Cargando…

Estimating snakebite incidence from mathematical models: A test in Costa Rica

BACKGROUND: Snakebite envenoming is a neglected public health challenge that affects mostly economically deprived communities who inhabit tropical regions. In these regions, snakebite incidence data is not always reliable, and access to health care is scare and heterogeneous. Thus, addressing the pr...

Descripción completa

Detalles Bibliográficos
Autores principales: Bravo-Vega, Carlos A., Cordovez, Juan M., Renjifo-Ibáñez, Camila, Santos-Vega, Mauricio, Sasa, Mahmood
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6907855/
https://www.ncbi.nlm.nih.gov/pubmed/31790407
http://dx.doi.org/10.1371/journal.pntd.0007914
_version_ 1783478612839628800
author Bravo-Vega, Carlos A.
Cordovez, Juan M.
Renjifo-Ibáñez, Camila
Santos-Vega, Mauricio
Sasa, Mahmood
author_facet Bravo-Vega, Carlos A.
Cordovez, Juan M.
Renjifo-Ibáñez, Camila
Santos-Vega, Mauricio
Sasa, Mahmood
author_sort Bravo-Vega, Carlos A.
collection PubMed
description BACKGROUND: Snakebite envenoming is a neglected public health challenge that affects mostly economically deprived communities who inhabit tropical regions. In these regions, snakebite incidence data is not always reliable, and access to health care is scare and heterogeneous. Thus, addressing the problem of snakebite effectively requires an understanding of how spatial heterogeneity in snakebite is associated with human demographics and snakes’ distribution. Here, we use a mathematical model to address the determinants of spatial heterogeneity in snakebite and we estimate snakebite incidence in a tropical country such as Costa Rica. METHODS AND FINDINGS: We combined a mathematical model that follows the law of mass action, where the incidence is proportional to the exposed human population and the venomous snake population, with a spatiotemporal dataset of snakebite incidence (Data from year 1990 to 2007 for 193 districts) in Costa Rica. This country harbors one of the most dangerous venomous snakes, which is the Terciopelo (Bothrops asper, Garman, 1884). We estimated B. asper distribution using a maximum entropy algorithm, and its abundance was estimated based on field data. Then, the model was adjusted to the data using a lineal regression with the reported incidence. We found a significant positive correlation (R(2) = 0.66, p-value < 0.01) between our estimation and the reported incidence, suggesting the model has a good performance in estimating snakebite incidence. CONCLUSIONS: Our model underscores the importance of the synergistic effect of exposed population size and snake abundance on snakebite incidence. By combining information from venomous snakes’ natural history with census data from rural populations, we were able to estimate snakebite incidence in Costa Rica. The model was able to fit the incidence data at fine administrative scale (district level), which is fundamental for the implementation and planning of management strategies oriented to reduce snakebite burden.
format Online
Article
Text
id pubmed-6907855
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-69078552019-12-27 Estimating snakebite incidence from mathematical models: A test in Costa Rica Bravo-Vega, Carlos A. Cordovez, Juan M. Renjifo-Ibáñez, Camila Santos-Vega, Mauricio Sasa, Mahmood PLoS Negl Trop Dis Research Article BACKGROUND: Snakebite envenoming is a neglected public health challenge that affects mostly economically deprived communities who inhabit tropical regions. In these regions, snakebite incidence data is not always reliable, and access to health care is scare and heterogeneous. Thus, addressing the problem of snakebite effectively requires an understanding of how spatial heterogeneity in snakebite is associated with human demographics and snakes’ distribution. Here, we use a mathematical model to address the determinants of spatial heterogeneity in snakebite and we estimate snakebite incidence in a tropical country such as Costa Rica. METHODS AND FINDINGS: We combined a mathematical model that follows the law of mass action, where the incidence is proportional to the exposed human population and the venomous snake population, with a spatiotemporal dataset of snakebite incidence (Data from year 1990 to 2007 for 193 districts) in Costa Rica. This country harbors one of the most dangerous venomous snakes, which is the Terciopelo (Bothrops asper, Garman, 1884). We estimated B. asper distribution using a maximum entropy algorithm, and its abundance was estimated based on field data. Then, the model was adjusted to the data using a lineal regression with the reported incidence. We found a significant positive correlation (R(2) = 0.66, p-value < 0.01) between our estimation and the reported incidence, suggesting the model has a good performance in estimating snakebite incidence. CONCLUSIONS: Our model underscores the importance of the synergistic effect of exposed population size and snake abundance on snakebite incidence. By combining information from venomous snakes’ natural history with census data from rural populations, we were able to estimate snakebite incidence in Costa Rica. The model was able to fit the incidence data at fine administrative scale (district level), which is fundamental for the implementation and planning of management strategies oriented to reduce snakebite burden. Public Library of Science 2019-12-02 /pmc/articles/PMC6907855/ /pubmed/31790407 http://dx.doi.org/10.1371/journal.pntd.0007914 Text en © 2019 Bravo-Vega et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Bravo-Vega, Carlos A.
Cordovez, Juan M.
Renjifo-Ibáñez, Camila
Santos-Vega, Mauricio
Sasa, Mahmood
Estimating snakebite incidence from mathematical models: A test in Costa Rica
title Estimating snakebite incidence from mathematical models: A test in Costa Rica
title_full Estimating snakebite incidence from mathematical models: A test in Costa Rica
title_fullStr Estimating snakebite incidence from mathematical models: A test in Costa Rica
title_full_unstemmed Estimating snakebite incidence from mathematical models: A test in Costa Rica
title_short Estimating snakebite incidence from mathematical models: A test in Costa Rica
title_sort estimating snakebite incidence from mathematical models: a test in costa rica
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6907855/
https://www.ncbi.nlm.nih.gov/pubmed/31790407
http://dx.doi.org/10.1371/journal.pntd.0007914
work_keys_str_mv AT bravovegacarlosa estimatingsnakebiteincidencefrommathematicalmodelsatestincostarica
AT cordovezjuanm estimatingsnakebiteincidencefrommathematicalmodelsatestincostarica
AT renjifoibanezcamila estimatingsnakebiteincidencefrommathematicalmodelsatestincostarica
AT santosvegamauricio estimatingsnakebiteincidencefrommathematicalmodelsatestincostarica
AT sasamahmood estimatingsnakebiteincidencefrommathematicalmodelsatestincostarica