JMIR Publications

ABSTRACT

Background:

The range expansion of ticks transmitting zoonotic diseases in Canada poses significant challenges for public health surveillance. Active monitoring of tick populations at a national scale is resource-intensive and logistically complex. Crowdsourced data can provide a cost-effective and scalable approach to augment surveillance. In this study, we assess the value of crowdsourced data from eTick, a platform where the public can submit photographs of ticks for expert identification.

Objective:

Focusing on Quebec, our objectives were to: (1) characterise spatial patterns in eTick submissions and identify socio-ecological factors associated with their occurrence and frequency and (2) assess the ability of indicators derived from eTick data to predict census subdivisions (CSDs) with established tick populations.

Methods:

We conducted a retrospective ecological assessment for the effectiveness of crowdsourced data to detect established tick populations in Quebec, Canada, over the period 2020–2023. First, we analysed spatial patterns of ticks submissions by performing spatial scan analyses for human and animal-origin data separately. Then, using zero-inflated regression models, we assessed socio-ecological factors associated with the occurrence and counts of tick submissions, at the CSD-year level. Next, we used logistic regression models to assess the value of using eTick surveillance data to predict the presence of an established tick population in a CSD. Finally, we calculated thresholds for eTick submissions to maximize sensitivity (Se), specificity (Sp), or a balance of both metrics for detecting established tick populations, and developed scenarios to interpret linear predictors in terms of submission counts across different socio ecological contexts.

Results:

Spatial scan analyses revealed significant clusters of submissions of ticks found on humans in southern Quebec, whereas clusters of tick submissions found on domestic animals extended further northwards. This suggests that exposure rates differ between humans and domestic animals and that using data from ticks found on domestic animals may detect biting ticks over a broader area. Zero-inflated models identified socio-economic, climatic, and land cover factors significantly associated with submission rates. Logistic regression models assessing the ability of eTick indicators to identify municipalities with established tick populations showed that models including significant socio-ecological factors performed best. Predictive performance was similar for ticks from humans (AUC = 0.86), animals (AUC = 0.83), and all submissions combined (AUC = 0.85). Using the best model adjusted for population size, median income, median age, and cumulative degree-days above 0°C, an optimal threshold of 3.32 crowdsourced tick submissions allowed the detection of CSDs with established tick populations while maximizing sensitivity and specificity (both 0.76).

Conclusions:

This study supports the reliability of crowdsourced passive surveillance to detect established tick populations; and highlights the potential of crowdsourced data to augment traditional systems for monitoring ticks and tick-borne disease risk.