(2023) 21:85
Liu et al. BMC Medicine
https://doi.org/10.1186/s12916-023-02784-z

BMC Medicine

RESEARCH ARTICLE

Open Access

Assessing the impacts of COVID‑19
vaccination programme’s timing and speed
on health benefits, cost‑effectiveness,and relative affordability in 27 African countries
Yang Liu1,2* , Simon R. Procter1,2, Carl A. B. Pearson1,2,3, Andrés Madriz Montero4, Sergio Torres‑Rueda4,
Elias Asfaw5, Benjamin Uzochukwu6, Tom Drake7, Eleanor Bergren4, Rosalind M. Eggo1,2, Francis Ruiz4,
Nicaise Ndembi8,9, Justice Nonvignon5,10†, Mark Jit1,2† and Anna Vassall4†

Abstract
Background The COVID-19 vaccine supply shortage in 2021 constrained roll-out efforts in Africa while populations
experienced waves of epidemics. As supply improves, a key question is whether vaccination remains an impactful andcost-effective strategy given changes in the timing of implementation.
Methods We assessed the impact of vaccination programme timing using an epidemiological and economic
model. We fitted an age-specific dynamic transmission model to reported COVID-19 deaths in 27 African countries toapproximate existing immunity resulting from infection before substantial vaccine roll-out. We then projected healthoutcomes (from symptomatic cases to overall disability-adjusted life years (DALYs) averted) for different programme
start dates (01 January to 01 December 2021, n = 12) and roll-out rates (slow, medium, fast; 275, 826, and 2066 doses/
million population-day, respectively) for viral vector and mRNA vaccines by the end of 2022. Roll-out rates used were
derived from observed uptake trajectories in this region. Vaccination programmes were assumed to prioritise thoseabove 60 years before other adults.
We collected data on vaccine delivery costs, calculated incremental cost-effectiveness ratios (ICERs) compared to no
vaccine use, and compared these ICERs to GDP per capita. We additionally calculated a relative affordability measureof vaccination programmes to assess potential nonmarginal budget impacts.
Results Vaccination programmes with early start dates yielded the most health benefits and lowest ICERs comparedto those with late starts. While producing the most health benefits, fast vaccine roll-out did not always result in thelowest ICERs. The highest marginal effectiveness within vaccination programmes was found among older adults. Highcountry income groups, high proportions of populations over 60 years or non-susceptible at the start of vaccination
programmes are associated with low ICERs relative to GDP per capita. Most vaccination programmes with small ICERs
relative to GDP per capita were also relatively affordable.

†

Justice Nonvignon, Mark Jit and Anna Vassall contributed equally to thiswork.
*Correspondence:
Yang Liuyang.liu@lshtm.ac.uk
Full list of author information is available at the end of the article© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
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regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/. The Creative Commons Public Domain Dedication waiver (http://​creat​iveco​
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Liu et al. BMC Medicine(2023) 21:85

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Conclusion Although ICERs increased significantly as vaccination programmes were delayed, programmes starting
late in 2021 may still generate low ICERs and manageable affordability measures. Looking forward, lower vaccine pur‑chasing costs and vaccines with improved efficacies can help increase the economic value of COVID-19 vaccination
programmes.
Keywords Vaccination, COVID-19 | SARS-CoV-2, Economic evaluation, Affordability, Mathematical models, Decisionmaking, Programme evaluation, Public health interventions

Background
Since COVID-19 vaccines were first authorised in late2020, many countries achieved considerable coverage ina short period [1], reducing disease and economic burdens enormously. However, accruing COVID-19 vaccine
coverage in many African countries has been slow before
2022, with one of the largest contributors being vaccine
shortage [2]. Given roll-out efforts by the end of 2021, it
would be a substantial challenge for the region to achieve
the 70% coverage target by mid-2022 as set out by theWorld Health Organization (WHO) and the Africa Centres for Disease Control and Prevention (Africa CDC)
[3–5].
The vaccine shortage has eased significantly since early2022 [4]. By this time, however, many African Union
member states had already experienced several epidemic
waves involving multiple variants of concern [1, 6]. A
key question that these countries face is whether rolling out COVID-19 vaccines remains an impactful strategy representing good value for money, having missed
key windows of opportunities from early- to mid-2021.
Continuing vaccination raises issues in respect of bothcost-effectiveness and affordability in the region due topotentially low health impacts and high health opportunity costs to other health services due to nonmarginal
budgetary impact [7, 8].
This study aims to inform future decisions about vaccine roll-out and investment by retrospectively examining the impacts of implementation timing and speed on
COVID-19 transmission using a combined epidemiological and economic modelling approach. This approachallowed us to factor in the potentially high seroprevalence of the region, the emergence of multiple variants ofconcerns, and population characteristics key to the transmission of SARS-CoV-2 (e.g. population age structure,contacts, non-pharmaceutical interventions) and theeconomic evaluation of vaccination programmes (grossdomestic product per capita (GDP per capita), generalpublic health expenditures).
Given that the COVID-19 vaccine roll-out occurred
relatively late and slowly among African countries, we
focused on answering two key questions: (i) how much
health impacts and value for money could have beenachieved had the vaccine roll-out been earlier and faster?

(ii) could late COVID-19 vaccine roll-out efforts (in late2021) still provide considerable health impacts and value
for money? To answer these questions, we evaluateddifferent vaccine roll-out scenarios in terms of healthimpacts, cost-effectiveness, and relative affordability by
vaccine types among Africa Union member states.
This is the first multi-country regional analysis of
COVID-19 vaccine strategies for Africa that links epidemiological models and economic evaluation with findings that reflect on between-country heterogeneities. The
lessons learned could inform regional decision-makers
on future vaccine roll-out decisions in Africa, particularly
if reformulated vaccines become available in response to
future variants. To our knowledge, this is also the firststudy to empirically examine the impacts of implementation timing while appraising COVID-19 vaccine policies.

