Dataset Information

Local and global effects of climate on dengue transmission in Puerto Rico.

ABSTRACT: The four dengue viruses, the agents of dengue fever and dengue hemorrhagic fever in humans, are transmitted predominantly by the mosquito Aedes aegypti. The abundance and the transmission potential of Ae. aegypti are influenced by temperature and precipitation. While there is strong biological evidence for these effects, empirical studies of the relationship between climate and dengue incidence in human populations are potentially confounded by seasonal covariation and spatial heterogeneity. Using 20 years of data and a statistical approach to control for seasonality, we show a positive and statistically significant association between monthly changes in temperature and precipitation and monthly changes in dengue transmission in Puerto Rico. We also found that the strength of this association varies spatially, that this variation is associated with differences in local climate, and that this relationship is consistent with laboratory studies of the impacts of these factors on vector survival and viral replication. These results suggest the importance of temperature and precipitation in the transmission of dengue viruses and suggest a reason for their spatial heterogeneity. Thus, while dengue transmission may have a general system, its manifestation on a local scale may differ from global expectations.

SUBMITTER: Johansson MA

PROVIDER: S-EPMC2637540 | biostudies-literature | 2009

REPOSITORIES: biostudies-literature

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Local and global effects of climate on dengue transmission in Puerto Rico.

Johansson Michael A MA Dominici Francesca F Glass Gregory E GE

PLoS neglected tropical diseases 20090217 2

The four dengue viruses, the agents of dengue fever and dengue hemorrhagic fever in humans, are transmitted predominantly by the mosquito Aedes aegypti. The abundance and the transmission potential of Ae. aegypti are influenced by temperature and precipitation. While there is strong biological evidence for these effects, empirical studies of the relationship between climate and dengue incidence in human populations are potentially confounded by seasonal covariation and spatial heterogeneity. Usi ...[more]

PMID: 19221592

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Project description:Mountains receive a greater proportion of precipitation than other environments, and thus make a disproportionate contribution to the world's water supply. The Luquillo Mountains receive the highest rainfall on the island of Puerto Rico and serve as a critical source of water to surrounding communities. The area's role as a long-term research site has generated numerous hydrological, ecological, and geological investigations that have been included in regional and global overviews that compare tropical forests to other ecosystems. Most of the forest- and watershed-wide estimates of precipitation (and evapotranspiration, as inferred by a water balance) have assumed that precipitation increases consistently with elevation. However, in this new analysis of all known current and historical rain gages in the region, we find that similar to other mountainous islands in the trade wind latitudes, leeward (western) watersheds in the Luquillo Mountains receive lower mean annual precipitation than windward (eastern) watersheds. Previous studies in the Luquillo Mountains have therefore overestimated precipitation in leeward watersheds by up to 40%. The Icacos watershed, however, despite being located at elevations 200-400 m below the tallest peaks and to the lee of the first major orographic barrier, receives some of the highest precipitation. Such lee-side enhancement has been observed in other island mountains of similar height and width, and may be caused by several mechanisms. Thus, the long-reported discrepancy of unrealistically low rates of evapotranspiration in the Icacos watershed is likely caused by previous underestimation of precipitation, perhaps by as much as 20%. Rainfall/runoff ratios in several previous studies suggested either runoff excess or runoff deficiency in Luquillo watersheds, but this analysis suggests that in fact they are similar to other tropical watersheds. Because the Luquillo Mountains often serve as a wet tropical archetype in global assessments of basic ecohydrological processes, these revised estimates are relevant to regional and global assessments of runoff efficiency, hydrologic effects of reforestation, geomorphic processes, and climate change.

Project description:BackgroundThe incidence and severity of dengue in Latin America has increased substantially in recent decades and data from Puerto Rico suggests an increase in severe cases. Successful clinical management of severe dengue requires early recognition and supportive care.MethodsFatal cases were identified among suspected dengue cases reported to two disease surveillance systems and from death certificates. To be included, fatal cases had to have specimen submitted for dengue diagnostic testing including nucleic acid amplification for dengue virus (DENV) in serum or tissue, immunohistochemical testing of tissue, and immunoassay detection of anti-DENV IgM from serum. Medical records from laboratory-positive dengue fatal case-patients were reviewed to identify possible determinants for death.ResultsAmong 10,576 reported dengue cases, 40 suspect fatal cases were identified, of which 11 were laboratory-positive, 14 were laboratory-negative, and 15 laboratory-indeterminate. The median age of laboratory-positive case-patients was 26 years (range 5 months to 78 years), including five children aged < 15 years; 7 sought medical care at least once prior to hospital admission, 9 were admitted to hospital and 2 died upon arrival. The nine hospitalized case-patients stayed a mean of 15 hours (range: 3-48 hours) in the emergency department (ED) before inpatient admission. Five of the nine case-patients received intravenous methylprednisolone and four received non-isotonic saline while in shock. Eight case-patients died in the hospital; five had their terminal event on the inpatient ward and six died during a weekend. Dengue was listed on the death certificate in only 5 instances.ConclusionsDuring a dengue epidemic in an endemic area, none of the 11 laboratory-positive case-patients who died were managed according to current WHO Guidelines. Management issues identified in this case-series included failure to recognize warning signs for severe dengue and shock, prolonged ED stays, and infrequent patient monitoring.

Project description:BackgroundPredictive models can serve as early warning systems and can be used to forecast future risk of various infectious diseases. Conventionally, regression and time series models are used to forecast dengue incidence, using dengue surveillance (e.g., case counts) and weather data. However, these models may be limited in terms of model assumptions and the number of predictors that can be included. Machine learning (ML) methods are designed to work with a large number of predictors and thus offer an appealing alternative. Here, we compared the performance of ML algorithms with that of regression models in predicting dengue cases and outbreaks from 4 to up to 12 weeks in advance. Many countries lack sufficient health surveillance infrastructure, as such we evaluated the contribution of dengue surveillance and weather data on the predictive power of these models.MethodsWe developed ML, regression, and time series models to forecast weekly dengue case counts and outbreaks in Iquitos, Peru; San Juan, Puerto Rico; and Singapore from 1990-2016. Forecasts were generated using available weekly dengue surveillance, and weather data. We evaluated the agreement between model forecasts and actual dengue observations using Mean Absolute Error and Matthew's Correlation Coefficient (MCC).ResultsFor near term predictions of weekly case counts and when using surveillance data, ML models had 21% and 33% less error than regression and time series models respectively. However, using weather data only, ML models did not demonstrate a practical advantage. When forecasting weekly dengue outbreaks 12 weeks in advance, ML models achieved a maximum MCC of 0.61.ConclusionsOur results identified 2 scenarios when ML models are advantageous over regression model: 1) predicting dengue weekly case counts 4 weeks ahead when dengue surveillance data are available and 2) predicting weekly dengue outbreaks 12 weeks ahead when dengue surveillance data are unavailable. Given the advantages of ML models, dengue early warning systems may be improved by the inclusion of these models.

Dataset Information

Local and global effects of climate on dengue transmission in Puerto Rico.

Publications

Local and global effects of climate on dengue transmission in Puerto Rico.

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