2026/03/04 by Wenwu Tang, Tianyang Chen, Zachery Slocum +12 · 1 voice
Medicine · Environmental Science · #SARS-CoV-2 detection and testing #Air Quality Monitoring and Forecasting #COVID-19 impact on air quality
paper · doi:10.1080/15230406.2026.2629334
The COVID-19 pandemic has precipitated profound socioeconomic and public health ramifications globally. Wastewater-based epidemiology has been increasingly utilized for the surveillance and mitigation of COVID-19 outbreaks and transmission. The implementation of wastewater surveillance methodologies at a small scale has demonstrated considerable cost-effectiveness as an alternative to individual clinical testing, particularly in high-density environments such as academic institutions. Wastewater surveillance necessitates the acquisition and analysis of complex spatiotemporal data, requiring sophisticated interpretation and integration with complementary epidemiological parameters to effectively inform intervention strategies. The systematic management and analysis of these multivariate datasets present formidable logistical and computational challenges for timely decision making. This investigation advances geospatial science through a novel web-based spatial decision support system (SDSS) framework to resolve these challenges. This study encompasses the main campus of the University of North Carolina at Charlotte, where we implemented a spatiotemporal data model that revolutionizes management of multidimensional space-time data of wastewater surveillance. We employ advanced spatiotemporal analysis incorporating innovative cluster detection algorithms and uncertainty quantification to elucidate latent spatio-temporal patterns of SARS-CoV-2 virus abundance, which conventional approaches consistently fail to detect. This investigation conclusively demonstrates the superiority of integrated space-time cluster pattern analysis from both wastewater surveillance and clinical test results, quantifying their differential robustness when subjected to spatiotemporal uncertainties. The SDSS framework developed here represents a breakthrough in automated management, analytics, and dissemination of spatiotemporal wastewater epidemiological data. Our framework delivers transformative capabilities for informing evidence-based prevention strategies and establishing targeted intervention protocols for COVID-19 outbreaks within complex institutional environments.