Estimating the COVID-19 infection fatality ratio accounting for seroreversion using statistical modelling

BackgroundThe infection fatality ratio (IFR) is a key statistic for estimating the burden of coronavirus disease 2019 (COVID-19) and has been continuously debated throughout the COVID-19 pandemic. The age-specific IFR can be quantified using antibody surveys to estimate total infections, but requires consideration of delay-distributions from time from infection to seroconversion, time to death, and time to seroreversion (i.e. antibody waning) alongside serologic test sensitivity and specificity. Previous IFR estimates have not fully propagated uncertainty or accounted for these potential biases, particularly seroreversion.MethodsWe built a Bayesian statistical model that incorporates these factors and applied this model to simulated data and 10 serologic studies from different countries.ResultsWe demonstrate that seroreversion becomes a crucial factor as time accrues but is less important during first-wave, short-term dynamics. We additionally show that disaggregating surveys by regions with higher versus lower disease burden can inform serologic test specificity estimates. The overall IFR in each setting was estimated at 0.49-2.53%.ConclusionWe developed a robust statistical framework to account for full uncertainties in the parameters determining IFR. We provide code for others to apply these methods to further datasets and future epidemics. Brazeau et al. use a statistical modelling approach to estimate COVID-19 infection fatality ratios from seroprevalence data. The authors' model accounts for seroreversion over the course of the pandemic, as well as other important uncertainties such as serologic test characteristics. Plain language summaryLarge-scale outbreaks of infectious diseases such as COVID-19, known as epidemics, can be monitored via statistics like the probability of death once infected, or infection fatality ratio (IFR). Measuring the levels of antibodies (proteins produced by the immune system to target the virus) in peoples' blood can show how many have been previously infected. The number of deaths and infections are used to calculate the IFR, but this calculation is challenging due to time delays during the natural course of illness as well as imperfect antibody tests and declining antibody levels over time. We develop a mathematical model that can account for these factors to provide accurate IFR estimates. We tested our model using several different datasets. We provide code for other researchers, which can be used to obtain more accurate IFR estimates both during COVID-19 and future epidemics.

Automated summary

The authors developed a statistical model that accounted for uncertainties in estimating the infection fatality ratio (IFR) of COVID-19, considering factors such as seroreversion and serologic test characteristics. By analyzing serologic studies from various countries, they found that time plays a crucial role in seroreversion, but is less significant during the initial phase of the pandemic. By disaggregating surveys by regions with varying disease burden, they were able to better estimate the specificity of serologic tests.