Disaggregation modelling is a method of predicting disease risk at high resolution using aggregated response data. High resolution disease mapping is an important public health tool to aid the optimisation of resources, and is commonly used in assisting responses to diseases such as malaria. Current disaggregation regression methods are slow, inflexible, and do not easily allow non-linear terms. Neural networks may offer a solution to the limitations of current disaggregation methods. This project aimed to design a neural network which mimics the behaviour of disaggregation, then benchmark it against current methods for accuracy, flexibility and speed. Cross-validation and nested cross-validation tested neural networks against traditional disaggregation for accuracy and execution speed was measured. Neural networks did not improve on the accuracy of current disaggregation methods, although did see an improvement in execution time. The neural network models are more flexible and offer potential for further improvements on all metrics. The R package 'Kedis' (Keras-Disaggregation) is introduced as a user-friendly method of implementing neural network disaggregation models.

翻译：分解建模是一种利用聚合响应数据以高分辨率预测疾病风险的方法。高分辨率疾病制图是优化资源配置的重要公共卫生工具，常用于辅助应对疟疾等疾病。现有的分解回归方法存在速度慢、灵活性差、不易引入非线性项等局限性。神经网络可能为现有分解方法的局限性提供解决方案。本研究旨在设计一种模拟分解行为的神经网络，并在准确性、灵活性和速度方面将其与现有方法进行基准比较。通过交叉验证和嵌套交叉验证，测试神经网络与传统分解方法的准确性及执行速度。结果表明，神经网络并未在准确性上超越现有分解方法，但在执行时间上有所改善。神经网络模型具有更高的灵活性，并有望在所有指标上实现进一步改进。本文介绍了R语言包'Kedis'（Keras-分解），作为实现神经网络分解模型的用户友好型工具。