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Seroprevalence and infection attack rate of COVID-19 in Indian cities.
Fei, Yiming; Xu, Hainan; Zhang, Xingyue; Musa, Salihu S; Zhao, Shi; He, Daihai.
  • Fei Y; Department of Math and Stats, The Hang Seng University, Hong Kong, China.
  • Xu H; Department of Math and Stats, McMaster University, Canada.
  • Zhang X; Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong, China.
  • Musa SS; Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong, China.
  • Zhao S; Department of Mathematics, Kano University of Science and Technology, Wudil, Nigeria.
  • He D; JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China.
Infect Dis Model ; 7(2): 25-32, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1796731
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.
Keywords

Full text: Available Collection: International databases Database: MEDLINE Type of study: Observational study / Prognostic study Language: English Journal: Infect Dis Model Year: 2022 Document Type: Article Affiliation country: J.idm.2022.03.001

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Observational study / Prognostic study Language: English Journal: Infect Dis Model Year: 2022 Document Type: Article Affiliation country: J.idm.2022.03.001