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Study of Non-Pharmacological Interventions on COVID-19 Spread

Avaneesh Singh*, Saroj Kumar Chandra, Manish Kumar Bajpai
PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, 482005, India
* Corresponding Author: Avaneesh Singh. Email:
(This article belongs to this Special Issue: Computer Modelling of Transmission, Spread, Control and Diagnosis of COVID-19)

Computer Modeling in Engineering & Sciences 2020, 125(3), 967-990. https://doi.org/10.32604/cmes.2020.011601

Received 20 May 2020; Accepted 16 October 2020; Issue published 15 December 2020

Abstract

COVID-19 disease has emerged as one of the life threatening threat to the society. A novel beta coronavirus causes it. It began as unidentified pneumonia of unknown etiology in Wuhan City, Hubei province in China emerged in December 2019. No vaccine has been produced till now. Mathematical models are used to study the impact of different measures used to decrease pandemic. Mathematical models have been designed to estimate the numbers of spreaders in different scenarios in the present manuscript. In the present manuscript, three different mathematical models have been proposed with different scenarios, such as screening, quarantine, and NPIs, to estimate the number of virus spreaders. The analysis shows that the numbers of COVID-19 patients will be more without screening the peoples coming from other countries. Since every people suffering from COVID-19 disease are spreaders. The screening and quarantine with NPIs have been implemented to study their impact on the spreaders. It has been found that NPI measures can reduce the number of spreaders. The NPI measures reduce the spread function’s growth and provide decision makers more time to prepare with in dealing with the disease.

Keywords

Coronavirus; COVID-19; mathematical modelling; epidemic

Cite This Article

Singh, A., Chandra, S. K., Bajpai, M. K. (2020). Study of Non-Pharmacological Interventions on COVID-19 Spread. CMES-Computer Modeling in Engineering & Sciences, 125(3), 967–990.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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