Aedes-AI: Neural Network Models of Mosquito Abundance
This work addresses mosquito control and disease risk estimation, but it is incremental as it applies existing neural network methods to a new domain without major innovations.
The authors tackled the problem of modeling mosquito abundance by developing three neural network models (feed-forward, LSTM, and GRU) to replace a mechanistic model, finding that they could replicate spatiotemporal features and discussing how data augmentation affects performance.
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 time series that emphasize specific dynamical behaviors 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.