Challenges on the implementation of wastewater-based epidemiology as a prediction tool: the paradigm of SARS-CoV-2

Wastewater Based Epidemiology (WBE) has been identified as a tool for monitoring and predicting patterns of SARS-CoV-2 in communities. Several factors may lead to a day-to-day variation in the measurement of viral genetic material. Wastewater samples are systematically collected from the two major wastewater treatment plants in Crete, Greece. Physico-chemical factors were tested, viral concentration was determined by RT-real time PCR and the results were normalized. The influence of restriction measures, rain and physico-chemical agents was addressed. Statistics together with machine learning (ML) were applied to predict human cases. 781 samples were analyzed. RNA concentration was reduced during lockdown and was impacted by rain. Fluctuations in pH and total solids' concentrations were associated with changes in viral load. Conductivity was mainly related to chloride ions. In Heraklion, wastewater viral load preceded human cases by three days on average. Cross- correlation estimates did not perform likewise in Chania. According to ML, the ratio of sewage RNA measurements to reported cases decreased in comparison to the first wave, due to different variants, climatological parameters, testing rate and behaviors related to seeking healthcare. The model developed showed a close approximation between recorded and predicted cases. Parameters such as total solids, pH, conductivity, rain and inhibitors can significantly impact the recovery of viral RNA. The correlation between viral load in wastewater and human cases is not straightforward. The application of ML may fill some but not every gap. Existing models cannot be directly applied to different Wastewater Treatment Plants or countries.