Models for Predicting Aedes aegypti Larval Indices Based on Satellite Images and Climatic Variables

Forecasting models were developed for predicting Aedes aegypti larval indices in an endemic area for dengue (cities of Tartagal and Orán, northwestern Argentina), based on the Breteau and House indices and environmental variables considered with and without time lags. Descriptive models were first developed for each city and each index by multiple linear regressions, followed by a regional model including both cities together. Finally, two forecasting regional models (FRM) were developed and evaluated. FRM2 for the Breteau index and House index fit the data significantly better than FRM1. An evaluation of these models showed a higher correlation FRM1 than for FRM2 for the Breteau index (R = 0.83 and 0.62 for 3 months; R = 0.86 and 0.67 for 45 days) and the House index (R = 0.85 and 0.79 for 3 months; R = 0.79 and 0.74 for 45 days). Early warning based on these forecasting models can assist health authorities to improve vector control.