论文标题
Covid-19的第一原理机器学习建模
First-principles machine learning modelling of COVID-19
论文作者
论文摘要
自世界卫生组织在2020年1月30日宣布爆发以来，2019年冠状病毒病（Covid-19）改变了世界，承认爆发是2020年3月11日的流行病。正如政客和科学顾问所说的那样，目标是“使曲线变平”，或“推动峰值”，或“推高峰值”，或类似的言语，或类似的Virus sprecrus sprecrus serverus serverus serverus server of virus sprecress of virus spread sprecress of virus spread spread of virus spread sprecress。官方建议的核心是数学模型和数据，这些模型和数据提供了有关感染，恢复和死亡人数演变的估计。通过推断数据的接触，恢复和死亡率（已确认情况），每天都会提高模型的准确性。提出了一个数据驱动的模型，该模型提出了{\ it is can} data {\ it and}的第一原理。任何新数据可用时，该模型都可以快速重新训练。该方法可以应用于更详细的流行模型，几乎没有概念修改。
The coronavirus disease 2019 (COVID-19) has changed the world since the World Health Organization declared its outbreak on 30th January 2020, recognizing the outbreak as a pandemic on 11th March 2020. As often said by politicians and scientific advisors, the objective is "to flatten the curve", or "push the peak down", or similar wording, of the virus spreading. Central to the official advice are mathematical models and data, which provide estimates on the evolution of the number of infected, recovered and deaths. The accuracy of the models is improved day by day by inferring the contact, recovery, and death rates from data (confirmed cases). A data-driven model trained with {\it both} data {\it and} first principles is proposed. The model can quickly be re-trained any time that new data becomes available. The method can be applied to more detailed epidemic models with virtually no conceptual modification.
