1. Time-specific ecological niche modeling predicts spatial dynamics of vector insects and human dengue cases A. Townsend Peterson, Carmen Martinez-Campos, Yoshinori Nakazawa, Enrique Martinez-Meyer Peterson, A. T., C. Martínez-Campos, Y. Nakazawa, and E. Martínez-Meyer. 2005. Time-specific ecological niche modeling predicts spatial dynamics of vector insects and human dengue cases. Transactions of the Royal Society of Tropical Medicine and Hygiene 99:647-655.

2. Dengue Is caused by a virus from the genus Flavovirus. Transmitted by a mosquito (Aedes aegypti). Tropical and subtropical regions of the world. Create time specific ecological niche models that help us understand the spatial and temporal dynamics of the mosquitoes. Predict future outbreaks.

3. Data Point-occurrence of Aedes aegypti drawn from larval surveys (Laboratorio de Entomologia, InDRE) Monthly samples from eastern and southern Mexico. Data from April to December 1995. Mosquitoes InDRE: Instituto de Diagnostico y Referencia Epidemiologica.

4. Environmental data Monthly maximum value composites of NDVI for 1995. From AVHRR. Topographic variables from USGS Hydro-1k: DEM, slope, aspect and topographic index.

5. Methods All points all NDVI

6. Methods Data2 Data1 Data4 Data3 Data5 Month2 Month1 Month4 Month3 Month5 MODIS NDVI DATA Model2 Model1 Model4 Model3 Model5 Include information about conditions from previous months: NDVI(t) - NDVI(t-1) NDVI(t) - NDVI(t-2)

7. Time specific predictions Time specific model

8. Methods: Transmission cycle Human case data drawn from cases tested by InDRE. -Incubation in mosquito -Infection of human -Incubation in human -7 days for taking sera after onset of symptoms. (Total: 18days)

9. Methods: Future - Models for each month - Models projected to all other months - Average of models from two previous months (t-1 and t-2) projected to the current (t) - June  August - July  August - Overlay of occurrences (t) - August - Evaluate predictions

10. Predict future distribution Average models from previous months

11. Predict future distribution

12. Percent correctly predicted Statistical significance Month (1995) Number of test pointsAny >50% of models>80% of modelsAny>50% of models>80% of models June22100.072.754.5*** July28100.082.167.9X** August40100.075.035.0+** September25100.080.016.0X* October1994.778.921.1X November25100.076.052.0X + December22100.095.581.8X+* * Strong significance + Marginally significant X Not significant Predict future distribution

13. Predict future outbreaks

14. Conclusions Time-specific models seem to perform better. Recover the spatial and temporal dynamics of the disease through ENM. Predict areas where an outbreak is more likely to occur. -- Inclusion of human population data/variables.