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[Preprint]. 2020 Nov 16:2020.11.13.20231266.
doi: 10.1101/2020.11.13.20231266.

Estimating the cumulative incidence of SARS-CoV-2 infection and the infection fatality ratio in light of waning antibodies

Kayoko Shioda  1 , Max Sy Lau  2 , Alicia Nm Kraay  3 , Kristin N Nelson  3 , Aaron J Siegler  3 , Patrick S Sullivan  3 , Matthew H Collins  4 , Joshua S Weitz  5 , Benjamin A Lopman  3
Affiliations

Estimating the cumulative incidence of SARS-CoV-2 infection and the infection fatality ratio in light of waning antibodies

Kayoko Shioda et al. medRxiv. .

Update in

Abstract

Background: Serology tests can identify previous infections and facilitate estimation of the number of total infections. However, immunoglobulins targeting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been reported to wane below the detectable level of serological assays. We estimate the cumulative incidence of SARS-CoV-2 infection from serology studies, accounting for expected levels of antibody acquisition (seroconversion) and waning (seroreversion), and apply this framework using data from New York City (NYC) and Connecticut.

Methods: We estimated time from seroconversion to seroreversion and infection fatality ratio (IFR) using mortality data from March-October 2020 and population-level cross-sectional seroprevalence data from April-August 2020 in NYC and Connecticut. We then estimated the daily seroprevalence and cumulative incidence of SARS-CoV-2 infection.

Findings: The estimated average time from seroconversion to seroreversion was 3-4 months. The estimated IFR was 1.1% (95% credible interval: 1.0-1.2%) in NYC and 1.4% (1.1-1.7%) in Connecticut. The estimated daily seroprevalence declined after a peak in the spring. The estimated cumulative incidence reached 26.8% (24.2-29.7%) and 8.8% (7.1-11.3%) at the end of September in NYC and Connecticut, higher than maximum seroprevalence measures (22.1% and 6.1%), respectively.

Interpretation: The cumulative incidence of SARS-CoV-2 infection is underestimated using cross-sectional serology data without adjustment for waning antibodies. Our approach can help quantify the magnitude of underestimation and adjust estimates for waning antibodies.

Funding: This study was supported by the US National Science Foundation and the National Institute of Allergy and Infectious Diseases.

Keywords: Antibody; COVID-19; SARS-CoV-2; ascertainment bias; case ascertainment ratio; cumulative incidence; infection fatality ratio; seroconversion; seroprevalence; seroreversion; waning antibody.

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Conflict of interest statement

Conflicts of interest BAL reports personal fees from Takeda Pharmaceutical, personal fees from CDC Foundation, and personal fees from Hall Booth Smith, P.C., outside the submitted work. Other authors do not have conflicts of interest.

Figures

Figure 1.
Figure 1.. Structure of the analytic framework.
aOnset of infectiousness for asymptomatic cases; bData from Georgia Department of Public Health; cIyer et al. medRxiv 2020. Abbreviations: SD, standard deviation.
Figure 2.
Figure 2.. Cumulative density function for the estimated Weibull distribution for time from seroconversion to seroreversion (New York City and Connecticut).
Lines and shaded areas represent the posterior median and 95% credible intervals, respectively.
Figure 3.
Figure 3.. Estimated case ascertainment ratio in New York City and Connecticut in 2020.
The case ascertainment ratio was calculated in Equation 3. Lines represent the 50th percentile of the posterior distributions, and shaded areas represent 95% credible intervals.
Figure 4.
Figure 4.. Estimated daily seroprevalence and cumulative incidence of SARS-CoV-2 infection in New York City and Connecticut in 2020.
Black dots and bars represent the point estimates and 95% confidence intervals for the seroprevalence reported by CDC. Abbreviations: SARS-CoV-2, severe acute respiratory syndrome coronavirus 2; CDC, Centers for Disease Control and Prevention.
See this image and copyright information in PMC

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