Patz J A, Strzepek K, Lele S, Hedden M, Greene S, Noden B, Hay S I, Kalkstein L, Beier J C
Department of Enviromental Health Sciences, John Hopkins School of Hygiene and Public Health, Baltimore, MD 21205-2179, USA.
Trop Med Int Health. 1998 Oct;3(10):818-27. doi: 10.1046/j.1365-3156.1998.00309.x.
While malaria transmission varies seasonally, large inter-annual heterogeneity of malaria incidence occurs. Variability in entomological parameters, biting rates and entomological inoculation rates (EIR) have been strongly associated with attack rates in children. The goal of this study was to assess the weather's impact on weekly biting and EIR in the endemic area of Kisian, Kenya. Entomological data collected by the U.S. Army from March 1986 through June 1988 at Kisian, Kenya was analysed with concurrent weather data from nearby Kisumu airport. A soil moisture model of surface-water availability was used to combine multiple weather parameters with landcover and soil features to improve disease prediction. Modelling soil moisture substantially improved prediction of biting rates compared to rainfall; soil moisture lagged two weeks explained up to 45% of An. gambiae biting variability, compared to 8% for raw precipitation. For An. funestus, soil moisture explained 32% variability, peaking after a 4-week lag. The interspecies difference in response to soil moisture was significant (P < 0.00001). A satellite normalized differential vegetation index (NDVI) of the study site yielded a similar correlation (r = 0.42 An. gambiae). Modelled soil moisture accounted for up to 56% variability of An. gambiae EIR, peaking at a lag of six weeks. The relationship between temperature and An. gambiae biting rates was less robust; maximum temperature r2 = -0.20, and minimum temperature r2 = 0.12 after lagging one week. Benefits of hydrological modelling are compared to raw weather parameters and to satellite NDVI. These findings can improve both current malaria risk assessments and those based on El Niño forecasts or global climate change model projections.
虽然疟疾传播具有季节性变化，但疟疾发病率在年际间存在很大的异质性。昆虫学参数、叮咬率和昆虫学接种率（EIR）的变化与儿童的发病率密切相关。本研究的目的是评估天气对肯尼亚基西安流行地区每周叮咬率和EIR的影响。美国陆军于1986年3月至1988年6月在肯尼亚基西安收集的昆虫学数据与附近基苏木机场的同期天气数据进行了分析。利用地表水可利用性的土壤湿度模型，将多个天气参数与土地覆盖和土壤特征相结合，以改进疾病预测。与降雨量相比，对土壤湿度进行建模能显著提高对叮咬率的预测；滞后两周的土壤湿度可解释高达45%的冈比亚按蚊叮咬变异性，而原始降水量仅为8%。对于嗜人按蚊，土壤湿度可解释32%的变异性，在滞后4周后达到峰值。两种按蚊对土壤湿度的反应差异显著（P < 0.00001）。研究地点的卫星归一化植被指数（NDVI）也有类似的相关性（r = 0.42，冈比亚按蚊）。建模的土壤湿度可解释高达56%的冈比亚按蚊EIR变异性，在滞后6周时达到峰值。温度与冈比亚按蚊叮咬率之间的关系不太显著；最高温度r² = -0.20，最低温度r² = 0.12（滞后一周后）。将水文模型的优势与原始天气参数和卫星NDVI进行了比较。这些发现可以改进当前的疟疾风险评估以及基于厄尔尼诺预测或全球气候变化模型预测的评估。
