ItemArticle

An Effective Strategy and Mathematical Model to Predict the Sustainable Evolution of the Impact of the Pandemic Lockdown

Title
An Effective Strategy and Mathematical Model to Predict the Sustainable Evolution of the Impact of the Pandemic Lockdown
Creator
Harjule P.; Poonia R.C.; Agrawal B.; Saudagar A.K.J.; Altameem A.; Alkhathami M.; Khan M.B.; Hasanat M.H.A.; Malik K.M.
Description
There have been considerable losses in terms of human and economic resources due to the current coronavirus pandemic. This work, which contributes to the prevention and control of COVID-19, proposes a novel modified epidemiological model that predicts the epidemics evolution over time in India. A mathematical model was proposed to analyze the spread of COVID-19 in India during the lockdowns implemented by the government of India during the first and second waves. What makes this study unique, however, is that it develops a conceptual model with time-dependent characteristics, which is peculiar to Indias diverse and homogeneous societies. The results demonstrate that governmental control policies and suitable public perception of risk in terms of social distancing and public health safety measures are required to control the spread of COVID-19 in India. The results also show that Indias two strict consecutive lockdowns (21 days and 19 days, respectively) successfully helped delay the spread of the disease, buying time to pump up healthcare capacities and management skills during the first wave of COVID-19 in India. In addition, the second waves severe lockdown put a lot of pressure on the sustainability of many Indian cities. Therefore, the data show that timely implementation of government control laws combined with a high risk perception among the Indian population will help to ensure sustainability. The proposed model is an effective strategy for constructing healthy cities and sustainable societies in India, which will help prevent such a crisis in the future. 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Source
Healthcare (Switzerland), Vol-10, No. 5
Date
2022-01-01
Publisher
MDPI
Subject
COVID-19; epidemiology; mathematical model; ordinary differential equations; SEIHRD
Coverage
Harjule P., Department of Mathematics, Malaviya National Institute of Technology (MNIT), Jaipur, 302017, India; Poonia R.C., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, India; Agrawal B., Department of Computer Science Engineering, Indian Institute of Information Technology Kota, MNIT Campus, Jaipur, 302017, India; Saudagar A.K.J., Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia; Altameem A., Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia; Alkhathami M., Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia; Khan M.B., Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia; Hasanat M.H.A., Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia; Malik K.M., Department of Computer Science and Engineering, Oakland University, Rochester, 48309, MI, United States
Rights
All Open Access; Gold Open Access; Green Open Access
Relation
ISSN: 22279032
Format
Online
Language
English
Type
Article

Collection

Articles

Citation

Harjule P.; Poonia R.C.; Agrawal B.; Saudagar A.K.J.; Altameem A.; Alkhathami M.; Khan M.B.; Hasanat M.H.A.; Malik K.M., “An Effective Strategy and Mathematical Model to Predict the Sustainable Evolution of the Impact of the Pandemic Lockdown,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/15084.

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