论文标题
通过机器学习，确定性和随机的SIR模型对确认病例进行了证实的病例的比较预测
Comparative prediction of confirmed cases with COVID-19 pandemic by machine learning, deterministic and stochastic SIR models
论文作者
论文摘要
在本文中，我们提出了一种具有数值近似值的机器学习技术和SIR模型（确定性和随机病例），以预测几天和接下来的三周内感染Covid-19的病例数。像[1]中的[2]中的公共数据一样，我们估算参数并做出预测，以帮助如何找到控制情况的具体动作。根据乐观的估计，某些国家的大流行很快就会结束，而对于世界上大多数国家，反流行的袭击将不晚于5月初。
In this paper, we propose a machine learning technics and SIR models (deterministic and stochastic cases) with numerical approximations to predict the number of cases infected with the COVID-19, for both in few days and the following three weeks. Like in [1] and based on the public data from [2], we estimate parameters and make predictions to help on how to find concrete actions to control the situation. Under optimistic estimation, the pandemic in some countries will end soon, while for most of the countries in the world, the hit of anti-pandemic will be no later than the beginning of May.
