Original Research
Modeling of the ecological niches of the anopheles spp in Ecuador by the use of geo-informatic tools

https://doi.org/10.1016/j.sste.2016.12.001Get rights and content

Abstract

Ecuador in the northwestern edge of South America is struggling by vector-borne diseases with an endemic-epidemic behavior leading to an enormous public health problem. Malaria, which has a cyclicality in its dynamics, is closely related to climatic, ecological and socio-economic phenomena. The main objective of this research has been to compare three different prediction species models, the so-called Maxent, logistic regression and multi criteria evaluation with fuzzy logic, in order to determine the model which best describes the ecological niche of the Anopheles spp species, which transmits malaria within Ecuador. After performing a detailed data collection and data processing, we applied the mentioned models and validated them with a statistical analysis in order to discover that the Maxent model has been the model that best defines the distribution of Anopheles spp within the territory. The determined sites, which are of high strategic value and important for the increasing national development, will now be able to initiate preventive countermeasures based on this study.

Introduction

At present day a variety of diseases transmitted by vector and rodent-borne which are related with water and food-borne, respiratory infections or related to sudden temperature changes are increasingly associated with environmental changes that favor their appearance, reappearance or spreading and even in opposite way their temporary or permanent reduction or disappearance (Rotela et al., 2007, Tourre et al., 2008, Porcasi et al., 2012, Wu et al., 2016). Worldwide around 3.3 billion people are at risk of malaria, 30% of them are of high risk and around 200 million people are finally even infected with this disease (World Health Organization, 2015). While 80% of malaria cases occur in Africa, Latin America is not exempt where annually some 2.7 million people become infected. Malaria is endemic in the tropics, reaching some 90% of all infections in the Amazon basin and with less proportion in Central America and southern Mexico (San Sebastián et al., 2000, Rodríguez et al., 2003, Hakre et al., 2004). While the risk of infection is relatively low in large cities, such risk is higher in rural areas of Bolivia, Colombia, Ecuador, Peru and Venezuela (Mendez et al., 2000, Snow et al., 2005, Yalcindag et al., 2012).

Although Ecuador is located in an equatorial area, it has a great diversity of climates due to a varied topography and the influence of marine currents such as Humboldt and the El Niño Southern Oscillation (Kovats, 2000, Berz et al., 2001, Gallup et al., 2003, Hales et al., 2003, Toulkeridis, 2013, Toulkeridis and Zach, 2016); therefore, the presence of important tropical diseases such as malaria in the Ecuadorian terrain is relatively common due to its cyclical behavior which is closely related to climate and socioeconomic patterns. In Ecuador malaria causes direct and indirect economic losses affecting hereby mainly the poorest part of the society. Malaria mainly affects children and the economically active population, both groups are in the age range of 0–39 years old. Although there are no updated data in Ecuador on the economic impact of malaria calculations, a study carried out by Ruiz and Roeger in 1994 determined that such loss corresponded to a 16 to 20.8% of the basic monthly salary for each person infected with malaria (Ruiz and Roeger, 1994).

The modeling of ecological niche and associated species is a topic which in recent years gained worldwide a significant relevance due to the great potential of given applications, which highlights the vector-borne research of parasitic diseases (Gubler, 1998, Bergquist, 2001, Hunter, 2003, Peterson and Shaw, 2003, Moffett et al., 2007, Lafferty, 2009, González et al., 2010, Kulkarni et al., 2010). Within this context, research in Ecuador about such vector-borne diseases and their modeled distributions has been scarce to none, due to the fact that there has been either lack of information, inexperience on the subject or the absence of resources. Therefore, our main aim in this study has been to obtain the potential knowledge of the modeled geographical distribution of the ecological niche of the malaria vector (Anopheles spp) in this tropical territory, which may serve as an input for territorial management and planning in the control and eradication of diseases caused by such vectors.

Section snippets

Methodology

The core of this study has been the methodology used. Therefore, we have taken into consideration a number of different steps to describe detailed the methodological framework used in the process to accomplish with the main objectives of this research, namely, the collection of the data and how we processed them as well as the application of the three different ecological niche models. The methodological framework demonstrating all stages of this research is presented in Fig. 1.

Results and discussion

In order to choose the best fitting model of the ecological niche for Anopheles spp, we performed comparisons and statistical tests, which were applied to all three models.

Conclusions

This study has been able to highlight the importance of geospatial research focused on the biological and epidemiological field as it allows a good and quick overview for a subsequent useful contribution to regional planning and public health. Nonetheless, one of the critical points to improve may be the quality of the model results is the selection of variables and quantify their contribution when trying to evaluate a phenomenon, hence the importance of a preliminary analysis of environmental

Acknowledgments

We thank the Universidad de las Fuerzas Armadas ESPE for logistic and financial support. We would like to express our thanks to Prof. Lawson for his editorial handling and also the two anonymous reviewers who improved significantly the manuscript with their extensive and constructive comments.

References (51)

  • A. Agresti et al.

    Categorical data analysis

    (2011)
  • M. Barbet-Massin et al.

    Selecting pseudo-absences for species distribution models: how, where and how many?

    Methods Ecol Evol

    (2012)
  • G. Berz et al.

    World map of natural hazards–a global view of the distribution and intensity of significant exposures

    Nat. Hazards

    (2001)
  • S. Carver

    Integrating multi-criteria evaluation with geographical information systems

    Int J Geogr Inf Syst

    (1991)
  • T. Delgado

    Evolución de la diversidad vegetal en Ecuador ante un escenario de cambio global (Tesis doctoral)

    (2011)
  • J.R. Eastman

    IDRISI Andes guide to GIS and image processing

    (2006)
  • A. Fielding et al.

    A review of methods for the assessment of prediction errors in conservation presence/absence models

    Environ Conserv

    (1997)
  • D.H. Foley et al.

    Geographic distribution and ecology of potential malaria vectors in the Republic of Korea

    J Med Entomol

    (2009)
  • J.L. Gallup et al.

    Is geography destiny? lessons from Latin America

    (2003)
  • C. González et al.

    Climate change and risk of leishmaniasis in North America: predictions from ecological niche models of vector and reservoir species

    PLoS Negl Trop Dis

    (2010)
  • D.J. Gubler

    Resurgent vector-borne diseases as a global health problem

    Emerg Infect Dis

    (1998)
  • A. Guisan et al.

    Predicting species distribution: offering more than simple habitat models

    Ecol Lett

    (2005)
  • S. Hakre et al.

    Spatial correlations of mapped malaria rates with environmental factors in Belize, Central America

    Int J Health Geogr

    (2004)
  • S. Hales et al.

    Impacts on health of climate extremes

    Climate change and health: risks and responses

    (2003)
  • D.J. Hand et al.

    A simple generalisation of the area under the ROC curve for multiple class classification problems

    Mach Learn

    (2001)
  • View full text