Nuanced risk assessment for emerging infectious diseases
Authors: David N Fisman, Gabriel M Leung, Marc Lipsitch
Journal: The Lancet (2014)

At the outset of a potential epidemic, a priority is to assess whether the new disease will spread on a sustained basis—the assessment of transmissibility.1Lipsitch M Finelli L Heffernan RT Leung GM Redd SC for the 2009 H1N1 Surveillance GroupImproving the evidence base for decision making during a pandemic: the example of 2009 influenza A/H1N1.Biosecur Bioterror. 2011; 9: 89-115PubMed Google Scholar The most widely used metric is the basic reproduction number, R0. However, early estimates of this epidemiological construct are sometimes given unwarranted emphasis in assessment of pandemic potential, as discussed by Romulus Breban and colleagues in The Lancet.2Breban R Riou J Fontanet A Interhuman transmissibility of Middle East respiratory syndrome coronavirus: estimation of pandemic risk.Lancet. 2013; 382: 694-699Summary Full Text Full Text PDF PubMed Scopus (299) Google Scholar A more nuanced understanding of pathogen emergence and R0 is needed to formulate appropriate disease-control policies. R0, similar to most statistical constructs, represents a distribution, and might have region-specific or population-specific heterogeneities resulting from biological and contextual factors. Initial epidemiological assessments are often based on non-representative cases and can be biased. We discuss specific examples relevant to emerging influenza strains and coronaviruses, but the notions are generalisable to the emergence of any pathogen. The fundamental importance of R0 is that when it is more than 1, even slightly, self-sustaining exponential growth of case numbers becomes possible (ie, an epidemic can occur, and is likely to do so as R0 surpasses about 1·5–2). However, the R0 of a particular pathogen is not one number, but is the mean of population distributions of infectivities and contact patterns. For example, Mary Mallon (known as Typhoid Mary) was a cook whose role in food preparation made her personal R0 (the number of secondary cases of infection deriving from direct or indirect contact with typhoid bacilli she shed) probably far higher than the population average; she is thought to have infected 54 others with Salmonella typhi.3Leavitt J Typhoid Mary: captive to the public's health. Beacon Press, Boston, MA1996Google Scholar The detection of Mary Mallon as a shedder of S typhi might be related partly to the fact that she had such a high personal R0. Such interindividual variability has been referred to as superspreading,4Lipsitch M Cohen T Cooper B et al.Transmission dynamics and control of severe acute respiratory syndrome.Science. 2003; 300: 1966-1970Crossref PubMed Scopus (1106) Google Scholar and has been documented for several pathogens.5Lloyd-Smith JO Schreiber SJ Kopp PE Getz WM Superspreading and the effect of individual variation on disease emergence.Nature. 2005; 438: 355-359Crossref PubMed Scopus (1691) Google Scholar These nuances should be considered in assessments of emerging respiratory pathogens such as influenza A (H7N9) and Middle East respiratory syndrome coronavirus (MERS-CoV).6Gao HN Lu HZ Cao B et al.Clinical findings in 111 cases of influenza A (H7N9) virus infection.N Engl J Med. 2013; 368: 2277-2285Crossref PubMed Scopus (579) Google Scholar, 7Mailles A Blanckaert K Chaud P et al.First cases of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) infections in France, investigations and implications for the prevention of human-to-human transmission, France, May 2013.Euro Surveill. 2013; 18: 20502Crossref PubMed Google Scholar Breban and coworkers' findings2Breban R Riou J Fontanet A Interhuman transmissibility of Middle East respiratory syndrome coronavirus: estimation of pandemic risk.Lancet. 