Aedes-AI: Neural network models of mosquito abundance.
Aedes
/ virology
Animals
Computational Biology
Databases, Factual
/ statistics & numerical data
Humans
Models, Biological
Mosquito Vectors
/ virology
Neural Networks, Computer
Population Dynamics
/ statistics & numerical data
Spatio-Temporal Analysis
Stochastic Processes
Systems Analysis
United States
/ epidemiology
Vector Borne Diseases
/ epidemiology
Weather
Journal
PLoS computational biology
ISSN: 1553-7358
Titre abrégé: PLoS Comput Biol
Pays: United States
ID NLM: 101238922
Informations de publication
Date de publication:
11 2021
11 2021
Historique:
received:
22
04
2021
accepted:
21
09
2021
revised:
03
12
2021
pubmed:
20
11
2021
medline:
18
12
2021
entrez:
19
11
2021
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 34797822
doi: 10.1371/journal.pcbi.1009467
pii: PCOMPBIOL-D-21-00740
pmc: PMC8641871
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e1009467Subventions
Organisme : NIGMS NIH HHS
ID : T32 GM132008
Pays : United States
Déclaration de conflit d'intérêts
The authors have declared that no competing interests exist.
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