贝叶斯地理统计模型，考虑调查点间的空间关联性，量化疾病与影响因素的关系，估算没有调查数据的地方疾病的患病风险，产生高精度的疾病分布地图，指导干预措施用于最需要的地方。在资源有限的情况下，需要整合所有已知调查数据进行分析，不同调查方案、调查时间、诊断方法、人群结构、报告形式等，构成了地理信息数据的异质性问题。实际工作中，有两种异质性对分析结果影响较大：点、面数据异质性和年龄异质性，目前尚没有很好的地理统计模型解决以上问题。本课题拟开发针对以上异质性的地理统计模型，结合其他异质性问题，构建多层次地理统计联合模型。被忽视的热带病（NTD）是亚洲贫困地区最重要的传染病之一，作为应用实例，本课题拟收集亚洲NTD的大量调查数据，应用开发的新模型对数据进行整合分析，更准确的估算该类疾病的高精度分布地图，并结合疾病的动态传播模型，预测干预措施下患病风险的动态变化地图，为疾病防治提供新方法、新知识和新思路。

High-resolution maps depicting the geographical distribution of disease risk assist disease control by delivering control interventions at areas of highest risk. Bayesian geostatistical modeling is the most rigorous inferential approach for estimating the disease risk at areas without observed data by relating survey data with potential predictors and by taking into account the spatial correlation between survey locations. In the absence of single surveys covering the whole study region, survey data are compiled from bibliometric searches, heterogeneous between locations. There are two kinds of heterogeneity that draw special attention, that is point-areal heterogeneity and age heterogeneity. Until now, no practical geostatistical modes are developed yet to properly address them. The current proposed research aim to develop advanced geostatistical joint models to address data heterogeneity, particularly point-areal heterogeneity and age heterogeneity, and analyze data combined from different spatially geo-referenced prevalence surveys. As an application example, the developed models will be used to analyze survey data of neglected tropical diseases in Asia. These diseases were historically overlooked but are the most common chronic infections in the poorest areas in Asia. Data heterogeneity will be addressed and more precise high-resolution risk maps will be produced. In addition, with combination of transmission dynamic models, series of temporal change risk maps under proposed interventions will be predicted that allow estimate the resource input, as well as the time needed to achieve transmission control or elimination. The current proposed research will provide innovative methodology, up-to-date knowledge and new ideas for disease control and prevention.