Forecasting the abundance of disease vectors with deep learning

Detalhes bibliográficos
Autor(a) principal: Ceia-Hasse, Ana
Data de Publicação: 2023
Outros Autores: Sousa, Carla A., Gouveia, Bruna R., Capinha, César
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: http://hdl.handle.net/10362/164974
Resumo: Funding Information: We thank the personnel of the Regional Health Direction of the Autonomous Region of Madeira (Direção Regional da Saúde) involved in egg collection and analysis and the Portuguese Institute for Sea and Atmosphere (Instituto Português do Mar e da Atmosfera), namely Dr. Victor Prior, for providing the weather data. ACH, CAS and CC were supported by Portuguese National Funds through Fundação para a Ciência e a Tecnologia (ACH and CAS: PTDC/SAU-PUB/30089/2017 and GHTM-UID/Multi/04413/2013; CC: CEECIND/02037/2017, UIDB/00295/2020 and UIDP/00295/2020). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Funding Information: We thank the personnel of the Regional Health Direction of the Autonomous Region of Madeira (Direção Regional da Saúde) involved in egg collection and analysis and the Portuguese Institute for Sea and Atmosphere (Instituto Português do Mar e da Atmosfera), namely Dr. Victor Prior, for providing the weather data. ACH, CAS and CC were supported by Portuguese National Funds through Fundação para a Ciência e a Tecnologia (ACH and CAS: PTDC/SAU-PUB/30089/2017 and GHTM-UID/Multi/04413/2013; CC: CEECIND/02037/2017, UIDB/00295/2020 and UIDP/00295/2020). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Publisher Copyright: © 2023 The Authors
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spelling Forecasting the abundance of disease vectors with deep learningDengueForecastMachine learningMosquitoTime series classificationRA0421 Public health. Hygiene. Preventive MedicineQA75 Electronic computers. Computer scienceEcology, Evolution, Behavior and SystematicsEcologyModelling and SimulationEcological ModellingComputer Science ApplicationsComputational Theory and MathematicsApplied MathematicsInfectious DiseasesSDG 3 - Good Health and Well-beingSDG 9 - Industry, Innovation, and InfrastructureFunding Information: We thank the personnel of the Regional Health Direction of the Autonomous Region of Madeira (Direção Regional da Saúde) involved in egg collection and analysis and the Portuguese Institute for Sea and Atmosphere (Instituto Português do Mar e da Atmosfera), namely Dr. Victor Prior, for providing the weather data. ACH, CAS and CC were supported by Portuguese National Funds through Fundação para a Ciência e a Tecnologia (ACH and CAS: PTDC/SAU-PUB/30089/2017 and GHTM-UID/Multi/04413/2013; CC: CEECIND/02037/2017, UIDB/00295/2020 and UIDP/00295/2020). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Funding Information: We thank the personnel of the Regional Health Direction of the Autonomous Region of Madeira (Direção Regional da Saúde) involved in egg collection and analysis and the Portuguese Institute for Sea and Atmosphere (Instituto Português do Mar e da Atmosfera), namely Dr. Victor Prior, for providing the weather data. ACH, CAS and CC were supported by Portuguese National Funds through Fundação para a Ciência e a Tecnologia (ACH and CAS: PTDC/SAU-PUB/30089/2017 and GHTM-UID/Multi/04413/2013; CC: CEECIND/02037/2017, UIDB/00295/2020 and UIDP/00295/2020). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Publisher Copyright: © 2023 The AuthorsArboviral diseases such as dengue, Zika, chikungunya or yellow fever are a worldwide concern. The abundance of vector species plays a key role in the emergence of outbreaks of these diseases, so forecasting these numbers is fundamental in preventive risk assessment. Here we describe and demonstrate a novel approach that uses state-of-the-art deep learning algorithms to forecast disease vector abundances. Unlike classical statistical and machine learning methods, deep learning models use time series data directly as predictors and identify the features that are most relevant from a predictive perspective. We demonstrate for the first time the application of this approach to predict short-term temporal trends in the number of Aedes aegypti mosquito eggs across Madeira Island for the period 2013 to 2019. Specifically, we apply the deep learning models to predict whether, in the following week, the number of Ae. aegypti eggs will remain unchanged, or whether it will increase or decrease, considering different percentages of change. We obtained high predictive performance for all years considered (mean AUC = 0.92 ± 0.05 SD). Our approach performed better than classical machine learning methods. We also found that the preceding numbers of eggs is a highly informative predictor of future trends. Linking our approach to disease transmission or importation models will contribute to operational, early warning systems of arboviral disease risk.Instituto de Higiene e Medicina Tropical (IHMT)Global Health and Tropical Medicine (GHTM)Vector borne diseases and pathogens (VBD)RUNCeia-Hasse, AnaSousa, Carla A.Gouveia, Bruna R.Capinha, César2024-03-14T23:59:07Z2023-122023-12-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article10application/pdfhttp://hdl.handle.net/10362/164974eng1574-9541PURE: 83027956https://doi.org/10.1016/j.ecoinf.2023.102272info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T18:19:36Zoai:run.unl.pt:10362/164974Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:50:27.825432Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Forecasting the abundance of disease vectors with deep learning