Methods
We estimated the prevalence of infection-induced protection against COVID-19 by fitting a dynamic transmission model to country-level daily reported COVID-19
deaths. This procedure allowed us to capture different
epidemic histories experienced by African Union member states. Cumulative health outcomes and disabilityadjusted life years (DALYs) averted associated withdifferent vaccine roll-out scenarios were then simulated.
Combining health outcomes with data on the costs ofvaccine delivery and COVID-19-related health services,
we estimated the overall cost-effectiveness and relative
affordability from a health sector perspective by the endof 2022. This section provides essential information tounderstand the methods. Further details are presented in
Additional File 1.
All analyses were done in R (4.1.0). All data used are
publicly available. Code and intermediate results can beaccessed via our GitHub repository archived at Zenodo[9].
Characterising vaccine roll‑out scenarios

By the time most African Union member states achieved
1% COVID-19 vaccine coverage (August 2021), theUnited Kingdom (UK) had vaccinated 74.6% (as of 31
August 2021) of its population [1]. By February 2022,
vaccine coverage in over 35% of African Union member

Liu et al. BMC Medicine(2023) 21:85

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Fig. 1 Model parameterisation and vaccine roll-out scenarios setup. (a) Observed vaccine uptake among African Union member states. Each line
represents a country, and its colour indicates the roll-out rate levels (orange - slow; red - medium; blue - fast). (b) Vaccine supply trajectories of 36
vaccine roll-out scenarios (12 programmes starting date × 3 roll-out rates) and the population level vaccine coverage levels they realisestates was still below 10% (Fig. 1a) [1]. We explored possible alternative vaccine roll-out scenarios that could havetaken place in 2021 by varying two parameters: vaccination programme start date and vaccine roll-out rates. The
programme start date was defined as the date at which
the first person in a country was vaccinated and was varied between 01 January 2021 and 01 December 2021 in
1-month increments. We limited the testing window forstart dates to before 2022, as all African Union member
states had introduced COVID-19 vaccines by then [1].
Vaccine roll-out rate was defined as the number ofdoses administered per million population-day. In thisstudy, we varied this parameter along three levels: 275,
826, and 2066 doses/million population-day (hereafter ‘slow’, ‘medium’, and ‘fast’, respectively). These levels
were derived from observed vaccine roll-out trajectories
using a univariable linear model (cumulative doses per
million population-day ~ date) and represent the medianvaccine roll-out rates by tertile observed among African
Union members [1].
The vaccine roll-out scenarios combining these twoparameters (n = 12 start dates × 3 roll-out rates) are
visualised in Fig. 1b (age-specific example presented
in Additional File 1: Fig. S1). We capped the maximum
population-level vaccine coverage at 70%, consistent
with the target set by the WHO and the Africa CDC [3,5]. Assuming a two-dose schedule for COVID-19 vaccines, a country with a population size of n will receivea maximum of 1.4n doses of vaccines, likely via early andfast roll-out scenarios.
Older adults (60 + years of age) were prioritised in thevaccine roll-out process based on international guidelines and common practices observed in the region [10,11]. In the transmission model, older adults were alsocharacterised by higher susceptibility, higher percentagesof infection showing symptoms, and high percentages ofinfections progressing to severe, critical, and fatal outcomes. We assume the maximum vaccine uptake rates
among older adults to be 80% and other adults 60% [12].
Fitting and simulation using a dynamic transmission model

The epidemic model used in this study was an adaptationof CovidM, an age-specific dynamic transmission modelof COVID-19, which has been previously used to explore
the impact of different vaccination strategies in the UK,
European countries, and Pakistan [13–16]. Betweencountry heterogeneity has been captured by a wide rangeof health (e.g. age structure, life expectancy, reported
COVID-19-related deaths), behaviour (e.g. community
mobility, social contacts), economic (e.g. GDP per capita,
government public health expenditure, costs), and policy (e.g. COVID-19 response stringency index) factors
using local data. The conceptual diagram of the transmission model is presented in Additional File 1: Fig. S2[14]; the model is further described in Additional file 1:
Methods S1 [14, 17–22] and S3 [13, 14, 23–25]; complete
parameter tables and their data sources can be foundLiu et al. BMC Medicine(2023) 21:85

in Additional File 1: Table S1-6 [1, 3, 10–20, 22–44] by
category.
We fitted this model to country-level daily reported
COVID-19 deaths during 2020–2022, before the wideuse of COVID-19 vaccines, using maximum likelihood
estimation and differential evolutionary algorithm (fulloptimisation procedure described in Additional File 1:
Methods S2) [14, 45]. The following parameters were
estimated: (1) the basic reproduction numbers (R0); (2)infection introduction dates; (3) COVID-19 death reporting rate; and (4) variants of concern introduction dates.
The fitting process aims at approximating the immune
profiles (i.e. the levels of infection-induced immunity by
age and the distribution of non-susceptible individuals
within the population by compartments) against SARSCoV-2 before vaccine roll-out.
Among the 55 African Union member states, we
identified 27 with sufficient data for model fitting.1 The
remaining 28 member states were excluded from further
analysis due to either data sparsity (i.e. <  = 10 deaths/day
throughout the fitting period, n = 26) or potential reporting artefacts (i.e. with single day accounting for > 5% of
the cumulative COVID-19 deaths since early 2020, n = 2).
These countries, despite of interest among regional decision makers, are spatially clustered (Additional File 1:
Fig. S3), which makes reasonable extrapolation unfeasible. The epidemic curves describing COVID-19-related
deaths among 27 members included in this analysis arepresented in Additional File 1: Fig. S4-6 [1].
We explored vaccine roll-out scenarios with eithermRNA vaccines (with characteristics similar to those of
the Pfizer-BioNTech COVID-19 vaccines) or viral vector
vaccines (with characteristics similar to those of Oxford/
AstraZeneca vaccines). Six types of vaccine effect mechanisms were incorporated into the model: infection-, disease-, severe case-, critical case-, mortality-reducing, andonward transmission-blocking.
The vaccine efficacy estimates we used are presented inTable 1. An alternative efficacy estimate set based on thelower bounds of their uncertainty ranges in existing literature was tested as a sensitivity analysis. We assumed
infection-induced immunity among naïve individuals
to last 3 years on average [22]. Given the time horizonof this study, we did not consider the protection waning

