Abstract
The socio-economic and demographic changes that occurred over the past 50 years have dramatically expanded urban areas around the globe, thus bringing urban settlers in closer contact with nature. Ticks have trespassed the limits of forests and grasslands to start inhabiting green spaces within metropolitan areas. Hence, the transmission of pathogens causing tick-borne diseases is an important threat to public health. Using volunteered tick bite reports collected by two Dutch initiatives, here we present a method to model tick bite risk using human exposure and tick hazard predictors. Our method represents a step forward in risk modelling, since we combine a well-known ensemble learning method, Random Forest, with four count data models of the (zero-inflated) Poisson family. This combination allows us to better model the disproportions inherent in the volunteered tick bite reports. Unlike canonical machine learning models, our method can capture the overdispersion or zero-inflation inherent in data, thus yielding tick bite risk predictions that resemble the original signal captured by volunteers. Mapping model predictions enables a visual inspection of the spatial patterns of tick bite risk in the Netherlands. The Veluwe national park and the Utrechtse Heuvelrug forest, which are large forest-urban interfaces with several cities, are areas with high tick bite risk. This is expected, since these are popular places for recreation and tick activity is high in forests. However, our model can also predict high risk in less-intensively visited recreational areas, such as the patchy forests in the northeast of the country, the natural areas along the coastline, or some of the Frisian Islands. Our model could help public health specialists to design mitigation strategies for tick-borne diseases, and to target risky areas with awareness and prevention campaigns.
Highlights
Over the last couple of decades, urban areas have dramatically expanded [1]
Based on these three metrics, we think the that the ZIP and ZINB models are the ones performing better, since they present good correlation coefficients, reduced root-mean-squared deviation (RMSD) and are able to capture overdispersion or zero-inflation, respectively, which can open the door to multiple applications in ecological modelling and public health
In this work we illustrate how canonical machine learning algorithms like Random Forest (RF) may not perform well at modelling a skewed and zero-inflated distribution, and we present our algorithm-level solution to mitigate the bias towards the mean
Summary
Over the last couple of decades, urban areas have dramatically expanded [1]. In Europe, the development of low density residential areas in the periphery of cities has become the norm for urban growth. [1]. Several bird (e.g. thrushes) and mammal species (e.g. rodents, foxes, raccoons) have adapted their ethology to be able to live at the interface between forests and urban regions (e.g. more food, less predators) [4]. This means that the parasites and pathogens that several wildlife species carry get closer to residential areas and that, for instance, the hazard for tickborne diseases increases [5]. This behavior leads to a higher exposure to tick-borne diseases [8]
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