Estimating snakebite incidence from mathematical models: A test in Costa Rica
Estimating snakebite incidence from mathematical models: A test in Costa Rica
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DOI：
10.1371/journal.pntd.0007914
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发表时间：
2019-12-01
影响因子：
3.8
通讯作者：
Sasa, Mahmood
中科院分区：
文献类型：
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作者：
Bravo-Vega, Carlos A.;Cordovez, Juan M.;Sasa, Mahmood
背景毒蛇咬伤是一个被忽视的公共卫生挑战，主要影响居住在热带地区的经济贫困社区。在这些地区，蛇咬伤的发病率数据并不总是可靠的，获得卫生保健的机会很少，而且种类繁多。因此，有效地解决蛇咬伤问题需要了解蛇咬伤的空间异质性如何与人口统计学和蛇的分布相关联。在这里，我们使用一个数学模型来解决蛇咬伤的空间异质性的决定因素，并估计像哥斯达黎加这样的热带国家的蛇咬伤发病率。方法和发现我们结合了遵循质量作用定律的数学模型，其中发病率与暴露的人类种群和毒蛇种群成正比，与哥斯达黎加蛇咬伤发病率的时空数据集(193个地区的数据从1990年到2007年)相结合。这个国家有一种最危险的毒蛇，那就是Terciopelo(Bothrops Asper，Garman，1884)。我们使用最大熵算法估计了B.Asper分布，并根据野外数据估计了它的丰度。然后，通过与报告的发病率进行线性回归，将模型调整到数据。我们发现我们的估计与报告的发病率之间存在显著的正相关(R-2=0.66，p值<；0.01)，表明该模型在估计蛇咬伤发生率方面具有良好的性能。结论我们的模型强调了暴露人口数量和蛇的数量对蛇咬伤发生率的协同作用的重要性。通过结合毒蛇的自然历史信息和农村人口的人口普查数据，我们能够估计哥斯达黎加的蛇咬伤发生率。该模型能够较好地拟合精细行政级别(区级)的发病数据，为实施和规划以减轻蛇咬负担为导向的管理策略奠定了基础。
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 (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.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.