RATIONAL POLICY OVER
PANIC.
Re-evaluating Pandemic Risk within the Global
Pandemic Prevention, Preparedness and Response
AgendaA report on zoonosis spill-over risk assessments within global pandemic
preparedness policy prepared by the Re-Evaluating the Pandemic
Preparedness And REsponse agenda (REPPARE) research group at the
University of Leeds.

February 2024

Rational Policy over Panic.
Re-evaluating Pandemic Risk within the Global
Pandemic Prevention, Preparedness and Response
AgendaFebruary 2024
A report on zoonosis spill-over risk assessments within global
pandemic preparedness policy prepared by the RE-evaluating thePandemic Preparedness And REsponse agenda (REPPARE) researchgroup at the University of Leeds.

The REPPARE research group is comprised of Professor Garrett
Wallace Brown, Dr David Bell, Dr Blagovesta Tacheva, and Mr
Jean von Agris (University of Leeds).

Suggested Citation: D. Bell, G.W. Brown, B. Tacheva & J. von Agris (2024). Rational
Policy over Panic: Reexamining Pandemic Risk within the Global Pandemic Prevention,
Preparedness and Response Agenda, REPPARE Report, University of Leeds, UK:
https://essl.leeds.ac.uk/dir-record/research-projects/1260/reevaluating-the-pandemicpreparedness-and-response-agenda-reppareThis research has been supported by the Brownstone Institute, USA.

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Table of Contents
Table of Contents .............................................................................................................................................. 1
List of Figures ..................................................................................................................................................... 3
Glossary ............................................................................................................................................................... 6
Executive Summary........................................................................................................................................... 8
1.

2.

3.

Background ..............................................................................................................................................11
1.1.

Detecting outbreaks .......................................................................................................................14

1.2.

Burden and balance .......................................................................................................................16

1.3.

The imperative of reliable evidence ...........................................................................................18

Methodology ............................................................................................................................................20
2.1.

Aims ...................................................................................................................................................20

2.2.

Approach ..........................................................................................................................................20

The G20 evidence base ..........................................................................................................................24
3.1.

The G20 Bali Leaders Declaration, 15-16 November 2022. ..................................................24

3.2.

The Analysis of Pandemic Preparedness and Response (PPR) architecture, financing

needs, gaps and mechanisms – Prepared for the G20 Finance and Health Task Force .............27
3.3.

The G20 High Level Independent Panel (HLIP) Report ...........................................................29

3.4.

HLIP Report, Annex D ....................................................................................................................31

3.5.

Metabiota data contributing to the HLIP Report .....................................................................35

3.6.

Expansion on Metabiota data: Meadows et al. (2023) and Metabiota data presented to

CGD. 40

4.

5.

3.7.

Supporting references in Meadows ...........................................................................................44

3.8.

McKinsey and Co. report contributing to the HLIP report.....................................................45

3.9.

Summary of G20 evidence-base..................................................................................................48

WHO evidence base for pandemic risk ..............................................................................................50
4.1.

The WHO Future Surveillance Report ........................................................................................50

4.2.

Summary of WHO evidence base ...............................................................................................58

World Bank Report (2022): Putting Pandemics Behind Us ............................................................59
5.1.

The Report preface and overview. ..............................................................................................59

5.2.

An important note on the term ‘emerging infectious diseases’ (EIDs) ...............................61

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5.3.

Concerns with the introduction in the Report .........................................................................62

5.4.

Concerns regarding the importance of One Health reforms ...............................................65

5.5.

Questions on outbreak frequency ..............................................................................................66

5.6.

The Example of Nipah Virus Disease ..........................................................................................70

5.7.

Discussion on investments. ..........................................................................................................73

5.8.

Summary of World Bank Report .................................................................................................76

World Bank Technical Report (2022): Increasing investments in One Health to reduce risks ofemerging infectious diseases at the source. .............................................................................................77

7.

6.1.

Overview ...........................................................................................................................................77

6.2.

Annex 1. Classification of EID risk ...............................................................................................85

6.3.

Summary of World Bank Technical Report analysis ...............................................................87

Key evidence papers ..............................................................................................................................88
7.1.

Bernstein et al. (2022): The costs and benefits of primary prevention of zoonotic

pandemics.....................................................................................................................................................88
7.2.

Marani et al. (2021): Intensity and frequency of extreme novel epidemics ......................94

7.3.

Smith et al. (2014): Global rise in human infectious disease outbreaks ............................98

7.4.

Jones et al. (2008): Global trends in emerging infectious diseases .................................. 101

7.5.

Morand & Walther (2020, Updated 2023): The accelerated infectious disease risk in the

Anthropocene: more outbreaks and wider global spread.............................................................. 105
7.6.
8.

Other papers considered in the preparation of this report:.............................................. 108

Conclusion and Recommendations ................................................................................................. 110

Annex I. Assumptions and sources for analysis of mortality due to outbreaks listed in Annex D of
the G20 HLIP Report. ................................................................................................................................... 113

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List of Figures
Figure 1. Derivation for evidence for the statements on PPPR of the G20 Bali LeadersDeclaration. ..........................................................................................................................................25
Figure 2. (A) Annex D from the HLIP Report ‘A Global Deal for our Pandemic Age’, and (B)REPPARE assumptions for outbreaks listed and associated mortality. (See Annex I forsources of mortality estimates). ......................................................................................................32
Figure 3. Annex E from the HLIP Report, intended to demonstrate an exponential increase in
the frequency of epidemics (outbreaks) derived from zoonotic spillover. ............................37
Figure 4. Metabiota data on non-influenza zoonotic spillover events presented to the Center
for Global Development, 25 August 2021, derived from the same dataset prepared for the
HLIP report. ..........................................................................................................................................38
Figure 5. Reported non-influenza zoonotic spillover events and corresponding mortality, 1960
to 2020, from Figure 2 of Meadows et al., 2023. .........................................................................39
Figure 6. Meadows et al. (2023) data recharted, (A) including Ebola outbreaks and (B) excluding
Ebola outbreaks (note change in Y-axis intervals). ......................................................................43
Figure 7. Figure 1 from WHO Report; ‘Future Surveillance for Epidemic and Pandemic Diseases:
a 2023 Perspective’, showing “Major epidemic and pandemic events in the 21st century”.
.................................................................................................................................................................51
Figure 8. Fig. 1 from WHO handbook ‘Managing Epidemics: Key Facts About Major Epidemic
Diseases, Second Edition’, showing “Major epidemic and pandemic events in the 21st
century”. ................................................................................................................................................54
Figure 9. Acute public health events from WHO handbook ‘Managing Epidemics: Key Facts
About Major Epidemic Diseases, Second Edition’. (A) Acute public health events forselected diseases, and (B) infectious disease public health events by year. .........................55
Figure 10. Extracts from Morand (2020) Figures 3 and 4, showing (A) reported outbreakfrequency 1960 to 2020, and (B) relationship between outbreak frequency and animal
species density. ...................................................................................................................................67
Figure 11. Diversity of reported outbreaks over time in the GIDEON database, reported bySmith et al (2014). ...............................................................................................................................69
Figure 12. Areas of known human Nipah Virus infections, and range of the bat species
harbouring the virus. .........................................................................................................................71
Figure 13. Evolution of outbreaks in the World Bank technical report, based on Smith et al.
(2014). ....................................................................................................................................................78

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Figure 14. Figure 1 from Stephens et al. (2021), one of five key references for the World Bank
analysis, reproducing GIDEON data and demonstrating a reduction in recent reported
outbreaks..............................................................................................................................................80
Figure 15. Figure 5 from Morand and Lejaunie (2021), demonstrating the associations at acountry level of change in forest cover and infectious disease outbreaks. ..........................82
Figure 16. Extract from Fig. 2 in Morand (2020), illustrating human outbreak frequency in the
GIDEON dataset with wildlife species diversity and number of endangered species in acountry. .................................................................................................................................................83
Figure 17. Fig. 1 from Allen et al. (2017), illustrating identified major drivers of emerging
infectious disease events, after adjustments for reporting bias. ‘Global envir. Strat.’ Refers
to climate. .............................................................................................................................................86
Figure 18. Table of “all” zoonotic spillover events from 1918 to mid-2021, from Bernstein et al.
(2022). Outbreaks contributing most to the total are highlighted here for emphasis. ......90
Figure 19. Re-calculation of Bernstein et al. 2022 data (from Table 1 of their paper). First row
follows Bernstein, including Spanish Flu but averaging over 70 years. Subsequent rows
reflect 52 years from 1969 to 2020, since the last significant influenza pandemic. Column
second from right reflects total world population of 7.5 billion (approximating Bernstein’s
denominator) and right column reflects population at end of 2023. .....................................91
Figure 20. Fig. 1 from Bernstein et al. (2022), demonstrating a relatively constant frequency ofoutbreaks, but an increasing cost (larger diameter circles) over time. ..................................92
Figure 21. Fig 1 from Marani et al. (2021). Epidemic intensity and probability of occurrence(exceedance), 1600-1944, and cause of major epidemics. ........................................................95
Figure 22. From Figure S1 from supplementary material of Marani et al. 2021: Number of
epidemics (without consideration of numerical epidemic intensity) occurring in each year
on record. .............................................................................................................................................96
Figure 23. Change in outbreak diversity with time in Smith et al. (2014), suggesting a rapid
increase in pathogen detection and identification in emerging economies since 1980. 100
Figure 24. Figure 1 from Jones et al. (2008), showing temporal occurrence of infectious disease(EID) events recorded from 1940 to 2000. ................................................................................. 103
Figure 25. Figure 2 from Jones et al. (2008), showing geographical distribution of infectious
disease (EID) events recorded from 1940 to 2000. .................................................................. 104
Figure 26. Fig. 3. from Morand and Walther (2020, updated 2023), demonstrating thecorrelation with more developed surveillance capacity for outbreaks in the GIDEON
database. ........................................................................................................................................... 106

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Figure 27. Fig. 2 from Morand and Walther (2020, updated 2023), showing marked recent
reductions in disease number and outbreak numbers in the GIDEON database. ........... 107

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Glossary
AFROWorld Health Organization African Region (Regional Office)

CGD

Center for Global Development

COP

Conference of Parties

DALYs

Disability-adjusted life years

DRC

Democratic Republic of Congo

EID

Emerging infectious diseases

EMS

Emergency Medical Services

EU

European Union

G7Group of Seven (governments)

G20

Group of Twenty (governments)

HIV/AIDS

Human immunodeficiency virus / Acquired Immunodeficiency Syndrome

HLIP

High-Level Independent Panel (of the G20)

IHR

International Health Regulations

INB

Intergovernmental Negotiating Body (of WHO)

IPPPR

Independent Panel for Pandemic Preparedness and Response

IPSN

International Pathogen Surveillance Network

JID

Journal of Infectious Diseases

LMIC

Low- and middle-income country (s)MCP

Medical countermeasures platform (of WHO)

MERS

Middle East Respiratory Syndrome

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ODA

Official Development Assistance

PCR

Polymerase chain reaction (diagnostic technology)

PHEIC

Public Health Emergency of International Concern

PNAS

Proceedings of the National Academy of Sciences

PPR

Pandemic Preparedness and Response (often interchanged with PPPR)

PPPR

Pandemic Prevention, Preparedness and Response (often interchanged
with PPR)QALYs

Quality-adjusted life years

SARS1

Severe Acute Respiratory Virus -1

SMU

Standardized Mortality Units

UHC

Universal Health Coverage

UN

United Nations

WGIHR

Working Group on amendments to the International Health Regulations (of
WHO)

WHA

World Health Assembly

WHO

World Health Organization

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Executive Summary
Background
International health institutions are emphasizing an urgency to prioritize prevention andresponse to pandemics.
Pandemic risk is characterized as an “existential threat to humanity” and is being used to justify
proposed amendments to the International Health Regulations and a new legally binding
Pandemic Agreement. It also underlies unprecedented annual financial requests to support thisagenda, including over $10 billion in new Overseas Development Assistance and over $26 billion
in LMICs investment, with additional funds for One Health interventions.
The World Health Assembly will vote on the WHO instruments in May-June 2024.
Problem
The urgency and unprecedented scope of this agenda is based on interpretations of evidence
claimed to demonstrate increasing pandemic frequency and burden. It is therefore essential that
this evidence is correct, and its interpretation appropriate and objective. Any investment must
be weighed against competing health, social and economic priorities, and therefore carries broad
implications for global health, and risks to, global health.
Objective
This report investigates the evidence base supporting the assumption that natural pandemics
constitute a significant health burden, and will increase in frequency and severity. By doing so,
the report seeks to better determine to what degree the associated risk supports the trajectoryof current Pandemic Prevention, Preparedness and Response (PPPR) policy.
MethodThis report analyzes the data and evidentiary material cited within 8 key G20 (n=3), World Bank(n=2) and WHO (n=3) policy documents used to support current policy assumptions about
pandemic risk. Our analysis included key secondary citations (n=2) and academic sources (n=6)
directly referenced in the policy documents to support these claims. Additional third-level
academic references (n=15) found within these secondary citations were also considered as partof the overall analysis in this report. Our analysis focused on reported mortality and outbreakfrequency to determine trends in risk and demonstrated harm.

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Result
The report finds that the data and evidence is poorly supportive of current pandemic risk
assumptions, suggesting that the urgency is unwarranted and that more time is required toformulate policy that reflects the true risk of pandemics in the wider healthcare context.
In contrast, the data suggests that an increase in recorded natural outbreaks could be largely
explained by technological advancements in diagnostic testing over the past 60 years, whilecurrent surveillance, response mechanisms and other public health interventions havesuccessfully reduced burden in the past 10 to 20 years. COVID-19, if indeed of natural origin,appears as an outlier rather than part of an underlying trend.
Implications
Most data underlying the WHO, World Bank and G20 reports demonstrate a flattening of theincrease in outbreak reporting over the past two decades, with a reduction in mortality, and somein outbreak frequency, over the past 10 years. In this context, their claims regarding pandemic
risk indicate that diverting investment to this risk is of lesser urgency than claimed, while theevidence supporting the scope of investment is weak. Any new investment should therefore be
carefully weighed against investment in diseases of greater burden, particularly in low-resourced
settings. This raises concern that escalated urgency based on weak evidence could undermine
both.
Overall, mortality from these outbreaks is also historically low, and low in contrast to othercurrent health burdens. Analyses indicating annual mortality in the millions include pre-antibiotic
era Spanish Flu and the multi-decade HIV event. Both influenza and HIV have extensive
international mechanisms already in place (although there is room for improvement), withmortality trends reflecting improved management.
In this context, the analyses of WHO, the World Bank and the G20, and in certain cases thesources they cite, are disappointing in terms of scholarship and balance. They raise concern thata desire to address a perceived threat is driving analysis, rather than analysis objectively
determining the extent of threat. However well intentioned, this seems unlikely to effectively
address the needs of public health or the populations it serves. Disease outbreaks harm people
and shorten lives and must be addressed. This is best achieved on the basis of well-compiled
evidence and scholarly analysis if we aim to achieve effective pandemic preparedness and global
health policy coherence.
The evidence, assessed objectively, paints a picture of an increasing ability to identify and reportoutbreaks up to the period 2000 to 2010, followed by a reduction in burden consistent with anincreasing ability to successfully address these relatively low-burden events through current

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public health mechanisms. Whatever the underlying mechanisms, the data on which the majorinternational agencies base their claims strongly indicate that the imperative of using healthresources wisely in the context of overall public health and economic need should temper thecurrent prioritization of health threats that are poorly defined and almost certainly overstated.
Recommendations
There is a clear need to commission better evidence to accurately determine the scale andurgency of pandemic risk.
An appropriate determination of pandemic risk must account for recent advancements indiagnostic capacity, information sharing, and improving disease control mechanisms.
Understanding relative disease burden is crucial for identifying the cost-benefit of pandemic
investment and how to best select interventions and promote overall public health outcomes.
Given the poor evidence underlying risk assessment, it is prudent not to rush into new pandemic
initiatives such as the proposed WHO pandemic instruments until underlying assumptions
receive proper assessment based on robust evidence, recognized need, and overall benefit.
WHO Member States should support proportional pandemic preparedness efforts based on
substantiated evidence, careful deliberation, and rational reflection.

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1. Background
The pandemic preparedness, prevention, and response (PPPR) agenda is currently dominatinginternational public health, and prevalent in deliberations of multilateral bodies including theGroup of 20 (G20), Group of 7 (G7), United Nations (UN), European Union (EU), and World Bank.
The World Health Organization (WHO) is the primary focus for coordination of this agenda.
Supporters are seeking, and receiving, substantial funding from international official
development assistance (ODA) budgets, whilst domestic research agendas and spending arebeing similarly directed.
These evolving priorities are having a major impact on global health financing. Although COVID19 era ODA budgets saw an increase since 2019 in overall dispersals for health, 63.9% of that
increase was for COVID-19 response with another one billion dollars disbursed for infectious
disease control. Contemporaneously, ODA for basic health care fell from $3.4 billion in 2019 to$2.3 billion in 2020, a drop of 34.5 %, while nutrition declined by 10.1%. Although ODA for basic
health rose again in 2022, it has not recovered to 2019 funding levels, while ODA for COVID-19
and infectious disease control saw additional increases of $1 billion and $500 million respectively
in 2022. Furthermore, there is evidence indicating that national budgets are reallocating existing
resources to PPPR, resulting in increased vulnerabilities for universal health coverage (UHC) andthreatening to reverse positive health outcomes (Brown et al., 2022).1,2
The World Health Assembly (WHA), 3 the governance council of WHO, currently plans to consider
two significant PPPR instruments in its May 2024 meeting. These instruments are intended tobring substantial changes to the management of future pandemics and threats of pandemics,
and other health emergencies.
The first instrument is a set of over 300 proposed amendments to the International HealthRegulations (IHR) with the intent of strengthening the role of WHO in declaring and directing
responses to public health emergencies of international concern (PHEIC).4 The aim ofstrengthening the IHRs is to address perceived failures experienced during COVID-19 related tocooperation, policy alignment, and compliance. As stated in the IHRs, countries will undertake tocomply with WHO recommendations, treating WHO as the authority on these matters (Article
13A).

https://www.oecd.org/dac/financing-sustainable-development/ODA-2022-summary.pdf
https://www.brookings.edu/articles/global-health-financing-after-covid-19-and-the-new-pandemic-fund/3
https://www.who.int/about/accountability/governance/world-health-assembly4
https://apps.who.int/gb/wgihr/pdf_files/wgihr1/WGIHR_Compilation-en.pdf
12

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A second instrument, a new Pandemic Agreement (sometimes referred to as the ‘pandemic
treaty’) intended to have force under international law,5,6,7 seeks to establish a new governancebody (Conference of Parties - COP) with extensive mandate to develop new PPPR requirements
and regulations, funding mechanisms and implementation mechanisms to support WHO in
preparedness activities and response when a PHEIC is declared.8
Financial requirements to support current proposals for PPPR are high in terms of prior spending
on ODA for health. Estimates used by the World Bank, WHO and G20 propose approximately$31.5 billion in total annual funding for PPPR, including $26.4 billion in annual PPPR investments
by low-and middle-income countries (LMICs) and $4.7 billion required in new ODA funding toshore-up international efforts. These estimates assume that 25% of existing ODA already covers
international PPPR efforts and further assumes that LMICs will only require $7 billion in extra
ODA to fill national budget shortfalls. Thus, the total estimated ODA requirement for PPPR iscurrently $3.5 billion + $7billion = $10.5 billion.9,10
WHO, in its IHR amendments and proposed Pandemic Agreement, envisions increased regularfinancial contributions and potentially a mechanism for acquiring further ‘surge’ resources onrequest during a PHEIC. The World Bank has already established the Pandemic Fund to makefurther funds available for surveillance, diagnostics and related human resources. Comparedwith approximately $3.8 billion in current annual funding to WHO, and $3 billion in total
estimated funding globally for malaria, $10.5 billion in ODA for PPPR would constitute a major
increase and redirection in international public health funding.
As is claimed in the G20 High Level Independent Panel report ‘A Global Deal for our Pandemic
Age’:“without greatly strengthened proactive strategies, global health threats will emerge more
often, spread more rapidly, take more lives, disrupt more livelihoods, and impact theworld more greatly than before.”Moreover,https://www.who.int/news-room/questions-and-answers/item/pandemic-prevention--preparedness-and-response-accord
https://www.whitehouse.gov/briefing-room/statements-releases/2022/11/16/g20-bali-leaders-declaration/
7
https://www.consilium.europa.eu/media/66739/g20-new-delhi-leaders-declaration.pdf
8
https://inb.who.int/
9
https://thedocs.worldbank.org/en/doc/018ab1c6b6d8305933661168af757737-0290032022/original/PPR-FIF-WB-WhitePaper.pdf
10
https://pandemic-financing.org/report/foreword/
5
6

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Page 13 of 114“…countering the existential threat of deadly and costly pandemics must be the humansecurity issue of our times. There is every likelihood that the next pandemic will comewithin a decade…”
In other words, the G20’s report claims that: 1) there will be increasing infectious disease
outbreaks (epidemics), driven largely by escalating risk of major “spillover” from pathogens fromanimals (zoonosis), and; 2) some of these outbreaks will be severe, posing an “existential threat”
to humanity.

