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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%.

SUBMITTER: Campbell H

PROVIDER: S-EPMC8632419 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Seroprevalence survey is the most practical method for accurately estimating infection attack rate (IAR) in an epidemic such as influenza. These studies typically entail selecting an arbitrary titer threshold for seropositivity (e.g. microneutralization [MN] 1?40) and assuming the probability of seropositivity given infection (infection-seropositivity probability, ISP) is 100% or similar to that among clinical cases. We hypothesize that such conventions are not necessarily robust because different thresholds may result in different IAR estimates and serologic responses of clinical cases may not be representative. To illustrate our hypothesis, we used an age-structured transmission model to fully characterize the transmission dynamics and seroprevalence rises of 2009 influenza pandemic A/H1N1 (pdmH1N1) during its first wave in Hong Kong. We estimated that while 99% of pdmH1N1 infections became MN1?20 seropositive, only 72%, 62%, 58% and 34% of infections among age 3-12, 13-19, 20-29, 30-59 became MN1?40 seropositive, which was much lower than the 90%-100% observed among clinical cases. The fitted model was consistent with prevailing consensus on pdmH1N1 transmission characteristics (e.g. initial reproductive number of 1.28 and mean generation time of 2.4 days which were within the consensus range), hence our ISP estimates were consistent with the transmission dynamics and temporal buildup of population-level immunity. IAR estimates in influenza seroprevalence studies are sensitive to seropositivity thresholds and ISP adjustments which in current practice are mostly chosen based on conventions instead of systematic criteria. Our results thus highlighted the need for reexamining conventional practice to develop standards for analyzing influenza serologic data (e.g. real-time assessment of bias in ISP adjustments by evaluating the consistency of IAR across multiple thresholds and with mixture models), especially in the context of pandemics when robustness and comparability of IAR estimates are most needed for informing situational awareness and risk assessment. The same principles are broadly applicable for seroprevalence studies of other infectious disease outbreaks.

Project description:BackgroundThe first wave of SARS-CoV-2 infection in Chile occurred during the cold season reaching a peak by the end of June 2020, with 80 % of the cases concentrated in its capital, Santiago. The main objective of this study was to estimate the attack rate during this first wave of SARS-CoV-2 in a large, densely populated city with more than seven million inhabitants. Since the number of confirmed cases provides biased information due to individuals' potential self-selection, mostly related to asymptomatic patients and testing access, we measured antibodies against SARS-CoV-2 to assess infection prevalence during the first wave in the city, as well as estimate asymptomatic cases, and infection fatality ratio. To our knowledge this is one of the few population-based cross-sectional serosurvey during the first wave in a highly affected emerging country. The challenges of pandemic response in urban settings in a capital city like Santiago, with heterogeneous subpopulations and high mobility through public transportation, highlight the necessity of more accurate information regarding the first waves of new emerging diseases.MethodsFrom April 24 to June 21, 2020, 1326 individuals were sampled from a long-standing panel of household representatives of Santiago. Immunochromatographic assays were used to detect IgM and IgG antibody isotypes.ResultsSeroprevalence reached 6.79 % (95 %CI 5.58 %-8.26 %) in the first 107 days of the pandemic, without significant differences among sex and age groups; this figure indicates an attack rate 2.8 times higher than the one calculated with registered cases. It also changes the fatality rate estimates, from a 2.33 % case fatality rate reported by MOH to an estimated crude 1.00 % (CI95 % 0.97-1.03) infection fatality rate (adjusted for test performance 1.66 % [CI95 % 1.61-1.71]). Most seropositive were symptomatic (81,1 %).ConclusionsDespite the high number of cases registered, mortality rates, and the stress produced over the health system, the vast majority of the people remained susceptible to potential new epidemic waves. We contribute to the understanding of the initial spread of emerging epidemic threats. Consequently, our results provide better information to design early strategies that counterattack new health challenges in urban contexts.

Project description:BackgroundRecent developments in sequencing technologies make it possible to obtain genome sequences from a large number of isolates in a very short time. Bayesian phylogenetic approaches can take advantage of these data by simultaneously inferring the phylogenetic tree, evolutionary timescale, and demographic parameters (such as population growth rates), while naturally integrating uncertainty in all parameters. Despite their desirable properties, Bayesian approaches can be computationally intensive, hindering their use for outbreak investigations involving genome data for a large numbers of pathogen isolates. An alternative to using full Bayesian inference is to use a hybrid approach, where the phylogenetic tree and evolutionary timescale are estimated first using maximum likelihood. Under this hybrid approach, demographic parameters are inferred from estimated trees instead of the sequence data, using maximum likelihood, Bayesian inference, or approximate Bayesian computation. This can vastly reduce the computational burden, but has the disadvantage of ignoring the uncertainty in the phylogenetic tree and evolutionary timescale.ResultsWe compared the performance of a fully Bayesian and a hybrid method by analysing six whole-genome SNP data sets from a range of bacteria and simulations. The estimates from the two methods were very similar, suggesting that the hybrid method is a valid alternative for very large datasets. However, we also found that congruence between these methods is contingent on the presence of strong temporal structure in the data (i.e. clocklike behaviour), which is typically verified using a date-randomisation test in a Bayesian framework. To reduce the computational burden of this Bayesian test we implemented a date-randomisation test using a rapid maximum likelihood method, which has similar performance to its Bayesian counterpart.ConclusionsHybrid approaches can produce reliable inferences of evolutionary timescales and phylodynamic parameters in a fraction of the time required for fully Bayesian analyses. As such, they are a valuable alternative in outbreak studies involving a large number of isolates.

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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

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