Can COVID-19 symptoms as reported in a large-scale online survey be used to optimise spatial predictions of COVID-19 incidence risk in Belgium?

Although COVID-19 has been spreading throughout Belgium since February,2020, its spatial dynamics in Belgium remain poorly understood, partly due tothe limited testing of suspected cases during the epidemic’s early phase. Weanalyse data of COVID-19 symptoms, as self-reported in a weekly online sur-vey, which is open to all Belgian citizens. We predict symptoms’ incidence usingbinomial models for spatially discrete data, and we introduce these as a covari-ate in the spatial analysis of COVID-19 incidence, as reported by the Belgiangovernment during the days following a survey round. The symptoms’ inci-dence is moderately predictive of the variation in the relative risks based on theconfirmed cases; exceedance probability maps of the symptoms’ incidence andconfirmed cases’ relative risks overlap partly. We conclude that this frameworkcan be used to detect COVID-19 clusters of substantial sizes, but it necessitatesspatial information on finer scales to locate small clusters.

Publication year:2020

Keywords:A1 Journal article

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Accessibility:Open