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Detecting outbreaks

A disease outbreak may be detected by the spread of an unusual or characteristic illness (set ofsymptoms and signs) in a population, or by detection of a specific pathogen in the presence of
non-specific symptoms and signs. To be recorded, there must be communication channels,
recording mechanisms and an accessible record, and institutional interest and resources that
ensure these mechanisms are applied.
Symptom recognition depends on sufficiently trained people recognizing the patterns of disease
involved. Pathogen detection requires specific tests that detect signs of pathogen presence thatare accessible to the point of care, or linked through sample transport mechanisms. There must
be both resources and human interest available to apply such tests, on the part of the provider
and the affected population.
Prior to the development of microscopy two centuries ago, there was no way of detecting andrecognizing pathogens directly. Proteins (antigens) characteristic of pathogens and immune
responses mounted against them by the host have only been detectable within the past century,and detectable at point of care in the past few decades. Basic tests are still unavailable forcommon outbreak diseases in many populations.
PCR tests, critical to detect and distinguish between many human pathogens, were only
developed in the 1980s.11 Versions of PCR (and other nucleic acid amplification tests) suitable forpoint-of-care use have only become available within the past 20 years, remain limited in scope,and too expensive or resource-dependent for widescale use in many geographical settings.
Lateral flow tests for rapid detection of antigens and antibodies have been rolled out widely formore common diseases such as malaria,12 but remain inaccessible for many less common
pathogens.
Reporting of disease is dependent on electronic or paper-based reporting systems, and humanwillingness or capacity to use them. Reporting thoroughness has been transformed over the past
century by electronic communications, road networks, rising human population density, literacy,
and health service funding.
Therefore, when we discuss frequency of disease outbreaks in human populations, we depend
on a range of requirements being in place to record an event as having occurred, and theserequirements have changed dramatically over time and at different rates in different
geographies. The probability of an outbreak being detected and recorded now is greatly

1112

https://www.future-science.com/doi/10.2144/btn-2020-0057
https://www.nature.com/articles/nrmicro1525

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increased compared to a few decades ago in many geographies, and to a century ago in all
settings. This empirical reality must be understood in the interpretation of all data regarding
trajectories of disease outbreaks in human populations, and is critical to understanding thisreport.

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Burden and balance

The justification for the PPPR agenda rests on pandemics constituting a major and growing threat
to human wellbeing. This needs to be understood, therefore, both in terms of what constitutes
human wellbeing, and the significance of the threat itself.
WHO defines health as “a state of complete physical, mental and social well-being and not merely
the absence of disease or infirmity”. 13 There is no hierarchy of importance implied between these
aspects of health. The WHO definition is important when considering responses to outbreaks ofinfectious disease. Manifestations of infectious disease are predominantly physical, thoughsickness and death have obvious mental and social consequences. However, any response also
has direct social and potentially mental health consequences. Quarantine, travel restrictions,workplace closures and restrictions on social activities are obvious examples.
Any public health intervention must involve a balance of risk versus benefit. If restricted tophysical disease, this is somewhat simpler to assess. Local travel restrictions have potential
benefits in slowing the spread of a pathogen, with immediate physical risks limited to, perhaps,
inability to seek care for another ailment. However, they may have huge economic, mental andsocial consequences related to inability to work, obtain education, socialize with family or friends,
or attend important life events such as graduations, weddings and funerals.
While mental and social wellbeing are harder to quantify, several well accepted methods are used
to estimate relative physical burdens imposed by disease. The simplest of these are casenumbers and death counts. The former is poor beyond a very rough measure of spread of apathogen in a community. Obviously, severity and duration are critical to the impact of a disease,as is long-term disability versus rapid and complete recovery.
Mortality alone can also give only a poor measure of overall impact, as death of a young child will
foreshorten life far more than death of an elderly person. If most death from a disease occurs in
old age and in those with comorbidities expected to shorten life, such as with COVID-19, recorded
deaths may be high but the impact on average life expectancy will be small. Most would likely
have died due to age and/or comorbidities within a few years if COVID-19 had not occurred. In
contrast, most deaths from malaria occur at an age less than 5 years, with perhaps 70 years oflife lost per child. The death of a middle-aged woman, such as from HIV/AIDS or cervical cancer,
may also leave orphan children and a reduced family income, exposing them in turn to higher
health risks.

13

https://apps.who.int/gb/bd/PDF/bd47/EN/constitution-en.pdf?ua=1

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To address discrepancies in age, measures involving life-years lost are commonly used.14 Though
not widely used for COVID-19, these are standard metrics used by WHO and other agencies andare as relevant to outbreaks and pandemics as they are to endemic and non-communicabledisease. Disability-adjusted life years (DALYs) combine life-years lost with measures of impact ofillness (years of disability or lost healthy life years) on daily life, while quality-adjusted life years(QALYs) combine life-years gained by an intervention and the quality of life (lack of disability)
during those years. 15,16
Appropriate metrics of disease burden are vital to determining the relative risks and benefits ofany intervention. Directly, they allow estimation of the benefits of PPPR in terms of projected
reduction in burden of projected outbreaks against the projected impact of the measures taken
to avert or manage them. Indirectly, they allow this disease burden to be weighed against theopportunity cost of diverting resources to projected outbreaks rather than applying them toreduce the burdens of other diseases.
Opportunity costs can arise from reduction in resources available to address an alternate diseaseor health issue (physical, mental and/or social), or from economic costs that hinder the ability to
reduce disease burdens more generally. Such economic costs will be most acute in lower income
countries, where readily avoidable disease burdens tend to be higher. Including suchconsiderations in policy development is fundamental to good public health practice.

https://www.who.int/data/gho/indicator-metadata-registry/imr-details/159
https://www.who.int/data/gho/indicator-metadata-registry/imr-details/158
16
https://onlinelibrary.wiley.com/doi/full/10.1111/j.1524-4733.2009.00515.x
14
15

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The imperative of reliable evidence

The opportunity costs of diverting $10.5 billion to fill PPPR financing shortfalls while reallocating
considerable resources from other national and regional health, economic or other priorities arelikely to be significant. These sums are far larger than expenditure on other high-burden
infectious diseases such as malaria 17 and tuberculosis, 18 and may divert further funding fromsuch diseases to achieve PPPR priorities. For example, in 2017 the combined LMIC national
spending on high burden diseases such as HIV/AIDS and malaria equaled $25.3 billion, 19 justabove the amount being asked from LMICs for more targeted efforts of PPPR. Moreover, in 2019
the total amount of ODA on PPPR was $374 million,20 whereas the current ODA financial ask looks
to be 28 times that amount. Investment in nutrition and sanitation are also important healthalternatives, particularly given the spin-offs in this case, which include improving population
resilience to viral outbreaks as well as having broad positive benefits in reducing the burden ofother diseases and improving economic health (and accordingly future healthcare).
It is therefore vital that data on current risks from acute outbreaks of infectious diseases arereliable and provide adequate evidence to support the resource diversions that the PPPR agenda
is proposing. Similarly, modeling of anticipated future burdens must be based on reliable data
and defensible assumptions. One hallmark of evidence-based policy is that policy decisions
should be substantiated by rigorously established objective evidence and not based merely on
ideology, dogma or common belief. This enables appropriate allocation of resources among
competing health and economic priorities. Global health resources are already scarce andstretched; there is little doubt that decisions about pandemic preparedness will have significant
implications for global and local economies, health systems, and wellbeing.
The evidence supporting the PPPR agenda must therefore demonstrate that the resources being
directed to the PPPR agenda are proportionate to the burden of disease these health threatspose, compared to other disease and health priorities. In other words, justification for large scale
mobilization of financing and resources must be based on the burden of pandemic-causing
pathogens, and the frequency with which such pathogens cause significant outbreaks of disease.
The threat must justify the means and although it is prudent to prepare for public healthemergencies and pandemic risk, it is also sensible to assure that these preparations are reflectiveof the best available evidence concerning pandemic risk, and that any policy response is
proportional to that threat.

https://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2022
https://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-202219
https://www.thelancet.com/pdfs/journals/lancet/PIIS0140-6736(20)30608-5.pdf
20
Ibid.
17
18

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The REPPARE project is investigating the relative disease burden of outbreaks and competing
disease priorities in a separate series of reports. This current report investigates the assumptions
and evidence-base underwriting the current PPPR agenda as it relates to the frequency andseverity of pandemic threat. By doing so, the report seeks to better determine to what degree
the associated risk supports the trajectory of PPPR policy.
The report will proceed by examining the key PPPR policy documents and reports in turn,highlighting where pandemic risk claims are made and the evidence upon which these claims are
based. The report will then investigate key supporting studies individually to test the robustnessof the underpinning research and to make initial determinations about its reliability as anevidence-base. To be clear, the motivation for this investigation is not to propose that no
pandemic preparedness and response is necessary. Instead, this report aims to identify gaps andquestion the assumptions on which a huge shift in global health policy is being based and to
stimulate better reflections about the urgency and scale of PPPR.

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2. Methodology
2.1.

Aims

This report has four research aims:
1) To locate and outline the major claims of pandemic risk within the main policy documents(below) involved with the Pandemic Prevention, Preparedness and Response policy
agenda (PPPR).
2) To identify the evidence-base used within these policy documents to support these claimsof pandemic risk, particularly evidence supporting claims of increased outbreakfrequency and severity.
3) To analyse the robustness of this evidence-base.
4) To determine to what degree this evidence supports current PPPR assumptions andpolicy.

2.2.

Approach

The four aims were addressed with the following methods:
1) Locating and outlining main policy documents
Key policy documents were identified through a series of rapid online searches.
•

General searches of key intergovernmental organizations were conducted using
combinations of search terms that included “zoonosis”, “spillover”, “pandemic risk”,“emerging infectious disease”, “pandemic threat”, “epidemics” and “acute healthemergencies”;•

General searches of major institutional websites of the G7, G20, World Bank, World HealthOrganization and the United Nations were conducted;•

General internet searches (Google and other search engines) and alert requests wereused to help identify wider policy discussions, media foci, and institutional references
associated with pandemic risk.

Key selection criteria for inclusion were that the policy document or report:•

Explicitly gave pandemic risk assessments in support of policy recommendations;•

Was published after COVID-19, and;

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Was widely cited within the PPPR policy discourse.

2) Identification of the evidence baseThis report analyzed the data and evidentiary material cited within 8 key G20 (n=3), World Bank(n=2) and WHO (n=3) policy documents / reports used to support current policy assumptions
about pandemic risk. These documents represent the major post-COVID-19 PPPR policy
initiatives where pandemic risk is explicitly reassessed, often in the context of the emergence ofSARs-CoV-2, and were designed to explicitly provide evidentiary material for wider PPPR
initiatives such as the Pandemic Agreement, revision of the International Health Regulations(IHRs), the Pandemic Fund, the International Pathogen Surveillance Network (IPSN), and the
Medical Countermeasures Platform (MCP).
The policy documents and reports examined included:❖ The G20 Bali Leaders Declaration (November 2022).
❖ A Global Deal for a Pandemic Age: Report of the G20 High Level Independent Panel on
Financing the Global Commons for Pandemic Preparedness and Response (June 2021).
❖ Analysis of Pandemic Preparedness and Response (PPR) Architecture, Financing Needs,
Gaps and Mechanisms. World Health Organization and World Bank. Prepared for the G20
Joint Finance & Health Task Force (March 2022).
❖ Future Surveillance for Epidemic and Pandemic Diseases: A 2023 perspective. World
Health Organization (November 2023).
❖ Managing Epidemics: Key Facts about Major Deadly Disease. 2nd Edition. World HealthOrganization (2023).
❖ World Health Organization R&D Blueprint (2021).
❖ Putting Pandemics Behind Us: Investing in One Health to Reduce Risks of Emerging
Infectious Diseases. World Bank (2022).
❖ World Bank Technical Report: Increasing Investments in One Health to Reduce Risks ofEmerging Infectious Diseases at the Source (2022).
The study also included analysis of key secondary citations (n=2) and academic sources (n=5)
directly referenced in the policy documents for key evidentiary support.
Non-Academic Documents❖ Not the last pandemic: Investing now to reimagine public-health systems. McKinsey &Company (May 2021).
❖ Data on Non-Influenza Zoonotic Spillover Events. Metabiota (August 2021).

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Academic Publications❖ Bernstein, Aaron S., Amy W. Ando, Ted Loch-Temzelides, Mariana M. Vale, Binbin V. Li,
Hongying Li, Jonah Busch et al. "The costs and benefits of primary prevention of zoonotic
pandemics." Science

Advances 8,

no.

5(2022):

eabl4183.

https://doi.org/10.1126/sciadv.abl4183❖ Jones, Kate E., Nikkita G. Patel, Marc A. Levy, Adam Storeygard, Deborah Balk, John L.
Gittleman, and Peter Daszak. "Global trends in emerging infectious diseases." Nature 451,
no. 7181 (2008): 990-993. https://doi.org/10.1038/nature06536❖ Marani, Marco, Gabriel G. Katul, William K. Pan, and Anthony J. Parolari. "Intensity andfrequency of extreme novel epidemics." Proceedings of the National Academy of
Sciences 118, no. 35 (2021): e2105482118. https://doi.org/10.1073/pnas.2105482118❖ Morand, Serge, and Bruno A. Walther. "The accelerated infectious disease risk in the
Anthropocene: more outbreaks and wider global spread." BioRxiv (2020): 202004. doi: https://doi.org/10.1101/2020.04.20.049866❖ Smith, Katherine F., Michael Goldberg, Samantha Rosenthal, Lynn Carlson, Jane Chen, Cici
Chen, and Sohini

Ramachandran. "Global risein human infectious disease

outbreaks." Journal of the Royal Society Interface 11, no. 101 (2014): 20140950.
https://doi.org/10.1098/rsif.2014.0950
Additional third-level academic references (n=15) found within the non-academic and academic
secondary citations were also considered as part of the overall analysis in this report (see list
here).
Analysis of the documents proceeded in four stages:
Identification and categorization of documented pandemic risk assumptions, assessment andassociated policy response;•

Exegesis of stated pandemic risk;•

Identification and mapping of cited evidence-base, and;•Descriptive analysis of reported mortality and outbreak frequency within the reports.

3) Analysis of the evidence base
Analysis of the evidence-base focused on determining:•

Level of evidence used;•

Main conclusions and recommendations;•

Research methodologies;•

Overall coherency of argument / approach.

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Critical analysis of the evidence-based further focused on determining:•

Any misrepresentations of cited evidence;•

Methodological errors or shortcomings;•

Faulty research assumptions;•

Limitations and methodological confounders;•

Reliability of data.

4) Determination of fit of evidence to policy
Each policy document and its supporting evidentiary material was then analyzed individually todetermine to what degree the evidence supports current PPPR risk assumptions and policy. To
make this determination, the evidence was first judged on its own merits in terms of whether it
represents a robust risk assessment, whether there is appropriate research coherency, as wellas the overall convincingness of its conclusion. In case of the former, this included weighing theevidentiary material against existing counter-evidence and public health data as well as
reassessing the urgency of pandemic risk assumptions against wider global health contexts andburdens.

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3. The G20 evidence base
3.1.

The G20 Bali Leaders Declaration, 15-16 November 2022.

Meeting in Indonesia in 2022, the G20 group of nations expressed support for the PPPR pandemic
initiatives of WHO, the World Bank and partners to strengthen financial support for PPPR.21 Prior
to the meeting, the G20 commissioned two reports to help evidence its decision making on PPPR.
The first report of the ‘G20 High Level Independent Panel on Financing the Global Commons for
Pandemic Preparedness and Response’ (HLIP) was published in June 2021, with the specificmandate to “propose how finance can be organized, systematically and sustainably, to reduce
the world’s vulnerability to future pandemics.”22 The second report was commissioned from WHO
and the World Bank as a gap analysis and preparation white paper on financing PPPR, titled‘Analysis of Pandemic Preparedness and Response (PPR) architecture, financing needs, gaps andmechanisms’ (March 2022).23
Whereas the HLIP focused more on mobilizing financing, the WHO-World Bank report's aim wasto better identify country, regional and global level gaps in PPPR and to estimate the cost of
responding to those gaps. The first report had three independent co-chairs, multiple project
leads from different organizations, and used external consultations to inform their conclusions.
An analysis commissioned from McKinsey and Company contributed to the findings. As part oftheir remit, both reports were tasked to compile and analyze the evidence on outbreak risk andto provide some estimates on the social and economic costs of that risk. Both G20 reports were
produced in under five months and relied heavily on existing reports and academic literature on
pandemic risk and estimated gap costs (see Figure 1, below).

https://www.whitehouse.gov/briefing-room/statements-releases/2022/11/16/g20-bali-leaders-declaration/
https://pandemic-financing.org/report/foreword/
23
https://thedocs.worldbank.org/en/doc/5760109c4db174ff90a8dfa7d025644a-0290032022/original/G20-Gaps-in-PPRFinancing-Mechanisms-WHO-and-WB-pdf.pdf
21
22

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Figure 1. Derivation for evidence for the statements on PPPR of the G20 Bali LeadersDeclaration.

The official G20 Bali Leaders Declaration,24 released by the United States Government, devotes
Paragraphs 19-24 to support the initiatives behind the PPPR and health emergencies agenda. 25
The Declaration states G20 backing for the WHO Intergovernmental Negotiating Body (INB)
process to develop a “legally binding instrument” to strengthen PPPR,26 and the work of theWorking Group on amendments to the International Health Regulations (WGIHR).27
Of relevance to this report, the Declaration specifically mentioned COVID-19 and monkeypox
PHEICs as examples of the need for these new WHO instruments. Monkeypox was declared a
PHEIC by the WHO Director General (DG) in July 2022, after five recorded deaths globally after anequivocal determination of WHO’s IHR emergency committee. The PHEIC was ceased in April
2023 after 120 deaths globally. In the context of the findings below, this reflects a tendency toinclude very low-burden events in the justification for funding, underlining the need to assess

https://www.whitehouse.gov/briefing-room/statements-releases/2022/11/16/g20-bali-leaders-declaration/
Ibid.
26
https://inb.who.int/27
https://www.who.int/health-topics/international-health-regulations#tab=tab_1
24
25

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these events in the broader public health context that seems poorly addressed in the reports on
which the G20 and other international institutions have relied.