1

In alphabetical order: People’s Democratic Republic of Algeria, Republic ofAngola, Republic of Botswana, Republic of Cameroon, Democratic Republicof the Congo, Arab Republic of Egypt, Eswatini, Federal Democratic Republicof Ethiopia, Republic of Ghana, Republic of Kenya, Republic of Liberia, Libya,
Republic of Madagascar, Republic of Malawi, Kingdom of Morocco, Republicof Mozambique, Republic of Namibia, Federal Republic of Nigeria, Republicof Rwanda, Republic of Senegal, Somali Republic, Republic of South Africa,
Republic of The Sudan, Tunisian Republic, Republic of Uganda, Republic ofZambia, Republic of Zimbabwe.

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Table 1 Vaccine efficacy estimates by vaccine type
Outcome

Vaccine efficacy
Base case set | sensitivity analysis set
mRNA vaccines

Viral vector vaccines

1st dose

2nd dose

1st dose

2nd dose

Infection

0.7 | 0.55

0.85 | 0.7

0.7 | 0.55

0.75 | 0.65

Cases

0.7 | 0.55

0.9 | 0.85

0.7 | 0.55

0.8 | 0.7

Severe cases

0.85 | 0.75

0.95 | 0.9

0.85 | 0.75

0.9 | 0.8

Critical cases

0.85 | 0.73

0.95 | 0.93

0.85 | 0.75

0.93 | 0.78

Deaths

0.85 | 0.7

0.95 | 0.95

0.85 | 0.75

0.95 | 0.75

Onward transmission

0.47 | 0.47

0.47 | 0.47

0.47 | 0.47

0.47 | 0.47

In the context of this study, cases are defined as symptomatic infections; severecases are defined as those that require hospitalisation; critical cases are definedas those that require critical/intensive care units at some point during their
hospital stay. This includes cases that eventually proceed to death as their final
outcome. These estimates are broadly in line with Barnard et al. [16]. However,Barnard et al. did not provide estimates for efficacy against critical cases [16].
Based on the relative relationship we observed between vaccine efficacy
against severe cases (i.e. hospitalisations of at least 2 days long with acuterespiratory infection (ARI) code in the primary diagnosis field), critical cases(i.e. hospitalisations of at least 2 days long with ARI code in primary diagnosis
field and with either oxygen, ventilation, or intensive care unit use), and deaths
reported in the UK Health Security Agency Vaccine Surveillance Report (week12) [46], we assumed the vaccine efficacy against critical cases to be the averageof that against severe cases and that against death. The alternative estimatesused in the sensitivity analysis (i.e. the lower limits from their estimateduncertainty ranges) are from the same report [46]among fully vaccinated individuals. Decreased vaccine
efficacy due to immune evasiveness (e.g. against Omicron) was implemented through changes in susceptibility among vaccinated individuals. However, we assumed
immune evasiveness only affects infection- and diseasereducing vaccine efficacies—other types of vaccine efficacies remained constant.
We projected four health outcomes under each vaccine roll-out scenario: (a) symptomatic infections; (b)severe cases that require hospitalisation; (c) critical casesthat require intensive care unit (ICU) admission; and(d) deaths (see Additional File 1: Table S2 for parameter sources and Additional File 1: Methods S4 [14, 15,
23] for technical details). All outcomes were aggregatedfrom 01 January 2021 to 31 December 2022. We kept
the time horizon relatively short due to the uncertainty
around future emerging variants. However, vaccination
programmes that start late may require time to present
impacts. We extended the time horizon to 30 June 2023
as a sensitivity analysis.
Calculating DALYs

We used projected health outcomes to calculate DALYs
incurred, which capture both mortality and morbidity burden. Mortality outcomes were converted to yearsof life lost (YLLs) using country-specific life expectancy[26]. Morbidity outcomes (symptomatic infections,Liu et al. BMC Medicine(2023) 21:85

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Table 2 Vaccine unit costs by roll-out scenario and vaccine typeRoll-out scenario
Start date
01 August 2021

Vaccine type

Lower limit ($)

First quartile ($)

Median ($)

Third quartile ($)

Upper limit ($)

Roll-out rate
MediumViral vector vaccine

4.96

6.04

6.92

8.44

15.40

Fast

Viral vector vaccine

2.54

3.61

4.49

6.02

12.97

MediummRNA vaccine

14.2

15.27

16.15

17.68

24.63

Fast

mRNA vaccine

11.8

12.84

13.72

15.25

22.20

Summary of the vaccine unit costs by country (n = 27) estimated for vaccine roll-out efforts starting in 01 August 2021 using medium and fast roll-out rates byvaccine types. The underlying methods have been described in Additional File 1: Methods S7-9. Raw data behind these estimates were presented in Additional File 1:
Table S7-8. Further sample estimates by country are presented in Additional File 1: Table S9. In this study, vaccine delivery unit costs differ by country, vaccine type,vaccine roll-out rates, and programme duration

severe cases, critical cases and those who experiencelong-term health effects (“Long-COVID”)) were converted to years lived with disability (YLDs). We discounted YLLs over remaining life expectancy andadditionally discounted DALYs according to the year
in which COVID-19 health outcomes occurred. In linewith the WHO guidelines on the economic evaluation of
immunisation programmes, we used an annual discountrate of 3% [47]. More information on calculating DALYs
can be found in Additional File 1: Methods S5-6 [26,
48–51].
Estimating the vaccine delivery and health service costs

