Aedes-AI: Neural network models of mosquito abundance

Adrienne C. Kinney, Sean Current, Joceline Lega

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish (US)
Article numbere1009467
JournalPLoS computational biology
Issue number11
StatePublished - Nov 2021

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics


Dive into the research topics of 'Aedes-AI: Neural network models of mosquito abundance'. Together they form a unique fingerprint.

Cite this