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.
Highlights
Snakebite envenoming is mainly a disease of rural populations that affects up to 2.7 million people and kills as many as 95,000 worldwide per year [1,2,3,4]
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
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
Summary
Snakebite envenoming is mainly a disease of rural populations that affects up to 2.7 million people and kills as many as 95,000 worldwide per year [1,2,3,4]. The development of an appropriate response to snakebite requires a reasonable estimation of its burden at local and regional scales, and the understanding of the drivers of its spatiotemporal patterns Understanding these elements could contribute to: 1) public health planning by government authorities, 2) optimization of production and distribution of antivenom, 3) the design of control strategies to minimize snakebite incidence, and 4) correct training of the medical staff who will treat envenomed patients [7]. As it happens with other NTDs, epidemiological data of snakebite incidence is not readily available for several countries, especially in Tropical regions [8,9]
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