论文标题
通过使用气候变量和机器学习技术来评估哥斯达黎加的登革热风险
Assessing dengue fever risk in Costa Rica by using climate variables and machine learning techniques
论文作者
论文摘要
登革热是一种媒介传播的疾病，主要是热带和亚热带国家的流行，每年影响数百万，被认为是公共卫生的重大负担。它的地理分布使其对气候条件高度敏感。在这里，我们使用通用的添加剂模型探索气候变量的效果，用于位置，规模和形状（GAMLSS）和随机森林（RF）机器学习算法。使用报告的登革热案例，我们获得了可靠的预测。还测量了预测的不确定性。这些预测将作为卫生官员的意见，以进一步改善和优化登革热爆发之前的资源分配。
Dengue fever is a vector-borne disease mostly endemic to tropical and subtropical countries that affect millions every year and is considered a significant burden for public health. Its geographic distribution makes it highly sensitive to climate conditions. Here, we explore the effect of climate variables using the Generalized Additive Model for location, scale, and shape (GAMLSS) and Random Forest (RF) machine learning algorithms. Using the reported number of dengue cases, we obtained reliable predictions. The uncertainty of the predictions was also measured. These predictions will serve as input to health officials to further improve and optimize the allocation of resources prior to dengue outbreaks.
