Forecasting the abundance of disease vectors with deep learning

Ceia-Hasse, Ana; Sousa, Carla A.; Gouveia, Bruna R.; Capinha, César

Publication

Forecasting the abundance of disease vectors with deep learning

2023Journal article

dc.contributor.author	Ceia-Hasse, Ana
dc.contributor.author	Sousa, Carla A.
dc.contributor.author	Gouveia, Bruna R.
dc.contributor.author	Capinha, César
dc.date.accessioned	2024-06-25T14:23:42Z
dc.date.available	2024-06-25T14:23:42Z
dc.date.issued	2023
dc.description.abstract	Arboviral 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.	pt_PT
dc.description.version	info:eu-repo/semantics/publishedVersion	pt_PT
dc.identifier.citation	Ceia-Hasse, A., Sousa, C. A., Gouveia, B. R. & César Capinha (2023). Forecasting the abundance of disease vectors with deep learning. Ecological Informatics, 78, 102272. https://doi.org/10.1016/j.ecoinf.2023.102272	pt_PT
dc.identifier.doi	10.1016/j.ecoinf.2023.102272	pt_PT
dc.identifier.eissn	1878-0512
dc.identifier.issn	1574-9541
dc.identifier.uri	http://hdl.handle.net/10451/65130
dc.language.iso	eng	pt_PT
dc.peerreviewed	yes	pt_PT
dc.publisher	Elsevier	pt_PT
dc.relation	PTDC/SAU-PUB/30089/2017	pt_PT
dc.relation	GHTM-UID/Multi/04413/2013	pt_PT
dc.relation	CEECIND/02037/2017	pt_PT
dc.relation	UIDB/00295/2020	pt_PT
dc.relation	UIDP/00295/2020	pt_PT
dc.relation.publisherversion	https://www.sciencedirect.com/science/article/pii/S1574954123003011	pt_PT
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	pt_PT
dc.subject	Machine learning	pt_PT
dc.subject	Mosquito	pt_PT
dc.subject	Dengue	pt_PT
dc.subject	Forecast	pt_PT
dc.subject	Time series classification	pt_PT
dc.title	Forecasting the abundance of disease vectors with deep learning	pt_PT
dc.type	journal article
dspace.entity.type	Publication
oaire.citation.startPage	102272	pt_PT
oaire.citation.title	Ecological Informatics	pt_PT
oaire.citation.volume	78	pt_PT
person.familyName	Capinha
person.givenName	César
person.identifier.ciencia-id	7714-2A88-CDE3
person.identifier.orcid	0000-0002-0666-9755
person.identifier.rid	K-6439-2017
person.identifier.scopus-author-id	32867555000
rcaap.rights	openAccess	pt_PT
rcaap.type	article	pt_PT
relation.isAuthorOfPublication	4c666e7e-4ba8-4a41-8064-d26b3b9fc0f8
relation.isAuthorOfPublication.latestForDiscovery	4c666e7e-4ba8-4a41-8064-d26b3b9fc0f8