论文标题
EXO-SIR：一种流行病学模型，用于分析感染的外源扩散的影响
Exo-SIR: An Epidemiological Model to Analyze the Impact of Exogenous Spread of Infection
论文作者
论文摘要
诸如Covid-19和埃博拉病毒之类的流行病已经对人们的生活产生了重大影响。人民在流行病的传播中的流动性在流行传播中的影响很大。由于所考虑的人群本地因素而导致的疾病的扩散称为内源性扩散。由于外部因素，例如迁移，迁移率等，所引起的扩散称为外源扩散。在本文中，我们介绍了Exo-SIR模型，该模型的扩展以及模型的一些变体。我们模型中的新颖性是它捕获了病毒的外源和内源性扩散。首先，我们提出一项分析研究。其次，我们在有没有假设人群的接触网络的情况下模拟了Exo-SIR模型。第三，我们在真实数据集上实现有关COVID-19和埃博拉病毒的Exo-SIR模型。我们发现内源性感染受外源性感染的影响。此外，我们发现EXO-SIR模型预测峰值时间比SIR模型更好。因此，EXO-SIR模型对于政府在大流行时计划政策干预将有所帮助。
Epidemics like Covid-19 and Ebola have impacted people's lives significantly. The impact of mobility of people across the countries or states in the spread of epidemics has been significant. The spread of disease due to factors local to the population under consideration is termed the endogenous spread. The spread due to external factors like migration, mobility, etc. is called the exogenous spread. In this paper, we introduce the Exo-SIR model, an extension of the popular SIR model and a few variants of the model. The novelty in our model is that it captures both the exogenous and endogenous spread of the virus. First, we present an analytical study. Second, we simulate the Exo-SIR model with and without assuming contact network for the population. Third, we implement the Exo-SIR model on real datasets regarding Covid-19 and Ebola. We found that endogenous infection is influenced by exogenous infection. Furthermore, we found that the Exo-SIR model predicts the peak time better than the SIR model. Hence, the Exo-SIR model would be helpful for governments to plan policy interventions at the time of a pandemic.
