Associations between dengue and combinations of weather factors in a city in the Brazilian Amazon

• Published in: Revista panamericana de salud pública Vol. 20; no. 4; pp. 256 - 267
• Main Authors: Rosa-Freitas, Maria Goreti, Schreiber, Kathleen V, Tsouris, Pantelis, Weimann, Ellem Tatiani de Souza, Luitgards-Moura, José Francisco
• Format: Journal Article
• Language: English
• Published: United States Organización Panamericana de la Salud 01-10-2006
• Subjects: Brazil; Dengue - epidemiology; Diseases - Brazil; Environmental health - Brazil; Health Policy & Services; Humans; Public health - Brazil; Roraima, Brazil; Urban Health; Weather; Brazil; models; brotes de enfermedades; modelos biológicos; Dengue; forecasting; disease outbreaks; climate; Aedes; clima; Brasil; predicción; tiempo; weather; Brazil; biological
• ISSN: 1020-4989; 1680-5348; 1020-4989
• DOI: 10.1590/s1020-49892006000900006

Dengue has become the most important endemic disease in Brazil. The Amazonian state of Roraima has one of the highest incidence rates of dengue in the country. The objective of this study was to determine whether significant temporal relationships exist between the number of reported dengue cases and short-term climate measures for the city of Boa Vista, the capital of Roraima. If such relationships exist, that suggests that it may be possible to predict dengue case numbers based on antecedent climate, thus helping develop a climate-based dengue early-warning system for Boa Vista.
Seasonal Pearson product-moment correlations were developed between 3-week running averages of daily numbers of reported dengue cases for September 1998-December 2001 and certain meteorological variables (thermal, hydroclimatic, wind, atmospheric pressure, and humidity) up to 25 weeks before. Two-sample t tests were also applied to test for statistically significant differences between samples of daily dengue cases with above-average values and samples with below-average values for three-variable meteorological combinations. These multivariate combinations consisted of the three climate measures that together explained the greatest portion of the variance in the number of dengue cases for the particular season.
The strength of the individual averaged correlations varied from weak to moderate. The correlations differed according to the period of the year, the particular climatic variable, and the lag period between the climate indicator and the number of dengue cases. The seasonal correlations in our study showed far stronger relationships than had daily, full-year measures reported in previous studies. Two-sample t tests of multivariate meteorological combinations of atmospheric pressure, wind, and humidity values showed statistically significant differences in the number of reported dengue cases.
Relationships between climate and dengue are best analyzed for short, relevant time periods. Climate-based multivariate temporal stochastic analyses have the potential to identify periods of elevated dengue incidence, and they should be integrated into local control programs for vector-transmitted diseases.