整合城市污水监测与互联网搜索行为以加强传染病早期预警

Fu-Chang Deng; Hong Xu; Song-Zhe Fu; Qiao Yao; Jian-Qiu Qin; Cheng Yang; Yan-Feng Yao; Pu Li; Wei-Ying Tian; Xiao-Lei Wang; Ling-Shuang Lv; Xin Xia; Xia-Lu Lin; Rong-Qiu Zhang; Zhi-Nan Guo; Li-Lin Xiong; Shi-Fu Peng; Zhen Ding; Cao Chen; Yu Wang; En-Min Ding; Xi-Miao Zhao; Dan-Tong Hao; Hao-Ran Zhu; Shu-Ling Duan; Shu-Xian Li; Miao Sun; Xia Li; Jing Huang; Xiao Zhang; Liang Zhang; Hui-Hui Sun; Shu-Xin Hao; Jia-Yi Han; Yue Liu; Lan Zhang; Xiao-Yuan Yao; Guang-Ming Jiang; Tong Zhangm; John S. Ji; Song Tang; Bin Xu; Hong-Bing Shen; Xiao-Ming Shi

doi:10.1016/j.eng.2026.02.022

工程（英文） ›› 2026, Vol. 64 ›› Issue (9) : 389 -404. DOI: 10.1016/j.eng.2026.02.022

研究论文

整合城市污水监测与互联网搜索行为以加强传染病早期预警

Fu-Chang Deng ^a^,^b ,
Hong Xu ^c ,
Song-Zhe Fu ^d ,
Qiao Yao ^a^,^b ,
Jian-Qiu Qin ^c ,
Cheng Yang ^c ,
Yan-Feng Yao ^c ,
Pu Li ^c ,
Wei-Ying Tian ^c ,
Xiao-Lei Wang ^e ,
Ling-Shuang Lv ^e ,
Xin Xia ^e ,
Xia-Lu Lin ^f ,
Rong-Qiu Zhang ^f ,
Zhi-Nan Guo ^f ,
Li-Lin Xiong ^g ,
Shi-Fu Peng ^h ,
Zhen Ding ^h ,
Cao Chen ⁱ ,
Yu Wang ^a^,^b ,
En-Min Ding ^j ,
Xi-Miao Zhao ^a^,^b ,
Dan-Tong Hao ^a^,^b ,
Hao-Ran Zhu ^a^,^b ,
Shu-Ling Duan ^a^,^b ,
Shu-Xian Li ^a^,^b^,^k ,
Miao Sun ^a^,^b ,
Xia Li ^a^,^b ,
Jing Huang ^a^,^b ,
Xiao Zhang ^a^,^b ,
Liang Zhang ^a ,
Hui-Hui Sun ^a^,^b ,
Shu-Xin Hao ^a^,^b ,
Jia-Yi Han ^a^,^b ,
Yue Liu ^a^,^b ,
Lan Zhang ^a^,^b ,
Xiao-Yuan Yao ^a^,^b ,
Guang-Ming Jiang ^l ,
Tong Zhangm ^m ,
John S. Ji ⁿ ,
Song Tang ^a^,^b^,^k^,^o^,^* ,
Bin Xu ^c^,^* ,
Hong-Bing Shen ^o^,^p^,^* ,
Xiao-Ming Shi ^a^,^b^,^j^,^o^,^p^,^*

作者信息 +

Integrating Urban Wastewater Surveillance and Internet Search Behavior to Strengthen Early Warning for Infectious Diseases

Fu-Chang Deng ^a^,^b^,^# ,
Hong Xu ^c^,^# ,
Song-Zhe Fu ^d^,^# ,
Qiao Yao ^a^,^b^,^# ,
Jian-Qiu Qin ^c ,
Cheng Yang ^c ,
Yan-Feng Yao ^c ,
Pu Li ^c ,
Wei-Ying Tian ^c ,
Xiao-Lei Wang ^e ,
Ling-Shuang Lv ^e ,
Xin Xia ^e ,
Xia-Lu Lin ^f ,
Rong-Qiu Zhang ^f ,
Zhi-Nan Guo ^f ,
Li-Lin Xiong ^g ,
Shi-Fu Peng ^h ,
Zhen Ding ^h ,
Cao Chen ⁱ ,
Yu Wang ^a^,^b ,
En-Min Ding ^j ,
Xi-Miao Zhao ^a^,^b ,
Dan-Tong Hao ^a^,^b ,
Hao-Ran Zhu ^a^,^b ,
Shu-Ling Duan ^a^,^b ,
Shu-Xian Li ^a^,^b^,^k ,
Miao Sun ^a^,^b ,
Xia Li ^a^,^b ,
Jing Huang ^a^,^b ,
Xiao Zhang ^a^,^b ,
Liang Zhang ^a ,
Hui-Hui Sun ^a^,^b ,
Shu-Xin Hao ^a^,^b ,
Jia-Yi Han ^a^,^b ,
Yue Liu ^a^,^b ,
Lan Zhang ^a^,^b ,
Xiao-Yuan Yao ^a^,^b ,
Guang-Ming Jiang ^l ,
Tong Zhangm ^m ,
John S. Ji ⁿ ,
Song Tang ^a^,^b^,^k^,^o^,^* ,
Bin Xu ^c^,^* ,
Hong-Bing Shen ^o^,^p^,^* ,
Xiao-Ming Shi ^a^,^b^,^j^,^o^,^p^,^*

