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Modelling the seasonal dynamics of Aedes albopictus populations using a spatio-temporal stacked machine learning

Daniele Da Re1,2, Giovanni Marini3,4, Carmelo Bonannella5,6

  • 1Center Agriculture Food Environment, University of Trento, San Michele all'Adige, Italy. daniele.dare@fmach.it.

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Summary
This summary is machine-generated.

This study forecasts Aedes albopictus mosquito egg abundance using stacked machine learning. The model reveals seasonal egg-laying patterns and predicts abundance in new areas, aiding public health efforts.

Keywords:
ArthropodForecastInvasive speciesMosquitoPopulation dynamicsTime-series.

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Area of Science:

  • Ecology
  • Epidemiology
  • Machine Learning

Background:

  • Species phenology varies temporally and spatially, often modeled using correlative approaches linking species presence/abundance to abiotic factors.
  • Model algorithm choice significantly impacts outcomes, leading to inter-model variability.
  • Ensemble modeling, particularly stacked generalization, offers robust predictions by combining multiple models.

Purpose of the Study:

  • To forecast the weekly median number of Aedes albopictus mosquito eggs using a stacked machine learning model.
  • To analyze the 12-year seasonal egg-laying dynamics of Ae. albopictus.
  • To generate spatio-temporal forecasts for regions lacking conventional monitoring.

Main Methods:

  • Utilized a dataset of Ae. albopictus egg abundance from ovitraps and environmental predictors.
  • Applied a stacked machine learning model, incorporating predictions from multiple base learners into a meta-learner.
  • The meta-learner assimilated base learner predictions to generate a final forecast.

Main Results:

  • Successfully forecasted weekly median Ae. albopictus egg abundance.
  • Uncovered the species' 12-year seasonal egg-laying dynamics.
  • Generated spatio-temporal explicit forecasts for potential monitoring gaps.

Conclusions:

  • Established a robust methodological foundation for forecasting Ae. albopictus spatio-temporal abundance.
  • The stacked modeling framework is flexible and adaptable for public health applications.
  • Provides valuable insights for managing mosquito populations and related disease risks.