A joint spatial model to analyse self-reported survey data of COVID-19 symptoms and lagged surveillance-based COVID-19 incidence data
Creators
- 1. I-BioStat, Data Science Institute, Hasselt University, Martelarenlaan 42, B-3500 Hasselt, Belgium
- 2. I-BioStat, Data Science Institute, Hasselt University, Martelarenlaan 42, B-3500 Hasselt, Belgium, L-BioStat; Department of Public Health and Primary Care, Faculty of Medicine, KU Leuven, Kapucijnenvoer 35, B-3000 Leuven, Belgium
- 3. I-BioStat, Data Science Institute, Hasselt University, Martelarenlaan 42, B-3500 Hasselt, Belgium; Center for Health Economics Research and Modeling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium
- 4. Center for Health Economics Research and Modeling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium
Description
These datasets contain model results based on a synthetic data analysis and can be downloaded to be used in the supplementary material code of this paper, with the following abstract:
This work presents a joint spatial modelling 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 optimise 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 one 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.