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

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

SUBMITTER: Vranckx M 

PROVIDER: S-EPMC9349774 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature