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A Bayesian approach to estimating COVID-19 incidence and infection fatality rates.

ABSTRACT: Naive estimates of incidence and infection fatality rates (IFR) of coronavirus disease 2019 suffer from a variety of biases, many of which relate to preferential testing. This has motivated epidemiologists from around the globe to conduct serosurveys that measure the immunity of individuals by testing for the presence of SARS-CoV-2 antibodies in the blood. These quantitative measures (titer values) are then used as a proxy for previous or current infection. However, statistical methods that use this data to its full potential have yet to be developed. Previous researchers have discretized these continuous values, discarding potentially useful information. In this article, we demonstrate how multivariate mixture models can be used in combination with post-stratification to estimate cumulative incidence and IFR in an approximate Bayesian framework without discretization. In doing so, we account for uncertainty from both the estimated number of infections and incomplete deaths data to provide estimates of IFR. This method is demonstrated using data from the Action to Beat Coronavirus erosurvey in Canada.

SUBMITTER: Slater JJ

PROVIDER: S-EPMC11017123 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature

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A Bayesian approach to estimating COVID-19 incidence and infection fatality rates.

Slater Justin J JJ Bansal Aiyush A Campbell Harlan H Rosenthal Jeffrey S JS Gustafson Paul P Brown Patrick E PE

Biostatistics (Oxford, England) 20240401 2

Naive estimates of incidence and infection fatality rates (IFR) of coronavirus disease 2019 suffer from a variety of biases, many of which relate to preferential testing. This has motivated epidemiologists from around the globe to conduct serosurveys that measure the immunity of individuals by testing for the presence of SARS-CoV-2 antibodies in the blood. These quantitative measures (titer values) are then used as a proxy for previous or current infection. However, statistical methods that use ...[more]

PMID: 36881693

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Project description:ObjectivesTo estimate age-specific and sex-specific mortality risk among all SARS-CoV-2 infections in four settings in India, a major lower-middle-income country and to compare age trends in mortality with similar estimates in high-income countries.DesignCross-sectional study.SettingIndia, multiple regions representing combined population >150 million.ParticipantsAggregate infection counts were drawn from four large population-representative prevalence/seroprevalence surveys. Data on corresponding number of deaths were drawn from official government reports of confirmed SARS-CoV-2 deaths.Primary and secondary outcome measuresThe primary outcome was age-specific and sex-specific infection fatality rate (IFR), estimated as the number of confirmed deaths per infection. The secondary outcome was the slope of the IFR-by-age function, representing increased risk associated with age.ResultsAmong males aged 50-89, measured IFR was 0.12% in Karnataka (95% CI 0.09% to 0.15%), 0.42% in Tamil Nadu (95% CI 0.39% to 0.45%), 0.53% in Mumbai (95% CI 0.52% to 0.54%) and an imprecise 5.64% (95% CI 0% to 11.16%) among migrants returning to Bihar. Estimated IFR was approximately twice as high for males as for females, heterogeneous across contexts and rose less dramatically at older ages compared with similar studies in high-income countries.ConclusionsEstimated age-specific IFRs during the first wave varied substantially across India. While estimated IFRs in Mumbai, Karnataka and Tamil Nadu were considerably lower than comparable estimates from high-income countries, adjustment for under-reporting based on crude estimates of excess mortality puts them almost exactly equal with higher-income country benchmarks. In a marginalised migrant population, estimated IFRs were much higher than in other contexts around the world. Estimated IFRs suggest that the elderly in India are at an advantage relative to peers in high-income countries. Our findings suggest that the standard estimation approach may substantially underestimate IFR in low-income settings due to under-reporting of COVID-19 deaths, and that COVID-19 IFRs may be similar in low-income and high-income settings.

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A Bayesian approach to estimating COVID-19 incidence and infection fatality rates.

Publications

A Bayesian approach to estimating COVID-19 incidence and infection fatality rates.

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