论文标题
COVID-19的分析，建模和表示：印度的案例研究
Analysis, Modeling, and Representation of COVID-19 Spread: A Case Study on India
论文作者
论文摘要
冠状病毒爆发是地球整个人口最具挑战性的大流行者之一。诸如隔离感染者和维持社会距离之类的技术是针对流行病19的唯一预防措施。对数据科学家面临的不确定的问题的实际估计是一个不确定的问题。现有文献中有大量技术，包括繁殖数量，病例死亡率等，以预测流行病和感染人群的持续时间。本文提出了一项案例研究，以分析与流行病（例如COVID-19）相关的数据进行分析，建模和表示。我们进一步提出了一种用于估计特定区域中感染传播状态的算法。这项工作还提出了一种算法，用于估算易感感染和恢复模型的流行病的末期。最后，本文介绍了经验和数据分析，以研究传播概率，接触率，传染性和对流行病扩散的影响的影响。
Coronavirus outbreak is one of the most challenging pandemics for the entire human population of the planet Earth. Techniques such as the isolation of infected persons and maintaining social distancing are the only preventive measures against the epidemic COVID-19. The actual estimation of the number of infected persons with limited data is an indeterminate problem faced by data scientists. There are a large number of techniques in the existing literature, including reproduction number, the case fatality rate, etc., for predicting the duration of an epidemic and infectious population. This paper presents a case study of different techniques for analysing, modeling, and representation of data associated with an epidemic such as COVID-19. We further propose an algorithm for estimating infection transmission states in a particular area. This work also presents an algorithm for estimating end-time of an epidemic from Susceptible Infectious and Recovered model. Finally, this paper presents empirical and data analysis to study the impact of transmission probability, rate of contact, infectious, and susceptible on the epidemic spread.
