A spatial model to jointly analyze self-reported survey data of COVID-19 symptoms and official COVID-19 incidence data

This work presents a joint spatial modeling framework to improve estimation of the spatial distribution of the latent COVID-19 incidence in Belgium, based on test-confirmed COVID-19 cases and crowd-sourced symptoms data as reported in a large-scale online survey. Correction is envisioned for stochastic dependence between the survey's response rate and spatial COVID-19 incidence, commonly known as preferential sampling, but not found significant. Results show that an online survey can provide valuable auxiliary data to optimize spatial COVID-19 incidence estimation based on confirmed cases in situations with limited testing capacity. Furthermore, it is shown that an online survey on COVID-19 symptoms with a sufficiently large sample size per spatial entity is capable of pinpointing the same locations that appear as test-confirmed clusters, approximately 1 week earlier. We conclude that a large-scale online study provides an inexpensive and flexible method to collect timely information of an epidemic during its early phase, which can be used by policy makers in an early phase of an epidemic and in conjunction with other monitoring systems. The resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation - Flanders (FWO) and the Flemish Government. The data used in this manuscript were provided by the Belgian public health institute (Sciensano). This research received funding from the Flemish Government (AI Research Program). Authors Beutels, Faes, and Hens acknowledge funding from the European Union’s Horizon 2020 research and innovation programme - project EpiPose (No. 101003688). Authors Beutels, Hens, Neyens, and Van Damme acknowledge funding from the Research Foundation Flanders (No. G0G1920N). Authors Faes and Neyens acknowledge funding from the Research Foundation Flanders (No. G0G9820N). Author Neyens acknowledges funding by the Internal Funds KU Leuven (project number 3M190682).