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EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-312175

ABSTRACT

Objectives: Serological surveys were used to infer the infection attack rate in different populations. The sensitivity of the testing assay, Abbott, drops fast over time since infection which make the serological data difficult to interpret. In this work, we aim to solve this issue. Methods. We collect longitudinal serological data of Abbott to construct a sensitive decay function. We use the reported COVID-10 deaths to infer the infections, and use the decay function to simulate the seroprevalence and match to the reported seroprevalence in 12 Indian cities. Results. Our model simulated seroprevalence match the reported seroprevalence in most (but not all) of the 12 Indian cities we considered. We obtain reasonable infection attack rate and infection fatality rate for most of the 12 Indian cities. Conclusions. Using both reported COVID-19 deaths data and serological survey data, we infer the infection attack rate and infection fatality rate with increased confidence.

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