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Rapid evolution of SARS-CoV-2 has resulted in the emergence of numerous variants, posing significant challenges to public health surveillance. Clinical genome sequencing, while valuable, has limitations in capturing the full epidemiological dynamics of circulating variants in the general population. This study aimed to monitor the SARS-CoV-2 variant community dynamics and evolution using receptor-binding domain (RBD) amplicon sequencing of wastewater samples. We sequenced wastewater from El Paso, Texas, over 17 months, compared the sequencing data with clinical genome data, and performed biodiversity analysis to reveal SARS-CoV-2 variant dynamics and evolution. We identified 91 variants and observed waves of dominant variants transitioning from BA.2 to BA.2.12.1, BA.4&5, BQ.1, and XBB.1.5. Comparison with clinical genome sequencing data revealed earlier detection of variants and identification of unreported outbreaks. Our results also showed strong consistency with clinical data for dominant variants at the local, state, and national levels. Alpha diversity analyses revealed significant seasonal variations, with the highest diversity observed in winter. By segmenting the outbreak into lag, growth, stationary, and decline phases, we found higher variant diversity during the lag phase, likely due to lower inter-variant competition preceding outbreak growth. Our findings underscore the importance of low transmission periods in facilitating rapid mutation and variant evolution. Our approach, integrating RBD amplicon sequencing with wastewater surveillance, demonstrates effectiveness in tracking viral evolution and understanding variant emergence, thus enhancing public health preparedness. [Display omitted] • Ninety-one SARS-CoV-2 variants were identified, with waves of variants transitioning. • Wastewater sequencing detects early variant circulation and unreported outbreaks. • Seasonal variations in variant diversity observed, with peak diversity in winter. • Higher biodiversity was found for SARS-CoV-2 variants during outbreak lag phases. • RBD amplicon sequencing of wastewater effectively tracks variant community dynamics. [ABSTRACT FROM AUTHOR]

• Dynamic models provide a mechanistic understanding of WBS data. • WBS data provides unique information that enhances dynamic models. • Between-host models can maximize the epidemiological potential of WBS data. • Within-host models can provide insight on viral shedding dynamics. • Integrating WBS and dynamic models will yield a robust predictive monitoring system. Wastewater surveillance has proved to be a valuable tool to track the COVID-19 pandemic. However, most studies using wastewater surveillance data revolve around establishing correlations and lead time relative to reported case data. In this perspective, we advocate for the integration of wastewater surveillance data with dynamic within-host and between-host models to better understand, monitor, and predict viral disease outbreaks. Dynamic models overcome emblematic difficulties of using wastewater surveillance data such as establishing the temporal viral shedding profile. Complementarily, wastewater surveillance data bypasses the issues of time lag and underreporting in clinical case report data, thus enhancing the utility and applicability of dynamic models. The integration of wastewater surveillance data with dynamic models can enhance real-time tracking and prevalence estimation, forecast viral transmission and intervention effectiveness, and most importantly, provide a mechanistic understanding of infectious disease dynamics and the driving factors. Dynamic modeling of wastewater surveillance data will advance the development of a predictive and responsive monitoring system to improve pandemic preparedness and population health. [Display omitted] [ABSTRACT FROM AUTHOR]