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The Analysis of Pandemic Preparedness and Response (PPR)
architecture, financing needs, gaps and mechanisms – Prepared
for the G20 Finance and Health Task ForceIn advance of the G20 in Indonesia, a PPPR gap analysis was conducted by the WHO and WorldBank to estimate the costs of appropriate PPPR investment at country, regional and global levels.
The report concludes by suggesting that suitable PPPR requires $31.1 billion per year with anestimated gap of $10.5 billion a year needed in new ODA. As part of the justification for theseinvestments the report laid out the following pandemic risks:“Pathogens will emerge and re-emerge with the potential to cause disease, death, anddisruption of a magnitude equal to or greater than SARS-CoV-2. Outbreaks of infectious
pathogens have been a defining feature of human history, and any analysis of prevailingtrends strongly suggests that outbreaks of pathogens of pandemic potential are set tocontinue to increase in frequency for the foreseeable future.”“The intensification of, and interaction between, factors such as ecological degradation,
climate change, conflict and resource competition, mass population movement anddisplacement, urbanization, global travel and trade, and changes in agricultural practices
continue to multiply the risks of emergence and re-emergence of epidemic and pandemic
threats.”
To substantiate these claims the report cites a webinar organized by the Center of Global
Development (CGD - also a project lead for the HLIP),28,29 which included research from Metabiota
Inc. Citing this webinar and the associated Metabiota research, the G20 report concluded that:“Modeling suggests that the probability of a zoonotic event in which a pathogen with apandemic potential similar to SARS-CoV2 jumps from another species into humans is
between 2.5% and 3.3% annually, putting the chances of a similar outbreak between 47%
and 57% within the next 25 years.”
As will be detailed below, Metabiota (a California-based consultancy now absorbed by Ginkgo
Bioworks) and McKinsey & Company were the primary evidence sources used by the G20 within

28
29

https://www.cgdev.org/event/whats-next-predicting-frequency-and-scale-future-pandemics
https://www.cgdev.org/blog/the-next-pandemic-could-come-soon-and-be-deadlier

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both commissioned pandemic preparedness reports.30,31 These reports constitute the mainevidence-base that underwrote the subsequent 2022 G20 Declaration.

https://www.ginkgobioworks.com/
https://www.mckinsey.com/industries/public-sector/our-insights/not-the-last-pandemic-investing-now-to-reimagine-publichealth-systems#/
30
31

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The G20 High Level Independent Panel (HLIP) Report

The HLIP of the G20 published a commissioned report ‘A Global Deal for our Pandemic Age’, inJune 2021. Its findings regarding pandemic risk and the need for urgent action were primarily
based on analysis by Metabiota Inc. and McKinsey & Company, with additional input fromunspecified sources. 32
Key findings of the HLIP report, which forms the basis for the G20 considering pandemics a major
and increasing threat, are that pandemics and disease outbreaks are increasing in frequency andimpact, with a high likelihood of recurrence of a major pandemic within one to two decades:“without greatly strengthened proactive strategies, global health threats will emerge more
often, spread more rapidly, take more lives, disrupt more livelihoods, and impact theworld more greatly than before.
…countering the existential threat of deadly and costly pandemics must be the humansecurity issue of our times. There is every likelihood that the next pandemic will comewithin a decade…”(High Level Summary. Page 5)
The Report claims that considerable increases in financial support are essential to avoid andmitigate outbreaks, including increased effort by low- and middle-income countries (LMIC):“Low- and middle-income countries will need to add about 1% of GDP to public spendingon health over the next five years….
Governments must collectively commit to increasing international financing for pandemic
prevention and preparedness by at least US$75 billion over the next five years, or US$15
billion each year with sustained investments in subsequent years…
The Panel assesses this to be the absolute minimum in new international investments
required in the global public goods that are at the core of effective pandemic prevention
and preparedness. The estimate excludes other investments that will contribute toresilience against future pandemics while benefiting countries in normal times.…”(High Level Summary. Page 6)
The Report further asserts that such investment will reap major returns, based on its assessmentof the costs and risks of outbreak:32

https://pandemic-financing.org/report/foreword/

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Page 30 of 114“besides being a scientific and moral imperative. They will materially reduce the risk ofevents whose costs to government budgets alone are 700 times as large as the additional
international investments per year that we propose, and 300 times as large as the total
additional investments if we also take into account the domestic spending necessary.”(High Level Summary. Page 6)
The High-Level Summary recommends the establishment of commitments for such funding forcountries, including expansion and greater financing for WHO. Below we examine the specificevidence base on which the above claims and recommendations are made. In doing so, several
questions emerge regarding the reliability of the existing evidence-base, suggesting the need fora better understanding of pandemic risk and what assumptions should underwrite emerging
PPPR policies.

In the Overview of the HLIP Report,33 a basic claim is asserted:“Even as we fight this pandemic, we must face the reality of a world at risk of more frequent
pandemics.”
The support for this assertion is laid out on page 20 of the HLIP’s report:a) “The last two decades have seen major global outbreaks of infectious diseases every
four to five years, including SARS, H1N1, MERS and COVID-19. (See Annex D.)”b) “There has been an acceleration of zoonotic spillovers over the last three decades. (SeeAnnex E.)”c) “Scientists attribute the increased frequency of infectious disease outbreaks topopulation growth and increased human encroachment on the natural environment;
the loss of the world’s biodiversity; the growth of the wildlife trade; increasing
urbanization, crowded living conditions and increased mobility; and the broader
consequences of a warming environment on the life cycle of pathogens and thegeographical spread of insect-borne diseases.”Claim ‘c’ will be discussed later in Section 4 and Section 6. Here we examine the assertions in ‘a’and ‘b’.

33

Ibid.

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HLIP Report, Annex D

Annex D lists “Major Infectious Disease Outbreaks in the Past Two Decades” (Figure 2A), as
justification for the assertion of major infectious disease outbreaks every 4 to 5 years for the past2 decades (HLIP report, page 20). No reference or attribution is provided, and no data sources
given. Specifically, there is no associated mortality or other impact data provided, nor is thegeographic location of the outbreak given.
Based on the largest outbreaks identifiable for each disease commencing in the year listed, weanalyzed the impact of these listed outbreaks (Figure 2B). Sources for our assumptions are listed
in Annex I. Annex D appears to contain errors, as some years listed were not the largest outbreaks
recorded for the disease in the past two decades. For instance, the Enterovirus 71 outbreak
presumably refers to the outbreak in Taiwan of that year. However, recorded mortality for thisdisease was far higher in mainland China in 2008-2012.
While the major West African Ebola outbreak is listed, a further outbreak in 2017 may be an error,
as only 3 recorded deaths could be located in reports of that year. The intent may have been torefer to 2018, where two larger outbreaks occurred in the Democratic Republic of Congo. We
have included these two outbreaks on the assumption that ‘Ebola 2017’ as listed in Annex D
meant to refer to the more significant outbreaks in 2018 in the Democratic Republic of Congo(Figure 2B).
COVID-19 was included in the HLIP table. We have excluded this from analysis, as it is dealt withlater, and the origins of the outbreak (natural spill-over versus laboratory-mediated) remain
controversial.

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Figure 2. (A) Annex D from the HLIP Report ‘A Global Deal for our Pandemic Age’, and (B) REPPARE
assumptions for outbreaks listed and associated mortality. (See Annex I for sources ofmortality estimates).

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As Figure 2B demonstrates, the only outbreak in this list that killed over 20,000 people (prior to
COVID-19) was the H1N1 influenza pandemic of 2009 (Swine Flu) with 163,000 deaths. Remaining
mortality once this is excluded is dominated by the West African Ebola outbreak, which resulted
in 11,325 deaths. The H1N1 influenza outbreak killed far less people than normal seasonal
influenza does each year (approx. 290,000 to 650,000).34 The next largest outbreak (Ebola, 2014)
was largely confined to three countries. Ebola historically occurs in quite geographically confinedareas due to its reliance on close contact for transmission and readily distinguishable illness.
For contrast, endemic diseases such as tuberculosis and malaria are responsible forapproximately 1,380,00035 and 620,000 36 deaths annually respectively, each dwarfing the
combined total of all outbreaks listed in the HLIP report table (under 200,000, and under 25,000
excluding influenza – Figure 2B). In context, the West African Ebola outbreak, the largest in
history, thus resulted in the equivalent of 4 days of global tuberculosis mortality, while the Swine
Flu outbreak of 2009 had less than half the average mortality of seasonal influenza.37
The third largest outbreak listed by the G20 HLIP report was the cholera outbreak in 2010, which
was confined to Haiti, and thought to have originated from poor sanitation in a UN compound.38
Cholera once caused major outbreaks (peaking between 1852-1859) and was the subject of thefirst international agreements on pandemics.39 Improved water and sewage sanitation, its impact
has reduced greatly to a point where the Haiti outbreak was unusual, and there has been a n
decline in relative burden since the 19th century.
While there are many outbreaks overlooked in Annex D of the HLIP report, it contains most major
events (see note on Enterovirus 71) and is the only evidence quoted for the Report’s claim ofmajor outbreaks every 4 to 5 years. This claim requires outbreaks with mortality equivalent to 1
to 3 days of tuberculosis mortality (approximately 3,800 deaths) 40 to be considered ‘major
outbreaks.’

Iuliano AD, Roguski KM, Chang HH, Muscatello DJ, Palekar R, Tempia S, Cohen C, Gran JM, Schanzer D, Cowling BJ, Wu P,
Kyncl J, Ang LW, Park M, Redlberger-Fritz M, Yu H, Espenhain L, Krishnan A, Emukule G, van Asten L, Pereira da Silva S,
Aungkulanon S, Buchholz U, Widdowson MA, Bresee JS; Global Seasonal Influenza-associated Mortality Collaborator Network.
Estimates of global seasonal influenza-associated respiratory mortality: a modelling study. Lancet. 2018 Mar
31;391(10127):1285-1300. doi: 10.1016/S0140-6736(17)33293-2. Epub 2017 Dec 14. Erratum in: Lancet. 2018 Jan 19;: PMID:
29248255; PMCID: PMC5935243.
35
https://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2022
36
https://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-202237
https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(17)33293-2/fulltext
38
https://www.ijdh.org/wp-content/uploads/2013/07/The-Cholera-Outbreak-in-Haiti_Where-and-How-it-Begin.pdf
39
https://www.thelancet.com/article/S0140-6736(02)11244-X/fulltext
40
https://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2022
34

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The assertions of the HLIP report based on this table are therefore poorly evidenced. However,the quality of data, and lack of referencing or indication of source data, also raises questions on
the quality of underlying work involved in preparation of the report.

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Metabiota data contributing to the HLIP Report

Metabiota Inc. was based in San Francisco, California, USA,41 and specialized in data analysis
including health-related data. It is no longer extant as an independent entity, but reports being
acquired by Cameroon-based HEADA (a non-profit organization) 42 .with Ginkgo Bioworks
acquiring Metabiota’s epidemiology and disease monitoring group.43 The team of epidemiological
data experts responsible for the analysis used by G20 was also integrated into Ginkgo Bioworks
and is now present there.
Metabiota Inc. are listed as one of two major sources for outbreak data justifying proposed
financing in the HLIP Report. Data in Annex E is sourced to Metabiota and is the basis of theassertion of “an acceleration of zoonotic spillovers over the last three decades.” Annex E (Figure3) itself makes an extraordinary epidemiologic claim for non-influenza wildlife zoonoses that:“There has been a clear exponential increase of such epidemics, which increased infrequency by a factor of about 3 every 20 years.”and for influenza, that there has been a dramatic change (worsening) of the threat:“There have been around 10 influenza spillover events each year in recent years, comparedto hardly any 25 years ago.”
Figure 3 illustrates a marked increase in frequency of outbreaks of both non-influenza andinfluenza zoonotic spillover events. The curves indicate one or less non-influenza events annuallyprior to 1960, and one or less influenza spillover events annually prior to 1995, but now morethan 20 non-influenza and 10 influenza events annually.
The second chart on influenza ‘spillover’ events is difficult to interpret. Influenza deaths aretrending down in the United States (where data is relatively good) over the past few decades.
Furthermore, available global estimates are relatively flat, with around 600,000 deaths per year
for the last few decades and despite increases in population. Thus, Metabiota’s claim of anincrease from 1 to 10 spill-over events per year from 1995 to 2000 seems unlikely to refer to areal change in seasonal influenza. It is possible that the increase refers to advances in detection.
What is more, if only less common influenza variants are considered such as highly pathogenic
avian influenza (HPAI) types H5 and H7, then mortality has greatly declined over the pasthttps://web.archive.org/web/20230727112046/https:/www.metabiota.com/news
https://web.archive.org/web/20230727112046/https:/www.metabiota.com/news
43
https://www.prnewswire.com/news-releases/ginkgo-bioworks-announces-strategic-purchase-of-epidemiological-datainfrastructure-assets-301609075.html
4142

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century.44 WHO equally notes that mortality from ‘Bird Flu’, of which we most frequently hear,
has been declining (Figure 3).45
Unfortunately, the HLIP Report does not provide Metabiota’s sources for the charts in Annex E(Figure 4). REPPARE contacted former Metabiota employees now at Ginkgo Bioworks who
confirmed that the dataset used is the same as that used in a Metabiota presentation to theCenter for Global Development (CGD) on August 25th, 2021, which was a source also cited in the
HLIP.46 Ginkgo Bioworks also confirmed that the dataset also appears in a more recent academic
article in the British Medical Journal in 2023, co-authored by Metabiota personnel (Meadows etal., 2023).47 In that article the authors analyzed the Metabiota database of 3150 outbreaks,
including all outbreaks recorded by WHO since 1963 as well as “historically-significant” prioroutbreaks (Figure 5). The data used in Meadows et al. (2023) is available in the article’s
supplementary information, and former Metabiota staff confirmed to REPPARE that the dataset
used in that article, as in the earlier analyses, is now commercially available through Concentric
by Ginkgo Bioworks.

https://ourworldindata.org/influenza-deaths
https://cdn.who.int/media/docs/default-source/global-influenza-programme/2023_march_tableh5n1.pdf
46
https://www.cgdev.org/blog/the-next-pandemic-could-come-soon-and-be-deadlier
47
https://gh.bmj.com/content/bmjgh/8/11/e012026.full.pdf?with-ds=yes
44
45

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Figure 3. Annex E from the HLIP Report, intended to demonstrate an exponential increase in
the frequency of epidemics (outbreaks) derived from zoonotic spillover.

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Figure 4. Metabiota data on non-influenza zoonotic spillover events presented to the Center for
Global Development, 25 August 2021, derived from the same dataset prepared for the HLIP
report.48

48

https://www.cgdev.org/blog/the-next-pandemic-could-come-soon-and-be-deadlier

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Figure 5. Reported non-influenza zoonotic spillover events and corresponding mortality, 1960
to 2020, from Figure 2 of Meadows et al., 2023.

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Expansion on Metabiota data: Meadows et al. (2023) andMetabiota data presented to CGD.

Meadows et al. (2023) state that they (i.e. Ginkgo Bioworks, incorporating Metabiota’s
epidemiology team) have 3,150 outbreaks in their database, including those reported by the
WHO since 1963, and prior “historically significant” outbreaks. 49 In the BMJ article, they
concentrate on outbreaks of zoonotic origin from 1963 to 2019 that are not vector-borne, and of
not more than 5 years duration (to exclude endemic diseases), with at least 50 deaths recorded
for that pathogen, added over all outbreaks in the data (e.g. an Ebola outbreak with 10 deaths
would be included because all Ebola outbreaks add up to more than 50 deaths). They further
excluded influenza because specifically targeted surveillance programs increased detection.
These exclusion criteria were considered to reduce reporting bias.
The outcome, and therefore the basis for the HLIP report data, is 75 outbreak events with 17,232
deaths in 24 countries, caused by Filoviruses (Ebola, Marburg), SARS Coronavirus 1, Nipah virus,
and Machupo virus. Machupo virus causes localized outbreaks in Bolivia linked to an endemic
species of mouse, while Nipah has been confined to Southeast Asia. Most of these outbreaks
involved less than 50 deaths.50
The chart of frequency of events in Figure 5, and those in Figure 3 and 4 from the HLIP Reportand Metabiota’s CGD presentation respectively, demonstrate an obvious increase in event
frequency. Applying models to this data shows an exponential increase in frequency. This could
be explained by a rapid increase in actual events, which does not necessarily imply an increasein risk as it could simply reflect a rise in total population. However, the increase in reported events
could also be explained, at least in part, by the following:
•

•

•

49
50

Increased surveillance effort:oEconomic growth – more labs, roadsoRising donor interest in outbreaksoRising total donor fundingoRising emphasis on publication

Expansion of diagnostic capacity:oDevelopment of PCR (1983), and gradual expansion of PCR useoDevelopment of improved and lower cost antigen testsoExpansion of access to lower cost serology tests

Multiple other reasons for increased detection, reporting and recording.

https://gh.bmj.com/content/bmjgh/8/11/e012026.full.pdf?with-ds=yes
Ibid.

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The curves produced by Metabiota indicate that very few outbreaks occurred before the 1970s.
This seems to lend weight to the influence of the confounders listed above, particularly
technological advancement in detection, rather than showing a true exponential increase. The
potential for this confounder to be important is confirmed by Meadows et al. in the limitations
section of their paper, where this increase in reporting frequency did not consider thedevelopment of new surveillance and diagnostic technologies, which would have enabled better(or in some cases any) detection. As outlined above, PCR testing was only developed in the 1980s
and has steadily become more accessible in laboratories over the last 30 years. 51 Antigen andpoint-of-care serology tests were only widely available in the past couple of decades, and genetic
sequencing only very recently. 52
Since 1960, we also have had significant improvements in road transport, clinic access and digital
information sharing. As a result, this limitation in the Meadows study raises questions about how
advancements in detection technology could account for the large increase in reported
outbreaks, since most small and localized outbreaks will have been missed 60 years ago. As justone example, HIV/AIDS was missed for at least 40 years before identification in the 1980s. 53
What the above suggests is that there are certainly known spillover effects and that these do
occur with some frequency. What is less reliable from the Metabiota data is the claim that there
is an increased frequency of zoonosis and/or that the increase in reporting cannot be fully or
partly explained by advancements in detection technologies. Determining the former would
require further research that could control for this latter variable.
Analysis of the mortality trends for these diseases raises similar concerns. Meadows et al.
indicate that the data represents an exponential increase of 8.7% annually, and that: “If theseannual rates of increase continue, we would expect the analysed pathogens to cause four times thenumber of spillover events and 12 times the number of deaths in 2050 than in 2020.” 54
The mortality chart in Figure 6A on which Metabiota’s team have based their conclusions shows
2 outlier points driving the upswing of the curve between 2010 and 2019. These are the West
African Ebola outbreak of 2014 and the Democratic Republic of Congo (DRC) Ebola outbreaks of2017 (which we have assumed are the 2018 and 2018-2020 DRC outbreaks). The 2014 outbreakin particular was unusual due to a relatively slow response by authorities and is by far the largest
such recorded outbreak in history. 55

https://www.future-science.com/doi/10.2144/btn-2020-0057
https://www.nature.com/articles/nrmicro1525
53
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3234451/
54
https://gh.bmj.com/content/bmjgh/8/11/e012026.full.pdf?with-ds=yes
55
https://speakingofmedicine.plos.org/2014/08/22/global-response-ebola-fever-epidemic-took-long/;
https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(15)00946-0/fulltext
51
52

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As a thought experiment, Figure 6B shows the Metabiota mortality data from the 75 outbreaks
since 1963 with Ebola virus outbreaks removed. The trend is quite different when Ebola is
excluded, showing three outbreaks, all with mortality well below 1000 people, dominating thedata set. The figure illustrates a relative increase in mortality in the 1997 to 2003 period (SARS1,
Marburg and Nipah virus outbreaks), and very little mortality since. It is clear that an exponential
increase in mortality is no longer present when Ebola is excluded, and the data suggests thatsuch zoonotic events may be becoming less serious in terms of mortality (consistent with Smithet al. (2014), discussed below).56
The reanalysis also illustrates the very low overall burden of mortality resulting from such events.
In terms of annualized mortality over the 1963 to 2019 period, the 75 outbreaks produce anaverage of just 302 deaths per year in total, 34 if Ebola is excluded, and 17 if SARS1 is also
excluded. As a result, in relative terms, the risk of mortality from these events, even when all
events are included, is low and further suggests that perceptions of risk are disproportional tothe historical disease mortality record.

https://royalsocietypublishing.org/doi/10.1098/rsif.2014.0950?url_ver=Z39.882003&rfr_id=ori%3Arid%3Acrossref.org&rfr_dat=cr_pub++0pubmed
56

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Figure 6. Meadows et al. (2023) data recharted, (A) including Ebola outbreaks and (B) excluding
Ebola outbreaks (note change in Y-axis intervals).

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Supporting references in Meadows

Meadows et al. (2023) quote three studies to back their assertion of significant increases in thefrequency of emerging infectious disease outbreaks; Smith et al. (2014)57, Jones et al. (2008)58 andthe WHO Regional Office for Africa (2022).59 Smith et al. and Jones et al., are discussed in moredetail later in this report.
These studies are part of the wider literature on pandemic risk and will be examined in greater
detail below. However, given that these studies are cited in the Meadows et al. article ascorroboration of increasing outbreak frequency, which is the basis of the G20’s evidence-base ascited from Metabiota, it is useful to summarize here.
In brief, the article by Smith et al. (2014) notes an increase in outbreak frequency of modest size
compared to Meadows et al., but a reduction in mortality rate from such outbreaks once internet
usage (as a proxy for communication access) is adjusted for. 60 Jones et al. (2008) show an overall
increase in outbreaks from the 1940s to the year 2000, but a reduction from the 1980s to the1990s.61 They note that much of the increase is attributable to secondary infections in HIVinfected patients (immune-deficiency) and also due to antimicrobial resistance (each mutation isclassed as a new outbreak in their analysis). The WHO AFRO reference cited in Meadows et al. isa blog stating a 63% increase in zoonotic outbreaks occurred in the African Region from 20012011 to 2012-2022.62 No reference is given for this statement and no data provided, and REPPARE
was unable to obtain responses from the WHO AFRO office to clarify this.
The supporting references quoted by Meadows et al. therefore do not support a rapid andconsistent increase in risk of mortality from zoonotic outbreaks, (with the possible exception of
unreferenced WHO data), though Smith et al. (up to 2010) and WHO (unreferenced) note anincrease in frequency of reported events. As discussed elsewhere, this is likely to be significantly
influenced by improved detection and reporting.

https://royalsocietypublishing.org/doi/10.1098/rsif.2014.0950?url_ver=Z39.88-2003&rfr_id=ori%3Arid%
3Acrossref.org&rfr_dat=cr_pub++0pubmed
58
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5960580/
59
https://www.afro.who.int/news/africa-63-jump-diseases-spread-animals-people-seen-last-decade60
https://royalsocietypublishing.org/doi/10.1098/rsif.2014.0950?url_ver=Z39.88-2003&rfr_id=ori%3Arid%
3Acrossref.org&rfr_dat=cr_pub++0pubmed
61
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5960580/
62
https://www.afro.who.int/news/africa-63-jump-diseases-spread-animals-people-seen-last-decade
57

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3.8.

