论文标题
EXO-SIR：一个流行病学模型，用于分析Covid-19在印度的外源性感染的影响
Exo-SIR: An Epidemiological Model to Analyze the Impact of Exogenous Infection of COVID-19 in India
论文作者
论文摘要
流行病学模型是捕获流行动力学的数学模型。该病毒的扩散有两种路线 - 外源性和内源性。外源性扩散来自研究的人群，内源性扩散在研究中。尽管某些模型考虑了外源性感染来源，但他们尚未研究外源性和内源性扩散之间的相互作用。在本文中，我们介绍了一种新型模型 - Exo -SIR模型，该模型捕获了病毒的外源和内源性扩散。我们分析以找出COVID19大流行期间内源性和外源性感染之间的关系。首先，我们在不假设人群的任何接触网络的情况下模拟EXO-SIR模型。其次，通过假设联系网络是一个无标度网络来模拟它。第三，我们在有关COVID19的真实数据集上实现了EXO-SIR模型。我们发现内源性感染也受到外源性感染率最小的影响。同样，我们发现在存在外源性感染的情况下，内源性感染峰变得更高，并且峰发生在较早的情况下。这意味着，如果我们考虑对像Covid19这样的大流行的反应，那么我们应该为早期和更高的病例做好准备，而不是SIR模型表明是否存在外源性来源。
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.
