Songzhe Fu

Wastewater-based epidemiology (WBE) is a promising tool for monitoring respiratory pathogens, yet the rapid in-sewer decay of viruses limits its application in low-incidence settings. This study investigates the decay rates of respiratory syncytial virus (RSV) in wastewater under various conditions to enhance the utility of WBE for forecasting RSV outbreaks. Laboratory-scale recirculating sewer systems were employed to simulate in-sewer decay of RSV at different temperatures, organic concentrations, and total suspended solids (TSS) levels. A first-order decay model revealed a significant temperature dependence, with the decay rate increasing approximately 30-fold from 4 to 35 °C. Conversely, higher TSS concentrations provided substantial protection to RSV particles, extending the T₉₀ by up to sevenfold, whereas SCOD exhibited minimal impact. Based on these findings, a multiple linear regression model was established to identify key predictors. This study underscores the importance of understanding RSV decay kinetics for accurate WBE and highlights the value of kinetic modelling in correcting in-sewer viral loss for optimized wastewater surveillance.

Intensive aquaculture and animal farming along riverbanks have emerged as significant drivers of downstream public health risks, facilitating the transmission of zoonotic pathogens and antimicrobial resistance (AMR) genes from farm effluents into natural water systems. In this study, we conducted a comprehensive 12-week water monitoring program at the Wei River in Shandong, China, using a combination of rapid detection techniques (RPA-LFD) and whole-genome sequencing to trace the origins of detected pathogens. RPA-LFD screening revealed the sequential appearance of Vibrio parahaemolyticus, Aeromonas veronii, norovirus GII, and Brucella spp. in surface water from March onward, coinciding with documented wastewater discharge events from upstream shrimp and fox farms. Subsequent isolation efforts confirmed the presence of V. parahaemolyticus and A. veronii in both river water and shrimp samples, while Brucella abortus was isolated from fox feces and water samples. Whole-genome sequencing of bacterial isolates revealed that V. parahaemolyticus strains from water and shrimp shared identical sequence types (ST150 and ST809) and resistance gene profiles, indicating a clonal relationship. Similarly, B. abortus isolates from water and fox feces differed by fewer than five SNPs, confirming farm-to-water transmission. Norovirus GII.3 and GII.6 sequences from water and fecal samples clustered phylogenetically with regional clinical strains, suggesting local circulation and environmental dissemination. Our findings highlight the critical role of river water monitoring as an early warning system for pathogen spread, emphasizing the need for integrated surveillance systems that monitor both water quality and the health of upstream farms and wildlife populations. The combined use of RPA-LFD and whole-genome sequencing provides a robust framework for real-time detection and source tracing of zoonotic pathogens, offering valuable insights for future environmental monitoring and public health interventions.

Rural tropical regions face escalating threats from zoonotic AIV and dengue virus but lack sewered infrastructure for conventional wastewater surveillance. We implemented surface water-based surveillance (SWBS) in peri-urban Dhaka (Bangladesh) and Ruili (China) from July to November 2023 and coupled it with machine learning-enhanced digital epidemiology. Reverse transcription quantitative PCR (RT-qPCR) was employed to detect the M gene of AIV and to subtype H1, H5, H7, H9, and H10 in surface water. Wild bird feces (n = 40) were collected within 3 km of positive sites to source-track AIV. For the dengue virus, a serogroup-specific RT-qPCR assay targeting the CprM gene was used. Genomic sequencing of AIV and dengue virus was performed to elucidate phylogenetic relationships with local clinical strains. Clinical data related to dengue fever were also collected for correlation analysis. Meanwhile, 13 dengue-related keyword search volumes were harvested daily from Google, Bing and Baidu for four cities to reveal the relationship between dengue epidemics and the web search index. AIV H5 was detected in Dhaka city from week 38, peaking at week 39, while dengue virus was persistently detected from week 29 to week 45, aligning with clinical trends. Time-series cross-correlation analysis revealed that variations in surface water viral load led clinical case reports by approximately two weeks (max CCF = 0.572 at lag −2). In Ruili city, dengue virus was detected from week 32 to week 44. To sharpen sensitivity, 383 weekly web search series for 13 dengue keywords from four countries were screened; random-forest and XGBoost models retained five symptom queries that generated a composite index explaining 79% of variance in dengue RNA levels in an independent Ruili test set (n = 24) and reduced superfluous sampling by 35%. Phylogenetic analysis verified identity between water-derived and patient-derived DENV-2, confirming local transmission. The study demonstrates that AIV SWBS is optimally integrated with wild bird sampling for source attribution, whereas dengue SWBS achieves maximal efficiency when combined with real-time web search monitoring, providing tailored, low-cost early-warning modules for resource-constrained tropical settings.

