A Model for Highly Fluctuating Spatio-Temporal Infection Data, with Applications to the COVID Epidemic.

ABSTRACT: Spatio-temporal models need to address specific features of spatio-temporal infection data, such as periods of stable infection levels (endemicity), followed by epidemic phases, as well as infection spread from neighbouring areas. In this paper, we consider a mixture-link model for infection counts that allows alternation between epidemic phases (possibly multiple) and stable endemicity, with higher AR1 coefficients in epidemic phases. This is a form of regime-switching, allowing for non-stationarity in infection levels. We adopt a generalised Poisson model appropriate to the infection count data and avoid transformations (e.g., differencing) to alternative metrics, which have been adopted in many studies. We allow for neighbourhood spillover in infection, which is also governed by adaptiv

SUBMITTER: Congdon P 

PROVIDER: S-EPMC9179960 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature