Spatio-temporal small area surveillance of the COVID-19 pandemic

Abstract

The emergence of COVID-19 requires new effective tools for epidemiological surveillance. Spatio-temporal disease mapping models, which allow dealing with small units of analysis, are a priority in this context. These models provide geographically detailed and temporally updated overviews of the current state of the pandemic, making public health interventions more effective. These models also allow estimating epidemiological indicators highly demanded for COVID-19 surveillance, such as the instantaneous reproduction number $R_t$ , even for small areas. In this paper, we propose a new spatio-temporal spline model particularly suited for COVID-19 surveillance, which allows estimating and monitoring $R_t$ for small areas. We illustrate our proposal on the study of the disease pandemic in two Spanish regions. As a result, we show how tourism flows have shaped the spatial distribution of the disease in these regions. In these case studies, we also develop new epidemiological tools to be used by regional public health services for small area surveillance.

Keywords: COVID-19; Disease mapping; Instantaneous reproduction number; Spatio-temporal modelling.

Fig. 1

Overall time trends for the observed daily cases for both regions, Comunidad Valenciana and Castilla $y$ León in this order (top row). Estimated $R_t$ evolution for both regions (bottom left plot), and daily rates per 100,000 inhabitants for 4 municipalities in CV (bottom right plot). Vertical lines correspond to the starting day of each month of the period of study. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 2

Time trend of the observed daily cases for Valencia, Alicante, Massalfassar and Aigues, in this order, (left column) and time trend of $R_t$ for these same municipalities (right column). Vertical lines correspond to the starting day of each month of the period of study.

Fig. 3

Correlation plots for the coefficients of the basis functions for Comunidad Valencia (left) and Castilla $y$ León (right). Names of rows and columns correspond to the day (month/day format) showing the peak of the corresponding spline function.

Fig. 4

Risk levels according to smoothed weekly incidence rates and $R_t$’s (left) and its geographical representation (right). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)