Beugnet Frédéric, Chalvet-Monfray Karine, Loukos Harilaos
Merial, 29 Av T. Garnier, 69007 Lyon, France.
Geospat Health. 2009 Nov;4(1):97-113. doi: 10.4081/gh.2009.213.
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 degrees W and 30 degrees E and latitudes 37.75 degrees N and 62 degrees 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 veterinary clinics and tick samples, as well as comparisons with published surveys. The maps constructed with the model are available to veterinary practitioners on www.FleaTickRisk.com.
数学建模在流行病学中是一种相当新的工具。地理信息系统(GIS)与遥感(数据收集和分析)相结合可提供有价值的模型,但气候模型在寄生虫学和流行病学中的整合则不太常见。我们名为“跳蚤蜱虫风险”的模型旨在利用气象数据和预报来监测某些节肢动物的活动和密度。我们的寄生虫学模型使用整合了生物学参数的天气研究与预报(WRF)气象模型。WRF模型每天提供四次温度和湿度情况(分别在06:00、12:00、18:00和24:00)。其在欧洲的地理分辨率为27×27千米(西经10.5度至东经30度、北纬37.75度至北纬62度之间的区域)。该模型还提供每周预报。过去的数据通过地面站和气象卫星生成的当前气象数据进行比较和重新验证。WRF模型还包括源自美国地球物理调查生物群落地图的地理信息,其空间分辨率为30''(约900×900米)。WRF考虑了因山谷、海拔、湖泊和风力特性而产生的特定气候条件。蓖麻硬蜱、网纹革蜱、血红扇头蜱和猫栉首蚤指名亚种的生物学参数被转化为活动矩阵。该活动矩阵以百分比表示,在每个温度×湿度区间内从0到100。这些节肢动物的活动由它们感染宿主、吸食血液和繁殖的能力来定义。对于每种节肢动物,该矩阵是利用在最佳温度和湿度条件下收集的现有数据以及生命周期的时间来计算的。整合了WRF模型(气象数据+地理数据)和生物矩阵的数学模型提供两个指标:一个活动指标(范围从0到100),为前一周计算并对下一周具有预测性,以及一个累积指标(范围从0到1000),该指标考虑了过去12周的情况。每天为欧洲各地的每个地理点计算两次这些指标,并根据三种定义的生物群落类型进行校正:城市和郊区、农村地区以及荒野和森林。为了便于展示,指标在区间内计算,并以分组指标等压线的彩色地图形式呈现。我们假设蜱虫和跳蚤宿主的数量并不短缺,因此不会影响节肢动物的数量。然而,小气候和生物群落有重大影响,特别是对蜱虫种群,因此模型提供的结果必须由当地兽医等专家根据当地情况进行调整。对于跳蚤,该模型考虑了它们的户外活动而忽略了它们的室内生命周期。在2007年和2008年全年,利用定点兽医诊所和蜱虫样本以及与已发表调查的比较来验证数据的准确性。通过该模型构建的地图可在www.FleaTickRisk.com上供兽医从业者使用。