Local and Global Effects of Climate on Dengue Transmission in Puerto Rico

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

Forecast of dengue incidence using temperature and rainfall

INTRODUCTION: An accurate early warning system to predict impending epidemics enhances the effectiveness of preventive measures against dengue fever. The aim of this study was to develop and validate a forecasting model that could predict dengue cases and provide timely early warning in Singapore. METHODOLOGY AND PRINCIPAL FINDINGS: We developed a time series Poisson multivariate regression model using weekly mean temperature and cumulative rainfall over the period 2000-2010. Weather data were modeled using piecewise linear spline functions. We analyzed various lag times between dengue and weather variables to identify the optimal dengue forecasting period. Autoregression, seasonality and trend were considered in the model. We validated the model by forecasting dengue cases for week 1 of 2011 up to week 16 of 2012 using weather data alone. Model selection and validation were based on Akaike's Information Criterion, standardized Root Mean Square Error, and residuals diagnoses. A Receiver Operating Characteristics curve was used to analyze the sensitivity of the forecast of epidemics. The optimal period for dengue forecast was 16 weeks. Our model forecasted correctly with errors of 0.3 and 0.32 of the standard deviation of reported cases during the model training and validation periods, respectively. It was sensitive enough to distinguish between outbreak and non-outbreak to a 96% (CI = 93-98%) in 2004-2010 and 98% (CI = 95%-100%) in 2011. The model predicted the outbreak in 2011 accurately with less than 3% possibility of false alarm. SIGNIFICANCE: We have developed a weather-based dengue forecasting model that allows warning 16 weeks in advance of dengue epidemics with high sensitivity and specificity. We demonstrate that models using temperature and rainfall could be simple, precise, and low cost tools for dengue forecasting which could be used to enhance decision making on the timing, scale of vector control operations, and utilization of limited resources

Study of the relationship between Aedes (Stegomyia) aegypti egg and adult densities, dengue fever and climate in Mirassol, state of São Paulo, Brazil

The purpose of this study was to examine the relationship between Aedes aegypti egg and adult density indices, dengue fever and climate in Mirassol, state of São Paulo, Brazil, between November 2004-November 2005. Weekly collections of adults and eggs were made using, respectively, manual aspirators and oviposition traps that produced four entomological indices (positivity and average of females and eggs). Weekly incidence coefficients were calculated based on dengue cases. Each week, the data obtained from entomological indices were related to each other, dengue, and climate variables. The first index to show an association with dengue transmission was the female average, followed by female positivity and egg average. Egg positivity did not show a relationship with risk for dengue, but was sensitive to identifying the presence of the vector, principally in dry seasons. The relationship between climatic factors, the vector and the disease found in this study can be widely employed in planning and undertaking dengue surveillance and control activities, but it is a tool that has not been considered by the authorities responsible for controlling the disease. In fact, this relationship permits the use of information about climate for early detection of epidemics and for establishing more effective prevention strategies than currently exist