Aedes-AI: Neural Network Models of Mosquito Abundance

• URL: http://arxiv.org/abs/2104.10771v1
• Date: Wed, 21 Apr 2021 21:28:03 GMT
• Title: Aedes-AI: Neural Network Models of Mosquito Abundance
• Authors: Adrienne C. Kinney, Sean Current, Joceline Lega
• Abstract summary: We develop a feed-forward neural network, a short-term memory gated neural network, and a recurrent unit. We evaluate the networks in their ability to replicate recurrent features of mosquito populations by a mechanistic model. We conclude with an outlook on how such equation-free models may facilitate vector control or estimation of disease risk at arbitrary spatial scales.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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