论文标题
SIR模型和机器学习技术对COVID-19的大流行分析用于预测
Analysis of the COVID-19 pandemic by SIR model and machine learning technics for forecasting
论文作者
论文摘要
这项工作是一项试验，我们提出了爵士和机器学习工具，以分析现实世界中的冠状病毒大流行。基于\ cite {datahub}的公共数据，我们估算主要关键大流行参数，并对现实世界（专门针对塞内加尔）的拐点和可能的结束时间进行预测。世界卫生组织通过世界卫生组织的2019年冠状病毒病在整个中国，然后在全世界迅速扩展。根据乐观的估计，某些国家的大流行很快就会结束，而在世界上大部分国家（美国，意大利等）中，反流行的袭击将不迟于4月底。
This work is a trial in which we propose SIR model and machine learning tools to analyze the coronavirus pandemic in the real world. Based on the public data from \cite{datahub}, we estimate main key pandemic parameters and make predictions on the inflection point and possible ending time for the real world and specifically for Senegal. The coronavirus disease 2019, by World Health Organization, rapidly spread out in the whole China and then in the whole world. Under optimistic estimation, the pandemic in some countries will end soon, while for most part of countries in the world (US, Italy, etc.), the hit of anti-pandemic will be no later than the end of April.
