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Forecasting the numbers of disease vectors with deep learning
Ana Ceia-Hasse, Carla A. Sousa, Bruna R. Gouveia, César Capinha
doi: https://doi.org/10.1101/2022.11.22.517519
Ana Ceia-Hasse
aGlobal Health and Tropical Medicine, Institute of Hygiene and Tropical Medicine, NOVA University of Lisbon, Rua da Junqueira 100, 1349-008 Lisbon, Portugal
bCentre of Geographical Studies, Institute of Geography and Spatial Planning, University of Lisbon, and Associated Laboratory TERRA, Rua Branca Edmée Marques, 1600-276 Lisbon, Portugal
Carla A. Sousa
aGlobal Health and Tropical Medicine, Institute of Hygiene and Tropical Medicine, NOVA University of Lisbon, Rua da Junqueira 100, 1349-008 Lisbon, Portugal
Bruna R. Gouveia
cRegional Health Direction, Government of Madeira, Rua 31 de Janeiro 54-55, 9054-511 Funchal, Portugal
dInteractive Technologies Institute - LARSyS, Caminho da Penteada, 9020-105 Funchal, Portugal
César Capinha
bCentre of Geographical Studies, Institute of Geography and Spatial Planning, University of Lisbon, and Associated Laboratory TERRA, Rua Branca Edmée Marques, 1600-276 Lisbon, Portugal
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Posted November 24, 2022.
Forecasting the numbers of disease vectors with deep learning
Ana Ceia-Hasse, Carla A. Sousa, Bruna R. Gouveia, César Capinha
bioRxiv 2022.11.22.517519; doi: https://doi.org/10.1101/2022.11.22.517519
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