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.

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.

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.

Environmental data

Monthly maximum value composites of NDVI for 1995. From AVHRR.

Topographic variables from USGS Hydro-1k: DEM, slope, aspect and topographic index.

Methods

Absent1 - 49% of models50 - 79% of models80 - 100% of models All points all NDVI

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)

Time specific predictions

Time specific model

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

NDVI(t) Elevation NDVI(t-2) NDVI(t-1) Slope Topographic index

Aspect

Cor

rela

tion

coef

ficie

nt

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)

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

Predict future distribution

Average models from previous months

Predict future distribution

    Percent correctly predicted  Statistical significance

Month (1995)Number of test

points Any>50% of models >80% of models Any >50% of models >80% of models

June 22 100.0 72.7 54.5 * * *

July 28 100.0 82.1 67.9 X * *

August 40 100.0 75.0 35.0 + * *

September 25 100.0 80.0 16.0 X *  

October 19 94.7 78.9 21.1 X    

November 25 100.0 76.0 52.0 X   +

December 22 100.0 95.5 81.8 X + *

* Strong significance+ Marginally significantX Not significant

Predict future distribution

Predict future outbreaks

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.