Nadim, Sk Shahid ; Ghosh, Indrajit ; Chattopadhyay, Joydev (2021) Short-term predictions and prevention strategies for COVID-19: A model-based study Applied Mathematics and Computation, 404 . p. 126251. ISSN 00963003
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Official URL: http://doi.org/10.1016/j.amc.2021.126251
Related URL: http://dx.doi.org/10.1016/j.amc.2021.126251
Abstract
An outbreak of respiratory disease caused by a novel coronavirus is ongoing from December 2019. As of December 14, 2020, it has caused an epidemic outbreak with more than 73 million confirmed infections and above 1.5 million reported deaths worldwide. During this period of an epidemic when human-to-human transmission is established and reported cases of coronavirus disease 2019 (COVID-19) are rising worldwide, investigation of control strategies and forecasting are necessary for health care planning. In this study, we propose and analyze a compartmental epidemic model of COVID-19 to predict and control the outbreak. The basic reproduction number and the control reproduction number are calculated analytically. A detailed stability analysis of the model is performed to observe the dynamics of the system. We calibrated the proposed model to fit daily data from the United Kingdom (UK) where the situation is still alarming. Our findings suggest that independent self-sustaining human-to-human spread ( ) is already present. Short-term predictions show that the decreasing trend of new COVID-19 cases is well captured by the model. Further, we found that effective management of quarantined individuals is more effective than management of isolated individuals to reduce the disease burden. Thus, if limited resources are available, then investing on the quarantined individuals will be more fruitful in terms of reduction of cases.
Item Type: | Article |
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Source: | Copyright of this article belongs to Elsevier Inc |
Keywords: | Coronavirus disease;Mathematical model;Basic reproduction number;Model calibration and prediction;Control strategies;United Kingdom |
ID Code: | 132143 |
Deposited On: | 14 Dec 2022 06:31 |
Last Modified: | 14 Dec 2022 06:31 |
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