Nowcasting epidemics of novel pathogens: lessons from COVID-19
Joseph T. Wu, Kathy Leung, Tommy Tsan‐Yuk Lam, Michael Y. Ni, Carlos King Ho Wong, Malik Peiris, GM Leung
Abstract
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. Recent advances in computational and laboratory sciences are helping researchers to address large-scale sustained emergencies, such as the COVID-19 pandemic, and constantly adapt to the emergence of new questions, data and findings in order to synthesize real-time evidence that will inform policy decisions.