论文标题
一台机器学习辅助全球诊断和比较工具，以评估Covid-19的隔离控制效果
A machine learning aided global diagnostic and comparative tool to assess effect of quarantine control in Covid-19 spread
论文作者
论文摘要
我们通过使用神经网络模块来增强经典的流行病学模型来开发全球适用的诊断COVID-19模型。我们的模型不依赖于先前的流行病，例如SARS/MERS，所有参数均通过用于公开可用的Covid-19数据中的机器学习算法进行了优化。该模型分解了对感染时间表的贡献，以分析和比较欧洲，北美，南美和亚洲在控制病毒传播方面采用的隔离控制政策的作用。对于所有考虑的大陆，我们的结果表明，该模型所学的隔离控制和各个政府所采取的行动之间的相关性通常很强。最后，我们在一个公共平台上为全球70个受影响的国家的隔离诊断结果主持了我们的隔离诊断结果，该平台可用于公共卫生官员和研究人员的明智决策。
We have developed a globally applicable diagnostic Covid-19 model by augmenting the classical SIR epidemiological model with a neural network module. Our model does not rely upon previous epidemics like SARS/MERS and all parameters are optimized via machine learning algorithms employed on publicly available Covid-19 data. The model decomposes the contributions to the infection timeseries to analyze and compare the role of quarantine control policies employed in highly affected regions of Europe, North America, South America and Asia in controlling the spread of the virus. For all continents considered, our results show a generally strong correlation between strengthening of the quarantine controls as learnt by the model and actions taken by the regions' respective governments. Finally, we have hosted our quarantine diagnosis results for the top 70 affected countries worldwide, on a public platform, which can be used for informed decision making by public health officials and researchers alike.