We collected data on country-specific unit costs per
vaccine delivered by vaccine type from a health sector
perspective in three countries (Ethiopia (a low-income
country), Nigeria (a lower-middle-income country), andSouth Africa (an upper-middle-income country)). For
South Africa, we included observed vaccine delivery
costs. However, at the time of this study, realistic vaccine
delivery costs were not available for any other country in
the region. Thus, in this study, we used a normative (i.e.
per-protocol) ingredient-based (i.e. itemised) approach(see Additional File 1: Methods S7) [52–54]. The unit
cost includes vaccine purchasing costs and related components and activities involved in the planning and delivery of the vaccine (e.g. planning and coordination, cold
chain, transportation, and waste disposal) [52]. Costs
were then validated by experts knowledgeable of country-level immunisation efforts. In the case of Ethiopia
and Nigeria, this validation step was part of the healthtechnology assessment process used to support decisionmaking around COVID-19 vaccinations.
We found that the unit cost of delivering mRNA vaccines to be substantially higher than that of viral vector
vaccines (Table 2). These differences are driven by vaccine purchasing costs (see itemised costs by country,
activity, and component in Additional File 1: Methods S7,
Additional File 1: Tables S7-8)—for which we used thelowest purchasing price reported in the public domain foreach vaccine [55, 56]. We extrapolated these vaccine unit
costs for other countries (see Additional File 1: Methods
S8 [57–60] for extrapolation methods; this process did
not differentiate by country-specific purchasing agreements) and other vaccination programme setups (i.e.
programme duration and daily vaccine roll-out rate, see
Additional File 1: Methods S9 for the extrapolation methods, and Additional File 1: Table S9 for sample extrapolation results). The extrapolation results indicate that faster
vaccine roll-out rates are associated with lower vaccine
unit costs due to fixed costs being spread between more
vaccine doses (Table 2). Our costing approach assumed
no increases in the price of scarce resources (e.g. healthcare workforce).
For COVID-19-related health service costs, we usedpreviously published country-specific estimates (Table 3)
and lengths of hospital stay [31, 48]. All costs were converted to US$2020 using GDP deflators [61]. Similarly to
DALYs, costs were discounted by 3% in line with WHO
guidelines [47].
Measuring cost‑effectiveness and relative affordability

We estimated incremental cost-effectiveness ratios(ICERs) from a healthcare payer perspective for different vaccination scenarios compared to a no-vaccination
scenario by dividing the differences in total costs between
alternatives (including vaccine delivery and COVID19 health service costs) by the differences in DALYs
incurred. Vaccination costs may be paid by country governments or external donors. To allow for between-country comparison, we calculated ICER as a proportion of
GDP per capita and thus intrinsically assuming a proportion of GDP per capita may approximate the underlying
health opportunity costs [62, 63].
We did not fix a single cost-effectiveness thresholdand instead focused our discussion on ICER as a proportion of GDP per capita. This is because there are noLiu et al. BMC Medicine(2023) 21:85

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Table 3 Health service costs
Health service endpoints

Lower limit ($)

First quartile ($)

Median ($)

Third quartile ($)

Upper limit ($)

Home-based care

4.84

17.22

22.11

44.52

266.24

Hospital-based care for severe cases

27.02

33.65

37.08

45.74

170.99Hospital-based care for critical cases

156.63245.10277.57336.061685.98

Management of fatal cases

65.29

65.29

65.29

65.29

65.29

Summary of the vaccine unit costs by country (n = 27) estimated for vaccine roll-out efforts starting 01 August 2021 using medium and fast roll-out rates by vaccine
types. The underlying methods have been described in Additional File 1: Methods S7-9. Further sample estimates by country are presented in Additional File 1:
Table S9

generally accepted cost-effectiveness thresholds for any
country in Africa or, indeed, most of the world [64]. Furthermore, COVID-19 vaccination is likely to have a large
budget impact, and hence the thresholds calculated at
the margin of budgets may not always apply [7, 8, 65].
In practice, a cost-effectiveness analysis using an overestimated cost-effectiveness threshold may incur highhealth opportunity costs in budget-constrained settings
as the programme under evaluation could affect existing
public health activities. In low- and middle-income countries, substantial uncertain remains around the appropriate cost-effectiveness threshold [63, 66]. In this study, forsimplicity, we assumed conditions where ICERs accountfor more than 50% of GDP per capita are less likely to beconsidered cost-effective [63, 66].
We ran additional multivariable regression analyses
to explore the country-specific metrics that can link to
ICERs as proportions of GDP per capita. We tested population size [26], proportion of population over 60 years[26], proportion of population non-susceptible at thestart of vaccine roll-out (model fitted in this study), vaccine unit cost (estimated in this study), government general health expenditure [33], and income group [34].
In this study, we defined the relative affordability of a
vaccine programme in a given year as the annual incremental cost of the programme divided by annual government general health expenditure [7, 8]. The annual
incremental cost of a programme is calculated by thesum of vaccination programme costs and the corresponding COVID-19-related healthcare costs minus the
COVID-19-related healthcare costs without any vaccine
during the entire time horizon and then standardised tothe annual level. Since the external donor contribution
may be uncertain (or redirected through different funding schemes) in the COVID-19 era, only domestic healthcare budgets were considered.
We estimated ICER as a proportion of GDP per capita
and relative affordability to evaluate COVID-19 vaccination programmes. The two underlying streams of data
used, GDP per capita and government general healthexpenditure, are not strictly correlated. As a result, costeffectiveness and affordability outcomes may lead todifferent conclusions—vaccination programmes may becost-effective but not affordable, and vice versa.

