Chinese Urban Wastewater Surveillance System for Early Warning of Infectious Diseases: Implementation and Efficacy — January 2023–June 2025

Jiayi Han¹; Xia Li¹; Shuxin Hao¹; Xiao Zhang¹; Liang Zhang¹; Fuchang Deng¹; Huihui Sun¹; Yue Liu¹; Yong Zhang²; Lin Wang¹; Xiaoyuan Yao¹; Lan Zhang^1,#; Song Tang^3,#; Xiaoming Shi^4,#; Hongbing Shen³

1. National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention & Chinese Academy of Preventive Medicine, Beijing, China;

2. National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention & Chinese Academy of Preventive Medicine, Beijing, China;

3. School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China;

4. Chinese Center for Disease Control and Prevention & Chinese Academy of Preventive Medicine, Beijing, China.

^# Corresponding authors: Xiaoming Shi, shixm@chinacdc.cn; Song Tang, tangsong@nieh.chinacdc.cn; Lan Zhang, zhanglan@nieh.chinacdc.cn

Wastewater surveillance has emerged as a powerful tool for public health monitoring, particularly during disease outbreaks. This report documents China's pioneering establishment of the first nationwide, comprehensive wastewater surveillance system with multi-scenario applications during the COVID-19 pandemic (January 2023–June 2025). The system integrated three components: national urban wastewater surveillance, inbound international aircraft wastewater surveillance, and pilot public health risk surveillance. This integrated framework demonstrated significant effectiveness in providing early warnings for COVID-19, polio, monkeypox, and other infectious disease outbreaks, while advancing variant tracking capabilities. These findings underscore the critical role of wastewater surveillance in augmenting existing public health infrastructure and improving outbreak detection capabilities. Implementing standardized protocols and developing collaborative networks will strengthen pandemic preparedness and enhance global public health resilience.

中国城市污水监测助力传染病早期预警：监测系统的实施与成效 — 2023年1月–2025年6月

韩嘉艺¹; 李霞¹; 郝舒欣¹; 张晓¹; 张良¹; 邓富昌¹; 孙惠惠¹;刘悦¹; 张勇²; 王林¹; 姚孝元¹; 张岚^1,#; 唐宋^3,#; 施小明^4,#; 沈洪兵³

1. 传染病溯源预警与智能决策全国重点实验室， 环境与人群健康重点实验室,环境与健康相关产品安全所, （中国预防医学科学院），北京，中国；

2. 病毒病预防控制所， （中国预防医学科学院），北京，中国；

3. 南京医科大学公共卫生学院，南京市，江苏省，中国；

4. （中国预防医学科学院），北京，中国。

^# 通信作者：施小明，shixm@chinacdc.cn；唐宋，tangsong@nieh.chinacdc.cn；张岚，zhanglan@nieh.chinacdc.cn。

污水监测已成为公共卫生监测的有力工具，尤其在疾病暴发期间发挥着重要作用。本文记录了在2023年1月至2025年6月期间，中国在新冠疫情背景下率先建成的全球首个多场景综合污水监测系统。该系统整合了三大组成部分：城市污水病原监测、入境航空器污水病原监测以及城市污水公共卫生风险试点监测。这一综合监测体系在实践中展现出显著成效，为新冠、脊灰、猴痘等疫情的早期预警提供了关键支撑，并提升了变异株追踪能力。研究结果凸显了污水监测在强化现有公共卫生基础设施、提升疫情发现能力方面的重要价值。推进标准化监测流程并建设协同网络，将进一步增强大流行防范能力，提升全球公共卫生韧性。

For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2025.267

Environmental Health Literacy Prevalence and Profiles among Shanghai Residents — Shanghai, China, 2020–2024

Fengchan Han¹; Ling Tong¹; Duo Wang¹; Zheng Wu¹; Hailei Qian¹; Yewen Shi¹; Chunyang Dong¹; Feier Chen¹; Chen Wu¹; Yangyang Ren¹; Mingjing Xu¹; Mengshuang Liu¹; Aimin Du¹; Zhenni Zhu¹; Yi He¹; Shaofeng Sui¹; Tian Chen^2,#; Jianghua Zhang^1,#

1. Division of Health Risk Factors Monitoring and Control/State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, Shanghai Municipal Center for Disease Control and Prevention (Shanghai Academy of Preventive Medicine), Shanghai, China;

2. Shanghai Preventive Medicine Association, Shanghai, China.

^# Corresponding author: Tian Chen, chentian@scdc.sh.cn; Jianghua Zhang, zhangjianghua@scdc.sh.cn.

