Utilizing wastewater surveillance to model behavioural responses and prevent healthcare overload during "Disease X" outbreaks.

ABSTRACT: During the COVID-19 pandemic, healthcare systems worldwide faced severe strain. This study, utilizing wastewater virus surveillance, identified that periodic spontaneous avoidance behaviours significantly impacted infectious disease transmission during rapid and intense outbreaks. To incorporate these behaviours into disease transmission analysis, we introduced the Su-SEIQR model and validated it using COVID-19 wastewater data from Beijing and Hong Kong. The results demonstrated that the Su-SEIQR model accurately reflected trends in susceptible populations and confirmed cases during the COVID-19 pandemic, highlighting the role of spontaneous collective avoidance behaviours in generating periodic fluctuations. These fluctuations helped reduce infection peaks, thereby alleviating pressure on

SUBMITTER: Chen W 

PROVIDER: S-EPMC11749008 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature