Evidence map›Paper›PMID 31790407›Full record

ArticlePLoS neglected tropical diseases2019

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

Carlos A Bravo-Vega, Juan M Cordovez, Camila Renjifo-Ibáñez, Mauricio Santos-Vega, Mahmood Sasa

Open access · goldAbstract read
In one paragraph

Article in PLoS neglected tropical diseases, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
3.8field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 40 citations in OpenAlex.

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  7. Identification of Risk Areas forAnimals : an open access journal from MDPI · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 2 countries.

Carlos A Bravo-VegaResearch Group in Mathematical and Computational Biology (BIOMAC), Department of biomedical engineering, University of los Andes, Bogotá, Colombia.ORCID 0000-0002-5588-7396
Juan M CordovezResearch Group in Mathematical and Computational Biology (BIOMAC), Department of biomedical engineering, University of los Andes, Bogotá, Colombia.
Camila Renjifo-IbáñezCorporación colombiana de investigación agropecuaria, AGROSAVIA, Bogotá, Colombia.
Mauricio Santos-VegaResearch Group in Mathematical and Computational Biology (BIOMAC), Department of biomedical engineering, University of los Andes, Bogotá, Colombia.
Mahmood SasaInstituto Clodomiro Picado and Escuela de Biología, Universidad de Costa Rica, San José, Costa Rica.
Universidad de Los Andes · COColombian Corporation for Agricultural Research - AGROSAVIA · COUniversidad de Costa Rica · CR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSnakebite 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

findingsWe 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 (R2 = 0.66, p-value < 0.01) between our estimation and the reported incidence, suggesting the model has a good performance in estimating snakebite incidence.

conclusionsOur 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.

Indexed as

Models, TheoreticalTopography, MedicalAnimalsBothropsCosta RicaHumansIncidenceSnake BitesTropical Climate

Identifiers

PMID31790407
PMCPMC6907855
OpenAlexW2990077119

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.