ArticleInfectious Disease Modelling2022
Seroprevalence and infection attack rate of COVID-19 in Indian cities.
Article in Infectious Disease Modelling, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Dynamics of SARS-CoV-2 seroassay sensitivity: a systematic review and modelling study.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2023Pooled it
- Assessing the dynamics and impact of COVID-19 vaccination on disease spread: A data-driven approach.Infectious Disease Modelling · 2024Article
- Plausible explanation for the third COVID-19 wave in India and its implications.Infectious Disease Modelling · 2023Article
- Trajectories of Seroprevalence and Neutralizing Activity of Antibodies against SARS-CoV-2 in Southern Switzerland between July 2020 and July 2021: An Ongoing, Prospective Population-Based Cohort Study.International journal of environmental research and public health · 2023Article
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Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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 makes 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-19 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 matchs the reported seroprevalence in most of the 12 Indian cities. 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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