During the span of August–October, 2017 a major outbreak of Dengue fever happened in Khyber Pakhtunkhwa province of Pakistan. Cases were reported from all the major cities and rural areas, but Peshawar was more severely hit with more than half of the total cases belonging to central Peshawar city. The epidemic patterns reveal that dengue fever cases were mostly reported for plain areas and also low altitude mountainous regions. We employed the principle of maximum entropy to establish the underlying distribution of dengue presences and background data. A geostatistical analysis was conducted by modelling the spatial structure of the dengue fever risk and estimating the prediction maps with corresponding uncertainty taking into account some of the most significant covariates. The prediction maps were created using binomial kriging with a binary logistic drift. The analysis was carried out for the whole province as well as subregions to have a closer look of the spatial distribution at local level. Our results show that our methodology performed well. Vector distribution, population density, and distance to roads were found to significantly affecting the spatial distribution of risk and gives very informative pattern.

2017年8月至10月，巴基斯坦开伯尔-普赫图赫瓦省发生了一次大规模的登革热疫情。据报道，所有主要城市和农村地区都有病例，但白沙瓦受灾更为严重，占白沙瓦市中心总数的一半以上。流行模式表明，登革热病例主要报告在平原地区和低海拔山区。我们采用最大熵原理来建立登革热存在和背景数据的基本分布。通过对登革热风险的空间结构建模并考虑到一些最重要的协变量，对具有相应不确定性的预测图进行了地统计学分析。预测图是使用二项Logistic漂移的二项式克里金法创建的。对整个省及次区域进行了分析，以便更仔细地了解地方一级的空间分布。我们的结果表明我们的方法学表现良好。发现媒介分布，人口密度和到道路的距离会显着影响风险的空间分布，并提供非常有用的信息。