Yale School of Public Health, Yale University, New Haven, Connecticut, USA.
School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada.
Ticks Tick Borne Dis. 2022 Sep;13(5):101969. doi: 10.1016/j.ttbdis.2022.101969. Epub 2022 May 21.
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 m 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.
莱姆病是加拿大安大略省不断扩大的蜱虫媒介，Ixodes scapularis 的范围，是一种新出现的公共卫生威胁。蜱密度是人类莱姆病风险的重要预测指标，通常通过拖拉采样进行主动蜱虫监测来测量，这种方法既费时又费资源。需要新的经济有效的工具来补充当前的监测活动。我们的目的是评估最大熵（Maxent）物种分布模型预测安大略省三个地区（渥太华、金斯顿和安大略省南部）中 I. scapularis 密度的能力，以确定其在预测莱姆病公共卫生风险方面的效用。通过拖拉采样在三个地区的 60 个地点收集了蜱虫。模型预测的生境适宜性是从先前构建的 Maxent 模型中计算出来的，方法是在每个地点的 1 公里半径内计算出平均预测生境适宜性。Spearman 相关系数用于量化模型预测的生境适宜性与蜱密度之间的连续关系，负二项回归用于量化蜱密度与模型预测的生境适宜性之间的关系。整个研究区域、金斯顿地区和渥太华地区的模型预测生境适宜性的 Spearman 相关系数分别为 0.517、0.707 和 0.537，表明存在中度正相关关系，并且模型能够预测蜱密度。回归分析进一步证明了蜱密度与模型预测的生境适宜性之间存在显著的正相关关系（p<0.001）。使用模型预测的生境适宜性的二分测量，发病率比-预测为“适宜”生境的地点每米的蜱虫数量与预测为“不适宜”生境的地点的蜱虫数量之比-为 33.95，表明在预测为适宜生境的地点，蜱密度明显更高。鉴于蜱密度是莱姆病风险的一个重要组成部分，使用 Maxent 模型预测高蜱密度地点的能力可能使其成为一种经济有效的工具，用于确定存在高莱姆病公共卫生风险的地理区域。
