Citizen Science Tick Observations Serve as an Early Warning System for Tick‐Borne Diseases

Abstract

<h3>Introduction</h3><p>Tick observation data collected through citizen science is increasingly utilised to map tick-borne infection risk areas indirectly, that is, based on the rate of tick encounters or occurrence of ticks. However, direct associations between tick observations and Lyme borreliosis (LB) cases have received little attention. In the current study, associations between weekly tick observations and LB cases were studied on a nationwide scale in Finland, in order to determine if tick observations precede cases in a predictable manner, and whether tick observations could be used to predict peaks in cases.</p><h3>Methods and Results</h3><p>Nationwide weekly electronic citizen science tick observation data from a tick surveillance website (www.punkkilive.fi/en) and Lyme borreliosis data from the Finnish Institute for Health and Welfare from 2021 to 2023 were utilised in the current study. Negative binomial models were fitted to assess whether tick observations explain variation in LB cases beyond simple seasonality, and to determine if weekly disease cases can be predicted based on tick observation data originating from either humans, pets (dogs & cats) or all sources. Disease cases followed observations with a three to four week lag. Tick observation data were observed to explain variation in LB cases beyond simple seasonality. Models only utilising observations from humans to predict disease cases had the best performance. Finally, differences in the phenology of the two human-biting tick species present in Finland were observed to influence temporal patterns of observations and LB cases on smaller spatial scales.</p><h3>Conclusions</h3><p>This study revealed that LB cases can be predicted utilising citizen science tick observation data. Consequently, crowdsourced tick observation data can be used to predict when peaks in disease cases are to be expected, allowing for specifically targeted awareness campaigns. This, in turn, may lead to symptoms being detected and recognised earlier, allowing for more rapid treatment and fewer sequelae. Guidance on setting up similar models is provided. Actors with access to such data are encouraged to set up similar early warning systems. This increased utility of the data can be leveraged to justify setting up tick observation services, as well as to motivate citizens to participate.</p>