Eurosurv. Wastewater-based reproduction numbers and projections of COVID-19 cases in three areas in Japan, November 2021 to December 2022

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• Shogo Miyazawa[SUP]1,*[/SUP] , Ting Sam Wong[SUP]2,3,*[/SUP] , Genta Ito[SUP]1[/SUP] , Ryo Iwamoto[SUP]1,4[/SUP] , Kozo Watanabe[SUP]5[/SUP] , Michiel van Boven[SUP]6,7[/SUP] , Jacco Wallinga[SUP]6,8[/SUP] , Fuminari Miura[SUP]5[/SUP]

Background

Wastewater surveillance has expanded globally as a means to monitor spread of infectious diseases. An inherent challenge is substantial noise and bias in wastewater data because of the sampling and quantification process, limiting the applicability of wastewater surveillance as a monitoring tool.

Aim

To 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 during 2021–22 as an example.

Results

Observed notified cases were matched with the range of cases estimated by our approach with wastewater data only, across different study areas and virus quantification methods, especially when the disease prevalence was high. 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.



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​https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2024.29.8.2300277