Modelling the seasonal dynamics of Aedes albopictus populations using a spatio-temporal stacked machine learning model
Abstract Various modelling techniques are available to understand the temporal and spatial variations of the phenology of species. Scientists often rely on correlative models, which establish a statistical relationship between a response variable (such as species abundance or presence-absence) and a...
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2025-01-01
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| author | Daniele Da Re Giovanni Marini Carmelo Bonannella Fabrizio Laurini Mattia Manica Nikoleta Anicic Alessandro Albieri Paola Angelini Daniele Arnoldi Federica Bertola Beniamino Caputo Claudio De Liberato Alessandra della Torre Eleonora Flacio Alessandra Franceschini Francesco Gradoni Përparim Kadriaj Valeria Lencioni Irene Del Lesto Francesco La Russa Riccardo Paolo Lia Fabrizio Montarsi Domenico Otranto Gregory L’Ambert Annapaola Rizzoli Pasquale Rombolà Federico Romiti Gionata Stancher Alessandra Torina Enkelejda Velo Chiara Virgillito Fabiana Zandonai Roberto Rosà |
| author_facet | Daniele Da Re Giovanni Marini Carmelo Bonannella Fabrizio Laurini Mattia Manica Nikoleta Anicic Alessandro Albieri Paola Angelini Daniele Arnoldi Federica Bertola Beniamino Caputo Claudio De Liberato Alessandra della Torre Eleonora Flacio Alessandra Franceschini Francesco Gradoni Përparim Kadriaj Valeria Lencioni Irene Del Lesto Francesco La Russa Riccardo Paolo Lia Fabrizio Montarsi Domenico Otranto Gregory L’Ambert Annapaola Rizzoli Pasquale Rombolà Federico Romiti Gionata Stancher Alessandra Torina Enkelejda Velo Chiara Virgillito Fabiana Zandonai Roberto Rosà |
| author_sort | Daniele Da Re |
| collection | DOAJ |
| description | Abstract Various modelling techniques are available to understand the temporal and spatial variations of the phenology of species. Scientists often rely on correlative models, which establish a statistical relationship between a response variable (such as species abundance or presence-absence) and a set of predominantly abiotic covariates. The choice of the modeling approach, i.e., the algorithm, is itself a significant source of variability, as different algorithms applied to the same dataset can yield disparate outcomes. This inter-model variability has led to the adoption of ensemble modelling techniques, among which stacked generalisation, which has recently demonstrated its capacity to produce robust results. Stacked ensemble modelling incorporates predictions from multiple base learners or models as inputs for a meta-learner. The meta-learner, in turn, assimilates these predictions and generates a final prediction by combining the information from all the base learners. In our study, we utilized a recently published dataset documenting egg abundance observations of Aedes albopictus collected using ovitraps. and a set of environmental predictors to forecast the weekly median number of mosquito eggs using a stacked machine learning model. This approach enabled us to (i) unearth the seasonal egg-laying dynamics of Ae. albopictus for 12 years; (ii) generate spatio-temporal explicit forecasts of mosquito egg abundance in regions not covered by conventional monitoring initiatives. Our work establishes a robust methodological foundation for forecasting the spatio-temporal abundance of Ae. albopictus, offering a flexible framework that can be tailored to meet specific public health needs related to this species. |
| format | Article |
| id | doaj-art-47c5a8e0e06444ffbbf23ec84adda630 |
| institution | Kabale University |
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| language | English |
| publishDate | 2025-01-01 |
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| spelling | doaj-art-47c5a8e0e06444ffbbf23ec84adda6302025-02-02T12:19:31ZengNature PortfolioScientific Reports2045-23222025-01-0115111210.1038/s41598-025-87554-yModelling the seasonal dynamics of Aedes albopictus populations using a spatio-temporal stacked machine learning modelDaniele Da Re0Giovanni Marini1Carmelo Bonannella2Fabrizio Laurini3Mattia Manica4Nikoleta Anicic5Alessandro Albieri6Paola Angelini7Daniele Arnoldi8Federica Bertola9Beniamino Caputo10Claudio De Liberato11Alessandra della Torre12Eleonora Flacio13Alessandra Franceschini14Francesco Gradoni15Përparim Kadriaj16Valeria Lencioni17Irene Del Lesto18Francesco La Russa19Riccardo Paolo Lia20Fabrizio Montarsi21Domenico Otranto22Gregory L’Ambert23Annapaola Rizzoli24Pasquale Rombolà25Federico Romiti26Gionata Stancher27Alessandra Torina28Enkelejda Velo29Chiara Virgillito30Fabiana Zandonai31Roberto Rosà32Center Agriculture Food Environment, University of TrentoResearch and Innovation Centre, Fondazione Edmund MachOpenGeoHub FoundationDepartment of Economics and Management & RoSA, University of ParmaFEM-FBK Joint Research Unit, Epilab-JRUInstitute of Microbiology, University of Applied Sciences and Arts of Southern Switzerland (SUPSI)Centro Agricoltura Ambiente “G.Nicoli” Regional Health Authority of Emilia–RomagnaResearch