Cabrera Maritza, Leake Jason, Naranjo-Torres José, Valero Nereida, Cabrera Julio C, Rodríguez-Morales Alfonso J
Centro de Investigación de Estudios Avanzados del Maule (CIEAM), Universidad Católica del Maule, Talca 3480094, Chile.
Facultad Ciencias de la Salud, Universidad Católica del Maule, Talca 3480094, Chile.
Trop Med Infect Dis. 2022 Oct 21;7(10):322. doi: 10.3390/tropicalmed7100322.
Dengue fever is a serious and growing public health problem in Latin America and elsewhere, intensified by climate change and human mobility. This paper reviews the approaches to the epidemiological prediction of dengue fever using the One Health perspective, including an analysis of how Machine Learning techniques have been applied to it and focuses on the risk factors for dengue in Latin America to put the broader environmental considerations into a detailed understanding of the small-scale processes as they affect disease incidence. Determining that many factors can act as predictors for dengue outbreaks, a large-scale comparison of different predictors over larger geographic areas than those currently studied is lacking to determine which predictors are the most effective. In addition, it provides insight into techniques of Machine Learning used for future predictive models, as well as general workflow for Machine Learning projects of dengue fever.
登革热在拉丁美洲和其他地区是一个严重且日益严重的公共卫生问题，气候变化和人口流动加剧了这一问题。本文回顾了从“同一健康”视角对登革热进行流行病学预测的方法，包括分析机器学习技术如何应用于登革热预测，并聚焦拉丁美洲的登革热风险因素，以便在详细理解影响疾病发病率的小规模过程时纳入更广泛的环境考量。虽然确定许多因素可作为登革热暴发的预测指标，但目前尚缺乏在比当前研究更大的地理区域对不同预测指标进行大规模比较，以确定哪些预测指标最有效。此外，本文还深入探讨了用于未来预测模型的机器学习技术，以及登革热机器学习项目的一般工作流程。
