Utilizing wastewater surveillance to model behavioural responses and prevent healthcare overload during “Disease X” outbreaks
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 the...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Emerging Microbes and Infections |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/22221751.2024.2437240 |
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| author | Wenxiu Chen Wei An Chen Wang Qun Gao Chunzhen Wang Lan Zhang Xiao Zhang Song Tang Jianxin Zhang Lixin Yu Peng Wang Dan Gao Zhe Wang Wenhui Gao Zhe Tian Yu Zhang Wai-yin Ng Tong Zhang Ho-kwong Chui Jianying Hu Min Yang |
| author_facet | Wenxiu Chen Wei An Chen Wang Qun Gao Chunzhen Wang Lan Zhang Xiao Zhang Song Tang Jianxin Zhang Lixin Yu Peng Wang Dan Gao Zhe Wang Wenhui Gao Zhe Tian Yu Zhang Wai-yin Ng Tong Zhang Ho-kwong Chui Jianying Hu Min Yang |
| author_sort | Wenxiu Chen |
| collection | DOAJ |
| description | 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”. |
| format | Article |
| id | doaj-art-92199af0dac944049918fcbe83820c1a |
| institution | Kabale University |
| issn | 2222-1751 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Emerging Microbes and Infections |
| spelling | doaj-art-92199af0dac944049918fcbe83820c1a2025-01-18T11:03:32ZengTaylor & Francis GroupEmerging Microbes and Infections2222-17512025-12-0114110.1080/22221751.2024.2437240Utilizing wastewater surveillance to model behavioural responses and prevent healthcare overload during “Disease X” outbreaksWenxiu Chen0Wei An1Chen Wang2Qun Gao3Chunzhen Wang4Lan Zhang5Xiao Zhang6Song Tang7Jianxin Zhang8Lixin Yu9Peng Wang10Dan Gao11Zhe Wang12Wenhui Gao13Zhe Tian14Yu Zhang15Wai-yin Ng16Tong Zhang17Ho-kwong Chui18Jianying Hu19Min Yang20National 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 ChinaNational 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 ChinaNational 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 ChinaBeijing Center for Disease Prevention and Control, Beijing, People’s Republic of ChinaNational 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 ChinaChina CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, People’s Republic of ChinaChina CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, People’s Republic of ChinaChina CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, People’s Republic of ChinaBeijing Drainage Group Co. LTD, Beijing, People’s Republic of ChinaBeijing Drainage Group Co. LTD, Beijing, People’s Republic of ChinaBeijing Drainage Group Co. LTD, Beijing, People’s Republic of ChinaBeijing Drainage Management Center, Beijing, People’s Republic of ChinaBeijing Drainage Management Center, Beijing, People’s Republic of ChinaChaoyang District Center for Disease Prevention and Control of Beijing, People’s Republic of ChinaNational 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 ChinaNational 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 ChinaHong Kong Environmental Protection Department, Hong Kong, People’s Republic of ChinaEnvironmental Microbiome Engineering and Biotechnology Lab, Department of Civil Engineering, Center for Environmental Engineering Research, The University of Hong Kong, Hong Kong SAR, People’s Republic of ChinaHong Kong Environmental Protection Department, Hong Kong, People’s Republic of ChinaCollege of Urban and Environment Sciences, Peking University, Beijing, People’s Republic of ChinaNational 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 ChinaDuring 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”.https://www.tandfonline.com/doi/10.1080/22221751.2024.2437240Behavioural avoidanceSu-SEIQR modelhealthcare overloadpandemicwastewater virus surveillanceDisease X |
| spellingShingle | Wenxiu Chen Wei An Chen Wang Qun Gao Chunzhen Wang Lan Zhang Xiao Zhang Song Tang Jianxin Zhang Lixin Yu Peng Wang Dan Gao Zhe Wang Wenhui Gao Zhe Tian Yu Zhang Wai-yin Ng Tong Zhang Ho-kwong Chui Jianying Hu Min Yang Utilizing wastewater surveillance to model behavioural responses and prevent healthcare overload during “Disease X” outbreaks Emerging Microbes and Infections Behavioural avoidance Su-SEIQR model healthcare overload pandemic wastewater virus surveillance Disease X |
| title | Utilizing wastewater surveillance to model behavioural responses and prevent healthcare overload during “Disease X” outbreaks |
| title_full | Utilizing wastewater surveillance to model behavioural responses and prevent healthcare overload during “Disease X” outbreaks |
| title_fullStr | Utilizing wastewater surveillance to model behavioural responses and prevent healthcare overload during “Disease X” outbreaks |
| title_full_unstemmed | Utilizing wastewater surveillance to model behavioural responses and prevent healthcare overload during “Disease X” outbreaks |
| title_short | Utilizing wastewater surveillance to model behavioural responses and prevent healthcare overload during “Disease X” outbreaks |
| title_sort | utilizing wastewater surveillance to model behavioural responses and prevent healthcare overload during disease x outbreaks |
| topic | Behavioural avoidance Su-SEIQR model healthcare overload pandemic wastewater virus surveillance Disease X |
| url | https://www.tandfonline.com/doi/10.1080/22221751.2024.2437240 |
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