为应对新发传染病对公共卫生的威胁，本项目组建了大数据学科和流行病学科联合的科研团队，拟通过大数据、深度学习与流行病学融合的技术路线，研发一套基于“大数据驱动范式”的传染病建模与防控决策支持技术。该研究建立在申请团队对国务院联防联控机制等部门的大数据疫情防控决策支持服务之上，研究内容包括：1）“基于可解释深度学习的疫情影响因素挖掘”，目的是将可解释深度学习引入疫情大数据的挖掘处理当中；2）“传染病学与大数据相融合的预测仿真建模”，目的是将大数据方法与流行病领域知识相结合，研发传染病的仿真预测模型；3）“面向常态化精准疫情防控的决策支持应用”，目的是选择城市突发疫情应急和海外输入疫情防控等典型场景，将项目关键技术在实际应用中进行验证。本项目的研究成果将被应用于国务院联防联控机制、中国疾控中心、北京市疫情防控办的常态化防控决策当中；该项目是“全景式PAGE框架”在公共卫生决策研究领域的具体应用。

In response to the threat of emerging infectious diseases to global public health, a team of researchers in the fields of big data and epidemiology has been established for the project. By introducing big data and deep learning to the context of public health, we developed a set of decision support techniques for epidemic prevention and control based on the big data-driven paradigm. Based on the team’s previous study of big data decision support services for COVID-19 prevention and control in collaboration with the Joint Prevention and Control Mechanism of the State Council, the research covers three aspects including i) mining of factors influencing infection based on interpretable deep learning techniques (aiming to introduce interpretable deep learning into the mining and processing of epidemic data), ii) predictive simulation modeling based on the integration of epidemiology and big data (aiming to develop a model for simulation and prediction of the pandemic through the combination of big data methods and knowledge of epidemiology), and iii) application of decision support for regular and precise epidemic prevention and control (aiming to apply key techniques of the project to typical scenarios such as urban response for emergent epidemic and the prevention and control of imported cases). As a specific application of the “panoramic PAGE framework” in the field of public health decision-making during COVID-19 pandemic, the research results of this project will be applied to the Joint Prevention and Control Mechanism of the State Council, Chinese Center for Disease Control and Prevention, and Beijing Epidemic Prevention and Control Office for decision-making about normalized epidemic prevention and control.