and Innovation Centre, Fondazione Edmund MachFondazione Museo Civico di RoveretoDipartimento di Sanità Pubblica & Malattie Infettive, Sapienza UniversityIstituto Zooprofilattico Sperimentale del Lazio e della ToscanaDipartimento di Sanità Pubblica & Malattie Infettive, Sapienza UniversityInstitute of Microbiology, University of Applied Sciences and Arts of Southern Switzerland (SUPSI)MUSE - Museo delle Scienze, Research and Museum Collection Office, Climate & Ecology UnitIstituto Zooprofilattico Sperimentale delle VenezieInstitute of Public HealthMUSE - Museo delle Scienze, Research and Museum Collection Office, Climate & Ecology UnitIstituto Zooprofilattico Sperimentale del Lazio e della ToscanaIstituto Zooprofilattico Sperimentale della Sicilia Department of Veterinary Medicine, University of BariIstituto Zooprofilattico Sperimentale delle Venezie Department of Veterinary Medicine, University of BariEID MediterranéeResearch and Innovation Centre, Fondazione Edmund MachIstituto Zooprofilattico Sperimentale del Lazio e della ToscanaIstituto Zooprofilattico Sperimentale del Lazio e della ToscanaFondazione Museo Civico di RoveretoIstituto Zooprofilattico Sperimentale della SiciliaInstitute of Public HealthDipartimento di Sanità Pubblica & Malattie Infettive, Sapienza UniversityFondazione Museo Civico di RoveretoCenter Agriculture Food Environment, University of TrentoAbstract Various modelling techniques are available to understand the temporal and spatial variations of the phenology of species. Scientists often rely on correlative models, which establish a statistical relationship between a response variable (such as species abundance or presence-absence) and a set of predominantly abiotic covariates. The choice of the modeling approach, i.e., the algorithm, is itself a significant source of variability, as different algorithms applied to the same dataset can yield disparate outcomes. This inter-model variability has led to the adoption of ensemble modelling techniques, among which stacked generalisation, which has recently demonstrated its capacity to produce robust results. Stacked ensemble modelling incorporates predictions from multiple base learners or models as inputs for a meta-learner. The meta-learner, in turn, assimilates these predictions and generates a final prediction by combining the information from all the base learners. In our study, we utilized a recently published dataset documenting egg abundance observations of Aedes albopictus collected using ovitraps. and a set of environmental predictors to forecast the weekly median number of mosquito eggs using a stacked machine learning model. This approach enabled us to (i) unearth the seasonal egg-laying dynamics of Ae. albopictus for 12 years; (ii) generate spatio-temporal explicit forecasts of mosquito egg abundance in regions not covered by conventional monitoring initiatives. Our work establishes a robust methodological foundation for forecasting the spatio-temporal abundance of Ae. albopictus, offering a flexible framework that can be tailored to meet specific public health needs related to this species.https://doi.org/10.1038/s41598-025-87554-yArthropodForecastInvasive speciesMosquitoPopulation dynamicsTime-series. |
| spellingShingle | Daniele Da Re Giovanni Marini Carmelo Bonannella Fabrizio Laurini Mattia Manica Nikoleta Anicic Alessandro Albieri Paola Angelini Daniele Arnoldi Federica Bertola Beniamino Caputo Claudio De Liberato Alessandra della Torre Eleonora Flacio Alessandra Franceschini Francesco Gradoni Përparim Kadriaj Valeria Lencioni Irene Del Lesto Francesco La Russa Riccardo Paolo Lia Fabrizio Montarsi Domenico Otranto Gregory L’Ambert Annapaola Rizzoli Pasquale Rombolà Federico Romiti Gionata Stancher Alessandra Torina Enkelejda Velo Chiara Virgillito Fabiana Zandonai Roberto Rosà Modelling the seasonal dynamics of Aedes albopictus populations using a spatio-temporal stacked machine learning model Scientific Reports Arthropod Forecast Invasive species Mosquito Population dynamics Time-series. |
| title | Modelling the seasonal dynamics of Aedes albopictus populations using a spatio-temporal stacked machine learning model |
| title_full | Modelling the seasonal dynamics of Aedes albopictus populations using a spatio-temporal stacked machine learning model |
| title_fullStr | Modelling the seasonal dynamics of Aedes albopictus populations using a spatio-temporal stacked machine learning model |
| title_full_unstemmed | Modelling the seasonal dynamics of Aedes albopictus populations using a spatio-temporal stacked machine learning model |
| title_short | Modelling the seasonal dynamics of Aedes albopictus populations using a spatio-temporal stacked machine learning model |
| title_sort | modelling the seasonal dynamics of aedes albopictus populations using a spatio temporal stacked machine learning model |
| topic | Arthropod Forecast Invasive species Mosquito Population dynamics Time-series. |
| url | https://doi.org/10.1038/s41598-025-87554-y |
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