 Estimating the parameters of susceptible-infected-recovered model of COVID-19 cases in India during lockdown periodsDilip Kumar Bagal, Arati Rath, Abhishek Barua and Dulu Patnaik Chaos, Solitons & Fractals, 2020, vol. 140, issue C Abstract: Owing to the pandemic scenario of COVID-19 disease cases all over the world, the outbreak prediction has become extremely complex for the emerging scientific research. Several epidemiological mathematical models of spread are increasing daily to forecast the predictions appropriately. In this study, the classical susceptible-infected-recovered (SIR) modeling approach was employed to study the different parameters of this model for India. This approach was analyzed by considering different governmental lockdown measures in India. Some assumptions were considered to fit the model in the Python simulation for each lockdown scenario. The predicted parameters of the SIR model exhibited some improvement in each case of lockdown in India. In addition, the outcome results indicated that extreme interventions should be performed to tackle this type of pandemic situation in the near future. Keywords: COVID-19; India; Lockdown; Python; SIR Model (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:140:y:2020:i:c:s0960077920305506 DOI: 10.1016/j.chaos.2020.110154 Access Statistics for this article Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros More articles in Chaos, Solitons & Fractals from Elsevier |
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