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?

Abstract

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.

Individual factors influencing COVID-19 vaccine acceptance in between and during pandemic waves (July–December 2020)

“…longitude-latitude coordinates of the centre of the postal code of a participant’s place of residence; whether the participant knew someone with severe COVID-19 symptoms; underlying medical conditions; and time, which is represented by the wave ID. The spatial effect was modelled by estimating a smooth two-dimensional spline and was validated through a sensitivity analysis in which a spatially-discrete geostatistical logistic model [33] was fitted. The main purpose of Analysis 1 was to attribute variation in vaccine willingness to multiple characteristics of participants that can be targeted in policy making.…”

Burden of COVID-19 on Primary Care: a Prospective Nationwide Observational Study

“…A Belgian study showed that the diversity in the number of COVID-19 cases could be partially explained by model-based symptom incidence predictions. 3 Therefore, based on these data, we might identify a window of opportunity to undertake actions in a timely manner to avoid an increase in hospitalizations.…”

“…2 On the 18th of March 2020, the Belgian government declared a national lockdown to contain the spread of the virus. 3 Primary care faced an unprecedented situation, as it plays a central role in the healthcare response to the COVID-19 pandemic. 4 Work organisation in primary care has drastically changed worldwide.…”

Infectious diseases epidemiology, quantitative methodology, and clinical research in the midst of the COVID-19 pandemic: Perspective from a European country

“…At the same time, it can be a component of an early warning system (Section 3.2) if the occurrence of symptoms is queried. One such example is the "Big Corona Study" (see also [93]), an online survey that can be filled in by all members of the public on every Tuesday since March 17, 2020; from June 2, 2020 onwards, the survey shifted to a bi-weekly frequency. It collects data about public adherence to measures taken by the government, contact behavior, mental and socio-economic distress, and spatio-temporal dynamics of COVID-19 symptoms' incidences.…”

“…1. Predicted probabilities for a citizen to experience at least one key COVID-19 symptom per municipality, based on extensions of a shared latent process model that corrects for preferential sampling [93].…”