Results
The fitted model could broadly reproduce the observed
epidemic history of COVID-19 before vaccine roll-out
by country (see Additional File 1: Fig. S4-6 for model fits
and S7 for fitted parameter distributions). The projected
impacts of viral vector vaccines on cases, deaths, andDALYs by roll-out scenario are presented in Fig. 2 (seeAdditional File 1: Fig. S8-10 for intermediate results andresults for using mRNA vaccines). We calculated relative reduction in disease burden instead of absolute differences to allow the comparison across countries withvarying population sizes.
For a given vaccination programme start date, fasterroll-out rates are associated with a substantially greater
disease burden averted compared to the no-vaccination
scenario. For example, compared to the no-vaccination
scenario, vaccination programmes starting in August2021 (when more than half of African Union member
states with data (n = 53) reached 1% population-level vaccine coverage) could have reduced deaths by an averageof 11.06% [median = 8.80%, interquartile range: 5.87–
16.20%; n = 27 (countries)] under slow roll-out, 24.19%[24.43%, 21.78–27.18%] under medium roll-out, and31.29% [32.57%, 25.72–36.68%] under fast roll-out.
For a given vaccine roll-out rate, earlier roll-out startdates are associated with greater disease burden averted
compared to the no-vaccination scenario. For example,
under medium roll-out, starting in January could have
reduced deaths by a mean of 50.30% [51.29%, 48.97–
54.95%]. However, starting the same programme inAugust 2021 could only have reduced deaths by a meanof 24.19% [24.32%, 21.78–27.18%]. These decreasing
trends in relative impact reduction are steeper for more
severe outcomes, which reflects different vaccine efficacies by outcome and the features of emerging variants ofconcern.
Given the same start date, faster programmes may be
associated with lower ICERs in relation to GDP per capita (Fig. 3). At all income levels, medium and fast roll-out

Liu et al. BMC Medicine(2023) 21:85

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Fig. 2 Outcomes associated with different vaccine roll-out scenarios for 27 African Union Members using viral vector vaccines. This figure presents
the relative reduction in outcomes as a result of different vaccine roll-out scenarios (i.e. combinations of vaccination programme start dates andvaccine roll-out rates). Relative reduction is defined as (1 - outcome with vaccination/outcome without vaccination). Greater relative reductions
indicate more effective vaccine roll-out scenarios. Results for intermediate health outcomes and for using mRNA vaccines can be found in
Additional File 1: Fig. S8-10. DALYs: disability-adjusted life years

scenarios are associated with lower ICERs relative to
GDP per capita compared to slow. However, mediumroll-out scenarios may result in lower ICERs relative to
GDP per capita compared to fast. This can be explainedby differential marginal effectiveness by age group as thefast roll-out scenarios quickly move into younger adult
populations (which are less cost-effective to vaccinate)after vaccinating older adults (Additional File 1: Methods S10). Compared to older adults, younger adults areless susceptible and less likely to progress to more severe
outcomes.
The results of multivariable regression analysis linking
country characteristics and ICER as a proportion of GDP
per capita revealed that a country’s income group was
statistically significant (using type I error rate of 0.05) andhad the largest explanatory power (i.e. effect size, Additional File 1: Fig. S11). Higher income groups were associated with low ICERs as proportions of GDP per capita.
In upper-middle-income countries, ICERs account for,on average, − 2.52% and 20% of GDP per capita using
viral vector and mRNA vaccines, respectively (Fig. 3C).
Vaccination programmes may be cost-saving. In lowincome countries, ICERs account for, on average, 82.59%
and 313.52% of GDP per capita using viral vector andmRNA vaccines, respectively (Fig. 3A). These vaccination
programmes are unlikely to be considered cost-effective.
In low-income countries, only viral vector vectors andonly when vaccination programmes start in the first halfof 2021 could the average ICERs as proportions of GDP
per capita have fallen below 50% (Fig. 3A).
The proportion of population above 60 years of age andthe proportion of non-susceptible individuals at the startof vaccination programmes were also statistically significant and had the next largest effect sizes. Higher proportions of population above 60 years of age were associatedwith lower ICERs as proportions of GDP per capita.
Higher proportions of non-susceptible population at thestart of the vaccination programme were associated with

Liu et al. BMC Medicine(2023) 21:85

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Fig. 3 ICERs in relation to GDP per capita by vaccine roll-out scenarios. A. Low-income countries. B. Lower-middle-income countries. C.
Upper-middle-income countries. Each y-axis tick represents a country; the y-axis is arranged based on ICERs as proportions of GDP per capita. The
baseline for comparison is the no-vaccination scenario (01 January 2021–31 December 2022)

higher ICERs as proportions of GDP per capita. All othervariables, except for government general health expenditure, were statistically significantly associated with ICERs
as proportions of GDP per capita but had smaller effectsizes relative to the aforementioned three variables.
Our sensitivity analyses around the time horizon showthat drawing the line on 31 December 2022 affects how
ICERs compare to GDP per capita (Fig. 4, top panels).
Of 972 country and roll-out scenario combinations (27countries × 36 scenarios), 34.88% and 19.65% returned
different results when we extended the time horizon by
6 months while using viral vector and mRNA vaccines,
respectively. These differences are expressed as deviations from the diagonal line in Fig. 4. Deviations above
the diagonal line indicate by extending the time horizon
ICERs got larger. Stratified analyses revealed that mostdeviations above the diagonal lines had late start dates;
most deviations below had early start dates. An extended
time horizon makes the observed contrast in ICER overstart date (Fig. 3) more extreme, indicating the robustness of our results to the analytical window.
Using lower vaccine efficacies affects ICERs as proportions of GDP per capita to a much lesser extent. We
expect low vaccine efficacy to be associated with highmedical costs and low overall QALYs averted comparedto the no vaccine used scenario and thus high ICERs as

proportions to GDP per capita. This is the case with bothviral vector and mRNA vaccines (Fig. 4, bottom panels; non-empty cells exist to the top left of the diagonalline). While using mRNA vaccines, lower vaccine efficacies may lead to lower ICERs as proportions to GDP per
capita (Fig. 4, bottom right panel; non-empty cells exist
on the bottom right of the diagonal line). In these cases,
lower vaccine efficacy leads to larger outbreaks earlier on,
resulting in more individuals being protected by hybrid
immunity by the time that the more transmissible strain
emerges.
The median relative affordability estimates for vaccination programmes involving mRNA vaccines implemented with slow, medium, and fast roll-out rates are
3.85% [interquartile range: 1.26–10.24%], 12.42% [3.43–
29.84%], and 26.13% [7.82–61.61%], respectively, and forviral vector vaccines 1.09% [0.18–3.02%], 3.24% [0.61–
6.61%], and 5.28% [0.70% -10.98%], respectively.
In the context of low- and middle-income countries,
scenarios where ICERs account for more than 50% of
GDP per capita are less likely to be cost-effective (Fig. 5,
dotted cells) [63, 66]. Scenarios where ICERs account forless than 50% of GDP per capita and where programme
cost less than 10% of government general health expenditure may be favourable policy options (Fig. 5, pattern-freecells). Some scenarios have negative net costs and ICERs,

