Ecological niche modelling for predicting the risk of cutaneous leishmaniasis in the Neotropical moist forest biome

A major challenge of eco-epidemiology is to determine which factors promote the transmission of infectious diseases and to establish risk maps that can be used by public health authorities. The geographic predictions resulting from ecological niche modelling have been widely used for modelling the future dispersion of vectors based on the occurrence records and the potential prevalence of the disease. The establishment of risk maps for disease systems with complex cycles such as cutaneous leishmaniasis (CL) can be very challenging due to the many inference networks between large sets of host and vector species, with considerable heterogeneity in disease patterns in space and time. One novelty in the present study is the use of human CL cases to predict the risk of leishmaniasis occurrence in response to anthropogenic, climatic and environmental factors at two different scales, in the Neotropical moist forest biome (Amazonian basin and surrounding forest ecosystems) and in the surrounding region of French Guiana. With a consistent data set never used before and a conceptual and methodological framework for interpreting data cases, we obtained risk maps with high statistical support. The predominantly identified human CL risk areas are those where the human impact on the environment is significant, associated with less contributory climatic and ecological factors. For both models this study highlights the importance of considering the anthropogenic drivers for disease risk assessment in human, although CL is mainly linked to the sylvatic and peri-urban cycle in Meso and South America.生态流行病学（eco-epidemiology）的核心挑战之一，在于明确推动传染病传播的相关因素，并构建可供公共卫生主管部门使用的疾病风险地图。基于物种出现记录与疾病潜在流行率所生成的生态位模型（ecological niche modelling）地理预测结果，已被广泛应用于媒介生物未来扩散的模拟工作。针对如皮肤利什曼病（cutaneous leishmaniasis, CL）这类具有复杂传播循环的疾病系统，由于其大量宿主与媒介物种间存在复杂的推断网络，且疾病的时空分布模式存在显著异质性，构建其风险地图往往极具挑战性。本研究的一项创新之处在于，以人类皮肤利什曼病病例数据为基础，在两个不同尺度下，针对新热带湿润森林生物群区（Neotropical moist forest biome，即亚马孙流域及周边森林生态系统）与法属圭亚那（French Guiana）周边区域，预测由人为、气候及环境因素驱动的利什曼病发病风险。本研究采用了此前从未被使用过的一致性数据集，并搭建了用于解读病例数据的概念与方法学框架，最终得到了具有高统计学支持度的风险地图。研究识别出的主要人类皮肤利什曼病高风险区域，均为人类对环境影响显著的区域，同时伴随贡献度较低的气候与生态因素。尽管在中美洲与南美洲，皮肤利什曼病主要与森林及城郊传播循环相关，但本研究的两类模型均强调，在人类疾病风险评估中纳入人为驱动因子的重要性。