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Hierarchical spatial modeling of the presence of Chagas disease insect vectors in Argentina. A comparative approach

Stochastic Environmental Research and Risk Assessment volume 31, pages 461–479 (2017)Cite this article

Abstract

We modeled the spatial distribution of the most important Chagas disease vectors in Argentina, in order to obtain a predictive mapping method for the probability of presence of the vector species. We analyzed both the binary variable of presence-absence of Chagas disease and the vector species richness in Argentina, in combination with climatic and topographical covariates associated to the region of interest. We used several statistical techniques to produce distribution maps of presence–absence for the different insect species as well as species richness, using a hierarchical Bayesian framework within the context of multivariate geostatistical modeling. Our results show that the inclusion of covariates improves the quality of the fitted models, and that there is spatial interaction between neighboring cells/pixels, so mapping methods used in the past, which assumed spatial independence, are not adequate as they provide unreliable results.

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Acknowledgements

We thank J. E. Rabinovich from Centro de Estudios Parasitologicos y de Vectores of Buenos Aires, Argentina for drawing our attention to this particular application problem and for providing access to the Chagas data base used. Work partially funded by grant MTM2013-43917-P from the Spanish Ministry of Science and Education, grant PAPIIT IN114814 of the Dirección General de Asuntos del Personal Académico of the Universidad Nacional Autónoma de México and Grant CONACYT number 241195.

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Authors and Affiliations

  1. Department of Mathematics, Universitat Jaume I, Campus Riu Sec, 12071, Castellón, Spain

    Pablo Juan & Jorge Mateu

  2. Research Institute in Applied Mathematics and Systems, Autonomus National University of Mexico, Mexico, D.F., Mexico

    Carlos Díaz-Avalos

  3. Red de Apoyo a la Investigación. Coordinación de la Investigación Científica, Universidad Nacional Autónoma de México, Mexico, D.F., Mexico

    Nancy R. Mejía-Domínguez

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  1. Pablo Juan
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  2. Carlos Díaz-Avalos
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  3. Nancy R. Mejía-Domínguez
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  4. Jorge Mateu
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Corresponding author

Correspondence to Pablo Juan.

Appendix

Appendix

See Figs. 5, 6, 7, 8, 9, Tables 4, 5, 6, 7 and 8.

Fig. 5
figure 5

Posterior probability of presence and standard deviation maps for autologistic models fitted without covariates using INLA-SPDE for the insect vector species considered in this study

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Fig. 6
figure 6

Posterior probability of presence and standard deviation maps for autologistic models fitted without covariates using MCMC for the insect vector species considered in this study

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Fig. 7
figure 7

Posterior probability of presence and standard deviation maps for autologistic models fitted with covariates using INLA-SPDE for the insect vector species considered in this study

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Fig. 8
figure 8

Posterior probability of presence and standard deviation maps for autologistic models fitted with covariates using MCMC for the insect vector species considered in this study

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Fig. 9
figure 9

Mean and sd maps for species richness models without covariates (first two columns from left to right) and with covariates (third and fourth columns) for the insect vector species considered in this study

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Table 4 Summary statistics for autologistic models fitted using MCMC: posterior mean, posterior standard deviation (SD) and posterior 95% credible interval for fixed effects
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Table 5 Summary statistics for autologistic models fitted with INLA-SPDE: posterior mean, posterior standard deviation (SD) and posterior 95% credible interval for fixed effects
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Table 6 Summary statistics for autologistic models fitted with INLA-SPDE: posterior mean, posterior standard deviation (SD) and posterior 95% credible interval for fixed effects
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Table 7 Summary statistics for autologistic models fitted with INLA-SPDE: posterior mean, posterior standard deviation (SD) and posterior 95% credible interval for fixed effects
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Table 8 Summary statistics for auto Poisson models fitted with INLA-SPDE: posterior mean, posterior standard deviation (SD) and posterior 95% credible interval for fixed effects
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Juan, P., Díaz-Avalos, C., Mejía-Domínguez, N.R. et al. Hierarchical spatial modeling of the presence of Chagas disease insect vectors in Argentina. A comparative approach. Stoch Environ Res Risk Assess 31, 461–479 (2017). https://doi.org/10.1007/s00477-016-1340-5

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Keywords

  • Binary spatial data
  • Chagas vector
  • Covariate and hierarchical Bayesian modeling