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)
* Corresponding Author: Avaneesh Singh. Email:
(This article belongs to this Special Issue: Computer Modelling of Transmission, Spread, Control and Diagnosis of COVID-19)
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.
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