大数据生态流行病学理论模型诠释

当今，数字技术作为一种新型的健康决定因素，它改变了社会、经济、城市和家庭，进而又影响了人类健康；人类进入了一个全新的数字虚拟世界。由此，在大数据生态流行病学理论范式中，现实世界生态流行病学模型与虚拟世界生态流行病学模型，组成既相对独立又相互博弈交互的共同体，它们均以表观遗传为中介，在以基因组为核心的多组学因素、现实世界健康决定因素、虚拟世界健康决定因素间相互博弈和相互依赖；由此形成了众多镶嵌层级内健康决定因素交互博弈的生态流行病学新病因论框架。现实世界与虚拟世界两个生态流行病学模型，通过同一数字健康模型，以数字技术作为“双刃剑”角色而发挥作用。

The theoretical model of big data eco-epidemiology

Under the background of today's big data and artificial intelligence era, eco-epidemiology has entered a stage of rapid development and application, but also faces many bottlenecks in the development of theoretical paradigms, design strategies and statistical methods. It is necessary to put forward new theoretical paradigms, new design strategies and new statistical methods of eco-epidemiology that suitable for the era of digital intelligence. To this end, this paper creates a big data eco-epidemiology model of the interactive game of health determinants in many mosaic levels in the "real world" and "virtual world" that are both relatively independent and interdependent; a new etiological model of eco-epidemiology in which the genome-centric multi-omics factors, the health determinants of the real world and the virtual world interact with epigenetics as an intermediary; and under the same digital health model, digital transformation of the eco-epidemiology paradigm could be realized using two enabling keys consisted of the "one health" and "digital health", the three perspectives of the integration of "individual-population-ecosystem", and the interactive game among the five dimensions of "citizen participation, education, environment, human-animal health care and digital health", in which digital technology is the catalyst and initiator of this digital transformation. This new theoretical paradigm of eco-epidemiology and its new systematic scientific methods for design and analysis (system dynamics model, network analysis and network dynamics model, multi-agent system model, and causal inference hypergraph model) together constitute the theoretical and methodological system of big data eco-epidemiology.