In 2019, the Chinese State Council launched the "Healthy China Initiative (2019-2030)", establishing explicit targets for residents' environmental and health literacy (EHL): reaching to 15% by 2022; to 25% and over by 2030. To identify knowledge gaps and guide targeted interventions, Shanghai implemented five consecutive EHL surveys between 2020 and 2024 with multi-stage random sampling design by using the Core Questionnaire for Assessing the EHL of Chinese Residents. Associations with EHL levels were examined using χ² tests, one-way analysis of variance, generalized linear models, and multivariate logistic regression analyses. The EHL were assessed among 11,220 residents aged 15–69 years. The mean EHL scores demonstrated steady improvement, and increased from 55.28±15.64 points in 2020 to 61.77±15.92 points (2021), 62.13±17.14 points (2022), 62.03±16.97 points (2023), and 63.14±18.21 points (2024) (P<0.001). The proportion achieving adequate EHL (≥70 points) increased correspondingly, with age-adjusted rates rising from 18.78% in 2020 to 30.18% (2021), 33.22% (2022), 33.84% (2023), and 42.88% (2024). Among the three primary dimensions, knowledge showed the greatest improvement, increasing from 7.12% to 39.93%. Participants surveyed in 2024 had 3.50-fold higher odds of achieving adequate EHL compared with those in 2020 (odds ratio=3.50; 95% confidence interval: 3.07, 4.00). Although educational attainment remained the primary determinant of EHL, targeted public health education campaigns significantly improved EHL among Shanghai residents between 2020 and 2024.

上海市居民环境健康素养现状与特征分析 — 上海市，中国，2020—2024年

韩凤婵¹，童玲¹，王朵¹，吴筝¹ ，钱海雷¹，施烨闻¹，东春阳¹，陈非儿¹，伍晨¹，任洋洋¹，徐铭婧¹，刘孟双¹，杜爱民¹，朱珍妮¹，何懿¹，隋少峰¹ ，陈田^2,#，张江华^1,#

1. 国家环境保护新污染物健康影响评估重点实验室/健康危害因素监测与控制所，上海市疾病预防控制中心（上海市预防医学科学院），上海，中国；

2. 上海预防医学会，上海，中国。

^# 通信作者：陈田, chentian@scdc.sh.cn; 张江华, zhangjianghua@scdc.sh.cn。

2019年，中国国务院启动"健康中国行动(2019–2030年) "，为居民环境与健康素养水平设定了明确目标：至2022年提升至15%，至2030年达到25%及以上。为识别环境健康知识科普宣传的薄弱点，促进采取针对性的改善措施，上海市于2020年至2024年间通过多阶段随机抽样方法连续开展了五轮环境健康素养调查，利用《中国居民环境与健康素养调查核心问卷》调查居民素养水平，并使用χ²检验、单因素方差分析、广义线性模型和多元Logistic回归分析探讨了与环境健康素养水平相关的因素。共评估11,220名15–69岁居民环境健康素养水平。各年的素养平均分呈现稳步上升趋势，从2020年的(55.28±15.64) 分分别增至2021–2024年的(61.77±15.92) 分、(62.13±17.14) 分、(62.03±16.97) 分和(63.14±18.21) 分(P<0.001) 。相应地，素养水平达标(得分≥70分) 的参与者比例逐年上升，经年龄调整后的素养水平达标率从2020年的18.78%显著提升至2021–2024年的30.18%、33.22%、33.84%和42.88%。在三个一级维度素养水平中，知识维度的提升最为显著，达标率从7.12%增至39.93%。2024年素养水平显著高于2020年(比值比=3.50；95%置信区间：3.07, 4.00) 。尽管教育程度仍是环境健康素养的主要决定因素，但在2020年至2024年间加强的科普宣传显著提升了上海市居民的环境健康素养水平。

