FleaTickRisk: a meteorological model developed to monitor and predict the activity and density of three tick species and the cat flea in Europe

Abstract

Abstract.Mathematical modelling is quite a recent tool in epidemiology. Geographical information system (GIS) combined with remote sensing (data collection and analysis) provide valuable models, but the integration of climatologic models in parasitology and epidemiology is less common. The aim of our model, called "FleaTickRisk", was to use meteorological data and forecasts to monitor the activity and density of some arthropods. Our parasitological model uses the Weather Research and Forecasting (WRF) meteorological model integrating biological parameters. The WRF model provides a temperature and humidity picture four times a day (at 6:00, 12:00, 18:00 and 24:00 hours). Its geographical resolution is 27 x 27 km over Europe (area between longitudes 10.5°W and 30°E and latitudes 37.75°N and 62°N). The model also provides weekly forecasts. Past data were compared and revalidated using current meteorological data generated by ground stations and weather satellites. The WRF model also includes geographical information stemming from United States Geophysical Survey biotope maps with a 30'' spatial resolution (approximately 900 x 900 m). WRF takes into account specific climatic conditions due to valleys, altitudes, lakes and wind specificities. The biological parameters of Ixodes ricinus, Dermacentor reticulatus, Rhipicephalus sanguineus and Ctenocephalides felis felis were transformed into a matrix of activity. This activity matrix is expressed as a percentage, ranging from 0 to 100, for each interval of temperature x humidity. The activity of these arthropods is defined by their ability to infest hosts, take blood meals and reproduce. For each arthropod, the matrix was calculated using existing data collected under optimal temperature and humidity conditions, as well as the timing of the life cycle. The mathematical model integrating both the WRF model (meteorological data + geographical data) and the biological matrix provides two indexes: an activity index (ranging from 0 to 100), calculated for the previous week and predictive for the coming week, and a cumulative index (ranging from 0 to 1000) which takes into account the past 12 weeks. The indexes are calculated twice a day for each geographical point all over Europe and are corrected based on three types of defined biotopes: urban and sub-urban areas, rural areas, and wilderness and forests. To clarify the presentation, indexes are calculated within intervals and are presented as colour maps grouping index isoclines. We hypothesised that the populations of tick and flea hosts are not lacking and therefore do not affect the numbers of arthropods. However, microclimates and biotopes have a major impact, especially on tick populations, and the results provided by the model must therefore be adjusted to local conditions by specialists, such as local veterinarians. Where fleas are concerned, the model takes into account their outdoor activity and ignores their indoor life cycle. The accuracy of the data was verified throughout 2007 and 2008, using sentinel veterinar...

Modelling the current distribution and predicted spread of the flea species Ctenocephalides felis infesting outdoor dogs in Spain

“…When faced with unfavourable climate conditions (too cold, too hot or too dry), flea populations enter a state of diapause as cocoons and wait for these conditions to improve [14]. Owing to the preference shown by the cat flea for the non-green land cover classes and lower rainfall values, a higher NDVI index was negatively correlated with C. felis infestation probability.…”

“…The probability of C. felis occurrence was scored from 0 to 1, where values close to 0 could indicate diapause rather than absence. As far as we are aware, the literature describes only one climate model (FleaTickRisk) developed to monitor and predict the activity and density of three tick species and the cat flea in Europe, according mainly to temperature and humidity data [14].…”

“…This type of information is readily available from several web sites, such as FleaTickRisk (http://www.fleatickrisk.com) and VectorMap (http:// vectormap.si.edu) [13,14]. With the introduction of geographic information systems (GIS) and other geospatial tools, there is now growing interest in modelling vector distributions based on climate and environmental drivers for epidemiology studies and follow-up of arthropod vectors [15][16][17].…”

“…In East Africa, scientists have had ample experience in surveying the bacterium Yersinia pestis, the causative agent of the plague, through GIS and Remote Sensing (RS) procedures [19][20][21][22][23]. However, so far only Beugnet et al [14] have modelled the distribution of the cat flea affecting pets based on climate forecasts for a large geographical area [14].…”

Geospatial Health: the first five years

“…In as many as seven studies (6.0%), tick-borne diseases were the focus of interest Estrada-Peña and Venzal, 2007;Rizzoli et al, 2007;Stein et al, 2008;Beugnet et al, 2009;EstradaPeña, 2009;Beugnet et al, 2011). The two most important animal diseases researched and published in Geospatial Health are avian influenza (H5N1), accounting for 5.2% (Cecchi et al, 2008;East et al, 2008a, b;Fiebig et al, 2009;Si et al, 2009;Williams et al, 2011), and bluetongue with a share of 1.7% (Ducheyne et al, 2007;Willgert et al, 2011).…”