Integrating Urban Wastewater Surveillance and Internet Search Behavior to Strengthen Early Warning for Infectious Diseases
Year: 2026
Type: article
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 ( R 2 ) = 0.92), diagnosed cases in fever clinics ( R 2 = 0.72), positive diagnoses in fever clinics ( R 2 = 0.86), and hospitalizations ( R 2 = 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 R 2 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 R 2 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.
Source: Engineering
Authors: 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 Zhang, John S. Ji, Song Tang, Bin Xu, Hong-Bing Shen, Xiao-Ming Shi
Language: English
Subfield: Infectious Diseases
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,300
Award: G1208054320
OpenAlex ID: W7134190897