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

2023-12Journal 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	2025-09-03T10:34:30Z
dc.date.available	2025-09-03T10:34:30Z
dc.date.issued	2023-12
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.	eng
dc.identifier.citation	Ceia-Hasse, A., Sousa, C. A., Gouveia, B. R., & Capinha, C. (2023). "Forecasting the abundance of disease vectors with deep learning". Ecological Informatics 78. 102272
dc.identifier.doi	https://doi.org/10.1016/j.ecoinf.2023.102272
dc.identifier.uri	http://hdl.handle.net/10400.26/58511
dc.language.iso	eng
dc.peerreviewed	n/a
dc.publisher	Elsevier
dc.rights.uri	N/A
dc.subject	Machine learning
dc.subject	Mosquito
dc.subject	Dengue
dc.subject	Forecast
dc.subject	Time series classification
dc.title	Forecasting the abundance of disease vectors with deep learning	por
dc.type	journal article
dspace.entity.type	Publication
oaire.citation.startPage	102272
oaire.citation.title	Ecological Informatics
oaire.citation.volume	78
oaire.version	http://purl.org/coar/version/c_970fb48d4fbd8a85