Elucidating user behaviours in a digital health surveillance system to correct prevalence estimates

Abstract

Estimating seasonal influenza prevalence is of undeniable public health importance, but remains challenging with traditional datasets due to cost and timeliness. Digital epidemiology has the potential to address this challenge, but can introduce sampling biases that are distinct to traditional systems. In online participatory health surveillance systems, the voluntary nature of the data generating process must be considered to address potential biases in estimates. Here we examine user behaviours in one such platform, FluTracking, from 2011 to 2017. We build a Bayesian model to estimate probabilities of an individual reporting in each week, given their past reporting behaviour, and to infer the weekly prevalence of influenza-like-illness (ILI) in Australia. We show that a model that corrects for user behaviour can substantially affect ILI estimates. The model examined here elucidates several factors, such as the status of having ILI and consistency of prior reporting, that are strongly associated with the likelihood of participating in online health surveillance systems. This framework could be applied to other digital participatory health systems where participation is inconsistent and sampling bias may be of concern.