Time-specific ecological niche modeling predicts spatial dynamics of vector insects and human dengue cases

Abstract

Numerous human diseases-malaria, dengue, yellow fever and leishmaniasis, to name a few-are transmitted by insect vectors with brief life cycles and biting activity that varies in both space and time. Although the general geographic distributions of these epidemiologically important species are known, the spatiotemporal variation in their emergence and activity remains poorly understood. We used ecological niche modeling via a genetic algorithm to produce time-specific predictive models of monthly distributions of Aedes aegypti in Mexico in 1995. Significant predictions of monthly mosquito activity and distributions indicate that predicting spatiotemporal dynamics of disease vector species is feasible; significant coincidence with human cases of dengue indicate that these dynamics probably translate directly into transmission of dengue virus to humans. This approach provides new potential for optimizing use of resources for disease prevention and remediation via automated forecasting of disease transmission risk.

Impact of Climate Change on Health and Disease in Latin America

“…In some coastal areas of the Gulf of Mexico, an increase in sea surface temperature (SST), minimum temperature and precipitation was associated with an increase in dengue transmission cycles (Hurtado-Díaz et al, 2007). Other studies in Mexico have reported similar results (Peterson et al, 2005). In Barbados, Puerto Rico and Dominica, climate variability has been linked to dengue incidence (Depradine and Lovell, 2004;Schreiber, 2001;RodriguezMorales, 2005).…”

Geographic Potential for Outbreaks of Marburg Hemorrhagic Fever

“…[45][46][47] However, its use should be cautious because it is a new tool and has only seen limited application to questions regarding disease transmission.…”

An ecoregional classification for the state of Roraima, Brazil: the importance of landscape in malaria biology

“…We developed ecological niche models and geographic predictions using the Genetic Algorithm for Rule-set Prediction (GARP, Stockwell and Peters 1999; Table I). Meteorological and bioclimate variables used in model development were 24 months of NDVI (greenness index) data from the AVHRR satellite (April 1992-March 1993, February 1995-January 1996, a suite of data that has proven useful in previous studies of vector distributions (Peterson et al 2005). The sample size requirement allows reasonable confidence in the accuracy of the predictive models (Stockwell & Peterson 2002).…”