Date of Award

January 2021

Document Type

Thesis

Degree Name

Master of Public Health (MPH)

Department

School of Public Health

First Advisor

Manisha A. Kulkarni

Second Advisor

Peter J. Krause

Abstract

Introduction: Lyme disease is an emerging public health threat in Ontario, Canada due to the range expansion of the tick vector, Ixodes scapularis. Tick abundance and density are important predictors of human Lyme disease risk and are typically measured using active tick surveillance (dragging). New cost-effective tools are needed to augment current surveillance activities.Objective: The objective of this study was to evaluate the ability of a Maximum entropy species distribution model to predict I. scapularis density and abundance 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. Methods: Ticks were collected via dragging at 60 sites across the three regions. Model-predicted habitat suitability was calculated as the mean predicted habitat suitability within a 1000m radius of the 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 dichotomized model-predicated habitat suitability. Results: The 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. Negative binomial regression found an incidence rate ratio of 33.95 in sites with model-predicted ‘suitable’ habitat compared to those with ‘not suitable’ habitat, indicating that the total number of ticks per site was significantly higher at sites situated in areas with predicted suitable habitat. Conclusions: These findings demonstrate that model-predicted habitat suitability is moderately associated with tick density, particularly in areas with known established tick populations, and therefore reflective of the risk of acquiring a tick bite. The use of this Maxent model may be a cost-effective tool for identifying areas in Ontario posing the greatest public health risk of Lyme disease.

Comments

This thesis is restricted to Yale network users only. It will be made publicly available on 06/01/2023

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