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• W3162139908 startingPage "111039" @default.
• W3162139908 abstract "The Severe Acute Respiratory Syndrome Coronavirus 2 (SAR-CoV-2) is the strain of coronavirus that causes coronavirus disease (COVID-19), the respiratory illness that resulted in COVID-19 pandemic in early December 2019. Due to lack of knowledge of the epidemiological cycle and absence of any type of vaccine or medications, the Government issued various non-pharmacological measures to end the COVID-19 pandemic. Several researchers applied the Susceptible-Infected-Recovered-Deceased (SIRD) compartmental epidemiology process model to identifying the effect of different governments intervention methods enforced to mollify the spread of COVID-19 epidemic. In this paper, we aim to provide a modified SIRD model for COVID-19 spread prediction. We have analyzed the data of the Northern and Southern states of India from January 30, 2020, to October 24, 2020 using the proposed SIRD model and existing SIRD model. We have made the predictions with reasonable assumptions based on real data, considering that the precise course of an epidemic is highly dependent on how and when quarantine, isolation, and precautionary measures were imposed. The proposed method gives better approximation values of new cases, R0 (Reproductive Number), daily deaths, daily infectious, transmission rate, and recovered individuals.Through the analysis of the reported results, the proposed SIRD model can be an effective method for investigating the effect of government interventions on COVID-19 associated transmission and mortality rate at the time of epidemic." @default.
• W3162139908 created "2021-05-24" @default.
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• W3162139908 date "2021-07-01" @default.
• W3162139908 modified "2023-09-29" @default.
• W3162139908 title "Modified SIRD Model for COVID-19 Spread Prediction for Northern and Southern States of India" @default.
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• W3162139908 doi "https://doi.org/10.1016/j.chaos.2021.111039" @default.
• W3162139908 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8120454" @default.
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