McKinsey and Co. report contributing to the HLIP report

The HLIP “primarily” relied on WHO and McKinsey for their financial assessments, summarized in
Annex H of their report. As stated on Page 48 of that report:“These are current best estimates based on WHO, McKinsey and others to size the various
requirements for future pandemic prevention and preparedness”.
This statement makes reference (page 81) to the WHO and World Bank report to the G20“Assessment of Gaps in Pandemic Preparedness” in 2020. This report was a “work-in-progress”
and was “replaced” in March 2022 by a final report titled “Analysis of Pandemic Preparedness andResponse (PPR) architecture, financing needs, gaps and mechanisms”. It was this later report thatwas presented to the G20 in Indonesia.
As outlined earlier, this report solely drew upon the evidence provided by Metabiota and
McKinsey & Company for its pandemic risk assessments. Thus, the assessments provided above
for Metabiota and Meadows et al. holds for this WHO and World Bank report, since there is no
new evidence provided. Aside from the Metabiota research examined above, the G20 reliedsignificantly on the McKinsey & Company’s report “Not the last pandemic: Investing now toreimagine public-health systems.”
The report by McKinsey & Company bases its conclusions on important assumptions concerning
pandemic and outbreak risk. Yet, no data is provided in their report to support these conclusions,which were subsequently accepted by the HLIP. Moreover, McKinsey’s 13-page report to the HLIP
is non-specific regarding risk, referencing general media reports and WHO summaries rather
than data. Therefore, it is not possible to draw firm conclusions on whether financial conclusions
from McKinsey are well supported by evidence. However, major assumptions underlying themraise concerns on reliability.
The McKinsey report makes several assumptions regarding the COVID-19 outbreak that areimportant to their conclusions. Briefly, they claim increased costs for countries “responding late”
to COVID-19, and “difficulties in managing second and third surges” as being evidence of the needfor greatly increased investment. Such statements raise questions on the veracity of subsequent
financial assumptions. For example, the OECD notes that Sweden, instituting relatively light
restrictions, has the lowest all-cause mortality of any OECD country.63 A lack of clear correlation
between strictness of restrictions and health outcomes raises questions regarding whether‘improve response’ of the type envisioned (e.g. lockdown-related approaches) are is aninvestment with clear returns in terms of health, or whether former lower-cost approaches are63

https://data.spectator.co.uk/covid-inquiry

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preferable.64 These issues require in-depth discussion and research, but are raised here tohighlight the assumptions and lack of quantifiable data throughout the report.
On evidence for outbreak frequency and severity – the primary justification for investment, theMcKinsey report is very sparse:•

On Page 2 the Independent Panel for Pandemic Preparedness and Response (IPPPR)65 isquoted “…if investment doesn’t occur now, ‘we will condemn the world to successive
catastrophes.’”

Importantly, the IPPPR report does not provide data to support thisassertion, but notes uncited opinions of an increase in outbreak frequency.
•

Again, on Page 2 the McKinsey report justifies its investment case by claiming increased
possibility of an outbreak with 25% mortality, which will disproportionately harm children.
However, such an outbreak has not occurred at broad scale since bubonic plague in the
14th Century, with the exception of virus outbreaks (smallpox and measles) in
immunologically-isolated populations of the Americas and Oceania in the 16th to 19th
Centuries. The former is readily addressable through hygiene and antibiotics, while
conditions for the latter (large, isolated populations) no longer exist. McKinsey do notoffer an explanation as to how such an event would recur nor do they provide further
evidence to support the 25% mortality claim.

•

On page 8, the McKinsey report notes that “zoonotic events, in which infectious diseases
make the jump from an animal to a human, touched off some of the most dangerous recent
epidemics, including of COVID-19, Ebola, MERS, and SARS”. Apart from COVID-19, these are
the only outbreaks discussed as evidence. Based on the Metabiota dataset describedabove, these diseases resulted in 16,237 total deaths globally from 1963 to 2019 (average
290/year). Including COVID-19 will obviously increase this, yet it is important to factor that
COVID-19 deaths occur predominantly in the unwell elderly and therefore cause relatively
lower life years to be lost. 66,67,68 Furthermore, the origin of COVID-19 is important here, as
McKinsey are referring to natural zoonotic spillovers, while the origins of COVID-19 are
disputed.69,70 If it is of natural origin, then based on the G20 data it could be understoodas an isolated event and not part of a trend. If SARS-CoV-2 is laboratory-modified,71 as
some have argued,72 then the massive effort underway to build surveillance for naturally
occurring threats would not be justified nor appropriate to the task.

https://apps.who.int/iris/bitstream/handle/10665/329438/9789241516839-eng.pdf?ua=1
https://theindependentpanel.org/wp-content/uploads/2021/05/COVID-19-Make-it-the-Last-Pandemic_final.pdf
66
https://www.sciencedirect.com/science/article/pii/S001393512201982X#bib42
67
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9420458/
68
https://covid.cdc.gov/covid-data-tracker/#demographics
69
https://www.bmj.com/content/382/bmj.p1556
70
https://www.dni.gov/files/ODNI/documents/assessments/Unclassified-Summary-of-Assessment-on-COVID-19-Origins.pdf
71
https://www.bmj.com/content/382/bmj.p1556
72
https://www.dni.gov/files/ODNI/documents/assessments/Unclassified-Summary-of-Assessment-on-COVID-19-Origins.pdf
6465

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Evidence within the McKinsey report referenced by the HLIP therefore poorly supports
the contention that PPPR investment on the scale of $85-130 billion over 2 years followed
by $20-50 billion annually (HLIP report, page 13) is justified or urgent.

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Summary of G20 evidence-baseWhile actual outbreaks, including those arising from zoonoses, may or may not be increasing, theevidence of the HLIP report, forming the basis for the G20 Bali Leaders Declaration, does notstrongly support either conclusion. The basis of the G20 recommendation, that “without greatly
strengthened proactive strategies, global health threats will emerge more often, spread more rapidly,
take more lives, disrupt more livelihoods, and impact the world more greatly than before” is notsupported. Most importantly, it is not supported by the HLIP report provided, or other evidence
from the entities this report relied on. It is inevitable that reporting of outbreaks will be influenced
by changes in both the capacity and incentive to report. These include the development of, andincreasing access to, major diagnostic platforms including PCR and point-of-care antigen andserology tests, as well as the development of communication infrastructure. Increased interest
from funders and research groups in outbreak detection may also be influencing the frequencyof recording.
COVID-19 has of course intervened, continuing through the publication of the HLIP report, as thefirst rapidly-spreading outbreak since 1969 to result in greater mortality than seasonal influenza
does each year. This mortality has occurred predominantly in the elderly and those withsignificant co-morbidities in higher-mortality high-income countries,73,74 a contrast to thepredominantly childhood deaths from malaria and young to middle-aged adults who die fromtuberculosis. Excess mortality rose over baseline but separating out COVID-19 mortality frommortality resultant from the ‘lockdown’ measures, reducing disease screening and managementin high-income countries and diverting resources from poverty-related diseases in low-income
countries makes actual burden estimates difficult.
However, if COVID-19 (for sake of argument here) is accepted as a natural event, then it should
obviously be included when determining risk. There are meaningful debates about the accuracyof how deaths were recorded and attributed to COVID-19.75,76 However, assuming the WHO iscorrect in its estimates, then the WHO records 7,010,568 deaths attributed to (or associated with)
the SARS-CoV-2 virus over 4 years, with most in the first 2 years (Figure 2). Allowing for population
increase, this is still higher than the 1.0 to 1.1 million deaths attributed to the influenza outbreaks
in 1957-58 and 1968-69, and the largest since the Spanish Flu that inflicted a mortality severalfold higher over a century earlier. With an average mortality of 1.7 million per year over 4 years,
COVID-19 is not greatly different from tuberculosis (1.3 million), but concentrated in aconsiderably older age group. Tuberculosis, however, continues before and will continue after
COVID-19, whereas Figure 2 indicates a rapidly waning COVID-19 outbreak. As the first event inhttps://covid.cdc.gov/covid-data-tracker/#demographics
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9420458/
75
https://www.bmj.com/content/382/bmj.p1556
76
https://www.dni.gov/files/ODNI/documents/assessments/Unclassified-Summary-of-Assessment-on-COVID-19-Origins.pdf
7374

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100 years of this magnitude, though little different from major endemic tuberculosis, and against
a background that does not demonstrate an overall increase in mortality from outbreak events,
it appears to be an outlier rather than evidence of a trend.
Given the above, there is poor support for a genuine exponential, rapid or continuing increase inmortality from zoonotic spillover-derived outbreaks in the data provided by Metabiota and othercontributors to the HLIP Report. An apparent increase was driven by an exceptional outbreak ofEbola Virus in West Africa in 2014, and a later Ebola virus outbreak in the DRC. Ebola virus
outbreaks have always been largely localized and time-limited, and due to the nature of thedisease, are likely to remain so.
The basis for the large benefits predicted to accrue from the recommended investments must
therefore be considered highly unreliable. Observations of increasing outbreaks are likely to be
partly or largely due to causes other than an increase in zoonotic spillover, and mortality risk
does not appear to have been increasing pre-Covid. The arguable exception is Ebola risk inCentral and West Africa, but these outbreaks remain localized. The largest sustainedtransmission was in just 3 countries and resulted in the equivalent of 3 days mortality fromendemic tuberculosis.
While COVID-19 has a higher recorded raw mortality, the evidence provided to the G20 Bali
meeting does not provide strong support for this being part of a trend rather than an exception.
Yet, outside general statements about globalization, increased speed of travel, urbanization, andincreased ecological interfacing, there is nothing concretely offered to think COVID-19 constitutes
the start of a new trend in zoonosis spillovers. This has important implications for assessing
relative disease burden, which is a key factor to understanding risk. This will be discussed in detailin a further report on pandemic disease burden and relational burdens to other infectious andnon-infectious diseases.

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4. WHO evidence base for pandemic risk
The World Health Organization (WHO) has made several references for the pressing need toprevent, prepare and respond to pandemic threats. Most of these statements have been briefcomments made during speeches by WHO personnel, in WHO memos, and in Director Generalreports.77 The need for increased policy response to pandemic threat has also been articulated
within key policy documents. Yet, as with the G20, these claims tend to lack citations and/or
disclosure of the underlying evidence.
This section of the report will examine key WHO documents where pandemic risk is explicitly
articulated and where those claims are supported with reference to some level of evidentiarymaterial.

4.1.

The WHO Future Surveillance Report

In late 2023 the WHO published its report “Future Surveillance for Epidemic and Pandemic
Diseases: a 2023 Perspective.” 78 The report was produced by the Strategic and Technical Advisory
Group on Infectious Hazards with Pandemic and Epidemic Potential. The report starts with theclaim that:“Since the beginning of the 21st century, the world has experienced major epidemics andpandemics every four to five years.”
Citing a joint statement of UN organizations from April 2020, the report further claims that:“The progression of COVID-19 has indeed proven that “no one is safe until everyone issafe” in this interconnected world.”
To substantiate the first claim the report provides a figure illustrating what the WHO suggests
are major epidemics or pandemics (see Figure 7).

7778

https://apps.who.int/gb/ebwha/pdf_files/WHA76/A76_10-en.pdf
https://www.who.int/publications/i/item/9789240080959

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Figure 7. Figure 1 from WHO Report; ‘Future Surveillance for Epidemic and Pandemic Diseases:
a 2023 Perspective’, showing “Major epidemic and pandemic events in the 21st century”.

Of the mentioned diseases, COVID-19 is responsible for almost the entire death toll. If you
exclude COVID-19 and the 2009 influenza pandemic (which is less severe than seasonal influenza)
we can see that these two events are responsible for roughly 90 percent of combined deaths.
Moreover, all listed diseases combined, including COVID-19, had an average death toll between
the years 2000 and 2022 that is less than a fourth of TB mortality or that of traffic accidents.79,80
Mortality alone, as discussed above in Section 1.2 of this report, can give only a poor measure ofoverall impact, as death of a young child will foreshorten life far more than death of an elderly
person. If most death from a disease occurs in old age and in those with comorbidities expected
to shorten life, such as with COVID-19,81,82,83 recorded deaths may be high but the impact on
average life expectancy will be small.
As a visual tool to provide an impression of increasing outbreak risk, Figure 7 is compelling.
However, the chart poorly reflects reality. Eight diseases are shown to be currently active in 2022,
compared to zero in the year 2000. Of these eight, Zika virus, cholera, Ebola and Mpox

https://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2022
https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries
81
https://www.sciencedirect.com/science/article/pii/S001393512201982X#bib42
82
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9420458/
83
https://covid.cdc.gov/covid-data-tracker/#demographics
79
80

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Page 52 of 114(monkeypox) had all been widely geographically distributed prior to the year 2000, with long
histories of human infection. The three influenza sub-types reflect the normal genetic drift of thisgroup of viruses. Only Covid-19, arguably a result of human interference, constitutes a truly new
outbreak in this period. Of the four remaining short-term outbreaks in the figure, only SARS was
unrecognized prior to 2000, with plague, yellow fever and cholera being long established endemic
diseases that are greatly reduced in outbreak severity within the past century. Rather thandemonstrating increasing risk, it demonstrates mostly low-level persistence of formerly more
severe disease risks.
In terms of the claim that “no one is safe until everyone is safe,” the Future Surveillance report does
not substantiate this claim nor does it lay out how such a broad claim might be proven. There isalso concern about its factuality, since it has been clear since early 2020 that children are at low
risk from SARS-CoV-2.84 In addition, several of the events highlighted in Figure 7 do not pose a
global spread risk, such as Ebola, which is largely confined to country and regional level
outbreaks, and why the WHO has in the past not declared all Ebola outbreaks a PHEIC. What thissuggests is that specific pathogens require specified approaches that respond to the individual
characteristics of the pathogen as well as the context in which the outbreak occurs (i.e. the needto distinguish between epidemics and pandemics). Although preparedness is necessary to
mitigate the threat of an outbreak, it is not immediately clear that all outbreaks should be treatedas a uniform global threat, nor should they receive the same response in every case.
WHO further notes within Future Surveillance that “300 to 400 infectious hazard events of publichealth concern are now detected annually,” apparently illustrating the prevalence of infectious
disease. This number is from the 2021 Annual Global Report on Public Health Intelligence
Activities85, which is part of the WHO Health Emergencies Programme. This report notes anincrease in substantiated public health events recorded in Emergency Medical Services (EMS)
over the 20 years leading to the COVID-19 pandemic, although the highest value thus far was
reached in 2009. Yet, within that report the authors acknowledge that the overall increasing trend
between 2013 and 2020 is “in part, due to an improved use of EMS and increased trainings for WHO
Regions, along with systematic engagement with States Parties to improve national surveillancesystems”. Despite this recognized confounder, the report goes on to assume that there is anincreased trend of public health events, and that this trend will continue “as climate change,(protracted) humanitarian crises, and disasters become more frequent”, none of which is further
substantiated with evidence or citations to evidentiary sources.
It is possible to find similar assumptions about environmental and social contributors repeatedin the Future Surveillance report. In the Introduction, the report lays out several global trends
https://www.sciencedirect.com/science/article/pii/S001393512201982X#bib42
https://cdn.who.int/media/docs/defaultsource/documents/emergencies/phi_2021_annual_report_web.pdf?sfvrsn=7482a7c9_2&download=true
84
85

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that plausibly contribute to the future emergence and re-emergence of pandemics, including“population and demographic changes, biodiversity and ecological changes, urbanization, livestock
production and expanded and higher-volume transportation networks” (p. 27). However, theseclaims are not supported by a citation to any source. As a result, there is an evidence gap and/or
under-evidenced assumption about how these environmental and social changes translate into
‘exacerbating factors of epidemic and pandemic risk’. Moreover, it is important to note that there
is contestation within the literature that explicitly examines the role of these factors. For example,
there is evidence to support a positive relationship between basic nutrition and immune
response to infections 86 as well as other adverse health affects from poor or contaminated foodsources.87 As a result, food production and land use can have important impacts on susceptibility
to disease. Yet, this holds for most diseases. Thus, it is a mainstay of basic population health andnot just relevant for infectious diseases. What the literature demonstrates is that the relationshipbetween these variables and pandemic risk is highly complex and multivariant, and that thesefactors can both exacerbate and help mitigate pandemic risk depending on how these sectors
are managed (see discussion below).
Managing Epidemics
In November 2023 the WHO released the 2nd Edition of the handbook Managing Epidemics: Key
Facts About Major Epidemic Diseases 88. In its Introduction, the handbook proclaims that:“Epidemics and pandemics of infectious diseases are occurring more often, and spreading
faster and further than ever, in many different regions of the world. The background factors of
this threat are biological, environmental and lifestyle changes, among others.”
The document goes on to describe that the “early years of the 21st century have already been deeply
scarred by so many major epidemics.” To substantiate the claim the handbook lists several
outbreaks, including, SARS, H1N1, MERS, Ebola, Zika, COVID-19 and a plague outbreak in
Madagascar that killed 209 people in 2017. Repeating the claim from its Introduction, thehandbook again asserts on Page 5 that “epidemics in the 21st century are spreading faster andfurther than ever”.
To visualize increased risk from outbreaks the following graphic is provided (Figure 8) which
differs from Figure 7 only in formatting:https://pubmed.ncbi.nlm.nih.gov/18207566/
https://pubmed.ncbi.nlm.nih.gov/28138818/
88https://www.who.int/publications/i/item/9789240083196
86
87

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Figure 8. Fig. 1 from WHO handbook ‘Managing Epidemics: Key Facts About Major Epidemic
Diseases, Second Edition’, showing “Major epidemic and pandemic events in the 21st century”.

To demonstrate an increased risk in outbreak frequency the handbook provides the table of‘acute public health events’ in Figure 9A. This is stated to be based on diseases from the WHO
R&D Blueprint priority list. Although these diseases correspond to those listed and tracked within
the Blueprint as priorities, the Blueprint itself does not provide the associated data related to therelationship between outbreaks and “health events”, or their impact. For example, sixty-three‘acute public health events’ were reported for Zika virus in 2016. These presumably refer tomultiple localities of the same outbreak(s) in South America that year, calling into question WHO’s
claim in Figure 9A that time and space linked reports were merged and counted as one event.
The numbers here in Figure 9A, being events a country chooses to report to WHO, are therefore
quite arbitrary. Malaria killed roughly 600,000 in each of these years but is recorded as a few
outbreaks per year. Measles remains endemic in many regions, as is yellow fever, while dengue
has frequent outbreaks across the tropics. However, by charting event numbers by year (Figure
9B), the WHO document provides a visual impression of increasing outbreaks. The event
numbers in Figure 9B don’t match those in Figure 9A and so must include a broader spread of
diseases, though the non-specific source, ‘acute public health events reported to WHO’, is the

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sole basis. The reader is to take on trust from WHO that this represents a real increase in risk,
though Figure 9A suggests that the vague methodology underlying these charts does not support
this.
Figure 9. Acute public health events from WHO handbook ‘Managing Epidemics: Key Facts About
Major Epidemic Diseases, Second Edition’. (A) Acute public health events for selected diseases,and (B) infectious disease public health events by year.