For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2025.269

Wastewater-based Surveillance of Salmonella Senftenberg as an Early-warning Indicator for Foodborne Outbreaks — Lianyungang City, Jiangsu Province, China, 2023–2025

Xiaolu Zhu¹, Zhiyang Yao¹, Jinli Huang¹, Enbo Tao¹, Jialing Zhang¹, Haipeng Li¹, Shengnan Cao¹, Li Chen¹, Huimin Qian^2,#

1. Lianyungang Center for Disease Control and Prevention, Lianyungang City, Jiangsu Province, China;

2. NHC Key Laboratory of Enteric Pathogenic Microbiology, Jiangsu Provincial Center for Disease Prevention and Control, Nanjing City, Jiangsu Province, China.

^# Corresponding author: Huimin Qian, jsqhm@jscdc.cn.

Salmonella Senftenberg ST14 is recognized for its heightened environmental persistence and distinct virulence factors. It represents a significant public health threat through severe foodborne outbreaks and potential systemic infections such as septicemia. The current surveillance systems remain reactive as they lack proactive capabilities. This study demonstrates that core-genome single-nucleotide polymorphism analysis (≤6 differences) confirmed clonal identity between outbreak-associated isolates and ST14 strains detected in wastewater. Phylogenetically linked strains were identified in wastewater samples 7–14 days before the outbreak and persisted for more than 3 weeks. Subsequent 6-month wastewater surveillance confirmed the sustained community circulation of ST14, corroborated by environmental contamination at the implicated facilities. A 24-month monitoring demonstrated no outbreak recurrence, indicating the absence of outbreak-associated strains in the wastewater. These findings suggest that wastewater-based surveillance can serve as early warning of emerging serotype-specific Salmonella outbreaks and in tracking transmission dynamics at the population level. Furthermore, WGS enhanced microevolutionary analysis provides distinct advantages for uncovering cryptic transmission chains and identifying undetected community-acquired foodborne infections, thereby enabling more targeted public health interventions.

基于污水的山夫登堡沙门氏菌监测作为食源性疾病暴发的早期预警：来自中国江苏省连云港市 2023–2025年的证据

朱晓露¹，姚志扬¹，黄金丽¹，陶恩波¹，张嘉陵¹，李海朋¹，曹胜男¹，陈莉¹，钱慧敏^2,# 

1. 连云港市疾病预防控制中心，连云港市，江苏省，中国；

2. 国家肠道病原微生物重点实验室，江苏省疾病预防控制中心，南京市，江苏省，中国。

^# 通信作者: 钱慧敏，jsqhm@jscdc.cn。

山夫登堡沙门氏菌 ST14 型因具备极强的环境存活性与独特毒力因子而备受关注，其可引发严重食源性疾病、诱发败血症等潜在全身性感染，对公共卫生构成重大威胁，而当前监测系统对其缺乏前瞻性预警能力。本研究通过核心基因组 SNP 分析（差异≤6 个），证实了疫情相关分离株与污水中检出的 ST14 菌株具有克隆一致性。关键发现在于，暴发前 7-14 天的污水中已检测到系统发育相关菌株，且该菌株在污水持续存在超过 3 周。后续 6 个月的污水监测进一步证实，ST14 型仍在社区中低水平流行，基于此线索启动的疾病暴发相关场所的再调查，确认了相关场所环境中ST14型的存在。 为期24 个月的污水监测证实，未再发现疾病暴发相关菌株的存在。这些结果表明，基于污水的监测模式，有望在新发沙门氏菌特异型血清型的暴发预警、人群社区水平传播动力学的追踪中发挥作用。此外，依托全基因组测序强化的微进化分析，在发现隐匿性传播链、识别未被察觉的社区获得性食源性感染方面具有独特优势，可为公共卫生干预措施的精准实施提供支撑。