title Forecasting the abundance of disease vectors with deep learning
spellingShingle Forecasting the abundance of disease vectors with deep learning
Ceia-Hasse, Ana
Dengue
Forecast
Machine learning
Mosquito
Time series classification
RA0421 Public health. Hygiene. Preventive Medicine
QA75 Electronic computers. Computer science
Ecology, Evolution, Behavior and Systematics
Ecology
Modelling and Simulation
Ecological Modelling
Computer Science Applications
Computational Theory and Mathematics
Applied Mathematics
Infectious Diseases
SDG 3 - Good Health and Well-being
SDG 9 - Industry, Innovation, and Infrastructure
title_short Forecasting the abundance of disease vectors with deep learning
title_full Forecasting the abundance of disease vectors with deep learning
title_fullStr Forecasting the abundance of disease vectors with deep learning
title_full_unstemmed Forecasting the abundance of disease vectors with deep learning
title_sort Forecasting the abundance of disease vectors with deep learning
author Ceia-Hasse, Ana
author_facet Ceia-Hasse, Ana
Sousa, Carla A.
Gouveia, Bruna R.
Capinha, César
author_role author
author2 Sousa, Carla A.
Gouveia, Bruna R.
Capinha, César
author2_role author
author
author
dc.contributor.none.fl_str_mv Instituto de Higiene e Medicina Tropical (IHMT)
Global Health and Tropical Medicine (GHTM)
Vector borne diseases and pathogens (VBD)
RUN
dc.contributor.author.fl_str_mv Ceia-Hasse, Ana
Sousa, Carla A.
Gouveia, Bruna R.
Capinha, César
dc.subject.por.fl_str_mv Dengue
Forecast
Machine learning
Mosquito
Time series classification
RA0421 Public health. Hygiene. Preventive Medicine
QA75 Electronic computers. Computer science
Ecology, Evolution, Behavior and Systematics
Ecology
Modelling and Simulation
Ecological Modelling
Computer Science Applications
Computational Theory and Mathematics
Applied Mathematics
Infectious Diseases
SDG 3 - Good Health and Well-being
SDG 9 - Industry, Innovation, and Infrastructure
topic Dengue
Forecast
Machine learning
Mosquito
Time series classification
RA0421 Public health. Hygiene. Preventive Medicine
QA75 Electronic computers. Computer science
Ecology, Evolution, Behavior and Systematics
Ecology
Modelling and Simulation
Ecological Modelling
Computer Science Applications
Computational Theory and Mathematics
Applied Mathematics
Infectious Diseases
SDG 3 - Good Health and Well-being
SDG 9 - Industry, Innovation, and Infrastructure
description Funding Information: We thank the personnel of the Regional Health Direction of the Autonomous Region of Madeira (Direção Regional da Saúde) involved in egg collection and analysis and the Portuguese Institute for Sea and Atmosphere (Instituto Português do Mar e da Atmosfera), namely Dr. Victor Prior, for providing the weather data. ACH, CAS and CC were supported by Portuguese National Funds through Fundação para a Ciência e a Tecnologia (ACH and CAS: PTDC/SAU-PUB/30089/2017 and GHTM-UID/Multi/04413/2013; CC: CEECIND/02037/2017, UIDB/00295/2020 and UIDP/00295/2020). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Funding Information: We thank the personnel of the Regional Health Direction of the Autonomous Region of Madeira (Direção Regional da Saúde) involved in egg collection and analysis and the Portuguese Institute for Sea and Atmosphere (Instituto Português do Mar e da Atmosfera), namely Dr. Victor Prior, for providing the weather data. ACH, CAS and CC were supported by Portuguese National Funds through Fundação para a Ciência e a Tecnologia (ACH and CAS: PTDC/SAU-PUB/30089/2017 and GHTM-UID/Multi/04413/2013; CC: CEECIND/02037/2017, UIDB/00295/2020 and UIDP/00295/2020). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Publisher Copyright: © 2023 The Authors
publishDate 2023
dc.date.none.fl_str_mv 2023-12
2023-12-01T00:00:00Z
2024-03-14T23:59:07Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/164974
url http://hdl.handle.net/10362/164974
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1574-9541
PURE: 83027956
https://doi.org/10.1016/j.ecoinf.2023.102272
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reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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