Influenza A virus (IAV) has become increasingly unpredictable in its seasonal patterns following the COVID-19 pandemic. A key challenge remains identifying optimal moments for public health interventions to inform evidence-based decisions. Real-time PCR was employed to quantify IAV concentrations in wastewater samples from 38 treatment plants in Shenzhen, China (n = 2,764), collected weekly from March 2023 to March 2024. The random forest model was used to estimate IAV infections based on viral concentrations and physico-chemical parameters. Baseline IAV concentrations were established using mean, geometric, and median values, revealing a seasonal IAV pattern with peaks in winter 2023 and spring 2024. The optimized random forest model (mean absolute error = 2,307, R² = 0.988) integrated IAV concentration, flow rate, wastewater temperature, chemical oxygen demand, total nitrogen, and phosphorus. The established baseline values of IAV concentration provides crucial evidence for optimal timing (early March and mid-November) of seasonal vaccination and nonpharmaceutical interventions. The baseline defined as the median provided 2 to 4 weeks early warning before influenza season onset (≥ 100 cases/100,000). The median baseline also allows early detection of abnormal increases in respiratory syncytial virus, supporting timely public health interventions. Wastewater surveillance of IAV, which encompasses the entire population, closely aligns with clinical monitoring trends, underscoring its vital role as a complementary tool in public health. The estimated number of infections and the baseline defined by median offer valuable insights to guide vaccination timing and non-pharmaceutical interventions, ultimately improving community health and disease control efforts.Highlightsλ By integrating clinical surveillance and wastewater monitoring data, dynamic baselines were established to detect real-time IAV concentration changes, enabling the early identification of epidemic risks.λ IAV baselines in wastewater guide optimal timing for vaccination and nonpharmaceutical interventions, especially in developing counties.λ The Random Forest model accurately predicts IAV cases using key physico-chemical parameters like IAV concentration, flow rate, wastewater temperature, chemical oxygen demand, total nitrogen and phosphorus.

The prolonged latency period and delayed reporting of clinical surveillance data have led to a sluggish response to Bordetella pertussis outbreaks in China. Wastewater-based surveillance (WBS) provides real-time, cost-effective monitoring of various viruses. To confirm whether this approach can be applied to B. pertussis, 271 patients infected with B. pertussis were enrolled to evaluate the fecal shedding patterns of the bacterium. Concurrently, we collected wastewater samples in a typical northern (Xi'an) and southern city (Nanchang) in China, from September 2023 to July 2024. The concentrations of B. pertussis in the sewage were detected using quantitative polymerase chain reaction (qPCR). Additionally, metagenomic sequencing with pre-enrichment was employed to trace genotypes of B. pertussis. We report that 40.2% of cases tested positive for B. pertussis in fecal samples. Sewage surveillance identified two waves of B. pertussis infection in Xi'an and one wave of B. pertussis outbreak in Nanchang. The concentration of B. pertussis in sewage showed a strong correlation with the dynamics of diagnosed cases. Notably, each peak of sewage B. pertussis concentration occurred 10 days prior to clinical surveillance, underscoring the value of WBS in early warning. Additionally, both clinical and wastewater surveillance demonstrated consistency in the prevalence estimation of B. pertussis infection cases. Finally, relative to clinical surveillance, wastewater sequencing also revealed the same genotypes of B. pertussis. This study presents the first comprehensive analysis of the fecal shedding of B. pertussis, thereby establishing the potential for early detection of B. pertussis outbreaks through WBS.

HolPase is a crucial enzyme catalyzing dephosphorylation of L-histidinol-phosphate (Hol-P) to L-histidinol, usually the last and most difficult enzyme to be identified in histidine biosynthesis due to its complex origin and evolution. We previously reported that PA0335 encode a HolPase belonging to the HAD family and inactivation resulted incomplete L-histidine auxotrophy in Pseudomonas aeruginosa, suggesting the presence of alternate HolPase enzymes. In this study, the alternative homologs of PA0335 encoded HolPase were identified and primarily characterized in P. aeruginosa. PA3255 and PA1143 code phosphatase with side HolPase activity. PA0335 play a key role in histidine biosynthesis, PA3255 respond mainly for the residual growth when PA0335 HolPase is absence. Moreover, all three genes, especially for PA3255, are required for the resistance of PA to ciprofloxacin and meropenem, while PA3255 and PA0335 are required for the full virulence but inactivation of PA1143 increased the pathogens of P. aeruginosa. PA0335 and PA3255 could be used as targets for P. aeruginosa infection treatment separately or synergistically due to their participates in histidine auxotrophy, full virulence and obvious antibiotics resistance in P. aeruginosa. The relation and regulation of the three phosphatases with HolPase or side HolPase activity need further study.