Forecasting COVID-19 spreading through an ensemble of classical and machine learning models: Spain's case study.

ABSTRACT: In this work the applicability of an ensemble of population and machine learning models to predict the evolution of the COVID-19 pandemic in Spain is evaluated, relying solely on public datasets. Firstly, using only incidence data, we trained machine learning models and adjusted classical ODE-based population models, especially suited to capture long term trends. As a novel approach, we then made an ensemble of these two families of models in order to obtain a more robust and accurate prediction. We then proceed to improve machine learning models by adding more input features: vaccination, human mobility and weather conditions. However, these improvements did not translate to the overall ensemble, as the different model families had also different prediction patterns. Additionally, machine

SUBMITTER: Heredia Cacha I 

PROVIDER: S-EPMC10127188 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature