论文标题
使用易感性感染的（S.I.R）模型预测印度的Covid 19增长
Forecasting COVID 19 growth in India using Susceptible-Infected-Recovered (S.I.R) model
论文作者
论文摘要
这项工作涵盖了对Covid 19的分析，并在不同的国家传播，并涉及Covid 19增长的主要特征，这是由于社会与接触结构所致的传播，该结构受参数\ b {eta}管辖的社会接触结构。该参数的依赖性\ b {eta}对社会传播水平的依赖性使人们对社会疏远所采取的措施的有效性有所了解。单独的算法使用Scipy在Python中进行了硬编码，该算法使用Scipy学习了社会接触结构，并为\ b {eta}赋予了合适的价值，这对结果的结果产生了重大影响。对印度流行病的预测已经进行了预测，发现在印度完成社会疏远的严格性不足以增长Covid 19。
This work covers the analysis of the COVID 19 spread in different countries and dealing the main feature of COVID 19 growth, which is the spread due to the social-contact structure, which is governed by the parameter \b{eta}. The dependency of this parameter \b{eta} on the transmission level in society gives a sense of the effectiveness of the measures taken for social distancing. A separate algorithm is hardcoded in python using Scipy which learns the social-contact structure and gives a suitable value for \b{eta}, which has a major impact on the outcome of the result. Forecasting for the epidemic spread in India was done, and it was found that the strictness at which social distancing in India is done, is insufficient for the growth of COVID 19.
