Wastewater-based epidemiology reveals spatiotemporal dynamics and diurnal temperature range effects on post-pandemic SARS-CoV-2 infection burden in Chongqing, China

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Background Wastewater-based epidemiology (WBE) is a cornerstone of post-pandemic SARS-CoV-2 surveillance, yet the environmental covariates and spatiotemporal heterogeneities of viral signals remain poorly characterized. Methods We conducted a 35-month longitudinal study across 19 sites in Chongqing, China, quantifying SARS-CoV-2N gene concentrations via RT-qPCR to assess the value of WBE and the correlation between viral concentrations and meteorological fluctuations. We employed Gaussian generalized additive models with REML estimation to quantify seasonal dynamics and lagged effects of diurnal temperature range (DTR). Results A total of 2,520 wastewater samples with complete data were obtained, with an overall N gene detection rate of 62.02%, a median flow-population normalized viral load of 8.40 log10 gc/day per 1,000 inhabitants, and median 1-day, 3-day and 7-day lagged DTR of 6.05 °C, 6.05 °C and 5.67 °C, respectively. Our results demonstrate a surveillance lead time for wastewater signals relative to clinical notifications. WBE detected a 2025 resurgence 2 months earlier than official reports. Wastewater SARS-CoV-2 circulation exhibited a distinct seasonal pattern, characterized by a primary peak in June and two secondary peaks in March and August. The spatiotemporal distribution patterns revealed by monthly heatmaps were highly consistent with the city-wide seasonal trends, and significant spatial heterogeneity was observed. The 7-day moving average DTR exhibited a complex nonlinear relationship with viral loads, initially decreasing, then increasing to a peak at 7–8 °C before declining again. Conclusion These findings indicate that WBE serves as a useful population-level proxy for infection burden and trends, complementing clinical surveillance. The statistical association between DTR and viral dynamics offers a predictive framework for refining early-warning systems and informs targeted interventions in complex urban environments.

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