Nadim Sk Shahid, Ghosh Indrajit, Chattopadhyay Joydev
Agricultural and Ecological Research Unit, Indian Statistical Institute, Kolkata 700108, West Bengal, India.
Department of Computational and Data Sciences, Indian Institute of Science, Bengalore 560012, Karnataka, India.
Appl Math Comput. 2021 Sep 1;404:126251. doi: 10.1016/j.amc.2021.126251. Epub 2021 Apr 1.
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.
自2019年12月以来，一种新型冠状病毒引发的呼吸道疾病疫情一直在持续。截至2020年12月14日，它已在全球范围内引发了一场疫情大爆发，确诊感染病例超过7300万例，报告死亡病例超过150万例。在这一疫情期间，当人际传播已经确立且全球范围内2019冠状病毒病（COVID-19）报告病例不断增加时，对控制策略进行调查和预测对于医疗规划来说是必要的。在本研究中，我们提出并分析了一种COVID-19的 compartments 疫情模型，以预测和控制疫情爆发。通过分析计算基本再生数和控制再生数。对该模型进行了详细的稳定性分析，以观察系统的动态变化。我们对所提出的模型进行了校准，以拟合来自英国的每日数据，该国的情况仍然令人担忧。我们的研究结果表明，独立的自我维持人际传播（ ）已经存在。短期预测表明，该模型很好地捕捉到了新COVID-19病例的下降趋势。此外，我们发现，对隔离个体进行有效管理比管理孤立个体更能有效减轻疾病负担。因此，如果资源有限，那么在减少病例方面，对隔离个体进行投资将更有成效。
