Predictive disease modeling attempts to exploit the complicated relationship between disease outbreaks and measurable environmental, biological, ecological and sociopolitical variables. Previous studies (3Á5) identified factors associated with dengue outbreaks such as: past cases, ambient temperature, precipitation, Normalized Difference Vegetation Index, Enhanced Vegetation Index, Southern Oscillation Index, sea surface temperature anomalies and socioeconomic factors. We obtained and preprocessed these variables to get one value per district per week. The epidemiological dengue fever case data used span 2001Á2009 and cover several districts in Loreto, Peru. We computed incidence rate per 1000 residents, enabling us to deal with significantly different population sizes in the different districts. The method predicts incidence rates 4 weeks in advance. We developed logistic regression (LR) models using part of the data set available. The second part, not previously used for model development, was used for testing. The ROC curve and positive predictive value (PPV) for the test set are shown in Fig. 1 . High specificity is easier to obtain than high sensitivity. The preliminary results are encouraging: when sensitivity is 0.375, we obtain a specificity of 0.987 and a PPV of 0.6.