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. 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.

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 (0.1 M), potassium dihydrogen phosphate (0.1 M) and concentrated nitric acid were used to obtain mobile, mobilizable, and pseudo total arsenic fractions. Soils were separated into fine (<0.25 mm) and large (0.25-0.75 mm) particle size fractions. Arsenic contents were correlated with the distribution of iron (R2=0.4827), manganese (R2=0.7674) and aluminum (R2=5459) in the particle size fractions, while such correlation was not observed for soil organic matter, indicating that the oxides/hydroxides of iron, manganese and aluminum control the distribution of arsenic in these soils. Sodium nitrate and potassium dihydrogen phosphate extractants used in this study extracted large portions of arsenic from most soil samples studied. This is especially true for the fine fraction where the extractable arsenic ranged from 9.2 to 51.3% with an average of 28.7 +/- 13.3%, whereas in the large fraction, arsenic ranged from 7.2 to 24.7% with an average of 15.4 +/- 6.4%. These extractants, however, release only small amounts of iron, manganese, and aluminum. It seems likely that arsenic can be released by sodium nitrate and potassium dihydrogen phosphate without significant dissolution of the oxides/hydroxides of iron, manganese, and aluminum in these soil samples.