PDF(Pubmed)
Abstract：
Human mobility has played a critical role in the spread of COVID-19. The understanding of mobility helps in getting information on the acceleration or control of the spread of disease. The COVID-19 virus has been spreading among several locations despite all the best efforts related to its isolation. To comprehend this, a multi-patch mathematical model of COVID-19 is proposed and analysed in this work, where-in limited medical resources, quarantining, and inhibitory behaviour of healthy individuals are incorporated into the model. Furthermore, as an example, the impact of mobility in a three-patch model is studied considering the three worst-hit states of India, i.e. Kerala, Maharashtra and Tamil Nadu, as three patches. Key parameters and the basic reproduction number are estimated from the available data. Through results and analyses, it is seen that Kerala has a higher effective contact rate and has the highest prevalence. Moreover, if Kerala is isolated from Maharashtra or Tamil Nadu, the number of active cases will increase in Kerala but reduce in the other two states. Our findings indicate that the number of active cases will decrease in the high prevalence state and increase in the lower prevalence states if the emigration rate is higher than the immigration rate in the high prevalence state. Overall, proper travel restrictions are to be implemented to reduce or control the spread of disease from the high-prevalence state to other states with lower prevalence rates.
摘要：
人的流动性在COVID-19的传播中发挥了关键作用。对移动性的理解有助于获得有关疾病传播的加速或控制的信息。尽管尽了最大的努力来隔离COVID-19病毒，但它已经在几个地方传播。为了理解这一点，在这项工作中，提出并分析了COVID-19的多补丁数学模型，在有限的医疗资源中，隔离,和健康个体的抑制行为被纳入模型。此外，作为一个例子，考虑到印度三个受灾最严重的州，研究了三补丁模型中流动性的影响，即喀拉拉邦,马哈拉施特拉邦和泰米尔纳德邦，作为三个补丁。从可用数据估计关键参数和基本再现数。通过结果和分析，可以看出，喀拉拉邦的有效接触率更高，患病率最高。此外，如果喀拉拉邦与马哈拉施特拉邦或泰米尔纳德邦隔离开来，喀拉拉邦的活跃病例数量将增加，但其他两个州的活跃病例数量将减少。我们的发现表明，如果移民率高于高流行状态下的移民率，则在高流行状态下，活跃病例的数量将减少，而在低流行状态下，活跃病例的数量将增加。总的来说,应实施适当的旅行限制，以减少或控制疾病从高流行州向其他患病率较低的州的传播。
