A model based study on the dynamics of COVID-19: Prediction and control
A model based study on the dynamics of COVID-19: Prediction and control
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DOI：
10.1016/j.chaos.2020.109889
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发表时间：
2020-07-01
影响因子：
7.8
通讯作者：
Kar, T. K.
中科院分区：
文献类型：
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作者：
Mandal, Manotosh;Jana, Soovoojeet;Kar, T. K.
由于没有疫苗及适当的治疗药物，近期COVID-19引起的大流行引起了人们对隔离策略及其他政府措施的关注，如封锁、媒体报道社会隔离及改善公共卫生等以控制疾病。数学模型可以帮助这些干预措施何时是疾病控制的最佳策略，以及它们如何影响疾病动态。基于此，本文建立了一个数学模型，引入了隔离类和政府干预措施，以减轻疾病的传播。我们用基本再生数研究了该模型的动力学行为。此外，我们进行了必要繁殖数的敏感性分析，发现减少暴露和易感人群的接触是实现疾病控制的最关键因素。为了减少受感染的个人，以及最小化的成本，实施政府控制措施，我们制定了一个最优控制问题，并确定最优控制。最后，我们预测了印度三个高度受影响的邦（马哈拉施特拉邦、德里和泰米尔纳德邦）的COVID-19短期趋势，并建议前两个邦需要进一步监测控制措施，以减少暴露和易感人群的接触。
As there is no vaccination and proper medicine for treatment, the recent pandemic caused by COVID-19 has drawn attention to the strategies of quarantine and other governmental measures, like lockdown, media coverage on social isolation, and improvement of public hygiene, etc to control the disease. The mathematical model can help when these intervention measures are the best strategies for disease control as well as how they might affect the disease dynamics. Motivated by this, in this article, we have formulated a mathematical model introducing a quarantine class and governmental intervention measures to mitigate disease transmission. We study a thorough dynamical behavior of the model in terms of the basic reproduction number. Further, we perform the sensitivity analysis of the essential reproduction number and found that reducing the contact of exposed and susceptible humans is the most critical factor in achieving disease control. To lessen the infected individuals as well as to minimize the cost of implementing government control measures, we formulate an optimal control problem, and optimal control is determined. Finally, we forecast a short-term trend of COVID-19 for the three highly affected states, Maharashtra, Delhi, and Tamil Nadu, in India, and it suggests that the first two states need further monitoring of control measures to reduce the contact of exposed and susceptible humans.