Liu et al. BMC Medicine(2023) 21:85

Page 9 of 15

Fig. 4 Results from sensitivity analyses. Univariable sensitivity analysis dimensions: (top) time horizon; (bottom) vaccine efficacies. In each panel,a total of 972 data points (27 countries × 36 vaccine roll-out scenarios) have been allocated into each cell based on value, with colours indicating
the overall count of data points in that cell. In the bottom right panel, for example, the dark cell on the top right indicates there are approximately
500 country-and-vaccine-roll-out-scenario combinations where the ICER relative to GDP per capita is greater than one in both the baseline analysis
and when low vaccine efficacies were implemented. Deviations from the diagonal line indicate there are country-and-vaccine-roll-out-scenario
combinations returning different results in terms of ICERs as proportions of GDP per capita resulting from the univariable sensitivity analysis

indicating they are cost-saving and health-improving.
Other scenarios (5.22% and 1.11% of country-and-rollout-scenario combinations for viral vector and mRNA
vaccines, respectively) have low ICERs as proportions of
GDP per capita but cost more than 10% of government
health expenditure (Fig. 5, striped cells). In these cases,

some COVID-19 vaccination programmes may still becost-effective yet unaffordable. Decision makers mayneed to adjust their perceived health opportunity costs
for a DALY, given COVID-19 is now a new and leading
source of health burden [7, 8]. The need for this adjustment may be mitigated by shifting costs to external(See figure on next page.)
Fig. 5 ICERs as proportions of GDP per capita and relative affordability of COVID-19 vaccine roll-out scenarios. In each panel, a total of 900 data
points (25 countries × 36 vaccine roll-out scenarios) have been allocated in each cell, with colours indicating the overall count of data points in thatcell. Only 25 of 27 countries with ICER results also had relative affordability results. Data on government general health expenditure for Somalia and
Libya were not available. In the top panel, for example, there are over 150 country-and-vaccine-roll-out-scenario combinations where the ICERs
account for 0–10% of the GDP per capita and where incremental costs of the vaccination programmes account for 0–10% of the country’s general
healthcare expenditure. An unfavourable conclusion (dotted pattern cells) is extremely unaffordable (> 100% of general healthcare expenditure) or
one where ICERs as proportions of GDP per capita are too high (> 0.5); a favourable conclusion (pattern-free cells) is one where ICERs as proportionsof GDP per capita are low (< = 0.5) and programme costs are also low (< = 10% of general healthcare expenditure); a mixed conclusion (striped
pattern cells) is one where ICERs as proportions of GDP per capita are low (< = 0.5), but programme costs are high (> = 10% of general healthcare
expenditure)Liu et al. BMC Medicine(2023) 21:85

Fig. 5 (See legend on previous page.)

Page 10 of 15

Liu et al. BMC Medicine(2023) 21:85

donors (e.g. via Gavi COVID-19 Vaccines Global Access(or COVAX), Advance Market Commitment) among
health sector payers.
Raw estimates behind the results presented above(including incremental costs, incremental DALYs, ICER,
GDP per capita, and ICERs as proportions of GDP per
capita) by each scenario and country can be downloaded
from our GitHub repository.

Discussion
Using a combined epidemiological and economic modelling approach, we explored how the timing and speedof implementation (i.e. vaccination programme startdate and vaccine roll-out rates) affect vaccination programmes’ health benefits, cost-effectiveness, and relative affordability among African countries. We foundthat vaccination programmes with earlier start dates leadto greater health benefits and are more likely to resultin small ICERs in relation to GDP per capita. Fast vaccination roll-out leads to the greatest health benefits but
does not always lead to the smallest ICERs in relation
to GDP per capita. This is due to differential marginal
effectiveness by age. Fast scenarios quickly move beyond
the vulnerable and thus prioritised population (defined
as 60 + years of age in this study, see Additional File 1:
Methods S10). These results should not be extrapolated
to other parts of the world without further validation;these results also should not be extrapolated to other vaccine products due to the fundamental differences in theunderlying epidemiology.
Higher income groups and greater proportions of population over 60 years of age were associated with lower
ICERs as proportions to GDP per capita. Greater proportions of populations non-susceptible at the start of vaccination programmes were associated with higher ICERs
as proportions of GDP per capita. The proportions of
populations non-susceptible at the start of vaccination
programmes were estimated by fitting a dynamic transmission model in this study. In practice, decision-making
based on this metric requires accurate serological surveys, which is particularly challenging to conduct in pandemic response settings.
There were missed opportunities in early- to mid-2021
for COVID-19 vaccination to save more lives in Africa.
However, even with a late start, certain vaccine types
and roll-out rates combinations may still generate relatively low ICERs. Two factors could further increase theeconomic value of vaccination programmes. First, ourresults are based on the known immune dynamics of
SARS-CoV-2 and the performance of current COVID-19
vaccines. New vaccines with better effectiveness against
emerging variants could decrease ICER by increasing
health benefits. Emerging variants with the protection

