ABSTRACT
Background: Wastewater surveillance has expanded globally to monitor the spread of infectious diseases. An inherent challenge is substantial noise and bias in wastewater data due to their sampling and quantification process, leading to the limited applicability of wastewater surveillance as a monitoring tool and the difficulty. Aim: In this study, we present an analytical framework for capturing the growth trend of circulating infections from wastewater data and conducting scenario analyses to guide policy decisions. Methods: We developed a mathematical model for translating the observed SARS-CoV-2 viral load in wastewater into effective reproduction numbers. We used an extended Kalman filter to infer underlying transmissions by smoothing out observational noise. We also illustrated the impact of different countermeasures such as expanded vaccinations and non-pharmaceutical interventions on the projected number of cases using three study areas in Japan as an example. Results: Our analyses showed an adequate fit to the data, regardless of study area and virus quantification method, and the estimated reproduction numbers derived from wastewater data were consistent with notification-based reproduction numbers. Our projections showed that a 10-20% increase in vaccination coverage or a 10% reduction in contact rate may suffice to initiate a declining trend in study areas. Conclusion: Our study demonstrates how wastewater data can be used to track reproduction numbers and perform scenario modelling to inform policy decisions. The proposed framework complements conventional clinical surveillance, especially when reliable and timely epidemiological data are not available.