{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Wu Y"],"funding":["Key R&D project in Shaanxi Province","National Science Foundation of China","Open fund of National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases"],"pagination":["2526"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12654024"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["13(11)"],"pubmed_abstract":["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 (<i>n</i> = 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"],"journal":["Microorganisms"],"pubmed_title":["Harnessing a Surface Water-Based Multifaceted Approach to Combat Zoonotic Viruses: A Rural Perspective from Bangladesh and China."],"pmcid":["PMC12654024"],"funding_grant_id":["2025NC-YBXM-104","2024NITFID310","82505614"],"pubmed_authors":["Yang X","Zahan N","Fu S","Du C","Long Y","Deng Z","Du X","Wu Y"],"additional_accession":[]},"is_claimable":false,"name":"Harnessing a Surface Water-Based Multifaceted Approach to Combat Zoonotic Viruses: A Rural Perspective from Bangladesh and China.","description":"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 (<i>n</i> = 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","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Nov","modification":"2026-05-19T03:26:56.994Z","creation":"2026-05-19T03:12:20.319Z"},"accession":"S-EPMC12654024","cross_references":{"pubmed":["41304212"],"doi":["10.3390/microorganisms13112526"]}}