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Community-level wastewater surveillance with machine learning methods to assess underreporting of COVID-19 case counts
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作者 Nathan Szeto Jianfeng Wu +9 位作者 Yili Wang Xin Li Zheshi Zheng Leyao Zhang Richard Neitzel Marisa Eisenberg J.Tim Dvonch Alfred Franzblau peter x.k.song Chuanwu Xi 《mLife》 2025年第6期715-718,共4页
Impact statement.COVID-19 remains an ongoing threat to public health,and reliable,continuous disease monitoring programs are essential for preventing future surges of infection.However,without mandated COVID-19 testin... Impact statement.COVID-19 remains an ongoing threat to public health,and reliable,continuous disease monitoring programs are essential for preventing future surges of infection.However,without mandated COVID-19 testing,accurate data of confirmed cases are unavailable.Instead,COVID-19 viruses may be tracked via wastewater samples from sewage manholes in areas of high social connectivity,where captured viral RNA data are biomarkers useful for monitoring and predicting community-level COVID-19 prevalence through machine learning techniques.We construct a prediction model of high sensitivity and specificity to provide evidence of significant underreporting of COVID-19 cases for the time period following the lifting of testing mandates. 展开更多
关键词 captured viral rna data machine learning public health disease monitoring community level wastewater surveillance sewage manholes UNDERREPORTING COVID case counts
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