Wimberly Michael C, Baer Adam D, Yabsley Michael J
Geographic Information Science Center of Excellence, South Dakota State University, Brookings, SD, USA.
Int J Health Geogr. 2008 Apr 15;7:15. doi: 10.1186/1476-072X-7-15.
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).
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.
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.
疾病地图在健康科学中的应用越来越广泛，其应用范围涵盖从个体病例诊断到区域和全球公共卫生评估。然而，关于新发传染病分布的数据往往仅来自有限数量的样本。我们比较了几种空间建模方法，以预测两种蜱传病原体的地理分布：查菲埃立克体（人类单核细胞埃立克体病的病原体）和嗜吞噬细胞无形体（人类粒细胞无形体病的病原体）。这些方法通过纳入空间自相关（病原体分布在空间上聚集的趋势）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建模。
纳入空间自相关或空间异质性均比标准逻辑回归模型有显著改进。对于在其地理范围内常见且分布高度聚集的查菲埃立克体，仅基于空间自相关的模型最为准确。对于具有更复杂人畜共患病循环且空间模式相对较弱的嗜吞噬细胞无形体，纳入空间自相关以及与环境变量的空间异质关系的模型最为准确。
空间自相关通过纳入空间模式作为未测量环境变量和空间过程的替代物，可以提高预测疾病风险模型的准确性。空间异质性也可以通过考虑不同区域影响环境驱动因素对疾病风险相对重要性的独特生态条件来提高预测准确性。
