Modelling and mapping tick dynamics using volunteered observations

Abstract

BackgroundTick 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.MethodTick 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.ResultsWe 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.ConclusionsThis 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.

Using volunteered observations to map human exposure to ticks

“…forests, urban parks). There are significant efforts in literature to quantify the hazard component 12 – 14 , but finding proxies of exposure is a harder task, due to the unavailability of human recreational metrics at the national scale. Quantifying recreational pressure in nature is of interest in fields as diverse as public health, forestry management or environmental science.…”

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

“…Latapia, & Ortega, 2015), tick presence (Swart et al, 2014), tick activity (Bennet,Halling,& Berglund,135 2006), or tick dynamics (Garcia-Martí et al, 2017), with a varying number of biotic and abiotic 136 parameters, and applied from local to continental spatial scales. In this work we use tick activity as a 137…”

“…which are the ones biting humans. Tick activity is extracted from a data-driven model that predicts daily 139 tick activity in forests and natural grasslands (Garcia-Martí et al, 2017). The map in Figure 3 shows 140 the predicted tick activity of this model, which is the average number of questing ticks per grid cell for 141 the entire study period (2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014).…”

“…to a uniform raster layer, the exposure metrics where calculated as the geographic distance between the 268 center of each grid cell and a set of selected real-world features in which we expect the co-ocurrence of 269 humans and ticks. As explained in Section 2.1, hazard metrics are extracted for forests and natural 270 grasslands using the model developed by (Garcia-Martí et al, 2017). Table 1 presents the 19 exposure 271 metrics and the 2 hazard metrics that were used to model tick bite risk in the Netherlands.…”

“…541 542 Tick activity per 1km grid cell. This tick activity estimation is provided by the data-driven 544 model in(Garcia-Martí et al, 2017), which is capable of predicting daily tick activity. We run this 545 model for each day during the period (2006-2014) and we calculated a robust long-term metric of 546 hazard, showing the maximum mean tick activity for the entire period.…”