Page 11 of 15

advantage of vaccine-induced immunity over infectioninduced immunity may have the same effect.
Second, our results show that vaccine roll-out scenarios are much more likely to be cost-effective whenusing viral vector vaccines than mRNA vaccines. The
relatively similar vaccine efficacy estimates against severe
health outcomes (Table 1) and the drastically different
vaccine unit costs (Table 2) may explain this. The main
driver of vaccine unit costs is vaccine price. Some lowincome countries may struggle to find COVID-19 vaccination programmes cost-effective after factoring inbudget constraints and potential health opportunity
costs. This highlights the importance of achieving evenlower vaccine purchasing prices than those we assumed
in this analysis. In this study, we estimated that the priceof viral vector vaccines may be reduced by 10.32–28.69%
and mRNA vaccines by 36.91-60.96% to reduce ICERs to
10–50% of GDP per capita (Additional File 1: Fig. S12).
We found a small number of countries where ICERs
were small in relation to GDP per capita, yet the programmes were relatively unaffordable. The implication isthat the investment in managing COVID-19, a new andleading cause of disease burden, may risk opportunity
costs for existing public health and health service issues.
Our affordability estimates are from a health sector perspective and have not explicitly accounted for the potential externalities on the local health systems and essential
health services. Unless development partners provide
subsidies that compensate for these effects, the perceived
opportunity cost of a DALY, which is often approximated using a fraction of GDP per capita, may need to
be further adjusted down from what we already know
them for in low- and middle-income settings (which arelow) [7, 8, 63, 66]. Affordability and ICERs in relation to
GDP per capita are two of many metrics that stakeholders base their decisions on while planning for vaccine
programmes.
This is the first study to examine the role of timing in
the cost-effectiveness of COVID-19 vaccination programmes. We highlighted key issues to consider in thenext pandemic (or next epidemic waves triggered byemerging variants of concern) to come. Timing has
already been shown to influence the health impacts ofvaccination programmes. Incorporating it into economic
evaluation would lead to more accurate and realistic costeffectiveness assessments [67, 68]. We fit a mathematicalmodel with a range of plausible vaccine effect mechanisms to the COVID-19 epidemic histories in 27 African
Union member states. Most existing modelling studies
on the economic evaluation of COVID-19 vaccination
strategies have either focused on a single country [69] orcity [15] or used hypothetical epidemic histories that areLiu et al. BMC Medicine(2023) 21:85

not based on data from any country [70], which hinders
the interpretability and generalisability of their results.
It is not the objective of this study to directly inform
COVID-19 vaccine policies in individual countries. A
regional analysis constrained us to use only data streams
available in all countries under consideration. Thus,
although we provided country-specific estimates in our
GitHub repository, we caution national-level stakeholders against interpreting these results out of the regional
context. Evidence for informing policies in individual
countries may be generated by better-fitted transmission models (e.g. channelling more locally available data
streams, such as more detailed non-pharmaceuticalinterventions and ICU occupancy) using the modelling
framework we presented here (code available throughour GitHub repository).
Our study has several limitations. First, our epidemic model has not captured the full complexity of theimmune dynamics against SARS-CoV-2, which involves
both vaccination and infections. For example, our model
assumed that vaccinated individuals who had prior infection histories were completely protected against SARSCoV-2. This design is intended to ensure that mostindividuals within the model receive a maximum of two
doses of vaccine. However, we cannot capture a small
number of breakthrough infections that may still happen.
These tend to be mild cases and, thus, should not alter
our results substantially [71]. Despite best efforts, there
may be additional factors that may affect the epidemiology of COVID-19 that we have not implemented in thismodel (e.g. baseline prevalence of chronic health conditions). Second, we assumed the vaccine supply shortage was the only reason for slow uptake. In practice,
uptake may also be slowed down by factors such as vaccine hesitancy [72]. The vaccine delivery unit costs may
have been underestimated in this study as the vaccination programmes may require greater efforts in termsof social mobilisation. Third, we did not use observed
costs of different vaccination programmes, as this data
is not yet available. The normative approach (i.e. perprotocol) we took did not account for the resource
wastes incurred in operations or the increased prices ofscarce resources that may be associated with the paceof the roll-out, healthcare utilisation rate, or potential
structural changes to local health systems as a result of
COVID-19 pandemic response. As a result, ICERs may
have been biased. Fourth, the cost-effectiveness analysis
we presented is from a country’s perspective and does
not consider COVID-19 transmission in nearby countries(e.g. transmission spillover). Finally, our analysis took ona health sector perspective and did not account for societal elements such as changes in productivity or the preservation of key functions (e.g. labour market, emergence

Page 12 of 15

response, healthcare, and education). These societal elements specific to the COVID-19 pandemic have beendifficult to estimate and may vary substantially between
countries—additional data and research on these elements could improve the comprehensiveness of vaccine
policy evaluation in the future.

Conclusions
We assessed the impact of COVID-19 vaccination programmes’ timing and speed on the health benefits, costeffectiveness, and relative affordability in 27 Africancountries. We found that earlier vaccination programmes
led to greater health benefits and generated lower ICERs
in relation to GDP per capita. Although fast vaccination
programmes yielded the greatest health benefits, they did
not always generate the lowest ICERs in relation to GDP
per capita as they covered larger proportions of individuals who were less vulnerable. Lower vaccine purchasing
costs and improved vaccine efficacies may improve theoverall cost-effectiveness and affordability of COVID-19
vaccination programmes.
Abbreviations
Africa CDC	Africa Centres for Disease Control and Prevention
ARI	Acute respiratory infection
CHEERS	Consolidated Health Economic Evaluation Reporting Standards
DALY	Disability-adjusted life yearGDP	Gross domestic product
ICER	Incremental cost-effectiveness ratio
ICU	Intensive care unit
UK	United KingdomWHO	World Health Organization
YLDs	Years lived with disability
YLLs	Years of life lost

