论文标题
印度基于贝叶斯优化事件的贝叶斯优化事件模型
Bayesian Optimized Event Based Epidemic Modeling in India
论文作者
论文摘要
大流行疫情在世界范围内创造了威胁生命的局势，有一段时间的感觉好像它在减慢世界。正在付出很多努力来减轻大流行的传播。本文的主要目的是基于使用分布功能和贝叶斯方法的大规模收集，在印度建立大流行蔓延的数学模型。随后，根据通用线性回归到达印度的航班来建模大流行差。我们验证了这些事件在印度确认的案件数量中的影响，并从积极的意义上提出了确认案件的数量。随后，使用进展系列研究了感染性的进展，并使用适当的收敛标准将曲线扁平化。我们通过政府发布的统计数据验证了理论方面。
Pandemic outbreak creates a life threatening situation around the world and for a while it feels as if it slows down the world. A lot of effort is being taken to mitigate the spread of the pandemics. The main objective of this paper is to build a mathematical model of the pandemic spread in India based on the mass gathering using distribution functions and Bayesian approach. Subsequently modeled the pandemic spread based on the flights arrived in India using generalized linear regression. We validated the effect of these events in the number of confirmed cases in India and formulated the number of confirmed cases in the positive sense. Subsequently, studied the progression of the infective using progression series and flattened the curve using an appropriate convergence criterion. We validated the theoretical aspects with the statistics released by the Government.
