  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.
