Although COVID-19 has been spreading throughout Belgium since February, 2020, its spatial dynamics in Belgium remain poorly understood, partly due to the limited testing of suspected cases during the epidemic's early phase. We analyse data of COVID-19 symptoms, as self-reported in a weekly online survey, which is open to all Belgian citizens. We predict symptoms' incidence using binomial models for spatially discrete data, and we introduce these as a covariate in the spatial analysis of COVID-19 incidence, as reported by the Belgian government during the days following a survey round. The symptoms' incidence is moderately predictive of the variation in the relative risks based on the confirmed cases; exceedance probability maps of the symptoms' incidence and confirmed cases' relative risks overlap partly. We conclude that this framework can be used to detect COVID-19 clusters of substantial sizes, but it necessitates spatial information on finer scales to locate small clusters. (C) 2020 Elsevier Ltd. All rights reserved. ; We thank Herman Van Oyen and Toon Braeye from the Belgian population health institute (Sciensano) for reading and commenting on our manuscript. This research received funding from the Flemish Government (AI Research Program). Authors NH and PB acknowledge funding from the European Union’s Horizon 2020 research and innovation programme - project EpiPose (No. 101003688 ). Authors TN, NH, PVD, and PB acknowledge funding from the Research Foundation Flanders (No. G0G1920N).