The potential of long-term wastewater-based surveillance to predict COVID-19 waves peak in Mexico.

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Wastewater-based surveillance (WBS) is valuable method for monitoring the dispersion of pathogens at a low cost. However, their impact on public health decision-making is limited because there is a lack of long-term analyses, especially in low- and middle-income countries. This study aimed to assess the effectiveness of using WBS to predict the occurrence of COVID-19 waves and estimate the prevalence of infection, emphasizing the impact of SARS-CoV-2 variants. During 17 months of influent monitoring of two wastewater treatment plants in Queretaro City, Mexico, wave prediction time was influenced by variant dispersion. Waves dominated by the Delta and Omicron variants circulation showed lead days values from 5 to 14 and 1 to 4 days, respectively. According to the Monte Carlo model, disease prevalence prediction by WBS aligned with clinically reported cases at wave onsets, but the variant's transmissibility explained the overestimation during peaks. This work provides new insights into the potential and limitations of using WBS as an epidemiological tool for detecting pathogens and predicting their occurrence. PRACTITIONER POINTS: Long-term wastewater monitoring allowed early prediction of COVID-19 case waves. The prediction capability is related to the variant presence and their infectivity. The prevalence estimated by wastewater surveillance was higher in all case waves. The prevalence estimation has limitations regarding variations in data input.