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http://hdl.handle.net/1942/33704
Title: | 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? | Authors: | NEYENS, Thomas FAES, Christel VRANCKX, Maren Pepermans, K HENS, Niel Van Damme, P MOLENBERGHS, Geert AERTS, Jan Beutels, P |
Issue Date: | 2020 | Publisher: | ELSEVIER SCI LTD | Source: | Spatial and spatio-temporal epidemiology (Print), 35 (Art N° 100379) | 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. (C) 2020 Elsevier Ltd. All rights reserved. | Keywords: | COVID-19;Disease mapping;Spatially correlated random effects;Integrated nested Laplace approximation;Self-reporting | Document URI: | http://hdl.handle.net/1942/33704 | ISSN: | 1877-5845 | e-ISSN: | 1877-5853 | DOI: | 10.1016/j.sste.2020.100379 | ISI #: | 000584362600014 | Rights: | 2020 Elsevier Ltd. All rights reserved. | Category: | A1 | Type: | Journal Contribution | Validations: | vabb 2022 |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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2020.05.18.20105627v1.full.pdf | Peer-reviewed author version | 1.13 MB | Adobe PDF | View/Open |
1-s2.0-S1877584520300575-main.pdf Restricted Access | Published version | 10.58 MB | Adobe PDF | View/Open Request a copy |
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