Inferring the COVID-19 infection fatality rate in the community-dwelling population: a simple Bayesian evidence synthesis of seroprevalence study data and imprecise mortality data

Abstract

Estimating the coronavirus disease-2019 (COVID-19) infection fatality rate (IFR) has proven to be particularly challenging –and rather controversial– due to the fact that both the data on deaths and the data on the number of individuals infected are subject to many different biases. We consider a Bayesian evidence synthesis approach which, while simple enough for researchers to understand and use, accounts for many important sources of uncertainty inherent in both the seroprevalence and mortality data. With the understanding that the results of one's evidence synthesis analysis may be largely driven by which studies are included and which are excluded, we conduct two separate parallel analyses based on two lists of eligible studies obtained from two different research teams. The results from both analyses are rather similar. With the first analysis, we estimate the COVID-19 IFR to be 0.31% [95% credible interval (CrI) of (0.16%, 0.53%)] for a typical community-dwelling population where 9% of the population is aged over 65 years and where the gross-domestic-product at purchasing-power-parity (GDP at PPP) per capita is $17.8k (the approximate worldwide average). With the second analysis, we obtain 0.32% [95% CrI of (0.19%, 0.47%)]. Our results suggest that, as one might expect, lower IFRs are associated with younger populations (and may also be associated with wealthier populations). For a typical community-dwelling population with the age and wealth of the United States we obtain IFR estimates of 0.43% and 0.41%; and with the age and wealth of the European Union, we obtain IFR estimates of 0.67% and 0.51%.

Assessing the burden of COVID-19 in developing countries: systematic review, meta-analysis and public policy implications

“…The elevated IFR in developing nations only becomes apparent when stratifying by age and adjusting for death under-reporting. Indeed, the quality of the vital statistics system tends to be linked to the overall level of economic development, and hence some previous studies of unadjusted data have incorrectly inferred that population IFRs are lower in developing countries than in high-income countries 27–30…”

“…Research conducted at various stages of the pandemic has found that individual preferences for testing can be associated with substantial bias in estimates of seroprevalence, with corresponding implications for estimates of population IFR 20 59. Such uncertainty can be incorporated into statistical models of prevalence and IFR 60 61. However, we did not follow such an approach here, because our statistical model already incorporates a number of other substantial sources of uncertainty.…”

Seroprevalence of SARS-CoV-2-specific anti-spike IgM, IgG, and anti-nucleocapsid IgG antibodies during the second wave of the pandemic: A population-based cross-sectional survey across Kashmir, India

“…The countries with lower case fatality rates as compared to global rates had reported lower infection fatality rates, as low as reported in our study. These countries and areas include France, Brazil, Canada, several regions in China, Tokyo (Japan), and Karachi (Pakistan) (42)(43)(44)(45). The reasons for lower mortality and lower infection fatality rates across different parts of the globe can be manifolds, including the social structure of the community, level of health care priority, age structure, overall development status of the community, and certain yet to be unfolded aspects like the effect of genetic makeup on the determinants of infections.…”