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To explain WHO’s claimed increase in outbreak frequency and scope the handbook outlines two
key drivers: 1) The “fast and intense mobility of people”, and; 2) Increased land use and agriculturalchanges, namely, “deforestation, urban sprawl and human encroachment into previously untouchedhabitats”. According to the handbook, these latter changes “intensify our interactions with wildlife
and the pathogens they harbor. Changing and intensified food production, from live poultry andanimal markets to deforestation for expanded large-scale agriculture, also leads to increased contact
between people and wildlife. Some of the animals that humans are increasingly in contact with (batsfor example) are likely sources of new pathogens.” Again, as with the 2023 WHO Future Surveillance
report, no evidence is cited to support these claims.
It is important to analyze these in turn.
First, it is unescapable that there is a direct connection between the increased volume of travel
and a higher chance of a given pathogen spreading more quickly. For example, global airline
passenger traffic increased fivefold between 1989 and 2019.89 This will undoubtedly increase thespeed in which a pathogen moves. However, increased travel also means that people have been
exposed to a greater number of pathogens and over time have formed immunities. As a result,although speed of travel and volume is a risk factor for new zoonosis (that needs to be consideredin any contingency planning), what is less likely is that reemerging pathogens will be reachingcompletely immune naïve populations.
Second, the evidence for land use change leading to increased disease emergence is less obvious.
Deforestation has slowed down substantially in the past 2 decades90. The same goes for the claimthat “ever increasing climate-mediated disasters create humanitarian emergencies where infectious
diseases can take hold and quickly spread”. Actually, deaths from natural disasters have decreased
massively compared to the first half of the 20th century 91, disasters themselves have notincreased 92 and the reason why economic losses have increased lies in societal change (larger,
more wealthy populations) rather than climate change.93 This suggests a more complicated story,
which further suggests the need for additional investigation and research. Lastly, the relationshipbetween climate change and health is a rather new field of study and the scientific data is now
only starting to emerge.
Third, the claim that “unprecedented levels of urbanization and swelling populations of citydwellers inescapably pose greater risks of infectious disease transmission” is also notstraightforward. Although urbanization accelerates speed of inflection by placing a greaternumber of human bodies in proximity, it can also facilitate a more targeted response. Moreover,
https://www.airlines.org/dataset/world-airlines-traffic-and-capacity/
https://www.fao.org/3/ca9825en/ca9825en.pdf
91
https://ourworldindata.org/natural-disasters#
92
https://www.forbes.com/sites/michaelshellenberger/2022/01/10/why-disasters-have-declined/
93
https://www.tandfonline.com/doi/full/10.1080/17477891.2020.1800440
89
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urbanization often also coincides with economic development and depending on urban
management, urbanization positively affects public health.94 Finally, the handbook mentions theincreased number of displaced people and how their often crowded and poor living conditions
benefits the spread of epidemics. Depending on level sanitation, nutrition, housing, andhumanitarian assistance, this is undoubtedly an exacerbating factor if poorly managed (e.g. Haiti
cholera outbreak due to poor sanitation in UN facilities). Where risk persists is in cases wherelarge and unexpected displacement occurs and where appropriate response to that crisis isunavailable or undelivered. The key word here is proper “managing” of situations where there isan increased risk of epidemics before conditions result in humanitarian disasters. Here, there isa clear role for the WHO and other UN agencies to act in the face of basic human health needs.
In sum, the WHO handbook Managing Epidemics paints an extremely bleak picture of a worldthat suffers with ever more epidemics, natural disasters, and other humanitarian emergencies.
Most of the foundational claims within the handbook remain under-evidenced and presumptive.
This points to the need for better evidence production to assess pandemic risk, but also in how
to best respond to those risks. By not doing so, the handbook constructs a future narrative that
contradicts several actual long-term trends while promoting a sense of urgency and unmitigated
risk.

94

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Summary of WHO evidence base

Whilst outbreaks may or may not be increasing, the evidence from WHO documentation does
not strongly support either conclusion. As a result, the foundation for the WHO’s general claimthat “Epidemics and pandemics of infectious diseases are occurring more often, and spreading fasterand further than ever, in many different regions of the world” is not well supported. In most cases
the WHO does not cite evidence to support its claims, or cites other WHO documents that in turnlack evidence to support a specific claim.
Moreover, there is no evidence in the data provided by WHO to support what they call“exacerbating factors” such as climate change and increased urbanization. Although there isevidentiary material available that could shed light on these relationships, it is not cited in the
WHO documentation. Moreover, the literature regarding these factors is complex anddemonstrate that factors such as increased urbanization both enhances certain risks while
mitigating others. A more thorough analysis and presentation of how these factors affectpandemic risk is required before the information presented by WHO can be used to legitimately
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5. World Bank Report (2022): Putting Pandemics Behind
Us
5.1.

The Report preface and overview.

The 2022 World Bank Group report ‘Putting Pandemics Behind Us: Investing in One Health to Reduce
Risks of Emerging Infectious Diseases’ was written with the intent of laying out a justification for,and costing of, a One Health Approach to prevent pandemics resulting from spillover events
transferring pathogens from animal to human populations. 95 An accompanying technical report,‘Increasing investments in One Health to reduce risks of emerging infectious diseases at thesource’, is discussed later.
The Executive Summary lays out the general theme and basic claims to be elaborated later:“We publish this report with a sense of urgency. As damaging as COVID-19 has been, thenumber of infectious disease outbreaks—from avian influenza to Middle East respiratory
syndrome to Ebola—has been increasing dramatically. Every year, zoonoses cause morethan a billion human infections and a million deaths —a trend that we must put an end
to because it jeopardizes human development and breeds larger outbreaks such as
COVID-19, bringing much higher death tolls.”
A phrase also similar across documents follows: 96,97,98,99“Effectively addressing the challenges posed by pandemics requires a departure from theold cycle of panic and neglect.”
It is unclear what this really means, as it suggests a frequent cycle of pandemics with a panicked
response, whereas there seems little evidence of this in recent decades. Arguments have been
made of a mishandled response to the 2009 Swine Flu pandemic, 100 but prior influenza
pandemics listed by WHO, in 1957 and 1968, passed with relatively little notice. 101 The SARS (2003)
and MERS (2012) were quite rapidly contained with mortality of each below 1000 people

https://documents.worldbank.org/en/publication/documentsreports/documentdetail/099530010212241754/p17840200ca7ff098091b7014001a08952e
96
https://blogs.worldbank.org/health/moving-away-panic-and-neglect-big-step-forward-pandemic-preparedness-andresponse97
https://news.un.org/en/story/2018/05/1010641
98
https://openknowledge.worldbank.org/entities/publication/32eceb2b-1f11-5c7b-9166-a62feec8e393
99
https://reliefweb.int/report/world/panic-and-neglect-investing-health-security-financing-pandemic-preparedness-national
100
https://link.springer.com/chapter/10.1007/978-3-319-68173-3_9101https://apps.who.int/iris/bitstream/handle/10665/329438/9789241516839-eng.pdf?ua=1
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globally,102,103 while the West African Ebola outbreak was characterized by a relatively low
mobilization of response but essentially then contained and managed similarly to other Ebola
outbreaks.104 Based on the disease burdens of listed outbreaks in this report, in comparison to
the major causes of mortality globally, the case for ‘neglect’ is unclear. The concern here, as
elsewhere in this analysis, is that a major global institution is appealing for increased funding fora program based, at least in part, on emotive language rather than evidence. In this case, theemotive language is echoed by multiple institutions and media, and may take on meaning byrepetition that may not be justified through fact.
One Health is explained by the World Bank as an approach to “…sustainably balance the health ofpeople, animals, and Ecosystem”. This is an important definition, as it does not clearly put humanhealth first, as public health traditionally has. This is important in phrasing that has been echoed
elsewhere in wording specifically placing the welfare of humans and other species on an equallevel.105 It would be of concern to many as a justification of public funding, though this lack ofclear prioritization of human health here is not discussed further and may not be the intent of
the World Bank text.
Four approaches to reduce risk are mentioned, centering around reduced spillover of zoonotic
pathogens, but nothing on building human resilience through nutrition or other approaches(shown so central in COVID-19). Rather, it focusses on building and strengthening technicalagencies and financial institutions - risking a vertical and one-size fits all approach to fix a problem
externally, rather than supporting populations to build resilience.
The last executive summary sentence repeats the phrase: “Investing in One Health based
prevention is the best way forward to break the cycle of panic and neglect”, but again – it is not clear
in what way there is a cycle nor what dynamics are involved in this cycle. Consequently, thelanguage of panic and neglect (or its sister phrase “panic and forget”) looks to be more about
motivating policy consensus than a description of an empirical condition. For example, preCOVID-19, the last acute pandemic above the level of normal influenza deaths was the 1968-1969
influenza pandemic (1 million deaths) 106 and there are now influenza surveillance programs andsystems in place aimed to mitigate against flu outbreaks.

https://www.who.int/publications/m/item/summary-of-probable-sars-cases-with-onset-of-illness-from-1-november-2002to-31-july-2003
103
https://www.who.int/health-topics/middle-east-respiratory-syndrome-coronavirus-mers#tab=tab_1
104
https://link.springer.com/chapter/10.1007/978-3-319-68173-3_9105
https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(23)00090-9/fulltext
106
https://apps.who.int/iris/bitstream/handle/10665/329438/9789241516839-eng.pdf?ua=1
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An important note on the term ‘emerging infectious diseases’(EIDs)The sub-title of this report, and the accompanying technical report, raises a significant
terminology issue underlying the approach currently common in public health regarding
infectious disease outbreaks. ‘Emerging disease’ implies a pathogen that has newly evolved or
crossed from other hosts (e.g. a zoonosis) to humans, rather than considering changes intechnology or approaches that may simply have enabled a pre-existing disease (or pathogen) to
be detected and characterized. In the context of rapidly evolving diagnostic technology, the term‘emerging disease’ should generally be used with caution, unless a clear origin can bedemonstrated, or the clinical picture is relatively unique.
HIV is an example of a pathogen that is likely to have newly emerged, though perhaps ageneration before it was recognized. It thus illustrates the importance of both size of outbreak(related to human population size), the geographical context in which it occurs, and thedevelopment and subsequent availability of methods to detect a pathogen. While probably
arising in areas in Africa that were remote from modern diagnostics and before recent advances
in molecular biology upon which much modern diagnosis depends, 107 it was only detected several
decades later when it caused illness in more technologically advanced communities where newly
developed laboratory techniques could be employed to identify and characterize the virus.
Small outbreaks such as those now attributed to Nipah Virus infections or Mpox (formerly
Monkeypox) are therefore likely to have persisted for centuries, associated with limited, localized
disease clusters with clinical features poorly distinguishable from other illnesses, in areas wherereporting was poor. They are therefore not necessarily ‘emerging’, but ‘newly characterized’.
Terminology matters here, as much of the urgency around pandemic preparedness hinges on aperception that new diseases are rapidly emerging. If infectious disease outbreaks are actuallyrelatively stable, then the approach, and certainly the urgency, will be very different.

107

https://perspectivesinmedicine.cshlp.org/content/1/1/a006841.full

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Concerns with the introduction in the Report

The Introduction to the World Bank’s report makes bold claims regarding the need for action in
addressing pandemics and One Health needs. These set a tone for the report but fit poorly withthe evidence provided and lack objectivity. The outbreaks under consideration by the World Bank
are generally very mild in terms of disease burden:“Before COVID-19, HIV/AIDS, Nipah virus disease, avian influenza, Ebola virus disease,severe acute respiratory syndrome (SARS), Middle East Respiratory Syndrome (MERS), andZika were among the diseases that emerged from animals in contact with humans overthe last few decades, and several of these fully realized their pandemic potential.”
HIV/AIDS is clearly a major disease and fits some ‘pandemic’ definitions in that it arose as a new
disease and spread worldwide, but probably took decades to become noticeable.108 It is notnecessarily justified to suggest such a situation would be repeated today – HIV spreads slowly
compared to respiratory viruses (such as SARS-CoV-2) and by direct human contact involving
exchange of body fluids, while the ability to detect the virus and diagnose the disease has beentransformed throughout most of the world since HIV emerged.
Ebola virus has never maintained transmission beyond three countries in an outbreak (the West
African outbreak, which killed less than 12,000 people, is the only outbreak to achieve this). 109
MERS and SARS killed less than 900 people each,110,111 Zika less than 400.112 The largest Nipah
virus outbreak (Malaysia in 1998) caused under 110 deaths. 113 Avian influenza mortality is lessthan 80 annually for the past two decades, and near zero for the past 6 years. 114 For context,
tuberculosis, a major endemic infectious disease, kills approximately 1.3 million people annually(or over 3,500 daily). 115
So, the Report’s contention that“several of these fully realized their pandemic potential”
does not apply beyond COVID-19 and HIV (a several decades event) unless we accept that
pandemics are generally mild events with little or no impact on overall mortality. If the latter istrue, the question arises of whether this is indeed a major health issue and being addressed
Ibid.
https://www.who.int/emergencies/situations/ebola-outbreak-2014-2016-West-Africa
110
https://www.who.int/health-topics/middle-east-respiratory-syndrome-coronavirus-mers#tab=tab_1
111
https://www.who.int/publications/m/item/summary-of-probable-sars-cases-with-onset-of-illness-from-1-november-2002to-31-july-2003
112
https://www.nejm.org/doi/pdf/10.1056/NEJMoa2101195
113
https://www.frontiersin.org/articles/10.3389/fmicb.2021.811157/full
114
https://cdn.who.int/media/docs/default-source/global-influenza-programme/2023_march_tableh5n1.pdf
115
https://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2022
108
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proportionately, or whether other drivers are influencing the prioritization of the PPPR agenda. 116
Adoption of a calm and more low-key response might be a more appropriate approach to PPPR
in the context of these outbreaks – reflecting previous approaches such as the WHO pre-2020
guidance for pandemic influenza, 117 rather than promoting unfounded concern regarding healthburden.
A further statement adds to this confusion regarding the report’s evidence base:“Every year, zoonotic diseases sicken billions of people, killing millions, with low- andmiddle-income countries being most vulnerable.”
Pre-COVID-19, HIV/AIDS is the only claimed zoonotic spillover associated with over a million
deaths annually over the past half century. It is unclear what other diseases are referred to, asmortality is low for other zoonotic spillover outbreaks listed in Section 3.4. of this report. The
statement presumably refers to seasonal influenza, for which international mechanisms are
already in place.
The Introduction then outlines the driver, repeated throughout the report, for the claimed
increase in outbreaks:“As humans extend their footprint on the planet, encroaching into natural habitats andaltering them, the potential for diseases to emerge has increased exponentially.”,
causing (later):“…an accelerating trend of pandemics…”
As discussed below, these claims are not supported by the data cited. As a note, it is also not welldemonstrated in other main PPPR documents examined earlier in this report.
Finally, the phrase: “risk anywhere becomes risk everywhere” appears derived from the WHO COVAX
slogan “No one is safe, until everyone is safe”, 118 but again seems inappropriate and unevidenced
when referring to disease outbreaks (this slogan is reproduced again in the 2023 WHO Future
Surveillance report – see above). Outbreaks over the past century have been highly heterogenous
in impact across populations. Most are defined by local ecology and host susceptibility. As an
example, people outside of Africa are not at risk from Ebola (nor are most Africans), and Nipah
virus is highly geographically defined (Figure 12, p. 71). The severity of respiratory viruses suchas SARS-CoV-2 (COVID-19) are highly dependent on age and comorbidities.119 The statement ishttps://www.tandfonline.com/doi/full/10.1080/13563467.2024.2304180
https://apps.who.int/iris/bitstream/handle/10665/329438/9789241516839-eng.pdf?ua=1
118
https://www.who.int/initiatives/act-accelerator/covax
119
https://covid.cdc.gov/covid-data-tracker/#demographics
116
117

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therefore illogical and seems inappropriate for a serious report, again potentially aimed toinfluence policy consensus and compliance based on emotive triggers rather than analysis ofdata.

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5.4.

Concerns regarding the importance of One Health reforms

The report claims on page 14 that One Health approaches successfully combated River Blindness.
River Blindness (Onchocerciasis) is not a zoonosis, but spread from human to human through a
blackfly insect vector. Control has been achieved predominantly through insecticide use(particularly DDT) and mass drug treatment (predominantly with ivermectin). 120 This is a strange
example of what the World Bank seeks to promote as One Health, but does illustrate that healthservices have long addressed environmental influences on disease (malaria is another example).
Further examples suggested as amenable to the One Health approach include COVID-19 (forwhich a natural spillover origin is widely questioned), 121 and Avian flu, Mpox and Langya virus.
Avian Flu and Mpox have a combined global mortality below 1000 over the past two
decades,122,123,124 while Langya virus has been reported from a few dozen human cases in Asian
countries, as a probable spillover from shrews or bats. 125
The urgency for investment in One Health is then referenced to a report by the Independent
Panel on Pandemic Preparedness and Response (IPPPR),126 which itself does not cite quantitative
data but describes the views of its expert contributors. The evidence base provided to support
the urgency and scope of World Bank’s proposed One Health approach is, therefore, weak.

https://www.sciencedirect.com/science/article/pii/S0001706X10002111
https://www.dni.gov/files/ODNI/documents/assessments/Unclassified-Summary-of-Assessment-on-COVID-19-Origins.pdf
122
https://cdn.who.int/media/docs/default-source/global-influenza-programme/2023_march_tableh5n1.pdf
123
https://iris.who.int/bitstream/handle/10665/365630/WER9803-29-36.pdf?isAllowed=y&sequence=1
124
https://www.who.int/news-room/fact-sheets/detail/monkeypox
125
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10129132/
126
https://theindependentpanel.org/wp-content/uploads/2021/05/COVID-19-Make-it-the-Last-Pandemic_final.pdf
120
121

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Questions on outbreak frequency

On page 17, the Report opens its discussion on pandemic frequency by stating:“The pace of EIDs [Emerging Infectious Diseases] has accelerated at an annual rate of 6.7
percent from 1980, with the number of outbreaks growing to several hundred every year
since 2000 (Morand 2020).”
The cited study, Morand (2020), 127 presents a more nuanced picture. Outbreak frequency
decreases after 2010, while a later analysis of the same Global Infectious Disease andEpidemiology Network (GIDEON) dataset by Morand, in Morand and Walther (2023),128 shows
disease frequency in 2018 returning to 1960 levels. As Figure 10A illustrates, Morand (2020) does
show an accelerating increase to several hundred per year from 1960, peaking around 2009. It
then consistently decreases over the subsequent (most recent) decade.
The reported risk per capita of becoming a case in an outbreak has been declining based on thesame GIDEON dataset (Smith et al., 2014).129 The overall increase since 1960 in reported outbreakfrequency forming the basis of GIDEON data takes no account of improved communications,road networks, development and expansion of PCR and other diagnostics (e.g. point of careserology and antigen tests) and laboratory capacity. Reporting of outbreak diversity is heavily
dominated by more highly developed countries, expanding in less developed countries later,reflecting expectation if the probability of detection and reporting are major drivers of anapparent increase in outbreak frequency (Figure 11).

https://www.sciencedirect.com/science/article/pii/S0006320720307655
https://www.biorxiv.org/content/10.1101/2020.04.20.049866v2
129
https://royalsocietypublishing.org/doi/10.1098/rsif.2014.0950?url_ver=Z39.88-2003&rfr_id=ori%3Arid%
3Acrossref.org&rfr_dat=cr_pub++0pubmed
127
128

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Figure 10. Extracts from Morand (2020) 130 Figures 3 and 4, showing (A) reported outbreakfrequency 1960 to 2020, and (B) relationship between outbreak frequency and animal species
density.

Smith et al. (2014) are then quoted as suggesting that“the richness of microbes causing diseases also increased significantly over that period.”

130

https://www.sciencedirect.com/science/article/pii/S000632072030765

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However, the World Bank authors fail to note that Smith et al. went on to show a per capita
reduction in cases (i.e. risk per person) once internet use (as a proxy for improved
communications) was taken into account.
The World Bank continues:“…and the yearly probability of an occurrence of large outbreaks could increase up to
threefold in the coming decades (Marani et al. 2021).”
The reference here (Marani 2021) is discussed elsewhere in this report. Yet here it is important
to note that the claimed 3x increase in outbreak frequency is a modelling result that ignores
reasons for increased detection and identification over the past few decades. Two studies arethen cited as supporting this finding; Jones et al (2008) and Daszak et al. (2001).131,132 These studies
do not cover the past two decades and do not lend direct support for this assumption (see below,
for a more detailed discussion of Jones et al.).

