Estimating the herd immunity threshold by accounting for the hidden asymptomatics using a COVID-19 specific model

Páez Chávez, Joseph and Kaushal, Shaurya and Rajput, Abhineet Singh and Bhattacharya, Soumyadeep and Vidyasagar, M and et al, . (2020) Estimating the herd immunity threshold by accounting for the hidden asymptomatics using a COVID-19 specific model. PLOS ONE, 15 (12). pp. 1-17. ISSN 1932-6203

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Abstract

A quantitative COVID-19 model that incorporates hidden asymptomatic patients is developed, and an analytic solution in parametric form is given. The model incorporates the impact of lock-down and resulting spatial migration of population due to announcement of lock-down. A method is presented for estimating the model parameters from real-world data, and it is shown that the various phases in the observed epidemiological data are captured well. It is shown that increase of infections slows down and herd immunity is achieved when active symptomatic patients are 10-25% of the population for the four countries we studied. Finally, a method for estimating the number of asymptomatic patients, who have been the key hidden link in the spread of the infections, is presented. © 2020 Kaushal et al.

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Item Type: Article
Additional Information: Sankhya Sutra Labs provided support for the study in the form of salaries for authors [SB and SA]. The specific roles of these authors are articulated in the 'author contributions' section. Science and Engineering Research Board (SERB) provided additional funding to MV. No additional funding was received for this research. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Uncontrolled Keywords: Asymptomatic Infections; COVID-19; France; Humans; Immunity, Herd; Italy; Japan; Models, Theoretical; Quarantine; SARS-CoV-2; Switzerland
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 02 Nov 2022 11:32
Last Modified: 02 Nov 2022 11:32
URI: http://raiith.iith.ac.in/id/eprint/11137
Publisher URL: http://doi.org/10.1371/journal.pone.0242132
OA policy: https://v2.sherpa.ac.uk/id/publication/17599
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