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Aedes-AI: Neural network models of mosquito abundance
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Journal Article
Aedes-AI: Neural network models of mosquito abundance
2021
Overview
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.
Publisher
Public Library of Science,Public Library of Science (PLoS)
Subject
/ Analysis
/ Animals
/ Computer and Information Sciences
/ Databases, Factual - statistics & numerical data
/ Datasets
/ Disease
/ Eggs
/ Humans
/ Humidity
/ Identification and classification
/ Medicine and Health Sciences
/ Methods
/ Population Dynamics - statistics & numerical data
/ Trends
/ United States - epidemiology
/ Vector Borne Diseases - epidemiology
/ Vector Borne Diseases - transmission
/ Vector Borne Diseases - virology
/ Weather
