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 adaptive regime-switching. Compared to existing models, the observational (in-sample) model is expected to better reflect the balance between epidemic and endemic tendencies, and short-term extrapolations are likely to be improved. Two case study applications involve COVID area-time data, one for 32 London boroughs (and 96 weeks) since the start of the COVID epidemic, the other for a shorter time span focusing on the epidemic phase in 144 areas of Southeast England associated with the Alpha variant. In both applications, the proposed methods produce a better in-sample fit and out-of-sample short term predictions. The spatial dynamic implications are highlighted in the case studies.

Evaluation of predictive capability of Bayesian spatio-temporal models for Covid-19 spread

“…2, or [ 19 ] for an examples). The result is that while it is possible to model peak behavior very closely, it usually requires the use of time dependent random effects ([ 11 , 20 ] Fig. 4 ), and so the predictive capabilities of such models are limited due to the overfitting.…”

Editorial: Epidemiological considerations in COVID-19 forecasting

“…Many studies of COVID dynamics have been at national level, but spatially disaggregated approaches (e.g. spatio-temporal forecasts) have been proposed, raising questions about localized diffusion between nearby populations ( 18 , 19 ).…”

Evaluation of Predictive capability of Bayesian Spatio-temporal models for Covid-19 spread

“…The importance of examining the complete spatio-temporal dynamic of an epidemic has been stressed previously [7]. Some recent approaches have also addressed space-time in ID modeling [8], [9]. While various time horizons are often evaluated for prediction accuracy, it is often clear that accuracy degrades quickly with extensive horizons [4].…”