Author information +

^a National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China

^b China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China

^c Nanning Center for Disease Control and Prevention, Nanning 530002, China

^d Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, School of Medicine, Northwest University, Xi’an 710069, China

^e Hunan Center for Disease Control and Prevention, Hunan Academy of Preventive Medicine, Changsha 410153, China

^f Xiamen Center for Disease Control and Prevention, Xiamen 361021, China

^g Department of Environmental Health, Nanjing Municipal Center for Disease Control and Prevention, Nanjing 210003, China

^h Department of Environment and Health, Jiangsu Center for Disease Control and Prevention, Nanjing 210009, China

ⁱ National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China

^j School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China

^k Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China

^l School of Civil, Mining, Environmental and Architectural Engineering, University of Wollongong, Wollongong 2522, Australia

^m Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Hong Kong 999077, China

ⁿ Vanke School of Public Health, Tsinghua University, Beijing 100084, China

^o School of Public Health, Nanjing Medical University, Nanjing 210029, China

^p Chinese Center for Disease Control and Prevention, Beijing 102206, China

^* E-mail addresses: tangsong@nieh.chinacdc.cn (S. Tang),

Tian1994@139.com (B. Xu),

hbshen@njmu.edu.cn (H.-B. Shen),

shixm@chinacdc.cn (X.-M. Shi).

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文章历史 +

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Abstract

Wastewater-based surveillance (WBS) has emerged as an effective tool for monitoring infectious diseases. However, its broader application is often constrained by operational resources and data complexities. Herein, we developed an integrated framework that synergistically integrated WBS with the Research Index, China’s leading online search query platform, to enhance early warning capability for infectious diseases using coronavirus disease 2019 (COVID-19) as a case study. A total of 1164 influent wastewater samples were collected from 12 wastewater treatment plants in Nanning, China, over a one-year period (February 2023-January 2024), and RNA was quantified using reverse transcription quantitative polymerase chain reaction (RT-qPCR). The 7-day flow-weighted moving average concentration (FWMAC) was calculated and evaluated in relation to 16 population surveillance indicators and 126 Baidu search terms. Remarkably, the 7-day FWMAC preceded clinical indicators by 1-7 days and demonstrated strong correlations with multiple epidemiological metrics, including reported cases (the coefficient of determination (R²) = 0.92), diagnosed cases in fever clinics (R² = 0.72), positive diagnoses in fever clinics (R² = 0.86), and hospitalizations (R² = 0.78). Distributed lag nonlinear models were employed to define actionable and clinically relevant risk thresholds. We then identified three key Baidu search terms (“second positive,” “four stages of COVID-19 clinical progression,” and “ibuprofen”). Their integrated search index improved model performance (with R² for diagnosed cases in fever clinics and hospitalizations increasing from 0.72 to 0.78 and 0.78 to 0.82, respectively). External validation across three additional major Chinese cities (Changsha, Xiamen, and Nanjing) further confirmed the model’s robustness and generalizability, yielding R² improvements of 36.4%, 30.0%, and 4.9% in random forest models, respectively. This integrative “wastewater-digital” framework represents a transformative and cost-effective early warning strategy for proactive public health responses.

关键词

Key words

Wastewater-based surveillance / Digital epidemiology / Baidu Research Index / Early warning

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Fu-Chang Deng,Hong Xu,Song-Zhe Fu,Qiao Yao,Jian-Qiu Qin,Cheng Yang,Yan-Feng Yao,Pu Li,Wei-Ying Tian,Xiao-Lei Wang,Ling-Shuang Lv,Xin Xia,Xia-Lu Lin,Rong-Qiu Zhang,Zhi-Nan Guo,Li-Lin Xiong,Shi-Fu Peng,Zhen Ding,Cao Chen,Yu Wang,En-Min Ding,Xi-Miao Zhao,Dan-Tong Hao,Hao-Ran Zhu,Shu-Ling Duan,Shu-Xian Li,Miao Sun,Xia Li,Jing Huang,Xiao Zhang,Liang Zhang,Hui-Hui Sun,Shu-Xin Hao,Jia-Yi Han,Yue Liu,Lan Zhang,Xiao-Yuan Yao,Guang-Ming Jiang,Tong Zhangm,John S. Ji,Song Tang,Bin Xu,Hong-Bing Shen,Xiao-Ming Shi. 整合城市污水监测与互联网搜索行为以加强传染病早期预警[J]. 工程（英文）, 2026, 64(9): 389-404 DOI:10.1016/j.eng.2026.02.022