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Short-term predictions and prevention strategies for COVID-19: A model-based study

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  • Nadim, Sk Shahid
  • Ghosh, Indrajit
  • Chattopadhyay, Joydev

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 (R0>1,Rc>1) 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.

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  • 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, Elsevier, vol. 404(C).
    • Handle: RePEc:eee:apmaco:v:404:y:2021:i:c:s0096300321003416
    DOI: 10.1016/j.amc.2021.126251
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    1. Lamia Hammadi & Hajar Raillani & Babacar Mbaye Ndiaye & Badria Aggoug & Abdessamad El Ballouti & Said Jidane & Lahcen Belyamani & Eduardo Souza de Cursi, 2023. "Uncertainty Quantification for Epidemic Risk Management: Case of SARS-CoV-2 in Morocco," IJERPH, MDPI, vol. 20(5), pages 1-29, February.
    2. Shao, Qi & Han, Dun, 2022. "Epidemic spreading in metapopulation networks with heterogeneous mobility rates," Applied Mathematics and Computation, Elsevier, vol. 412(C).
    3. Yang, Bo & Yu, Zhenhua & Cai, Yuanli, 2022. "The impact of vaccination on the spread of COVID-19: Studying by a mathematical model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 590(C).
    4. Meng, Xueyu & Han, Sijie & Wu, Leilei & Si, Shubin & Cai, Zhiqiang, 2022. "Analysis of epidemic vaccination strategies by node importance and evolutionary game on complex networks," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    5. Mugnaine, Michele & Gabrick, Enrique C. & Protachevicz, Paulo R. & Iarosz, Kelly C. & de Souza, Silvio L.T. & Almeida, Alexandre C.L. & Batista, Antonio M. & Caldas, Iberê L. & Szezech Jr, José D. & V, 2022. "Control attenuation and temporary immunity in a cellular automata SEIR epidemic model," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    6. Vashishth, Anil K. & Basaiti, Komal, 2024. "Modeling the effect of non-pharmaceutical measures and vaccination on the spread of two variants of COVID-19 in India," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 217(C), pages 139-168.

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