SEIRD model to study the asymptomatic growth during COVID-19 pandemic in India

© Indian Association for the Cultivation of Science 2020..

According to the current perception, symptomatic, presymptomatic and asymptomatic infectious persons can infect the healthy population susceptible to the SARS-CoV-2. More importantly, various reports indicate that the number of asymptomatic cases can be several-fold higher than the reported symptomatic cases. In this article, we take the reported cases in India and various states within the country till September 1, as the specimen to understand the progression of the COVID-19. Employing a modified SEIRD model, we predict the spread of COVID-19 by the symptomatic as well as asymptomatic infectious population. Considering reported infection primarily due to symptomatic, we compare the model predicted results with the available data to estimate the dynamics of the asymptomatically infected population. Our data indicate that in the absence of the asymptomatic infectious population, the number of symptomatic cases would have been much less. Therefore, the current progress of the symptomatic infection can be reduced by quarantining the asymptomatically infectious population via extensive or random testing. This study is motivated strictly toward academic pursuit; this theoretical investigation is not meant for influencing policy decisions or public health practices.

Medienart:

E-Artikel

Erscheinungsjahr:

2021

Erschienen:

2021

Enthalten in:

Zur Gesamtaufnahme - volume:95

Enthalten in:

Indian journal of physics and proceedings of the Indian Association for the Cultivation of Science (2004) - 95(2021), 12 vom: 01., Seite 2575-2587

Sprache:

Englisch

Beteiligte Personen:

Chatterjee, Saptarshi [VerfasserIn]
Sarkar, Apurba [VerfasserIn]
Karmakar, Mintu [VerfasserIn]
Chatterjee, Swarnajit [VerfasserIn]
Paul, Raja [VerfasserIn]

Links:

Volltext

Themen:

Asymptomatic
COVID-19
Computational
Journal Article
SARS
SEIRD

Anmerkungen:

Date Revised 18.02.2022

published: Print-Electronic

Citation Status PubMed-not-MEDLINE

doi:

10.1007/s12648-020-01928-8

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM318169932