Sivaraman Nirmal Kumar, Gaur Manas, Baijal Shivansh, Muthiah Sakthi Balan, Sheth Amit
Department of Computer Science and Engineering, The LNM Institute of Information Technology, Jaipur, India.
AI Institute, University of South Carolina, Columbia, USA.
Int J Data Sci Anal. 2022 Jun 6:1-16. doi: 10.1007/s41060-022-00334-z.
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.
像新冠疫情和埃博拉疫情这样的流行病对人们的生活产生了重大影响。人员在国家或州之间流动对疫情传播的影响很大。由于所考虑人群的本地因素导致的疾病传播被称为内源性传播。由迁移、流动等外部因素导致的传播被称为外源性传播。在本文中，我们介绍了外源性易感-感染-康复（Exo-SIR）模型，它是流行的易感-感染-康复（SIR）模型的扩展以及该模型的一些变体。我们模型的新颖之处在于它同时捕捉了病毒的外源性和内源性传播。首先，我们进行了一项分析研究。其次，我们在假设人群接触网络和不假设人群接触网络的情况下模拟了外源性易感-感染-康复模型。第三，我们在关于新冠疫情和埃博拉疫情的真实数据集上实现了外源性易感-感染-康复模型。我们发现内源性感染受外源性感染的影响。此外，我们发现外源性易感-感染-康复模型比易感-感染-康复模型能更好地预测峰值时间。因此，外源性易感-感染-康复模型将有助于政府在大流行期间规划政策干预措施。