131
132

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5960580/
https://doi.org/10.1016/S0001-706X(00)00179-0

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Figure 11. Diversity of reported outbreaks over time in the GIDEON database, reported by Smithet al. (2014). 133

https://royalsocietypublishing.org/doi/10.1098/rsif.2014.0950?url_ver=Z39.882003&rfr_id=ori%3Arid%3Acrossref.org&rfr_dat=cr_pub++0pubmed
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The Example of Nipah Virus Disease

The World Bank Report illustrates its concerns regarding pandemic risk through a section on the
Nipah Virus (A Cautionary Tale – The Story of Nipah Virus Disease. [page 17]). Analysis of the claims
here regarding Nipah Virus, a cause of recurrent severe encephalitis outbreaks, is important in
illustrating the overall approach taken within the Report.
The discussion starts with:“Events started in 1998 in Nipah, a suburb of Ipoh, Malaysia, when villagers experienced
febrile encephalitis for which there was no cure or treatment. Young people would behealthy one day and the next day their brains would swell up. They couldn’t walk or talk.
About half the patients died.”
It is then noted that the outbreak was initially misdiagnosed as Japanese Encephalitis (amosquito-borne illness). It was soon recognized that the illness was associated with contact with,and preceded by an unusual illness in, pigs. The report postulates that environmental changes
including deforestation promoted increased movement of fruit bats (that carry the virus) into
fruit orchards near pig farms (perhaps implausibly, as fruit bats commonly fly to orchards to eat
irrespective of deforestation, and to the increased density of pig rearing, promoting transmission
within farms). The Malaysian government culled approximately one million pigs during the
response, with considerable economic impact on rural communities.
Mortality from the Malaysian Nipah virus outbreak, the first and largest such outbreak recorded,
killed 105 people out of 376 total cases by 2014. 134,135,136 Subsequent outbreaks elsewhere haverecorded human to human transmission within villages and health facilities. 137,138 No further
outbreaks have been recorded in Malaysia, and outbreaks have been confined to very limited
geographical areas, despite the wider range of the major fruit bat species associated withspreading the virus (Figure 12). The World Bank Report notes 412 deaths in total globally at timeof publication (unreferenced in the supporting technical report). 139 The virus is carried by otherbat species in Asia and Africa, but without recognized transmission to humans. 140

https://link.springer.com/article/10.1007/s11908-006-0036-2#preview
Looi LM, Chua KB. Lessons from the Nipah virus outbreak in Malaysia. Malays J Pathol. 2007 Dec;29(2):63-7. PMID:
19108397
136
https://www.frontiersin.org/articles/10.3389/fmicb.2021.811157/full137
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2878219/
138
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3373078/
139
https://documents1.worldbank.org/curated/en/099042023145037128/pdf/P17840202eae520aa0ab910137cb6f1a142.pdf
140
https://www.who.int/news-room/fact-sheets/detail/nipah-virus
134
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Figure 12. Areas of known human Nipah Virus infections, and range of the bat species
harbouring the virus. 141

Reconsidering the evidence, the Nipah Virus story suggests a quite different scenario than the
one suggested by the World Bank. The geographic spread of identified infections strongly
suggests that the virus is not newly emerged, as does its presence in other bat species. The lackof any recurrence in Malaysia since its initial identification therefore suggests that this was an
unusual spillover of an endemic zoonotic agent rather than a newly emerged virus.
It took several months for the causative pathogen to be identified in Malaysia as there was notest available for a previously unrecognized virus. PCR tests, the main form of identification, were
invented less than 20 years earlier, and genomic sequencing barely available. It’s similarity to
Japanese Encephalitis, relative rarity and relative remoteness of most outbreaks suggest that141

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prior unrecognized outbreaks are likely to have occurred across the current range without being
characterized as a separate disease.
Nipah Virus infection causes a clinically serious disease, with high case fatality rate. 142 However,no recorded outbreak has caused more than 105 deaths. The lack of recurrence in Malaysia
suggests it is readily avoidable once its epidemiology is understood and mitigation mechanisms
are in place, though outbreaks are more common in India and Bangladesh.143 The main change
regarding Nipah Virus in recent decades is likely our ability to recognize it, rather than it emerging
de-novo.
The World Bank Report finishes its discussion on Nipah with:“But the Nipah story is also a tale of a changing climate, changing use of land and foodsystems, lack of adequate biosecurity on farms, urbanization, social inequities andtensions, human activities driving disease emergence, and total blindness to prevention.”
While more intensive farming seems a likely factor in promoting pig-pig, and therefore pig-humanspread (pigs are infected by proximity to fruit bats or bat excrement), the argument that forest
degradation would increase bat-pig interaction is poorly evidenced, and not well supported as a
general underlying cause of increased zoonotic spillover. 144,145 The link between Nipah Virusdisease and climate change remains unexplained in the Report, as do links to social inequities
and tensions. “Total blindness to prevention” may refer to a lack of ability to detect a disease if
no test, or understanding of its epidemiology, is available. The example in the Report surely
demonstrates that Nipah Virus Disease is largely an example of a newly recognized problem
being, after an initial large outbreak, quite well addressed through existing mechanisms.
The story of Nipah Virus is perhaps best suited as an example of the improved differentiation of
diseases as diagnostic capacity expands, and the ability to manage and contain such healththreats within existing health system capacity.

https://www.cdc.gov/vhf/nipah/symptoms/index.html
https://www.cdc.gov/vhf/nipah/index.html
144
https://www.nature.com/articles/s41893-019-0293-3
145
https://link.springer.com/article/10.1007/s10393-014-0941-z
142
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Discussion on investments.

The World Bank Report quotes the G20 High Level Independent Panel (HLIP) and a joint report of
the World Bank and WHO to summarize the initial financial requirements for PPPR:“The G20 High Level Independent Panel (HLIP) estimated the amount in international
financing for pandemic preparedness that would be required every year for five years at$15 billion, along with significant increases in domestic spending, to address current
gaps.”“Recently, WHO and the World Bank estimated the total amount at US$31.1 billion
annually, of which US$10.5 billion of international financing is needed annually for the
next five years (WHO and World Bank 2022).”
The Report (on page 27) then suggests this does not fully cover costs of the One Health approach.
As a response the report advocates, adding $2.1 billion for veterinary public health systems, $5
billion for farm biosecurity, and $3.2 to $4.4 billion to reduce deforestation; in total adding afurther $10.5 to $11.3 billion/year (it should also be noted that the $31.1 billion estimate fromthe 2022 report does not cover pandemic response financing and capacity building, which if
pegged to the WHO Joint Evaluation Exercise (JEE) is estimated to add another $124 billion over
five years to the total amount”).146
The claim on deforestation promoting pandemic risk is repeated in Box 3 on page 34, and again
on page 36 of the Report. The costing of mitigation of deforestation cites Allen et al. (2017). Allenet al. re-examined the data147. The authors found that the main associations with high EID
outbreaks were broadleaf evergreen (i.e. tropical) forests and high mammalian species richness.
This is not consistent with deforestation, where tree cover and mammalian species richness are
expected to be reduced.
While forest preservation is desirable for many reasons, its use here to justify billions of dollars
in funding to mitigate pandemic risk appears poorly supported by evidence.
Country ownership and equity.
Page 38 of the World Bank report discusses COVID-19 costs and country ownership. The
approach outlined here raises concerns. The report justifies a need to “mainstream” One Healthwith its associated costs on the basis that:

146
147

https://gh.bmj.com/content/8/1/e011172
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5654761/

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Page 74 of 114“Once the SARS-CoV-2 virus started spreading, it reached every corner of the globe, andno country was spared COVID-19’s health and economic losses. Pandemic risk may havebeen perceived as hypothetical and a concern for high-income countries, while endemic
diseases already burden lower-income countries. However, the burden of pandemics isheavy also in low- and middle-income countries along many dimensions,…”
This statement seems at odds with the direct health costs of COVID-19 in many low-income
countries, and the costs of diverting resources from other priorities (e.g. other health priorities
and economic development) to PPPR. In most sub-Saharan African countries the burden of
COVID-19 paled beside other higher burden, and currently increasing, endemic disease
burdens.148 The economic costs of lockdowns and imposed income loss in the COVID-19 publichealth response cannot reasonably be included in the pandemic burden in these countries
without demonstrating that such measures as school closures, market closures and closing of
employment in such situations were a necessary response. 149,150,151,152
The health burden of COVID-19 was clearly heterogenous, affecting predominantly older
populations with metabolic diseases who are predominantly in higher-income countries. 153,154,155
True country ownership and equity would therefore seem to mitigate against low-income
countries having to contribute or divert resources to this area, whilst higher burden healthpriorities persist. The Report does not seek to refute this, but leaves the issue unaddressed. In
this context of “country ownership”, the subsequent note that the proposed amendments to the
IHR, intended to strengthen PPPR, will be:“…enforcing minimum standards for what constitutes One Health spending…”
also suggests an emphasis against country ownership and control.
In finishing this section, the Report repeats the need to:“…correct the prevailing panic and neglect cycle…”

https://www.ajtmh.org/view/journals/tpmd/aop/article-10.4269-ajtmh.21-0899/article-10.4269-ajtmh.21-0899.xml
https://apps.who.int/iris/bitstream/handle/10665/329438/9789241516839-eng.pdf?ua=1
150
https://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2022151
https://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2022152
https://www.unaids.org/en/resources/fact-sheet
153
https://www.sciencedirect.com/science/article/pii/S001393512201982X#bib42
154
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9420458/
155
https://covid.cdc.gov/covid-data-tracker/#demographics
148
149

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This claim remains confusing in the context of PPPR, as no examples of panic are provided and,aside from the COVID-19 event, it is unclear when such conditions have been widely suggested
to have prevailed in recent decades. Certainly not widely and consistently.

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Summary of World Bank Report

In summary, the World Bank’s report is poorly evidenced and provides no solid support for
rapidly increasing pandemic risk. The references cited within the report to support thiscontention are selectively cited and appear sometimes to be mis-interpreted. There is no solid
recognition of competing priorities and potential opportunity costs of the resource allocation
proposed, beyond a general recognition that LMICs have a higher endemic infectious disease
burden than high income countries.
Fundamentally, both the report and its references, which rely heavily on analysis of the GIDEON
database, fail to take account of the development and expansion of diagnostics, and provide only
limited allowance for improving communications and surveillance, and funding for the same.
Claims of rising risk, together with emphasis on a perception of panicked historical responses,
are used to paint a picture of urgency and chaotic or absent response from current health systemmodels. However, the examples provided, such as Nipah Virus outbreaks, could equally serve toillustrate current success in mitigating potential harm, with acute outbreaks resulting in low
disease burdens and COVID-19 (predominately affecting older people in high income countries)
being an outlier. The argument for spending a further $10 billion annually on One Healthapproaches, not included in previous budgeting, and the over $31 billion previously proposed,
are therefore poorly supported.

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6. World Bank Technical Report (2022): Increasing
investments in One Health to reduce risks of emerging
infectious diseases at the source.
6.1.

Overview

The World Bank Technical Report was produced to support the 2022 Report ‘Putting Pandemics
Behind Us’.156,157 The report covers evidence for pandemic risk and reasons for emergence, andthen the financial implications. Here we discuss the first two issues, in the initial sections ‘Whatis the trend in emergence of infectious diseases?’, and ‘What drives emergence of infectious
diseases?’ and critique the assumptions and claims.
The report states in the first paragraph of the introduction that:“The burden of infectious diseases continues to grow, and humanity faces more outbreaks,some with the potential to become pandemics.”
and“The One Health approach offers a practical and successful framework to reduce
pandemic risks at the source.”
The first claim requires scrutiny. It is well recognized that the main improvements in life
expectancy in both high- and low-income countries has been achieved through reduction in therisk of infectious diseases. 158,159 This has occurred primarily through prevention and throughbuilding host resilience, improved sanitation, nutrition and general living conditions. Other
aspects of modern medicine; antibiotics and to a lesser extent vaccines, have also played a major
role.160 Life expectancy increase is continuing globally, with a 6.6-year increase recorded by WHO
between 2000 and 2019.
Non-communicable diseases are now globally the main cause of mortality, as the share from
infectious disease continuously declined for decades before the COVID-19 pandemic.161 For an
institution such as the World Bank to claim the opposite is highly misleading and misrepresents

https://documents.worldbank.org/en/publication/documents-reports/documentlist?qterm=P178402
https://documents1.worldbank.org/curated/en/099042023145037128/pdf/P17840202eae520aa0ab910137cb6f1a142.pdf
158
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2836340/
159
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7404362/
160
Ibid.
161
https://ourworldindata.org/burden-of-disease
156
157

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information in a way that weakens the evidence pool and its ability to provide suitable insights
for decision-making.
The emphasis of One Health approach is that it “recognizes that human and animal health areinterdependent and bound to the health of the ecosystems…” From this it forms the justification for
subsequent recommendations.
Section One of the Report, “trends in emergence of Infectious Diseases,” begins with:“Since 1980, the number of outbreaks per year has been steadily increasing (Figure 1a).
Between 1980 and 2012, the number of outbreaks increased at an average of 6.7 percent
per year.”This is based on Smith et al (2014), 162 who found that while the number of reported outbreaks
increased, the overall mortality rate (i.e. risk) from outbreaks was declining (Figure 13). Moreover,Smith et al. do not take into account changing detection and reporting capacity over this period(see Section 7.3 of this report).
Figure 13. Evolution of outbreaks in the World Bank technical report, based on Smith et al.
(2014).

https://royalsocietypublishing.org/doi/10.1098/rsif.2014.0950?url_ver=Z39.88-2003&rfr_id=ori%3Arid%
3Acrossref.org&rfr_dat=cr_pub++0pubmed
162

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Smith et al. based their analysis on the GIDEON database. 163 The World Bank authors note that
Morand et al. (2020), 164 extending the same database to 2020, demonstrated that outbreakfrequency then declines. As presented by the World Bank,“Although not seen in our dataset, graphs from Morand (2020) show a stagnation of theyearly outbreaks between 2013 and 2016, followed by a decline.”
This note is important, as the report was published in 2022 and there is no obvious reason not
to present a more recent dataset.
The GIDEON data is also reproduced in Stephens et al. (2021), 165 another source cited within theWorld Bank report. This study demonstrates the major reduction in reported medium (>100cases) and large (>1000 cases) outbreaks over the period 2009 to 2017 (Figure 14).
Therefore, the World Bank’s opening sentence that “the burden of infectious diseases continues togrow, and humanity faces more outbreaks, some with the potential to become pandemic” is contrary
to what the datasets within the cited research shows.

https://www.gideononline.com/
https://www.biorxiv.org/content/10.1101/2020.04.20.049866v2
165
https://royalsocietypublishing.org/doi/10.1098/rstb.2020.0535
163
164

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Figure 14. Figure 1 from Stephens et al. (2021), one of five key references for the World Bank
analysis, reproducing GIDEON data and demonstrating a reduction in recent reported
outbreaks.166

Figure 14 also underlines the danger of assuming that the increases in outbreak frequency seenin Figure 11 (p. 69) and derived from the GIDEON database represent a true increase. An increaseof approximately 6 times from 1980 does not sound biologically likely without quite dramatic
ecological changes. The major reporting areas in Smith et al. are North America and Europe andthese remain relatively stable, with Asia and later Africa becoming significant only in recent years.
In 1980 to 1984 North America accounted for 35% of global reports (page 6), which then reduced
relative to other regions over time. These trends strongly suggest that detection and reporting
capacity spreading to lower-income settings are major drivers (see discussion of Smith below). A
relative increase in viral versus bacterial outbreaks over time also supports this assumption, as

166

https://royalsocietypublishing.org/doi/10.1098/rstb.2020.0535

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techniques to detect viruses, particularly PCR and antigen detection, have been developed withaccess expanded more recently (see Section 1.1 of this report).
The authors of the World Bank report recognize the issue of reporting capacity on a geographic
basis (page 9):“As with all data on infectious diseases, a potential limitation is that some regions areoverrepresented in global incidence of infectious diseases because of better reporting andmonitoring and more frequent testing. In that sense, when looking at the baseline andevolution of outbreaks and deaths based on region, it is likely that numbers for Africa andSouth America are underestimated.”
This raises a key question with the underpinning methodology of the report, since it isinconsistent for the authors not to recognize that the same relationship must apply on a temporalbasis, having noted that the relative share of reporting from higher income areas reduced overtime in keeping with growing capacity in lower income settings. This is shown clearly in Figure 2of Smith et al. (2014),167 based on the same dataset and reproduced in Figure 11 above.
Section Two of the World Bank report, “What drives emergence of infectious disease?”, lays out
the basis for the proposed One Health response to perceptions of increasing risk. While listing awide range of drivers of infectious disease, including host susceptibility, the report focuses on
three:…land use change, agriculture industry change, and international travel and commerce
were the top three primary drivers.
and these form a basis for subsequent financial calculations.
The first, land use change, is discussed here, as the report emphasizes additional spending onforest preservation as a method of reducing outbreak risk:… increases in outbreaks of zoonotic and vector-borne diseases from 1990 to 2016 wereassociated with deforestation in tropical areas as well as reforestation in temperate
countries (Morand and Lajaunie 2021).
The cited paper of Morand and Lejaunie illustrates this (Figure 15). When assessed at a country
level as shown in Figure 16, this reveals an association with latitude, but with similar areas under
the curve indicating similar frequencies of association of outbreaks with both deforestation andreforestation. As tropical areas are undergoing most rapid deforestation and are also

https://royalsocietypublishing.org/doi/10.1098/rsif.2014.0950?url_ver=Z39.882003&rfr_id=ori%3Arid%3Acrossref.org&rfr_dat=cr_pub++0pubmed
167

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characterized by lower income countries, 168,169 the association may be explainable by a relative
increase in detection capacity incidental to deforestation in these countries.
Figure 15. Figure 5 from Morand and Lejaunie (2021), demonstrating the associations at acountry level of change in forest cover and infectious disease outbreaks. 170

Morand 2020, 171 also cited by the World Bank report, reinforces the argument that in somecircumstances, species richness is associated with human outbreak frequency, suggesting that
more intact ecosystems may be associated with a higher rate of zoonotic spillover events, at leastin some circumstances (Figure 16). A weaker correlation exists between the number of
threatened wildlife species associated with outbreak frequency (Figure 16), but extrapolating thisto forest degradation would rely on several assumptions that require more in-depth analysis.

https://www.science.org/doi/10.1126/science.1244693?url_ver=Z39.882003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed
169
https://www.nature.com/articles/sdata20184
170
https://www.frontiersin.org/articles/10.3389/fvets.2021.661063/full171
https://www.sciencedirect.com/science/article/pii/S0006320720307655
168

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Figure 16. Extract from Fig. 2 in Morand (2020), illustrating human outbreak frequency in the
GIDEON dataset with wildlife species diversity and number of endangered species in acountry.172

The example of Nipah Virus in Malaysia is discussed in Section 5 of this report. The Nipah Virus
example is also repeated in Annex 1 of the report, failing to apportion any significance to the factthat it was initially misdiagnosed, and only identified through use of newly developed diagnostic
technologies. The argument that it newly emerged is therefore poorly supported.

172

Ibid.

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The discussion on the relationship between land degradation and outbreaks is extended tofinancial returns in Section 3 (page 23):
“wildlife to humans are likely to occur overlays deforestation risk, high biodiversity, andaccessibility data. We estimate the annual cost of avoided deforestation for each of the
zoonotic spillover risk groups developed by Allen et al. (2017) (see Appendix 1 for moredetails on the approach developed by Allen et al. 2017).”
Allen et al. (2017) 173 showed the highest spillover events occurred in areas of tropical rainforest(evergreen broadleaf trees) with high mammalian species density (i.e. potentially intact forest
ecosystems rather than deforested or degraded areas). Their findings are therefore significantly
mischaracterized in the report, where they are associated with ‘land use changes’, while Allen etal. rather note the presence of ‘wildlife species richness, concluding that “Rather [than biodiversity
loss increasing risk], it is consistent with previous suggestions that the relationship between
biodiversity and disease risk is complex, context-specific and idiosyncratic.”174 Moreover, while there
are other good reasons to retain healthy tropical forest ecosystems, the findings of Allen et al.
actually signal perverse outcomes that may contribute to, not reduce, outbreaks arising fromwildlife spillover events. For example, the main policy recommendation made in this area is topreserve 50% of broad leaf (mostly tropical) forest that would otherwise be degraded – thehabitat associated with the highest rate of spillover events by Allen et al. Under this logic, theWorld Bank seeks health-sector investments to maintain pools from which the greatest level ofwildlife spillover events occur.

173
174

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5654761/
Ibid.

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6.2. Annex 1. Classification of EID risk
The Authors of this technical report note reliance on Jones et al. (2008) and Allen et al. (2017) todetermine the main drivers of Emerging Infectious Diseases (EIDs).
“There are two key papers that classify EIDs. Jones et al. (2008) analyze a database of 335
EID events (origins of EIDs) between 1940 and 2004. Allen et al. (2017) builds on Jones etal. (2008).”
Jones et al. (2008) is discussed in detail elsewhere. Their main finding was an increase in reported
EIDs from the 1940s, peaking in 1980-1990, and reducing somewhat in the 1990s. They foundthat:“human population density was a common significant independent predictor of EID eventsin all categories, …. Wildlife host species richness is a significant predictor for the
emergence of zoonotic EIDs with a wildlife origin.”
They did not find land degradation as the major driver, but rather conditions where more intact
ecosystems support high mammalian diversity.
Annex 1 states, regarding Allen et al.’s findings:“They find that the risk of disease emergence is elevated in tropical forest regions
experiencing anthropic land use changes related to agricultural practices and where
mammal biodiversity is high.”
This is a significant mischaracterization of the cited reference. Allen et al. state, at the start oftheir Results section:"After factoring out reporting effort (in the weighted model), evergreen broadleaf trees(median 7.6% of the model’s predictive power), human population density (6.9%), Global
Environmental Stratification (climate) (5.9%), and mammal species richness (an aspect of
biodiversity) (5.6%) had the largest relative influence over the distribution of EID events(Fig. 1).”Figure 1 from Allen et al. (Figure 17, below) illustrates their findings.