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​023-​02784-z.
Additional file 1: Supplemental Figures: Figure S1. Vaccine roll-out
trajectories by age group. Figure S2. Diagram of Transmission Model
Structure. Figure S3. Countries included and excluded from this analysis.
Figure S4-6. Performance of model fitting process, Part 1-3 (in alphabeti‑
cal order). Figure S7. Fitted parameters. Figure S8-10. Health Outcomes
Associated with Vaccine Roll-out Scenarios (by vaccine type and by healthoutcome). Figure S11. The association between the proportion of DALYs
averted attributable to older adults and the performance of medium
and fast scenarios. Figure S11. Effect sizes estimated in the multivariable
regression model linking country characteristics to ICERs as propor‑
tions of GDP per capita. Figure S12. Target vaccine price under different
perceived cost-effectiveness thresholds. Supplemental Tables: Table S1.
Model Equations. Table S2. Epidemic and Healthcare Process Parameters.
Table S3. Additional Data Sources. Table S4. Other vaccine and vaccina‑tion programme characteristics. Table S5. Variants of Concern Introduc‑tion. Table S6. List of countries with fitted models. Table S7. Itemised cost
per dose per activity for base countries - viral vector / AstraZeneca-likevaccine. Table S8. Itemised Cost per dose per activity for base coun‑tries - mRNA / Pfizer-like vaccine. Table S9. Cost per dose for countries
with fitted models. Table S10. CHEERS 2022 Checklist. Supplemental
Methods: Methods S1. Further Model Descriptions. Methods S2. Fitting

Liu et al. BMC Medicine(2023) 21:85

Process. Methods S3. Characterising behavioural change using data on
non-pharmaceutical intervention and mobility. Methods S4. Calculating
COVID-19 severe, critical, and death cases. Methods S5. Lengths of Stay
(LoSs). Methods S6. Calculating Disability-adjusted Life Years (DALYs).
Methods S7. Estimating Vaccine Delivery Costs. Methods S8. Extrapolat‑
ing unit costs from base countries to other countries in Africa. Methods
S9. Extrapolating vaccine unit costs for different roll-out scenarios. Meth‑ods S10. ICERs and Proportions of DALYs averted by those above 60 years.
Acknowledgements
The authors acknowledge the contribution of Dr Nicholas G. Davies and Dr
Rosanna C. Barnard to developing the general CovidM framework. The authors
thank the reviewers for helping us improve this work.
Authors’ contributions
YL, NN, JN, MJ, AV, FR, RME, and TD conceived the idea. YL, JN, MJ, and AV
designed the study. YL, SRP, AMM, ST-R, BSC, EB, EA, and FR collected thedata. YL, SRP, CABP, AMM, and ST-R analysed the data, built the models, andconducted the analysis. YL conducted the literature search and visualised
the data and results. YL and SRP wrote the manuscript. All co-authors havecontributed to interpreting the results, reviewing, and providing feedback to
the manuscript. All authors read and approved the final manuscript.
Funding
The contributions of YL, SRP, CABP, AMM, ST-R, EA, BSC (Uzochukwu), TD,
EB, RME, FR, JN, MJ, and AN are supported by the International Decision
Support Initiative, which is funded by the Bill and Melinda Gates Founda‑tion (OPP1202541). The contributions of CABP is supported by the World
Health Organization. MJ has received funding from the European Union’s
Horizon 2020 research and innovation programme—project EpiPose (Grant
agreement number 101003688); MJ and RME have received funding from the
National Institute for Health Research Health Protection Research Unit (NIHR
HPRU) in Modelling and Health Economics at Imperial College and LSHTM in
partnership with UKHSA. The European Commission is not responsible for any
use that may be made of the information it contains. The views expressed arethose of the author(s) and not necessarily those of the National Health Service(UK) (NHS), the NIHR, the Department of Health, or UKHSA. The contributionof RME is also supported by the Health Data Research UK (HDR UK) (grant:
MR/S003975/1). YL has also received funding from the Bill and Melinda Gates
Foundation via grant INV-003174.
Availability of data and materials
We used publicly available aggregate data and provided appropriate citations.
The CovidM modelling framework has been published previously and is avail‑able at https://​github.​com/​nicho​lasda​vies/​covidm-​alpha. All code used andcountry-specific intermediate results, original a GitHub repository, have beenarchived at Zenodo as Liu et al. (9). A CHEERS checklist (2022 version) is pre‑sented in Additional File 1: Table S10. CHEERS: Consolidated Health Economic
Evaluation Reporting Standards.

Declarations
Ethics approval and consent to participate
Our cost estimation and epidemiological modelling were based on
publicly available data sources as well as validation from the authors
and therefore did not require ethics approval. The use and collectionof data on Nigeria was based on a wider health technology assess‑ment exercise of COVID-19 vaccination which was approved by the
University of Nigeria Teaching Hospital Research Ethics Committee (Ref:
NHREC/05/01/2008B-FWA00002458-1RB00002323).
Consent for publication
Not applicable. This manuscript does not contain any individual person’s data
in any form.
Competing interests
One of the senior authors of this manuscript, Prof. Mark Jit (who contributed
equally with Dr Justice Nonvignon and Prof. Anna Vassall), is an editorial board

Page 13 of 15

member of the BMC Medicine for his expertise in Health Economics, Quality
and Reporting.
We declare no additional competing interests.
Author details
1
Department of Infectious Disease Epidemiology, Faculty of Epidemiology
and Population Health, London School of Hygiene and Tropical Medicine, Kep‑pel St, London, UK. 2 Centre for Mathematical Modelling of Infectious Diseases,
London School of Hygiene and Tropical Medicine, Keppel St, London, UK.
3South African DSI‑NRF Centre of Excellence in Epidemiological Model‑ling and Analysis, Stellenbosch University, Stellenbosch, Republic of South
Africa. 4 Department of Global Health & Development, Faculty of Public
Health and Policy, London School of Hygiene and Tropical Medicine, Keppel
St, London, UK. 5 Health Economics Programme, Africa Centres for Disease
Control and Prevention, Addis Ababa, Ethiopia. 6 Department of Community
Medicine, University of Nigeria Nsukka, Enugu Campus, Enugu, Nigeria.
7
Centre for Global Development, Great Peter House, Abbey Gardens, Great
College St, London, UK. 8 Institute of Human Virology, University of Maryland
School of Medicine, 725 W Lombard St, Baltimore, MD, USA. 9 Africa Centres
for Disease Control and Prevention, Addis Ababa, Ethiopia. 10 School of Public
Health, University of Ghana, Legon, Ghana.
Received: 10 June 2022 Accepted: 13 February 2023

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