Dengue Prediction in Latin America Using Machine Learning and the One Health Perspective: A Literature Review.

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.

Assessment of arsenic mobility in the soils of some golf courses in South Florida.

High concentrations of arsenic have been detected in soils and underlying groundwater of some South Florida golf courses, indicating the possible impact of the application of arsenic-containing herbicides. The mobility of arsenic in the soils from selected golf courses was studied using a simple two-step sequential extraction procedure. Sodium nitrate (0.