Wu Joseph T, Leung Kathy, Lam Tommy T Y, Ni Michael Y, Wong Carlos K H, Peiris J S Malik, Leung Gabriel M
WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
Laboratory of Data Discovery for Health (D24H), Hong Kong, China.
Nat Med. 2021 Mar;27(3):388-395. doi: 10.1038/s41591-021-01278-w. Epub 2021 Mar 15.
Epidemic nowcasting broadly refers to assessing the current state by understanding key pathogenic, epidemiologic, clinical and socio-behavioral characteristics of an ongoing outbreak. Its primary objective is to provide situational awareness and inform decisions on control responses. In the event of large-scale sustained emergencies, such as the COVID-19 pandemic, scientists need to constantly update their aims and analytics with respect to the rapidly evolving emergence of new questions, data and findings in order to synthesize real-time evidence for policy decisions. In this Perspective, we share our views on the functional aims, rationale, data requirements and challenges of nowcasting at different stages of an epidemic, drawing on the ongoing COVID-19 experience. We highlight how recent advances in the computational and laboratory sciences could be harnessed to complement traditional approaches to enhance the scope, timeliness, reliability and utility of epidemic nowcasting.
疫情即时预测大致是指通过了解正在发生的疫情的关键致病、流行病学、临床和社会行为特征来评估当前状况。其主要目标是提供态势感知并为控制应对决策提供信息。在发生大规模持续紧急情况时,例如新冠疫情,科学家需要不断更新其目标和分析方法,以应对新问题、数据和发现的快速演变,从而综合实时证据以供政策决策参考。在这篇观点文章中,我们借鉴正在发生的新冠疫情经验,分享我们对疫情不同阶段即时预测的功能目标、基本原理、数据要求和挑战的看法。我们强调如何利用计算科学和实验室科学的最新进展来补充传统方法,以扩大疫情即时预测的范围、提高其及时性、可靠性和实用性。