Objective: This article introduces Bayesian spatial–temporal modeling for social and health science research. We use the World Bank’s World Development Indicators data on youth unemployment and HIV risk in Africa to illustrate the utility of the Bayesian paradigm in modeling space–time changes in outcomes. Method: Data on adolescents and young adults were collected in 36 African countries from 1991 to 2014. We examined associations between HIV risk and youth unemployment rates using 16 Bayesian Poisson models incorporating spatial and temporal autocorrelations. Results: The best fit to the data was the model with spatially uncorrelated heterogeneity, temporally correlated random-walk autocorrelation, and spatial–temporal interaction. HIV risk factors are spatially uncorrelated across 36 countries but temporally correlated (i.e., country and time interaction) over the data collection period. The relationship between HIV risk and unemployment rate is statistically nonsignificant because of large spatial–temporal variations. Conclusions: This article demonstrates the capacity of Bayesian modeling to incorporate spatial (neighborhood) and temporal (historical) information to reflect not only the influences of space and time but also their interactions on the phenomenon of interest. The Bayesian framework holds great promise for improving the dynamic targeting of interventions and strategies to achieve desired outcomes.

中文翻译：

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时空动力学的贝叶斯建模：在社会和健康科学研究中的实践证明

目的：本文介绍了用于社会和健康科学研究的贝叶斯时空建模。我们使用世界银行关于非洲青年失业和艾滋病毒风险的世界发展指标数据来说明贝叶斯范式在模拟时空变化中的效用。方法：从1991年至2014年，在36个非洲国家中收集了青少年和年轻人的数据。我们使用16种结合时空自相关的贝叶斯泊松模型，研究了艾滋病毒风险与青年失业率之间的关联。结果：最适合数据的是具有空间无关的异质性，时间相关的随机游走自相关以及时空相互作用的模型。HIV危险因素在36个国家/地区在空间上不相关，但在时间上相关（即，国家和时间的互动）。由于时空变化较大，艾滋病毒风险与失业率之间的关系在统计学上不显着。结论：本文证明了贝叶斯建模融合空间（邻域）和时间（历史）信息的能力，不仅可以反映时空的影响，还可以反映它们对感兴趣现象的相互作用。贝叶斯框架对改善动态干预措施和策略以实现预期结果具有广阔的前景。本文展示了贝叶斯建模融合空间（邻域）和时间（历史）信息的能力，不仅可以反映时空的影响，还可以反映它们对感兴趣现象的相互作用。贝叶斯框架对改善动态干预措施和策略以实现预期结果具有广阔的前景。本文展示了贝叶斯建模融合空间（邻域）和时间（历史）信息的能力，不仅可以反映时空的影响，还可以反映它们对感兴趣现象的相互作用。贝叶斯框架对改善动态干预措施和策略以实现预期结果具有广阔的前景。