The effect of spatio-temporal sample imbalance in epidemiologic surveillance using opportunistic samples: an ecological study using real and simulated self-reported COVID-19 symptom data

Abstract

Open surveys complementing surveillance programs often yield opportunistically sampled data characterised by spatio-temporal imbalance. We set up our study to understand to what extent spatio-temporal statistical models using such data achieve in describing epidemiological trends. We used self-reported symptomatic COVID-19 data from two Belgian regions, Flanders and the Brussels-Capital Region. These data were collected in a large-scale open survey with spatio-temporally imbalanced participation rates. We compared incidence estimates of both self-reported symptoms and test-confirmed COVID-19 cases obtained through generalised linear mixed models correcting for spatio-temporal correlation. We additionally simulated symptom incidences under different sampling strategies to explore the impact of sample imbalance, sample size and disease incidence, on trend detection. Our study shows that spatio-temporal sample imbalance generally does not lead to bad model performances in spatio-temporal trend estimation and high-risk area detection. Except for low-incidence diseases, collecting large samples will often be more essential than ensuring spatio-temporally sample balance.