[1] The physical effects of hurricanes include deepening of the mixed layer and decreasing of the sea surface temperature in response to entrainment, curl-induced upwelling, and increased upper ocean cooling. However, the biological effects of hurricanes remain relatively unexplored. In this paper, we examine the passages of 13 hurricanes through the Sargasso Sea region of the North Atlantic during the years 1998 through 2001. Remotely sensed ocean color shows increased concentrations of surface chlorophyll within the cool wakes of the hurricanes, apparently in response to the injection of nutrients and/or biogenic pigments into the oligotrophic surface waters. This increase in post-storm surface chlorophyll concentration usually lasted 2-3 weeks before it returned to its nominal pre-hurricane level.

SignificanceForecasts routinely provide critical information for dangerous weather events but not yet for epidemics. Researchers develop computational models that can be used for infectious disease forecasting, but forecasts have not been broadly compared or tested. We collaboratively compared forecasts from 16 teams for 8 y of dengue epidemics in Peru and Puerto Rico. The comparison highlighted components that forecasts did well (e.g., situational awareness late in the season) and those that need more work (e.g., early season forecasts). It also identified key facets to improve forecasts, including using multiple model ensemble approaches to improve overall forecast skill. Future infectious disease forecasting work can build on these findings and this framework to improve the skill and utility of forecasts.

BackgroundDengue is the most common arboviral disease of humans, with more than one third of the world’s population at risk. Accurate prediction of dengue outbreaks may lead to public health interventions that mitigate the effect of the disease. Predicting infectious disease outbreaks is a challenging task; truly predictive methods are still in their infancy.MethodsWe describe a novel prediction method utilizing Fuzzy Association Rule Mining to extract relationships between clinical, meteorological, climatic, and socio-political data from Peru. These relationships are in the form of rules. The best set of rules is automatically chosen and forms a classifier. That classifier is then used to predict future dengue incidence as either HIGH (outbreak) or LOW (no outbreak), where these values are defined as being above and below the mean previous dengue incidence plus two standard deviations, respectively.ResultsOur automated method built three different fuzzy association rule models. Using the first two weekly models, we predicted dengue incidence three and four weeks in advance, respectively. The third prediction encompassed a four-week period, specifically four to seven weeks from time of prediction. Using previously unused test data for the period 4–7 weeks from time of prediction yielded a positive predictive value of 0.686, a negative predictive value of 0.976, a sensitivity of 0.615, and a specificity of 0.982.ConclusionsWe have developed a novel approach for dengue outbreak prediction. The method is general, could be extended for use in any geographical region, and has the potential to be extended to other environmentally influenced infections. The variables used in our method are widely available for most, if not all countries, enhancing the generalizability of our method.

Failure to consider anomalous propagation of microwave radiation in the troposphere may result in erroneous meteorological radar measurements. The most commonly occurring anomalous propagation phenomenon over the ocean is the evaporation duct. The height of this duct is dependent on atmospheric variables and is a major input to microwave propagation prediction models. This evaporation duct height is determined from an evaporation duct model using bulk measurements. Two current evaporation duct models in widespread operational use are examined. We propose and test a new model that addresses deficiencies in these two models. The new model uses recently refined bulk similarity expressions developed for the determination of the ocean surface energy budget in the Tropical Ocean Global Atmosphere Coupled Ocean-Atmosphere Response Experiment.Comparison of these models is made using data collected from a boat off Wallops Island, Virginia, during a range of seasons and weather conditions and from the tidal Potomac River during June and August. Independent evaporation duct height determinations are made using profile measurements from the same boat and are corroborated with fade measurements made with a nearby microwave link whenever possible. The proposed model performs better than the other (operational) models for the cases examined and has advantages of internal consistency.

Background: The District of Columbia (DC) Department of Health, under a grant from the US Centers for Disease Control and Prevention, established an Environmental Public Health Tracking Program. As part of this program, the goals of this contextual pilot study are to quantify short-term associations between daily pediatric emergency department (ED) visits and admissions for asthma exacerbations with ozone and particulate concentrations, and broader associations with socioeconomic status and age group.