Eyre Robert W, House Thomas, Xavier Gómez-Olivé F, Griffiths Frances E
Spectra Analytics, 70 Gracechurch Street, London, EC3V 0HR, UK.
Department of Mathematics, University of Manchester, Oxford Road, Manchester, M13 9PL, UK.
BMC Public Health. 2021 May 17;21(1):935. doi: 10.1186/s12889-021-10938-y.
Achieving food security remains a key challenge for public policy throughout the world. As such, understanding the determinants of food insecurity and the causal relationships between them is an important scientific question. We aim to construct a Bayesian belief network model of food security in rural South Africa to act as a tool for decision support in the design of interventions.
Here, we use data from the Agincourt Health and Socio-demographic Surveillance System (HDSS) study area, which is close to the Mozambique border in a low-income region of South Africa, together with Bayesian belief network (BBN) methodology to address this question.
We find that a combination of expert elicitation and learning from data produces the most credible set of causal relationships, as well as the greatest predictive performance with 10-fold cross validation resulting in a Briers score 0.0846, information reward of 0.5590, and Bayesian information reward of 0.0057. We report the resulting model as a directed acyclic graph (DAG) that can be used to model the expected effects of complex interventions to improve food security. Applications to sensitivity analyses and interventional simulations show ways the model can be applied as tool for decision support for human experts in deciding on interventions.
The resulting models can form the basis of the iterative generation of a robust causal model of household food security in the Agincourt HDSS study area and in other similar populations.
实现粮食安全仍然是全球公共政策面临的一项关键挑战。因此，了解粮食不安全的决定因素及其之间的因果关系是一个重要的科学问题。我们旨在构建南非农村地区粮食安全的贝叶斯信念网络模型，作为设计干预措施时决策支持的工具。
在此，我们使用来自阿金库尔健康与社会人口监测系统（HDSS）研究区域的数据，该区域位于南非低收入地区，靠近莫桑比克边境，并结合贝叶斯信念网络（BBN）方法来解决这个问题。
我们发现，专家启发和数据学习相结合产生了最可信的因果关系集，以及最佳预测性能，10倍交叉验证的布里尔分数为0.0846，信息奖励为0.5590，贝叶斯信息奖励为0.0057。我们将所得模型报告为有向无环图（DAG），可用于对改善粮食安全的复杂干预措施的预期效果进行建模。敏感性分析和干预模拟的应用展示了该模型可作为工具，为人类专家在决定干预措施时提供决策支持。
所得模型可构成在阿金库尔HDSS研究区域及其他类似人群中迭代生成稳健的家庭粮食安全因果模型的基础。
