COVID-19 case prediction via wastewater surveillance in a low-prevalence urban community: a modeling approach

Abstract

Estimating and predicting the epidemic size from wastewater surveillance results remain challenging for the practical implementation of wastewater-based epidemiology (WBE). In this study, by employing a highly sensitive detection method, we documented the time series of SARS-CoV-2 RNA occurrence in wastewater influent from an urban community with a 360,000 population in Japan, from August 2020 to February 2021. The detection frequency of the viral RNA increased during the outbreak events of COVID-19 and the highest viral RNA concentration was recorded at the beginning of January 2021, amid the most serious outbreak event during the study period. We found that: (1) direct back-calculation still suffers from great uncertainty dominated by inconsistent detection and the varying gap between the observed wastewater viral load and the estimated patient viral load, and (2) the detection frequency correlated well with reported cases and the prediction of the latter can be carried out via data-driven modeling methods. Our results indicate that wastewater virus occurrence can contribute to epidemic surveillance in ways more than back-calculation, which may spawn future wastewater surveillance implementations.

Efficacy of Linear Regression Modelling of SARS-CoV-2 cases based on local wastewater surveillance

“…Furthermore, further research is needed on virus shedding patterns in faeces, viral load concentration and their development over time (Zhu et al, 2022).…”

“…It may be worthwhile to train a separate model (and lag) for each slope of the data to enhance coherence. Different, more fine-tuned aggregation times could be investigated as indicated by some of the literature (Galani et al, 2022; Zhu et al, 2022). Furthermore, different testing efficiencies for establishing case numbers might impact lag time and shape parameters of the functional relation between wastewater and case data (Xiao et al, 2022).…”

“…The RKI data was filtered for the locality “SK Dresden” and daily reported cases were accumulated as moving sum for n previous days (being 7, 14, and 28) respectively for each sample at time point t . Accumulation for seven days was selected in accordance with the standard RKI reporting scheme, 14 days as it is handled often internationally, and 28 days was used as accumulation period in some previous studies (Galani et al, 2022; Medema et al, 2020; Zhu et al, 2022). Cases are represented as incidence rates, i.e.…”

Editorial: SARS-CoV-2 in water

“…Many studies have sought to estimate the number of cases in the community from the reported number of genome copies in a wastewater sample. Zhu et al (2022) review the uncertainty associated with estimating the number of cases in the catchment area using back-calculation and conclude that 'at the current stage, the uncertainty associated with the wastewater viral load is still a great hindrance to reliable back-calculation'. Their results, however, demonstrate a strong correlation between virus detection and reported cases, which they argue may guide towards novel wastewater surveillance strategies.…”

Assessing wastewater-based epidemiology for the prediction of SARS-CoV-2 incidence in Catalonia

“…These models allow for an extremely informative mechanistic approach; nevertheless, they entail the determination of several parameters that make their generalisation difficult to other sites. Different empirico-statistical models have also been explored to estimate the COVID-19 prevalence in two Greek municipalities 30 as well as to explore the prediction capacity of the incidence dynamics in specific communities like Sendai city in Japan 31 , Oklahoma city in USA 32 , and four Austrian communities 33 . These approaches are focused on particular contexts where the relationship between the area of influence of the wastewater plants and the epidemiological dynamics in this area can be straightforward and homogeneous.…”