The utility of a maximum entropy species distribution model for Ixodes scapularis in predicting the public health risk of Lyme disease in Ontario, Canada

Abstract

Lyme disease is an emerging public health threat in Ontario, Canada due to ongoing range expansion of the tick vector, Ixodes scapularis. Tick density is an important predictor of human Lyme disease risk and is typically measured using active tick surveillance via drag sampling, which is time and resource-intensive. New cost-effective tools are needed to augment current surveillance activities. Our objective was to evaluate the ability of a maximum entropy (Maxent) species distribution model to predict I. scapularis density in three regions of Ontario – Ottawa, Kingston, and southern Ontario – in order to determine its utility in predicting the public health risk of Lyme disease. Ticks were collected via drag sampling at 60 sites across the three regions. Model-predicted habitat suitability was calculated from a previously constructed Maxent model as the mean predicted habitat suitability within a 1-km radius of each site. Spearman's correlation coefficient was used to quantify the continuous relationship between model-predicted habitat suitability and tick density, and negative binomial regression was used to quantify the relationship between tick density and model-predicated habitat suitability. Spearman's correlation coefficients for the full study area, Kingston region, and Ottawa region were 0.517, 0.707, and 0.537, respectively, indicating a moderate positive relationship and ability of the model to predict tick density. Regression analysis further demonstrated a significant positive association between tick density and model-predicted habitat suitability (p< 0.001). Using a dichotomized measure of model-predicted habitat suitability, the incidence rate ratio – the ratio of ticks per m2 in sites predicted to have a ‘suitable’ habitat compared to those predicted to have ‘not suitable’ habitat – was 33.95, indicating that tick density was significantly higher at sites situated in areas with predicted suitable habitat. Given that tick density is an important component of Lyme disease risk, the ability to predict high tick density locations using the Maxent model may make it a cost-effective tool for identifying geographic areas that pose elevated public health risk of Lyme disease.