Wastewater-based reproduction rates for epidemic curve reconstruction.

ABSTRACT: We introduce a hierarchical Bayesian framework for reconstructing epidemic curves using under-reported case counts and wastewater data. Our approach models wastewater signals as differentiable Gaussian processes, enabling inference on their relative growth rates, which are used to define a wastewater-based reproduction rate. These estimates are incorporated into a binomially thinned Poisson autoregressive model for case counts using a modular inference strategy. We apply this framework to reconstruct the Covid-19 epidemic curve in Toronto, validating our model through out-of-sample forecasts and comparisons with independent serosurvey-based cumulative incidence estimates. We also apply the framework to New Zealand's Covid-19 data to reconstruct its epidemic curve and demonstrate improvemen

SUBMITTER: Somerset E 

PROVIDER: S-EPMC12533577 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature