OBJECTIVES We aimed to develop a prospective prediction tool on Crimean-Congo haemorrhagic fever (CCHF) to identify geographic regions at risk. The tool could support public health decision-makers in implementation of an effective control strategy in a timely manner. METHODS We used monthly surveillance data between 2004 and 2015 to predict case counts between 2016 and 2017 prospectively. The Turkish nationwide surveillance data set collected by the Ministry of Health contained 10 411 confirmed CCHF cases. We collected potential explanatory covariates about climate, land use, and animal and human populations at risk to capture spatiotemporal transmission dynamics. We developed a structured Gaussian process algorithm and prospectively tested this tool predicting the future year's cases given past years' cases. RESULTS We predicted the annual cases in 2016 and 2017 as 438 and 341, whereas the observed cases were 432 and 343, respectively. Pearson's correlation coefficient and normalized root mean squared error values for 2016 and 2017 predictions were (0.83; 0.58) and (0.87; 0.52), respectively. The most important covariates were found to be the number of settlements with fewer than 25 000 inhabitants, latitude, longitude and potential evapotranspiration (evaporation and transpiration). CONCLUSIONS Main driving factors of CCHF dynamics were human population at risk in rural areas, geographical dependency and climate effect on ticks. Our model was able to prospectively predict the numbers of CCHF cases. Our proof-of-concept study also provided insight for understanding possible mechanisms of infectious diseases and found important directions for practice and policy to combat against emerging infectious diseases.

中文翻译：

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了解土耳其克里米亚-刚果出血热动态的前瞻性预测工具。

目的我们旨在针对克里米亚-刚果出血热（CCHF）开发一种前瞻性预测工具，以识别有风险的地理区域。该工具可以支持公共卫生决策者及时实施有效的控制策略。方法我们使用2004年至2015年之间的每月监测数据来预测2016年至2017年之间的病例数。卫生部收集的土耳其全国监视数据集包含10 411例确诊的CCHF病例。我们收集了有关气候，土地利用以及处于危险中的动物和人类种群的潜在解释协变量，以捕获时空传播动态。我们开发了结构化的高斯过程算法，并对该工具进行了前瞻性测试，从而根据过去几年的案例来预测未来一年的案例。结果我们预测2016年和2017年的年度病例为438和341，而观察到的病例分别为432和343。2016年和2017年预测的Pearson相关系数和归一化均方根误差值分别为（0.83; 0.58）和（0.87; 0.52）。发现最重要的协变量是居民少于25 000的定居点的数量，纬度，经度和潜在的蒸散量（蒸发和蒸腾作用）。结论CCHF动态的主要驱动因素是农村人口处于危险之中，地理依赖性和气候对tick的影响。我们的模型能够前瞻性地预测CCHF病例数。