2013; 382: 694-699Summary Full Text Full Text PDF PubMed Scopus (299) Google Scholar suggested that MERS-CoV does not pose a pandemic threat, because the mean R0 for this pathogen (based on mean reported cluster sizes) is less than 1. We do not disagree with this conclusion, but would argue that such an analysis is overly simplistic for several reasons. For example, in a similar analysis8Fisman DM MERS-CoV—Eastern Mediterranean (11): Saudi Arabia, new death. [4] Transmissibility and cluster sizes. ProMED-mail.http://www.promedmail.org/promedprint.php?id=1738597Date: May 26, 2013Google Scholar we came to similar conclusions with regard to the mean R0 of MERS-CoV, but noted that a 22 case cluster (the size of the largest cluster reported at that time) would be unlikely—a probability of about 0·08%, similar to that of getting 10–11 heads in a row if tossing a fair coin—with an R0 of 0·76 (figure). Indeed, the divergence of reported cluster sizes from expectations emphasises the importance of heterogeneity in R0 to assessment of risk. We suggest that the R0 for MERS-CoV could be quite different in a health-care setting from that reported in the community. Severe acute respiratory syndrome coronavirus, which was predominantly spread in hospitals, might have had such heterogeneity; although it did not cause a large community-based pandemic, it was extremely disruptive. Estimated reproductive numbers can differ substantially in different geographies or populations according to the presence of disease-control infrastructure (eg, water improvement resources), differing contact patterns (eg, crowding), or the time of year at which R0 is estimated.9Altizer S Dobson A Hosseini P Hudson P Pascual M Rohani P Seasonality and the dynamics of infectious diseases.Ecol Lett. 2006; 9: 467-484Crossref PubMed Scopus (1086) Google Scholar Effective reproductive numbers (the product of R0 and the fraction of the population that is susceptible) can differ according to population demographic structure.10Greer AL Tuite A Fisman DN Age, influenza pandemics and disease dynamics.Epidemiol Infect. 2010; 138: 1542-1549Crossref PubMed Scopus (29) Google Scholar The 2009 influenza A (H1N1) virus had strikingly different reproductive numbers, and thus very different effects, in Indigenous Canadian populations and the general population of southern Canada. This difference might have resulted from differential crowding and a younger age distribution (those born before 1957 seem to have been protected against infection) in isolated First Nations reservations.11Mostaco-Guidolin LC Bowman CS Greer AL Fisman DN Moghadas SM Transmissibility of the 2009 H1N1 pandemic in remote and isolated Canadian communities: a modelling study.BMJ Open. 2012; 2: e001614Crossref Scopus (15) Google Scholar If the mean value of R0 is fixed, heterogeneity caused by differences between individuals in one setting (eg, superspreaders5Lloyd-Smith JO Schreiber SJ Kopp PE Getz WM Superspreading and the effect of individual variation on disease emergence.Nature. 2005; 438: 355-359Crossref PubMed Scopus (1691) Google Scholar) or by differences between settings (hospital vs community) both increase variation in cluster size and reduce the probability that any particular individual infection will cause an epidemic.4Lipsitch M Cohen T Cooper B et al.Transmission dynamics and control of severe acute respiratory syndrome.Science. 2003; 300: 1966-1970Crossref PubMed Scopus (1106) Google Scholar Pandemic risk estimates based on early, scarce information should be interpreted with caution, because the identification of highly infectious individuals and severe cases is more likely, and because of the greater availability of surveillance resources in high-income populations where transmission characteristics of pathogens might be atypical.12Cauchemez S Van Kerkhove MD Riley S Donnelly CA Fraser C Ferguson NM Transmission scenarios for Middle East Respiratory Syndrome Coronavirus (MERS-CoV) and how to tell them apart.Euro Surveill. 2013; 18: 20503Crossref PubMed Google Scholar As with any high-quality science, pandemic risk assessment as applied to emerging infectious diseases needs to incorporate many datapoints derived from unbiased epidemiological studies, ideally in the context of transparent information sharing and scientific cooperation. DF has received educational and research funds from Novartis Vaccines, and has been a paid consultant to Amgen. ML has received consulting income from Novartis, Pfizer, and AIR Worldwide. GML declares no conflicts of interest. The writing of this Comment was supported by Award Number U54GM088558 from the National Institute of General Medical Sciences to ML. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of General Medical Sciences or the National Institutes of Health.