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Figure 17. Fig. 1 from Allen et al. (2017), illustrating identified major drivers of emerging
infectious disease events, after adjustments for reporting bias. ‘Global envir. Strat.’ Refers to
climate.175

175

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5654761/

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Summary of World Bank Technical Report analysis

These findings suggest that the World Bank technical report has significantly mischaracterized its
keyreferences

regardingdriversofzoonotic

spillover.

Moreover,

based

on

this

mischaracterization, the World Bank is advocating for increased spending to address highspillover rates. While noting the apparent impact of reporting bias in raising rates in high income
countries (restated in Annex I), it fails to address the obvious extension of this to a temporal bias
in reporting, where the techniques used to identify and characterize many outbreaks in the lastfew decades did not even exist a few decades earlier. The basis of the proposed spending to
reduce deforestation in support of EID reduction, and more broadly the entire urgency ofaddressing what the World Bank claims is a rapidly increasing threat, appears unevidenced basedon their cited sources.

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7. Key evidence papers
7.1.

Bernstein et al. (2022): The costs and benefits of primaryprevention of zoonotic pandemics

Bernstein et al. published a heavily cited review in 2022 in Science Advances on the costs andbenefits of PPPR: “The costs and benefits of primary prevention of zoonotic pandemics.”176 This
paper is broadly consistent with the findings of the G20 Bali Leaders Declaration and the WorldBank examined above, concluding that savings in averted expenditure in a pandemic would
greatly exceed expenditure on PPPR. In this case accrued savings are calculated to be 20 times
the expenditure on PPPR.
The paper has been influential, with over 41,000 downloads, 39 citations and attention by 60
news outlets as of 11/12/2023.177 The analysis of Bernstein et al. formed the basis of the financial
claims in a significant Nature journal publication and blog on the justification for investing in PPPR
in May 2022.178
Key claims
The key claims of Bernstein et al. are that investment of approximately $20 billion annually inprevention and mitigation of zoonotic spillovers will realize savings of 20 times that amount incosts of human lives saved, and 10 times that amount in economic losses avoided. This is basedon an evaluation of “every novel viral zoonosis that has appeared since 1918 (to 2020) that killed atleast 10 people”. Lives lost are calculated as an average of 3.3 million every year (on a 2019 global
population estimate), with a costing of these lives at between $350 billion and $21 trillion dollars(a wide range of uncertainty depending on where the lives are lost). An estimated cost of $10
billion to ‘halve these losses’ then adopts the upper end of this range of lives lost (without
explanation), leading to a conclusion that $10 trillion will accrue in savings.
Methods and evidence
The spillover events (“all”) include the 1918-19 Spanish Flu, and the first 18 months (2020 to July2021) of recorded COVID-19 mortality. As apparent from Figure 18, the 1918-19 pre-antibiotic

https://www.science.org/doi/10.1126/sciadv.abl4183?url_ver=Z39.882003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed
177
Ibid.
178
https://www.nature.com/articles/d41586-022-01312-y
176

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event dominates the overall numbers. Most deaths in this outbreak are considered to have been
due to secondary bacterial infection, and so highly unlikely to occur in such numbers today.179
The cost of lives lost is calculated as Standardized Mortality Units (SMU); “where one SMU is thepercent of the population who die multiplied by 104” (described in Bernstein et al.’s Supplementarymaterials). This assumes all lives lost are of equal burden, and thus does not take into accountlife-years lost. This is significant when dealing with COVID-19, as an example, where average ageof death was commonly near 80 years, and usually in people with significant life-shortening comorbidities.
The authors include no spillover events between 1919 and 1951. They have then calculated
average annual lives lost from this data, using the 70 years (1950 to 2019) as a denominator andadjusting the global population and mortality rate for population in the year of each outbreak.
However, Spanish Flu deaths are included in this truncated (70 year) period of analysis, despite
occurring 30 years earlier and in the pre-antibiotic era. The authors note that failure to include
the Spanish Flu would change the results by “almost an order of magnitude”.
“To set the annual probability of any outbreak, we use the frequency of outbreaks since1950 (the first year at which reasonable mortality data are available for most outbreaks).
We observe 28 episodes in 70 years or about 40%...
Our core analysis includes Spanish influenza; this improves our ability to calibrate the tailof the distribution composed of severe events that only occur a few times in 100 years.”
The table of spillover events and their mortality at time of the event, and adjusted mortality per
million population, is shown in Figure 18.

179

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2599911/

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Figure 18. Table of “all” zoonotic spillover events from 1918 to mid-2021, from Bernstein et al.
(2022). Outbreaks contributing most to the total are highlighted here for emphasis.

A few concerns immediately stand out from Bernstein et al.’s data in Figure 18, that significantly
influence the paper’s conclusions:
1. Although outbreak mortality is averaged from 1950 onward, pre-antibiotic Spanish Flu
from 1918 is included. This event dominates mortality, and these deaths were dominatedby pre-antibiotic era secondary bacterial infections.
2. The complete lack of zoonotic spillover outbreaks between 1919 and 1951 drives aperceived increase in outbreak incidence. As with later outbreak frequency, the authors

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have taken no account of improved detection methods; development of, and then
expanded access to, serology tests, antigen detection, and PCR.
3. All pre-2020 numbers after the Spanish Flu are dominated by the 1957 and 1968 influenza
outbreaks, and HIV. HIV is unusually included here in a list of acute zoonotic spillover
events. Although it is probably due to a spillover event, it is not commonly consideredwithin the scope of the PPPR agenda as it is a several decades-long event and is now anendemic disease with well-developed mitigation programs.180
4. COVID-19 also dominates. There is controversy regarding the origin of this outbreak;whether it is a zoonotic spillover or a derivative of laboratory research. 181,182
5. The mortality of events excluding influenza and HIV/AIDS pre-Covid is rarely above 1 per
million population. Thus, there are only 5 events with mortality greater than that ofseasonal influenza in the 103 years considered by the authors.183
Re-evaluation of evidence
Bernstein et al. calculated 3.3 million deaths per year by including events over the 70 years to2021, adding Spanish Flu mortality from 2018-9, as mortality per million at time of the event. This
was then adjusted to approximately 7.5 billion population for 2021 (actual value not provided).
In response, we have also recalculated Bernstein results for a population of 8 billion (consistent
with the global; population at the end of 2023) (Figure 19). The figure also demonstrates whatthese numbers would be if calculated only from outbreaks over the 52 years from 1969 (after thelast significant influenza pandemic).
Figure 19. Re-calculation of Bernstein et al. 2022 data (from Table 1 of their paper). First row
follows Bernstein, including Spanish Flu but averaging over 70 years. Subsequent rows reflect
52 years from 1969 to 2020, since the last significant influenza pandemic. Column second fromright reflects total world population of 7.5 billion (approximating Bernstein’s denominator) andright column reflects population at end of 2023.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3234451/
https://www.bmj.com/content/382/bmj.p1556
182
https://www.dni.gov/files/ODNI/documents/assessments/Unclassified-Summary-of-Assessment-on-COVID-19-Origins.pdf
183
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6815659/
180
181

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As Figure 19 demonstrates, Bernstein et al.’s claims of 3.3 million per year average mortality fromzoonotic spillovers, and therefore their return on investment, are almost wholly driven by the
pre-antibiotic Spanish Flu event, with the remainder dominated by two influenza outbreaks overhalf a century ago, the HIV epidemic, and by the recent COVID-19 event. Excluding HIV/AIDS and
COVID-19 from the last 50 years results in a total global mortality form spillover outbreaks of lessthan 19,000 annually, the equivalent of about 5 days of tuberculosis mortality.184 There are strong
programs already in place for influenza surveillance globally, and it is highly unlikely that HIV
would be missed today as it was for an entire generation in central Africa in the period before
PCR and modern point of care diagnostics, road infrastructure and improvements incommunication. 185
The underlying drivers of Bernstein et al.’s conclusions are illustrated in the first figure in theirpaper (Figure 20). This demonstrates a fairly steady mortality rate from outbreaks recorded overthe past 60 years, but an increasing cost of response to outbreaks. One could claim that mortality
has been kept constant by increasing expenditure, but an equally valid interpretation would bea change in the type of response with no material benefit in lives saved. The data suggests, at aminimum, that analysis of the value of recent responses is warranted before they are used as
evidence of justifiable economic costs of outbreaks.
Figure 20. Fig. 1 from Bernstein et al. (2022), demonstrating a relatively constant frequency ofoutbreaks, but an increasing cost (larger diameter circles) over time.

184
185

https://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2022
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3234451/

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Summary
Bernstein et al. make claims to support a very high average annual mortality from zoonotic
spillovers, 3.3 million deaths, equivalent to WHO’s estimates of deaths with SARS-COV-2 during
the COVID-19 outbreak. These numbers are driven almost entirely by pre-antibiotic era Spanish
Flu mortality, and mortality from the 1957 and 1968 influenza outbreaks and HIV. The first 19
months of COVID-19 also contribute significantly, though natural origin is subject to debate.
Remaining mortality is dominated by Ebola virus outbreaks that are confined to few countries incentral and western Africa.
Claims of return on investment then assume that investment can achieve major reduction in
frequency and/or mortality, and that the highest end of the range of costs, $20 trillion annually,is realistic. For instance, Bernstein et al. claim that strategies that reduce the risk of any epidemic
by half would reduce mortality costs by up to $10 trillion. The authors suggest that the highest
cost intervention ($19 billion) is closing down wildlife farming in China, followed by curbing
deforestation ($1.53 to $9.59 billion). Given the lack of evidence for wildlife farming being the origin of the
major outbreaks listed – Spanish Flu and HIV, and the controversy regarding COVID-19 origins, the impact of
this expenditure requires further clarification. Similarly, the relationship between deforestation and outbreaks
is complicated,186,187 as discussed elsewhere in this report.
Bernstein et al.’s estimates on costs and return on investment therefore rely on enormous
assumptions that are not well supported by the evidence provided. Influenza and HIV, the main
drivers of their numbers, already have extensive existing surveillance programs in place, while itis unclear how COVID-19 would have been averted or mitigated through their proposed
investments (nor increased surveillance mechanisms if it was a result of laboratory escape). The
paper does not provide strong evidence either of increasing pandemic risk, or of return on PPPR
investment approaching the claim.

186
187

https://www.bmj.com/content/382/bmj.p1556
https://www.dni.gov/files/ODNI/documents/assessments/Unclassified-Summary-of-Assessment-on-COVID-19-Origins.pdf

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Marani et al. (2021): Intensity and frequency of extreme novel
epidemics

Marani et al. 2021 (Proceedings of the National Academy of Science (PNAS)) seek to determine
the risk of occurrence of epidemics of various intensity. The paper has been widely referenced
regarding the frequency of infectious disease outbreaks. The 2022 World Bank report "Putting
Pandemics behind us" references Marani as:188“the yearly probability of an occurrence of large outbreaks could increase up to threefoldin the coming decades (Marani et al. 2021)"
while Vora et al., 2022 (Nature) 189 cite it as a main reference for evidence of rapidly increasing
outbreak frequency.
Methods and evidence
A historical record of infectious disease epidemics was assembled from 1600 to 2020, based on
476 documented epidemics derived from a literature search. To calculate epidemic intensity
specifically, falling between 1600 and 1945 were selected (395 events), from which 182 withknown duration and an estimated mortality above a threshold of 0.001% deaths per year wereselected.
By basing the intensity calculations only on pre-1945 epidemics, the authors were able to exclude
epidemics that were (possibly) ended by the introduction of vaccines or effective treatments,
while outbreak frequency was based on all in the data set. Marani et al. assume the probability
distribution of epidemic intensity in this pre-1945 interval to be time-invariant, meaning that anincrease in the frequency of outbreaks would be expected to lead to an increase in the frequencyof severe epidemics.
“Epidemic intensity” is defined by mortality per year (Figure 21). Many of the listed outbreaks areeither bacterial or rickettsial infections for which antibiotics are now available and effective (e.g.
typhus or bubonic plague) or in which most actual death was associated with secondary bacterial
infection (Spanish Flu). 190 The main exceptions are smallpox (now eliminated through vaccination)and yellow fever (also relatively well controlled).

https://documents.worldbank.org/en/publication/documentsreports/documentdetail/099530010212241754/p17840200ca7ff098091b7014001a08952e
189
https://www.nature.com/articles/d41586-022-01312-y190
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2599911/
188

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Figure 21. Fig 1 from Marani et al. (2021). Epidemic intensity and probability of occurrence(exceedance), 1600-1944, and cause of major epidemics.

Considering the entire dataset from 1600 to 2020, the assumption of increasing frequency ofoutbreaks looks less solid (Figure 21). As the authors note, we see “an increase in the yearly
number of outbreaks until the end of World War II and subsequent rapid decline thereafter”,
even as global population markedly rises. This is at odds with other datasets in this report (e.g.
the GIDEON dataset illustrated in Morand and Walther (2020) where an increase in event isrecorded up to the decade of 2000 to 2010, 191 or that of Jones et al. (2008) showing a decline from1990.192 This earlier decline probably reflects the emphasis of Marani et al. on larger pandemics,
often of a bacterial (i.e. antibiotic-susceptible) aetiology.

191
192

https://www.biorxiv.org/content/10.1101/2020.04.20.049866v2
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Figure 22. From Figure S1 from supplementary material of Marani et al. 2021: Number of
epidemics (without consideration of numerical epidemic intensity) occurring in each year onrecord.

Most importantly for this report, Marani et al.’s data in Figure 22, found in their supplementary
data, contradicts both some conclusions of the paper (a threefold increase in severe pandemics
is likely) and the related citation in the World Bank’s Putting Pandemics Behind Us report(discussed above): “the yearly probability of an occurrence of large outbreaks could increase up
to threefold in the coming decades.” This is odd, and appears to be based on Marani et al.’smodelling based on pre-1945 (i.e. pre-frequency decline) data, and on two secondary sources
cited in turn within Marani et al.’s paper:
1. Jones et al. 2008.183 This (discussed elsewhere in this report) is misinterpreted as it does
not show an increase to the 1980s driven significantly by HIV and associated infections,and by antibiotic resistance mutations that are considered independent outbreaks (bothof which Marani exclude).
2. Daszak et al. 2001,184 which mainly discusses epidemic infectious disease outbreaks inwildlife, but also infections of very limited burden in humans and provides opinion, notspecific evidence for human cases.

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Marani et al. therefore appear to have somewhat mis-represented Jones et al.’s findings andproduced modelling that fails to reflect the changes they found post-1945, and then in turn theWorld Bank have inadvertently assumed a real increase pandemic risk.
The advent of modern antibiotics is probably important in the decline in outbreak frequency in
Figure 22 It seems likely (intuitively) that this will reduce intensity more than frequency, as it will
cure infections more than stop zoonotic spillovers or underlying virus transmission. An example
is influenza, where zoonotic spillover events occur irrespective of antibiotic use, but the mortalityof influenza is greatly reduced by treatment of secondary bacterial infections. By assuming a
constant probability distribution of epidemic intensity derived from pre-1945 data, Marani et al.’smodel implicitly assumes we still live in a world without antibiotics.
Conclusions and outcomes.
An initial error in calculations in the paper is corrected in a later erratum published in PNAS, May
2023. The corrected outcomes of Marani et al. predict a Spanish Flu-like event to occur once every
877 years. A COVID-19-like event is predicted to occur once every 209 years. Adjusting for a 3x
increase in outbreak frequency (discussed above as poorly justified), results in a predictedfrequency of once every 292 years (Spanish Flu) and 129 years (COVID-19).
By failing to take into account improved detection methods, and ignoring the reduction in
infectious disease intensity and frequency known to result from improved hygiene and sanitation
over the past few hundred years as well as the advent of antibiotics and modern medicine in thepast century, the authors thus conclude that a Spanish Flu-like event would recur once every 292
to 877 years, and COVID-19 every 129 to 209 years. This has two key implications for PPPR. First,
neither type of event is expected to occur within an average life span, suggesting that the risk
from such an event is much lower than current narratives portray. Second, that this lower risk
allows time for proper PPPR policy reflections that can triangulate evidence on risk, need andoptimal response.
This paper offers very poor justification for an increasing and existential pandemic threat. Marani
et al. actually show that pandemics such as the Spanish Flu, or even COVID-19, are rare. This iscontrary to claims elsewhere that their threat is high, another COVID-19 like event or worse islikely soon, and there is an urgency to get measures in place. Its use to justify World Bank
predictions and proposed financing and key conclusions of Vora et al. and others must therefore
suggest that such claims merit reassessment.

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Smith et al. (2014): Global rise in human infectious disease
outbreaks

Smith et al., (2014) is a widely cited paper as evidence of increasing frequency and risk ofinfectious disease outbreaks. It is cited by both Meadows et al. (2023) and Marani et al. (2021)
discussed elsewhere in this report. The paper assesses the diversity and number of recorded
outbreaks.
Methods and evidence
The authors analyze a dataset from 1980 to 2013. Exact bounds are unclear as text indicates a“33-year dataset (1980–2013)” which should therefore be 34 years, whilst figures indicate 1980 to2009 (for comparison) and Table 2 indicates up to 2010. This is not important to the conclusions.
A total of 12,102 outbreaks are recorded, including 215 diseases and 44 million cases. Bacteria
are the major cause (37%), viruses 33%, and then parasites, fungi and protozoans respectively.
Outbreaks are defined as incidence above normal background.
Smith et al.’s analysis controlled for a number of confounders: latitude, gross domestic product(GDP), press freedom, internet usage, population size and population density. Only internet
usage had a significant association. No attempt was made beyond this to directly addresschanges in reporting through development of increased access to and reporting of diagnostics
and surveillance techniques (PCR, as example, was first developed in 1983 and not widely
available until far later).
Figure 23 (Figure 2 in Smith et al.) indicates a marked increase in outbreak diversity in Africa, Asia
and Eastern Europe in particular. This is consistent with an increase in diagnostic capacity andreporting in these lower- and middle-income countries, and those recently emerged from behind
the Iron Curtain. This pattern at least strongly suggests that such factors are influencing total
numbers, as it is counterintuitive that pre-1980 outbreak diversity would have been far higher in
populations in more highly developed economies than in less developed. The association ofincreased internet use with greater reporting suggests some role for the development oftechnology use in reporting frequency.
Conclusions and outcomes
Smith et al. demonstrate an increase in a range of outbreak reporting (frequency and diversity)
over the three decades from 1980, but a reduction in per capita mortality. The increase infrequency appears likely in part or predominantly due to increased reporting capacity

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Page 99 of 114(development and increased access to diagnostic and reporting systems, particularly in emerging
economies).
The reduction in per capita risk runs strongly counter to claims elsewhere of increasing risk from
disease outbreaks, and is inconsistent with citations suggesting evidence for increasing risk
overall. The data would strongly support a hypothesis that the world is getting better at detecting
outbreaks, and identifying and distinguishing pathogens, whilst also improving capacity toaddress these challenges.