For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2025.268

A Multi-omics Framework Combining Genomics and Proteomics for Silicosis Prediction in Chinese Workers — Jiangsu Province, China, 2023–2024

Furu Wang^1,2; Qianqian Gao²; Chenjie Li¹; Lei Han²; Chuanfeng Zhang²; Zhengdong Zhang^1,#; Baoli Zhu^2,#

1. Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing City, Jiangsu Province, China;

2. Jiangsu Provincial Center for Disease Control and Prevention, Nanjing City, Jiangsu Province, China.

^# Corresponding author: Zhengdong Zhang, zdzhang@njmu.edu.cn; Baoli Zhu, zhubl@jscdc.cn.

In this study, we aimed to identify novel genetic loci and protein biomarkers associated with silicosis susceptibility in Chinese workers through integrated proteomic and genomic analyses and to develop an early diagnostic prediction model. A genome-wide association study (GWAS) was conducted on 163 patients with silicosis and 183 controls, followed by Olink proteomic profiling of 92 plasma proteins. Protein quantitative trait loci (pQTL) mapping, Mendelian randomization (MR), and Bayesian co-localization were used to infer causal relationships. A causal protein risk score (CPRS) model integrating genetic and proteomic data was developed and validated using 10-fold cross-validation. GWAS identified 16 novel risk loci (P<1×10⁻⁵), including rs6677666 (WLS) and rs2272528 (COL4A4). MR analysis revealed eight plasma proteins associated with silicosis risk, with MMP12, EGF, Gal_9, GZMA, and ICOSLG showing significant differential expression (P<0.05). The CPRS model combining these proteins demonstrated a high diagnostic accuracy (AUC=0.915), outperforming traditional clinical variables. This multi-omics study uncovered genetic and proteomic markers linked to silicosis susceptibility and established a robust predictive model. The integration of GWAS and proteomics offers novel insights into the pathogenesis of silicosis, and supports development of early detection and prevention policies for high-risk populations.

整合基因组学与蛋白质组学的多组学研究框架对中国工人矽肺病的预测 — 江苏省，中国，2023–2024年

王福如^1,2，高茜茜²，李辰杰¹，韩磊²，张钏沨²，张正东^1,#，朱宝立^2,#

1. 南京医科大学公共卫生学院现代毒理学教育部重点实验室，南京市，江苏省，中国；

2. 江苏省疾病预防控制中心，南京市，江苏省，中国。

^# 通信作者：张正东，zdzhang@njmu.edu.cn；朱宝立，zhubl@jscdc.cn。

本研究旨在通过整合蛋白质组学和基因组学分析，识别中国工人患矽肺病的新型遗传位点和蛋白质生物标志物，并开发早期诊断预测模型。我们进行了全基因组关联研究（Genome-wide association study, GWAS），纳入了163例矽肺病患者和183例对照者，随后对92种血浆蛋白进行了Olink蛋白质组学分析。采用pQTL分析、孟德尔随机化（Mendelian randomization, MR）和贝叶斯共定位来推断因果关系。开发了整合遗传和蛋白质组学数据的因果蛋白质风险评分（Causal protein risk score, CPRS）模型并使用10折交叉验证法对其进行验证。GWAS确定了16个新的风险位点（P<1 × 10⁻⁵），包括rs6677666（WLS）和rs2272528（COL4A4）。MR分析揭示了8种与矽肺病风险相关的血浆蛋白，其中MMP12、EGF、Gal_9、GZMA和ICOSLG表达差异显著（P<0.05）。结合这些蛋白质的CPRS模型显示出较高的诊断准确性（AUC=0.915），优于传统的临床变量。这项多组学研究揭示了与矽肺易感性相关的遗传和蛋白质组学标志物，并建立了一个可靠的预测模型。GWAS与蛋白质组学的结合为矽肺发病机制提供了新的视角，并为高危人群的有效公共卫生策略和综合预防政策提供了信息。

For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2025.270