通过通过联合建模,将有关物种的信息纳入稀有记录的疾病载体的分布模型,以改进它们的分布模型
Stacy Mowry1, Sean Moore1, Nicole L Achee1
1University of Notre Dame.
概括
准确的向量分布建模对于了解疾病风险至关重要. 联合建模相关的载体物种和疾病可以改善对记录不充分的载体的预测,减少不确定性并提高准确性.
科学领域:
- 生态生态学 生态生态学
- 流行病学 流行病学
- 生物统计学 生物统计学
背景情况:
- 准确的载体分布映射对于评估载体传播疾病风险至关重要.
- 物种分布模型 (SDM) 对鲜为记录的载体表现不佳.
- 全球变化需要改进分布预测工具,以应对新出现的载体威胁.
研究的目的:
- 开发和评估一个层次化的建模方法,以改善稀有文献载体物种的分布模型.
- 利用相关载体物种和相关疾病的相关数据来提高SDM的准确性.
- 评估结合相关物种和疾病数据对载体分布模型性能的影响.
主要方法:
- 将一个层次模型与来自多个载体物种及其相关人类疾病的数据相结合.
- 评估了12个模型,数据汇集策略和包括疾病数据的不同.
- 使用模拟数据集和实证数据对佛罗里达州的和传播疾病进行模型性能评估.
主要成果:
- 通过将相关载体物种的数据纳入,通过减轻过度拟合,显著降低了不确定性,并提高了鲜为人知的载体的准确性.
- 当已经包括了其他载体物种的数据时,疾病数据提供了边际改善.
- 疾病数据显著提高了模型的准确性,并在其他载体的数据无法获得时减少了过拟合和不确定性.
结论:
- 来自多个载体物种的相关数据的联合建模是改善数据有限物种分布模型的有价值的策略.
- 这种方法提高了准确性,减少了预测载体分布的不确定性,这对于疾病风险评估至关重要.
- 这些发现强调了利用相关物种数据的实用性,在这种信息稀缺时,疾病数据作为一种有价值的替代方案.
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