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Figure 23. Change in outbreak diversity with time in Smith et al. (2014), suggesting a rapid
increase in pathogen detection and identification in emerging economies since 1980. 193

This figure is often reproduced in policy documents. The same figure is also included in Section 5.5 (Figure 11), and repeatedhere for ease of engagement.
193

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Jones et al. (2008): Global trends in emerging infectious diseases

Jones et al. 2008 (Nature) is widely cited as evidence of increasing frequency of emerging
infectious disease (EID) outbreaks, including by Marani et al. (2021) and Meadows et al. (2023)
discussed in this report.
Methods and evidence
The authors assess the frequency of 335 EID events from 1940 to 2004. EIDs in their publication
include variants of species of bacteria (e.g. Eschericia coli serotypes and sub-types of Shigella andVibrio spp.) and new drug resistant mutations of known pathogens. The first emergence (i.e.
identified) event is recorded.
Most (60.3%) of EIDs are zoonoses, with 54% being bacterial or rickettsia.
The authors recognize the risk of reporting bias. To address this, they compare the number ofpublications in the Journal of Infectious Diseases (JID) with the frequency of recorded outbreaks.
The total number of JID articles from 1940-2004 (17,979) is compared against the reporting rateover time, and publications since 1973 are compared by geographic location of the study withthe spatial distribution of reported EID events.
While comparing with JID publication rates may provide some insight into the likelihood that an
outbreak will reach the public record, the relationship is likely to be poor, as it fails to accountfor:
1. Different geographies prioritizing different journals (including due to linguistic barriers).
2. PCR, point-of-care antigen testing and serology have been developed or access greatly
increased since 1940, with accessibility concentrated in high income countries. Point ofcare testing beyond microscopy was rare in low-income countries into the 1990s.
3. Differing pressures, reasons and available resources to compile and publish data.
4. Changing funding priorities – an increased interest in outbreaks in recent decades would
likely result in more outbreaks being detected and those detected more likely to bereported.
Conclusions and outcomes
The authors demonstrate a steady increase in reported EID events from 1940, peaking in the
1980s and then declining somewhat in the 1990s (Figure 24). Some categories (viruses andprions, and protozoa), EIDs of zoonotic origin, and vector-borne diseases, continue to show
increased reporting through the 1990s (Figure 24). The authors attribute the 1980s peak to host

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immunodeficiency secondary to the poorly controlled expansion of the HIV/AIDS epidemic
through the 1980s, with improved management in the following decade:“Increased susceptibility to infection caused the highest proportion of events during 1980–90(25.5%), and we therefore suggest that the spike in EID events in the 1980s is due largely to theemergence of new diseases associated with the HIV/AIDS pandemic”.
Human population density was a significant predictor of overall EID emergence (unlike Marani etal., above), and this may reflect the inclusion of bacterial outbreaks, including those consisting of
new antimicrobial phenotypes that are more likely to arise, transmit and be detected in highdensity settings. Wildlife host richness was also a significant factor, contrary to the suggestions
of Vora at al. (2023) and others that degradation of ecosystems is likely to increase zoonotic
spillover:“human population density was a common significant independent predictor of EID eventsin all categories, …. Wildlife host species richness is a significant predictor for the
emergence of zoonotic EIDs with a wildlife origin, with no role for human population
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Figure 24. Figure 1 from Jones et al. (2008), showing temporal occurrence of infectious disease(EID) events recorded from 1940 to 2000.

The second figure in Jones et al. (Figure 25) shows the geographic origins of EIDs. Reporting isoverwhelmingly dominated by the United States (especially the northeast), Europe, and Australia
and Japan, with few reports from Africa, South America or South or Central Asia. This apparent
correlation with higher income economies and laboratory capacity, rather than population andwildlife habitat, suggests that capacity to detect and differentiate EIDs is likely to be a major
driver, or the major driver, of reporting.

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Figure 25. Figure 2 from Jones et al. (2008), showing geographical distribution of infectious
disease (EID) events recorded from 1940 to 2000.

Inevitably, as new techniques more suitable to broad deployment become accessible, such as
point of care antigen and serology testing and the development and late deployment of PCR, an
increase in reporting from lower-resourced areas will rapidly drive overall EID detection.
Rather than providing compelling evidence of a steady increase in EID events, the data provided
by Jones et al. suggests that the likelihood of detection and related factors are at least a major
driver of reporting. Moreover, the HIV/AIDS epidemic underlines the importance of host fitness,with an overall decline in reporting despite increasing reporting capacity as HIV/AIDS came under
better control in the 1990s.
Reporting of zoonotic spillover and vector-borne events continued to increase through the study
period. Outbreaks such as Chikungunyah and Nipah virus would not have been detected andidentified across most geographies during the early decades of this study, as the technology
required (e.g. PCR) did not exist.

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Morand & Walther (2020, Updated 2023): The accelerated
infectious disease risk in the Anthropocene: more outbreaks andwider global spread

Morand and Walther (2020) 194 are a major reference for the World Bank Putting Pandemics
Behind Us report.
The authors rely on the GIDEON database archive of outbreaks:“Using the most complete, reliable, and up-to-date database on human infectious disease
outbreaks (GIDEON)”“…we show that the number of disease outbreaks, the number of diseases involved in theseoutbreaks, and the number of countries affected have increased during the entire
Anthropocene. Furthermore, the spatial distribution of these outbreaks is becoming moreglobalized…”This does not consider any change or increase in reporting.
“However, the imposition in the 1980s of the so- called Washington Consensus imposedby the International Monetary Fund, World Bank, and United States Department of theTreasury and the resulting the structural adjustment programs dramatically decreased
the public health and education capabilities of many developing countries (e.g., references68, 69) which should also have affected reporting rates. However, we see no evidence of aslowdown of our trends (Figures 1D-F) in the 1980s and 1990s. But still, when controllingfor population, annual outbreak numbers and annual disease numbers trend down overpast 10-20 years.”
The authors did test whether a reduction in funding due to imposed structural adjustments onsome lower-income countries in the 1980s and 1990s caused a noticeable reduction in reporting(at the time of the main HIV epidemic). Finding no clear effect, they then ignore potential effects
of an absence of modern diagnostic and reporting technologies before the 1980s that are now
widely used, such as PCR, point of care antigen tests and digital communications. Whilst
acknowledging the possibility of under-reporting in some ‘developing countries’ in the early partof the ‘Anthropocene’, they fail to incorporate this into their analysis.
The World Bank Technical Report of 2022 (page 6) noted a relative reduction in Africa’s share ofreports during the 1980s and 1990s based on the same data set, 195 lending more weight to the
hypothesis that reporting rates are a significant driver of an increase in reported outbreaks. The

194
195

https://www.biorxiv.org/content/10.1101/2020.04.20.049866v2
https://documents1.worldbank.org/curated/en/099042023145037128/pdf/P17840202eae520aa0ab910137cb6f1a142.pdf

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relatively low contribution of the African continent compared to North America and Europe in
Fig. 3 from Morand and Walther (Figure 26) supports this conclusion.
Figure 26. Fig. 3. from Morand and Walther (2020, updated 2023), demonstrating the correlation
with more developed surveillance capacity for outbreaks in the GIDEON database.

The authors find that:“From the 1940s to around 2010, the annual total outbreak number (Fig. 1D), the annual
total disease number (Fig. 1E), and the annual total country number (Fig. 1F) increased
exponentially (Supplementary Data S1).”
Adjustment for population, however, still shows a downward trend (Figure 27):
“When we control for the number of the global human population, the growing trend forthe annual total outbreak number (Fig. 2D) reverses downward around 2010, and thegrowing trend for annual total disease number (Fig. 2E) actually decreases shortly after
2000 and dramatically in 2020.”
In the Discussion, the authors therefore conclude:“We note, however, that the per capita trend for the annual total outbreak number slowed
down (Fig. 1D) and the per capita trend for the annual total disease number even reversed(Fig. 1E) after 2000. These results mirror results by Smith et al. who found that per capita
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Figure 27. Fig. 2 from Morand and Walther (2020, updated 2023), showing marked recent
reductions in disease number and outbreak numbers in the GIDEON database.

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7.6.

Other papers considered in the preparation of this report:

Badker R, Miller K, Pardee C, Oppenheim B, Stephenson N, Ash B, Philippsen T, Ngoon C, Savage P,
Lam C, Madhav N. Challenges in reported COVID-19 data: best practices andrecommendations for future epidemics. BMJ Glob Health. 2021 May;6(5):e005542. doi:
10.1136/bmjgh-2021-005542. PMID: 33958393; PMCID: PMC8103560.
Baker RE, Mahmud AS, Miller IF, Rajeev M, Rasambainarivo F, Rice BL, Takahashi S, Tatem AJ, Wagner
CE, Wang LF, Wesolowski A, Metcalf CJE. Infectious disease in an era of global change. Nat Rev
Microbiol. 2022 Apr;20(4):193-205. doi: 10.1038/s41579-021-00639-z. Epub 2021 Oct 13. PMID:
34646006; PMCID: PMC8513385.
Chadha MS, Comer JA, Lowe L, Rota PA, Rollin PE, Bellini WJ, Ksiazek TG, Mishra A. Nipah virusassociated encephalitis outbreak, Siliguri, India. Emerg Infect Dis. 2006 Feb;12(2):235-40. doi:
10.3201/eid1202.051247. PMID: 16494748; PMCID: PMC3373078.
Daszak P, Cunningham AA, Hyatt AD. Anthropogenic environmental change and the emergence of
infectious diseases in wildlife. Acta Trop. 2001 Feb 23;78(2):103-16. doi: 10.1016/s0001706x(00)00179-0. PMID: 11230820.
Dobson AP, Pimm SL, Hannah L, Kaufman L, Ahumada JA, Ando AW, Bernstein A, Busch J, Daszak P,
Engelmann J, Kinnaird MF, Li BV, Loch-Temzelides T, Lovejoy T, Nowak K, Roehrdanz PR, ValeMM. Ecology and economics for pandemic prevention. Science. 2020 Jul 24;369(6502):379-381.
doi: 10.1126/science.abc3189. PMID: 32703868.
Epstein JH, Field HE, Luby S, Pulliam JR, Daszak P. Nipah virus: impact, origins, and causes of
emergence. Curr Infect Dis Rep. 2006 Jan;8(1):59-65. doi: 10.1007/s11908-006-0036-2. PMID:
16448602; PMCID: PMC7088631.
Fan VY, Jamison DT, Summers LH. Pandemic risk: how large are the expected losses? Bull World HealthOrgan. 2018 Feb 1;96(2):129-134. doi: 10.2471/BLT.17.199588. Epub 2017 Dec 5. PMID:
29403116; PMCID: PMC5791779.
Gottdenker NL, Streicker DG, Faust CL, Carroll CR. Anthropogenic land use change and infectious
diseases: a review of the evidence. Ecohealth. 2014 Dec;11(4):619-32. doi: 10.1007/s10393014-0941-z. Epub 2014 May 23. PMID: 24854248.
Gurley ES, Montgomery JM, Hossain MJ, Bell M, Azad AK, Islam MR, Molla MA, Carroll DS, Ksiazek TG,
Rota PA, Lowe L, Comer JA, Rollin P, Czub M, Grolla A, Feldmann H, Luby SP, Woodward JL,
Breiman RF. Person-to-person transmission of Nipah virus in a Bangladeshi community.
Emerg Infect Dis. 2007 Jul;13(7):1031-7. doi: 10.3201/eid1307.061128. PMID: 18214175;
PMCID: PMC2878219.

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Madhav N, Oppenheim B, Gallivan M, Mulembakani P, Rubin E, Wolfe N. Pandemics: Risks, Impacts,and Mitigation. In: Jamison DT, Gelband H, Horton S, Jha P, Laxminarayan R, Mock CN, Nugent
R, editors. Disease Control Priorities: Improving Health and Reducing Poverty. 3rd ed.
Washington (DC): The International Bank for Reconstruction and Development / The World
Bank; 2017 Nov 27. Chapter 17. PMID: 30212163.
Madhav, N.K. (2023) Estimated future mortality from pathogens of epidemic and pandemic potential.
Center for Global Development.
Rohr JR, Barrett CB, Civitello DJ, Craft ME, Delius B, DeLeo GA, Hudson PJ, Jouanard N, Nguyen KH,
Ostfeld RS, Remais JV, Riveau G, Sokolow SH, Tilman D. Emerging human infectious diseases
and the links to global food production. Nat Sustain. 2019;2(6):445-456. doi: 10.1038/s41893019-0293-3. Epub 2019 Jun 11. PMID: 32219187; PMCID: PMC7091874.
Skowron K, Bauza-Kaszewska J, Grudlewska-Buda K, Wiktorczyk-Kapischke N, Zacharski M, Bernaciak
Z, Gospodarek-Komkowska E. Nipah Virus-Another Threat From the World of Zoonotic Viruses.
Front Microbiol. 2022 Jan 25;12:811157. doi: 10.3389/fmicb.2021.811157. PMID: 35145498;
PMCID: PMC8821941.
Viscusi WK, Masterman CJ. Income elasticities and global values of a statistical life. Journal of BenefitCost Analysis. 2017;8(2):226–50. doi:10.1017/bca.2017.12
Vora NM, Hannah L, Lieberman S, Vale MM, Plowright RK, Bernstein AS. Want to prevent pandemics?
Stop spillovers. Nature. 2022 May;605(7910):419-422. doi: 10.1038/d41586-022-01312-y.
PMID: 35551284.

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8. Conclusion and Recommendations
The WHO Future Surveillance Report summarizes the basis for urgent prioritization of policy
development and financial investment to prevent, identify and mitigate pandemics:“Epidemics and pandemics of infectious diseases are occurring more often, and spreading
faster and further than ever, in many different regions of the world.”
A thorough review of the evidence base on which WHO, the World Bank and G20 rely to promote
this important public health agenda demonstrates that this statement is contrary to their own
cited sources, and that over the past decade risk may actually be reducing. The implication is thatthe largest investment in international public health in history is based on misinterpretations of
key evidence as well as a failure to thoroughly analyze existing data.
While pandemics occur, and will continue to do so, the analysis in this report strongly indicatesthat the urgency expressed in key global health policy documents regarding natural pandemic
risk is exaggerated. Considerable public health harm may therefore accrue throughinappropriate diversion of resources if time is not taken to ensure responses are appropriate andproportionate. While the risk of outbreaks due to human manipulation of potential pathogens is
not addressed here, WHO, the World Bank and G20 proposals are clearly based on perceptionsof natural risk, particularly from zoonotic spillover events. The recent COVID-19 pandemic, if
considered of natural origin, appears as an outlier in the context of recent outbreak trajectory,
rather than indicative of a trend. However, when weighed against other endemic and chronic
disease burdens in standard metrics incorporating life-years lost, the mortality from COVID-19
will also overstate its relative impact.
Reported outbreaks have increased in the decades before the year 2000. However, it is inevitablethat reporting of such outbreaks will be influenced by changes in both the capacity and incentive
to report. These include the development of, and increasing access to, major diagnostic platforms
including PCR and point-of-care antigen and serology tests, as well as the development ofcommunication infrastructure. Fifty years ago, many pathogens now readily identifiable could
simply not be detected, or the diseases they cause be distinguished from other clinically similar
conditions. Increased interest from funders and research groups in outbreak detection may also
be influencing the frequency of recording, while human population increase should also
influence data. It is remarkable that reports backing the expenditure of tens of billions of dollars
annually have only partially taken these issues into account or ignored them altogether.
The development of improved diagnostic technologies not only impacts reporting rates but has
implications in understanding the term ‘emerging infectious disease’. While this frequently usedterm suggests that new pathogens are emerging, examples such as the Nipah Virus outbreaks

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discussed in the World Bank report Putting Pandemics Behind Us appear more likely to involve
identification of pre-existing diseases that were, and remain, of small overall burden. The
development of such technologies should reduce risk, and the impression of increasing risk due
to their use is a trap that needs to be understood and avoided.
Most data underlying the WHO, World Bank and G20 reports demonstrate a flattening of theincrease in outbreak reporting over the past two decades, with a reduction in mortality, and somein outbreak frequency, over the past 10 years. The exception in the Metabiota dataset is
explained by inclusion of Ebola virus disease outbreaks, a single geographically confined disease.
Overall, actually mortality from these outbreaks is also historically very low, and very low incontrast to other current health burdens. The much-quoted analysis of Bernstein et al. (2022)
achieves an annual mortality in the millions by including pre-antibiotic era Spanish Flu and themulti-decade HIV event. The first is inappropriate as a guide to mortality as most deaths werelikely due to secondary bacterial infection, whilst the latter is not commonly considered in the
PPPR agenda. Both influenza and HIV have extensive international mechanisms already in place(although there is room for improvement).
In this context, the analyses of WHO, the World Bank and the G20, and in certain cases thesources they cite, are disappointing in terms of scholarship and balance. They raise concern thata desire to address a perceived threat is driving analysis, rather than analysis objectively
determining the extent of threat. However well intentioned, this seems unlikely to effectively
address the needs of public health or the populations it serves. Disease outbreaks harm people
and shorten lives and must be addressed. This is best achieved on the basis of well-compiled
evidence and scholarly analysis.
The evidence, assessed objectively, paints a picture of an increasing ability to identify and reportoutbreaks up to the decade 2000 to 2010, followed by a reduction in burden consistent with anincreasing ability to successfully address these relatively low-burden events through current
public health mechanisms. Alternatively, it may reflect a general reduction in poverty, andthereby improved resilience against disease, over this period. Whatever the underlying
mechanisms, the data on which the major international agencies base their claims strongly
indicates that the imperative of using health resources wisely in the context of overall publichealth and economic need should outweigh the current urgency to put mechanisms in place forhealth threats that are poorly defined and almost certainly overstated.
In considering the development of PPPR policy, the following recommendations therefore arise:
1. There is a clear need to commission better evidence to accurately determine the scale
and urgency of pandemic risk.

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2. An appropriate determination of pandemic risk must account for recent advancements
in diagnostic capacity, information sharing, and improving disease control mechanisms.
3. Understanding relative disease burden is crucial for identifying the cost-benefit ofpandemic investment and how to best select interventions and promote overall publichealth outcomes.
4. Given the poor evidence underlying risk assessment, it is prudent not to rush into new
pandemic initiatives such as the proposed WHO pandemic instruments until underlying
assumptions receive proper assessment based on robust evidence, recognized need, andoverall benefit.
5. WHO Member States should support proportional pandemic preparedness efforts basedon substantiated evidence, careful deliberation, and rational reflection.
As WHO notes, health is a state of physical, mental and social wellbeing. Public health therefore
requires the balancing of a broad range of competing priorities, including the burden caused byoutbreaks if infectious disease. To achieve such balance, evidence must be rigorously sought,
carefully analyzed, and presented honestly and in context. In view of their influence, international
agencies working in health have a particular responsibility to ensure this happens. It is hopedthat the evaluation presented in this report will contribute to such an effort.

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Annex I. Assumptions and sources for analysis of mortality
due to outbreaks listed in Annex D of the G20 HLIP Report.
HLIP Report Annex D lists “Major Infectious Disease Outbreaks in the Past Two Decades”. Without
providing location or mortality. We have attempted to assign historic outbreaks to these diseases
and years, based on WHO, CDC and other published data. Data includes all recorded mortality
for full duration of outbreak starting in the year listed in the table.
Mortality for Chikungunya is difficult to determine as it may occur in the frail elderly and is difficult
to distinguish from other diseases. The 2014 outbreak may be intended to reflect an outbreak in
the Caribbean, but we were unable to determine mortality estimates. This may therefore
underestimate total outbreak mortality.
Zika figures are derived from a published analysis of the Brazil outbreak (Paixao et al., 2022). The
rate of excess mortality in babies recorded as having Zika congenital syndrome was estimatedfrom the mortality rates in Paixao et al., by deriving attributable risk (0.102). All mortality istherefore in these infants and children.
OutbreakMortality

Notes

…

Discussed separately

Source for mortality estimate(HLIP Annex D)2019 SARS-CoV-2

elsewhere in thisreport.
2018 Lassa

114

Nigeria2017 Zika

362

Assumed to be 2016-2017 Ebola

3

https://www.who.int/emergencies/disease-outbreaknews/item/20-april-2018-lassa-fever-nigeria-en
https://www.nejm.org/doi/pdf/10.1056/NEJMoa2101195

2017 outbreak.
DRC

https://www.cdc.gov/vhf/ebola/outbreaks/drc/2017may.html(2018 Ebola)

33

DRC (Bikoro)(2018-2020)

2287

DRC (n Kivu, Ituri, S

https://www.who.int/emergencies/disease-outbreak-Kivu).

news/item/2020-DON284

https://www.cdc.gov/vhf/ebola/outbreaks/drc/2018may.html

2014

0

Chikungunya

Location of 2014
outbreak unclear.
Mortality is low, but
may occur among theelderly.

2014 Ebola

11,325

West Africa outbreak.

2012 MERS

858

Globalhttps://www.who.int/emergencies/situations/ebolaoutbreak-2014-2016-West-Africa
https://www.who.int/health-topics/middle-eastrespiratory-syndrome-coronavirus-mers#tab=tab_1

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Rational Policy over Panic

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2010 Cholera

9,792

Haiti (2010-2019)2009 H1N1

164,000

Median of WHO

https://apps.who.int/iris/bitstream/handle/10665/32943

estimate 123,000-

8/9789241516839-eng.pdf?ua=1

https://www.who.int/emergencies/disease-outbreaknews/item/2022-DON415

Influenza

203,000.
2004 H5N1

32Southeast Asia

https://www.ncbi.nlm.nih.gov/books/NBK22148/

774

Globalhttps://www.who.int/publications/m/item/summary-of-

Influenza
2003 SARS-CoV-1

probable-sars-cases-with-onset-of-illness-from-1november-2002-to-31-july-2003

2001 Enterovirus

26

Taiwan2001 Nipah

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC918885
5/

71
45

Bangladesh

https://www.who.int/emergencies/disease-outbreaknews/item/2023-DON490