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\begin{document}

\title{Seroprevalence and COVID-19 deaths in Indian Cities}



\author[1]{Daihai He}%
\author[2]{Yiming Fei}%
\author[3]{Shi Zhao}%
\author[4]{Hainan Xu}%
\author[1]{Xingyue Zhang}%
\author[1]{Salihu Musa}%
\affil[1]{The Hong Kong Polytechnic University}%
\affil[2]{The Hang Seng University of Hong Kong}%
\affil[3]{The Chinese University of Hong Kong}%
\affil[4]{McMaster University}%


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  \date{May 19, 2024}


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\begin{abstract}
Population-based sero-epidemiological studies are widely used to
estimate the proportion of a population infected (infection attack rate,
IAR) with SARS-CoV-2. However, the accuracy of the estimates relies on
the design of the study (e.g. sample size) and the sensitivity (e.g.
decay of sensitivity) of the assay used. This study aims to resolve
these issues with the seroprevalence of COVID-19 and infection attack
rates in 12 Indian cities as examples. We examine serological data that
used Abbott to reconstruct a sensitivity decay function and use it to
infer attack rates and seroprevalence based on reported COVID-19 death
in these cities. We find that the reconstructed seroprevalence matched
with the reported scenario reasonably well in most cities, where Abbott
or similar assay was likely used, but failed in two cities, where
non-Abbott assay was likely used. We propose an approach to connect the
serological data and the reported COVID-19 deaths with the testing
sensitive decay function to increase the confidence in estimating the
size of the epidemic.%
\end{abstract}%



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\textbf{Hosted file}

\verb`2021-09-03_seroprevalence_COVID_India.docx`  available at \url{https://authorea.com/users/331590/articles/711816-seroprevalence-and-covid-19-deaths-in-indian-cities}

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