  We present artificial neural networks as a feasible replacement for a
mechanistic model of mosquito abundance. We develop a feed-forward neural
network, a long short-term memory recurrent neural network, and a gated
recurrent unit network. We evaluate the networks in their ability to replicate
the spatiotemporal features of mosquito populations predicted by the
mechanistic model, and discuss how augmenting the training data with both
actual and artificially created time series affects model performance. We
conclude with an outlook on how such equation-free models may facilitate vector
control or the estimation of disease risk at arbitrary spatial scales.
