Enhanced spatial models for predicting the geographic distributions of tick-borne pathogens
Enhanced spatial models for predicting the geographic distributions of tick-borne pathogens
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DOI：
10.1186/1476-072x-7-15
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发表时间：
2008-04-15
影响因子：
4.9
通讯作者：
Yabsley, Michael J.
中科院分区：
文献类型：
--
作者：
Wimberly, Michael C.;Baer, Adam D.;Yabsley, Michael J.
背景：疾病地图在健康科学中的应用越来越广泛，其应用范围从个体病例的诊断到公共卫生的区域和全球评估。然而，有关新发传染病分布的数据通常只能从有限数量的样本中获得。我们比较了几种用于预测两种蜱传病原体地理分布的空间建模方法：查菲埃利希体（人类单核埃利希体病的病原体）和嗜吞噬细胞无形体（人类粒细胞无形体病的病原体）。这些方法通过结合空间自相关（病原体分布在空间中聚集的趋势）和空间异质性（环境关系在空间上变化的可能性）扩展了基于逻辑回归的环境模型。结果：结合空间自相关或空间异质性导致比标准逻辑回归模型有了实质性的改进。对于查菲埃里希菌来说，它在其地理范围的边界内很常见，并且具有高度聚集的分布，因此仅基于空间自相关的模型是最准确的。对于具有更复杂的人畜共患循环和相对较弱的空间格局的嗜噬细胞食管杆菌，结合空间自相关和与环境变量的空间异质关系的模型是最准确的。结论：空间自相关可以通过将空间格局作为未测量的环境变量和空间过程的代理来提高预测疾病风险模型的准确性。空间异质性还可以通过考虑不同地区独特的生态条件来提高预测准确性，这些条件会影响环境驱动因素对疾病风险的相对重要性。
Background: Disease maps are used increasingly in the health sciences, with applications ranging from the diagnosis of individual cases to regional and global assessments of public health. However, data on the distributions of emerging infectious diseases are often available from only a limited number of samples. We compared several spatial modelling approaches for predicting the geographic distributions of two tick-borne pathogens: Ehrlichia chaffeensis, the causative agent of human monocytotropic ehrlichiosis, and Anaplasma phagocytophilum, the causative agent of human granulocytotropic anaplasmosis. These approaches extended environmental modelling based on logistic regression by incorporating both spatial autocorrelation (the tendency for pathogen distributions to be clustered in space) and spatial heterogeneity (the potential for environmental relationships to vary spatially).Results: Incorporating either spatial autocorrelation or spatial heterogeneity resulted in substantial improvements over the standard logistic regression model. For E. chaffeensis, which was common within the boundaries of its geographic range and had a highly clustered distribution, the model based only on spatial autocorrelation was most accurate. For A. phagocytophilum, which has a more complex zoonotic cycle and a comparatively weak spatial pattern, the model that incorporated both spatial autocorrelation and spatially heterogeneous relationships with environmental variables was most accurate.Conclusion: Spatial autocorrelation can improve the accuracy of predictive disease risk models by incorporating spatial patterns as a proxy for unmeasured environmental variables and spatial processes. Spatial heterogeneity can also improve prediction accuracy by accounting for unique ecological conditions in different regions that affect the relative importance of environmental drivers on disease risk.