Using epidemiological survey data to infer geographic distributions of leishmaniasis vector species

Abstract

An important aspect of tropical medicine is analysis of geographic aspects of risk of disease transmission, which for lack of detailed public health data must often be reduced to an understanding of the distributions of critical species such as vectors and reservoirs. We examine the applicability of a new technique, ecological niche modeling, to the challenge of understanding distributions of such species based on municipalities in the state of São Paulo in which a group of 5 Lutzomyia sandfly species have been recorded. The technique, when tested based on independent occurrence data, yielded highly significant predictions of species' distributions; minimum sample sizes for effective predictions were around 40 municipalities.

Current Knowledge of Leishmania Vectors in Mexico: How Geographic Distributions of Species Relate to Transmission Areas

“…Because one of the criteria set by the World Health Organization for linking species to disease transmission is that vector and reservoir geographic distributions must coincide with human case distributions, 68 our comparisons of modeled potential distributions enabled us to assess disease transmission in places where entomologic surveillance has scarcely been conducted. 4 The distribution of the only proven CL vector in Mexico, Lu. olmeca , does not overlap all of the known CL cases in the country, suggesting that other sand fly species must be involved as vectors in those areas.…”

“…1 Recent research in ecological niche modeling, integrating point occurrence data with digital environmental maps, provides a useful and powerful framework for understanding the eco-epidemiologic geography of zoonotic diseases because their transmission cycles involve different sylvatic (enzootic) and domestic reservoir and vector species, each responding to environmental variation, according to its own ecological niche. [2][3][4][5][6] The ecological niche is defined as the set of environmental conditions in which species can maintain populations without immigrational subsidy, and provides a framework for testing hypotheses regarding roles of environmental variables in shaping distributional patterns of species and evaluating how different species' ecological niches relate to one another. 3,7 An interesting and complex disease system from the point of view of the variety of species involved in the transmission cycle and public health relevance are the leishmaniases, a group of diseases with different clinical manifestations, caused by parasites transmitted by sand fly vectors (Diptera: Psychodidae: Phlebotominae) among mammal reservoir hosts.…”

Ecological Niche Modeling of Seventeen Sandflies Species (Diptera, Psychodidae, Phlebotominae) from Venezuela

“…Some studies have demonstrated deterioration in predictive performance as sample sizes are decreased [26,42]. In others, accuracy models were produced with few records maybe due to ecologically specialized species with smallest geographic extent of occurrence and very low tolerance [33].…”

“…This technique has been used in modeling distribution of diseases, such as dengue, malaria, and West Nile and yellow fever vectors, such as Aedes Meigen 1818 [19], Anopheles Meigen 1818 [20][21][22], Culex Linnaeus 1758 [23], and Haemagogus Williston 1896 [24], respectively. In addition, ENM has been used to examine the potential distribution of Lutzomyia França 1924 in America [25][26][27][28][29][30] and Phlebotomus Rondani 1840 in western Asia [31]. Recently, Foley et al [32] proposed a new map service that allows free public online access to global sandfly collection records and habitat suitability models.…”

Infectious Diseases, Biodiversity and Global Changes: How the Biodiversity Sciences May Help

“…Using appropriate algorithms in a GIS containing layers of environmental information (such as topography, climate, and vegetation), epidemiological and spatial risk stratification can be achieved from data on the location of vectors or pathogens. This approach has been used in the case of Chagas disease and for vectors of leishmaniasis and filovirus infections (Peterson et al, 2002(Peterson et al, , 2004a. Moreover, using scenarios of climate change, it is then possible to project scenarios of pathogen and vector distribution changes.…”