A Maximum Entropy Model of the Distribution of Dengue Serotype in Mexico

Author:

Annan Esther1ORCID,Lubinda Jailos2ORCID,Treviño Jesús3ORCID,Messer William45ORCID,Fonseca Dina6ORCID,Wang Penghua7ORCID,Pilz Jurgen8ORCID,Lintner Benjamin9ORCID,Angulo-Molina Aracely10ORCID,Gallego-Hernández Ana L.10ORCID,Haque Ubydul1112ORCID

Affiliation:

1. Center for Health and Wellbeing, School of Public and International Affairs, Princeton University, Princeton, NJ, USA

2. Malaria Atlas Project, Telethon Kids Institute, 6009, Nedlands, WA, Australia

3. Department of Urban Affairs at the School of Architecture, Universidad Autónoma de Nuevo León, San Nicolás de los Garza, Nuevo Léon 66455, Mexico

4. Department of Molecular Microbiology and Immunology, Oregon Health and Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA

5. Department of Medicine, Division of Infectious Disease, Oregon Health and Science University, Portland, OR, USA

6. Center for Vector Biology, Rutgers University, New Brunswick, NJ, USA

7. Department of Immunology, School of Medicine, U Conn Health, Farmington, CT 06030, USA

8. Department of Statistics, University of Klagenfurt, Klagenfurt, Austria

9. Department of Environmental Sciences, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA

10. Departamento de Ciencias Químico-Biológicas, Universidad of Sonora, Hermosillo 83000, Mexico

11. Rutgers Global Health Institute, New Brunswick, NJ, USA

12. Department of Biostatistics and Epidemiology, School of Public Health, Rutgers University, Piscataway, NJ, USA

Abstract

Pathogen strain diversity is an important driver of the trajectory of epidemics. The role of bioclimatic factors on the spatial distribution of dengue virus (DENV) serotypes has, however, not been previously studied. Hence, we developed municipality-scale environmental suitability maps for the four dengue virus serotypes using maximum entropy modeling. We fit climatic variables to municipality presence records from 2012 to 2020 in Mexico. Bioclimatic variables were explored for their environmental suitability to different DENV serotypes, and the different distributions were visualized using three cutoff probabilities representing 90%, 95%, and 99% sensitivity. Municipality-level results were then mapped in ArcGIS. The overall accuracy for the predictive models was 0.69, 0.68, 0.75, and 0.72 for DENV-1, DENV-2, DENV-3, and DENV-4, respectively. Important predictors of all DENV serotypes were the growing degree days for December, January, and February, which are an indicator of higher temperatures and the precipitation of the wettest month. The minimum temperature of the coldest month between −5°C and 20°C was found to be suitable for DENV-1 and DENV-2 serotypes. Respectively, above 700–900 mm of rainfall, the suitability for DENV-1 and DENV-2 begins to decline, while higher humidity still favors DENV-3 and DENV-4. The sensitivity concerning the suitability map was developed for Mexico. DENV-1, DENV-2, DENV-3, and DENV-4 serotypes will be found more commonly in the municipalities classified as suitable based on their respective sensitivity of 91%, 90%, 89%, and 85% in Mexico. As the microclimates continue to change, specific bioclimatic indices may be used to monitor potential changes in DENV serotype distribution. The suitability for DENV-1 and DENV-2 is expected to increase in areas with lower minimum temperature ranges, while DENV-3 and DENV-4 will likely increase in areas that experience higher humidity. Ongoing surveillance of municipalities with predicted suitability of 89% and 85% should be expanded to account for the accurate DENV serotype prevalence and association between bioclimatic parameters.

Publisher

Hindawi Limited

Subject

General Veterinary,General Immunology and Microbiology,General Medicine

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