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Title: A spatial model to jointly analyze self-reported survey data of COVID-19 symptoms and official COVID-19 incidence data
Authors: VRANCKX, Maren 
FAES, Christel 
HENS, Niel 
Beutels, Philippe
Van Damme , Pierre
Pepermans, Koen
NEYENS, Thomas 
Issue Date: 2022
Publisher: WILEY
Source: Biometrical journal (1977),
Status: Early view
Abstract: 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.
Notes: Neyens, T (corresponding author), Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat, Data Sci Inst, Martelarenlaan 42, B-3500 Hasselt, Belgium.; Neyens, T (corresponding author), Katholieke Univ Leuven, Leuven Biostat & Stat Bioinformat Ctr, Kapucijnenvoer 35, B-3000 Leuven, Belgium.
Keywords: bivariate conditional autoregressive random effect;COVID-19;disease mapping;preferential sampling;survey data
Document URI:
ISSN: 0323-3847
e-ISSN: 1521-4036
DOI: 10.1002/bimj.202100186
ISI #: WOS:000822964800001
Rights: 2022 Wiley-VCH GmbH This article has earned an Open Data badge for making publicly available the digitally-shareable data necessary to reproduce the reported results. The data is available in the Supporting Information section. This article has earned an open data badge “Reproducible Research” for making publicly available the code necessary to reproduce the reported results. The results reported in this article could fully be reproduced.
Category: A1
Type: Journal Contribution
Appears in Collections:Research publications

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