Modelling tick bite risk by combining random forests and count data regression models.

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.

Temporal-Spatial Variation in Questing Tick Activity in the Netherlands: The Effect of Climatic and Habitat Factors.

Longitudinal studies are fundamental in the assessment of the effect of environmental factors on tick population dynamics. In this study, we use data from a 10-year study in 11 different locations in the Netherlands to gauge the effects of climatic and habitat factors on the temporal and spatial variation in questing tick activity. Marked differences in the total number of ticks were found between locations and between years. We investigated which climatic and habitat factors might explain this variation. No effects of climatic factors on the total number of ticks per year were observed, but we found a clear effect of temperature on the onset of tick activity. In addition, we found positive associations between (1) humus layer thickness and densities of all three stages, (2) moss and blackberry abundance and larval densities, and (3) blueberry abundance and densities of larva and nymphs. We conclude that climatic variables do not have a straightforward association with tick density in the Netherlands, but that winter and spring temperatures influence the onset of tick activity. Habitats with apparently similar vegetation types can still differ in tick population densities, indicating that local composition of vegetation and especially of wildlife is likely to contribute considerably to the spatial variation in tick densities.

Using volunteered observations to map human exposure to ticks.

Lyme borreliosis (LB) is the most prevalent tick-borne disease in Europe and its incidence has steadily increased over the last two decades. In the Netherlands alone, more than 20,000 citizens are affected by LB each year. Because of this, two Dutch citizen science projects were started to monitor tick bites. Both projects have collected nearly 50,000 geo-located tick bite reports over the period 2006-2016. The number of tick bite reports per area unit is a proxy of tick bite risk. This risk can also be modelled as the result of the interaction of hazard (e.g. tick activity) and human exposure (e.g. outdoor recreational activities). Multiple studies have focused on quantifying tick hazard. However, quantifying human exposure is a harder task. In this work, we make a first step to map human exposure to ticks by combining tick bite reports with a tick hazard model. Our results show human exposure to tick bites in all forested areas of the Netherlands. This information could facilitate the cooperation between public health specialists and forest managers to create better mitigation campaigns for tick-borne diseases, and it could also support the design of improved plans for ecosystem management.

Modelling and mapping tick dynamics using volunteered observations.

BACKGROUND: Tick populations and tick-borne infections have steadily increased since the mid-1990s posing an ever-increasing risk to public health. Yet, modelling tick dynamics remains challenging because of the lack of data and knowledge on this complex phenomenon. Here we present an approach to model and map tick dynamics using volunteered data. This approach is illustrated with 9 years of data collected by a group of trained volunteers who sampled active questing ticks (AQT) on a monthly basis and for 15 locations in the Netherlands. We aimed at finding the main environmental drivers of AQT at multiple time-scales, and to devise daily AQT maps at the national level for 2014. METHOD: Tick dynamics is a complex ecological problem driven by biotic (e.g. pathogens, wildlife, humans) and abiotic (e.g. weather, landscape) factors. We enriched the volunteered AQT collection with six types of weather variables (aggregated at 11 temporal scales), three types of satellite-derived vegetation indices, land cover, and mast years. Then, we applied a feature engineering process to derive a set of 101 features to characterize the conditions that yielded a particular count of AQT on a date and location. To devise models predicting the AQT, we use a time-aware Random Forest regression method, which is suitable to find non-linear relationships in complex ecological problems, and provides an estimation of the most important features to predict the AQT. RESULTS: We trained a model capable of fitting AQT with reduced statistical metrics. The multi-temporal study on the feature importance indicates that variables linked to water levels in the atmosphere (i.e. evapotranspiration, relative humidity) consistently showed a higher explanatory power than previous works using temperature. As a product of this study, we are able of mapping daily tick dynamics at the national level. CONCLUSIONS: This study paves the way towards the design of new applications in the fields of environmental research, nature management, and public health. It also illustrates how Citizen Science initiatives produce geospatial data collections that can support scientific analysis, thus enabling the monitoring of complex environmental phenomena.