Chen Wenxiu, An Wei, Wang Chen, Gao Qun, Wang Chunzhen, Zhang Lan, Zhang Xiao, Tang Song, Zhang Jianxin, Yu Lixin, Wang Peng, Gao Dan, Wang Zhe, Gao Wenhui, Tian Zhe, Zhang Yu, Ng Wai-Yin, Zhang Tong, Chui Ho-Kwong, Hu Jianying, Yang Min
National Engineering Research Center of Industrial Wastewater Detoxication and Resource Recovery, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, People's Republic of China.
University of Chinese Academy of Sciences, Beijing, People's Republic of China.
Emerg Microbes Infect. 2025 Dec;14(1):2437240. doi: 10.1080/22221751.2024.2437240. Epub 2025 Jan 18.
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 healthcare systems. However, the effect of these spontaneous behaviours on mitigating healthcare overload was limited. Consequently, we incorporated healthcare capacity constraints into the model, adjusting parameters to further guide population behaviours during the pandemic, aiming to keep the outbreak within manageable limits and reduce strain on healthcare resources. This study provides robust support for the development of environmental and public health policies during pandemics by constructing an innovative transmission model, which effectively prevents healthcare overload. Additionally, this approach can be applied to managing future outbreaks of unknown viruses or "Disease X".
在新冠疫情期间，全球医疗系统面临巨大压力。本研究利用污水病毒监测发现，在快速且严重的疫情暴发期间，周期性的自发规避行为对传染病传播产生了显著影响。为了将这些行为纳入疾病传播分析，我们引入了Su-SEIQR模型，并利用北京和香港的新冠污水数据对其进行了验证。结果表明，Su-SEIQR模型准确反映了新冠疫情期间易感人群和确诊病例的趋势，突出了自发集体规避行为在产生周期性波动方面的作用。这些波动有助于降低感染峰值，从而减轻医疗系统的压力。然而，这些自发行为对缓解医疗负担过重的效果有限。因此，我们将医疗能力限制纳入模型，调整参数以在疫情期间进一步引导人群行为，旨在将疫情控制在可控范围内并减轻医疗资源的压力。本研究通过构建创新的传播模型，为疫情期间环境与公共卫生政策的制定提供了有力支持，有效防止了医疗负担过重。此外，这种方法可应用于未来未知病毒或“X疾病”暴发的管理。
