Modelling dengue fever in endemic areas is important to mitigate and improve vector-borne disease control to reduce outbreaks. This study applied artificial neural networks (ANNs) to predict dengue fever outbreak occurrences in San Juan, Puerto Rico (USA), and in several coastal municipalities of the state of Yucatan, Mexico, based on specific thresholds. The models were trained with 19 years of dengue fever data for Puerto Rico and six years for Mexico. Environmental and demographic data included in the predictive models were sea surface temperature (SST), precipitation, air temperature (i.e., minimum, maximum, and average), humidity, previous dengue cases, and population size. Two models were applied for each study area. One predicted dengue incidence rates based on population at risk (i.e., numbers of people younger than 24 years), and the other on the size of the vulnerable population (i.e., number of people younger than five years and older than 65 years). The predictive power was above 70% for all four model runs. The ANNs were able to successfully model dengue fever outbreak occurrences in both study areas. The variables with the most influence on predicting dengue fever outbreak occurrences for San Juan, Puerto Rico, included population size, previous dengue cases, maximum air temperature, and date. In Yucatan, Mexico, the most important variables were population size, previous dengue cases, minimum air temperature, and date. These models have predictive skills and should help dengue fever mitigation and management to aid specific population segments in the Caribbean region and around the Gulf of Mexico.

Purpose: Bottled water consumption continues to break records worldwide and its environmental impact is often underestimated by the consumer. Many factors affect individuals’ choices to consume tap water and bottled water including perceived health risks and water quality. The University of South Florida (USF) has joined a nationwide initiative to become carbon-neutral, and reducing bottled water consumption was a chosen strategy. The purpose of this study was to assess the risk perceptions and drinking water choices of the USF-Tampa campus community.Design/methodology/approach: A sample of 561 students, faculty and staff members responded to an online survey. The survey contained questions about the use of bottled water and tap water, reusable water bottles, risk perceptions and demographics.Findings: The results revealed that certain groups – undergraduate students and ethnic/racial minorities (e.g. black/African American, Hispanic/Latino) – drank significantly more bottled water. Among political ideologies, Liberals drank the least bottled water. Females and minorities had significantly greater risk perceptions of the tap water on campus. Important perceived benefits were tap water being less expensive and better for the environment than bottled water. Important perceived barriers were poor tasting tap water and the desire for filtered water.Originality/value: The findings suggest the need for public health campaigns to increase awareness of health, environmental and financial consequences of bottled water consumption. Such campaigns should aim to discourage bottled water and any potential increased sugar-sweetened beverage consumption while promoting tap water consumption.

Enterococci concentration variability at Escambron Beach, San Juan, Puerto Rico, was examined in the context of environmental conditions observed during 2005–2015. Satellite-derived sea surface temperature (SST), turbidity, direct normal irradiance, and dew point were combined with local precipitation, winds, and mean sea level (MSL) observations in a stepwise multiple regression analyses (Akaike Information Criteria model selection). Precipitation, MSL, irradiance, SST, and turbidity explained 20% of the variation in observed enterococci concentrations based upon these analyses. Changes in these parameters preceded increases in enterococci concentrations by 24 h up to 11 days, particularly during positive anomalies of turbidity, SST, and 480–960 mm of accumulated (4 days) precipitation, which relates to bacterial ecology. Weaker, yet still significant, increases in enterococci concentrations were also observed during positive dew point anomalies. Enterococci concentrations decreased with elevated irradiance and MSL anomalies. Unsafe enterococci concentrations per US EPA recreational water quality guidelines occurred when 4-day cumulative precipitation ranged 481–960 mm; irradiance < 667 W·m⁻²; daily average turbidity anomaly >0.005 sr⁻¹; SST anomaly >0.8 °C; and 3-day average MSL anomaly <−18.8 cm. This case study shows that satellite-derived environmental data can be used to inform future water quality studies and protect human health.