COVID-19 incidence estimates and forecast by metaprediction for the Comunidad de Madrid *
- Resource Type
- Conference
- Authors
- Martinez, Aymar Cublier; Munoz Organero, Mario; Morina, David; Barroso, Diana Gomez; Singh, David E.
- Source
- 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Bioinformatics and Biomedicine (BIBM), 2023 IEEE International Conference on. :3362-3369 Dec, 2023
- Subject
- Bioengineering
Computing and Processing
Engineering Profession
Robotics and Control Systems
Signal Processing and Analysis
COVID-19
Pandemics
Soft sensors
Computational modeling
Time series analysis
Europe
Predictive models
SARS-CoV-2
agent-based simulation
short-term forecast
machine learning model
meta-model
- Language
- ISSN
- 2156-1133
Epidemiological mathematical models have been proved crucial in supporting the decision-making of the health authorities during the COVID-19 pandemic. In this context, this work presents two contributions. The first one is a methodology to integrate different data sources into a single time series that provides realistic COVID-19 incidence rates considering both the reported and unreported cases in Spain and Comunidad de Madrid. The second contribution is a novel ensemble forecast model that uses as input the predictions of three different COVID-19 forecasts models. These approaches have been used to provide forecast predictions in the scope of PredCov project, supporting both the Spanish and the European Union -via the European Centre for Disease Prevention and Control-health authorities. The output generated by the ensemble model provides a combined -and more accurate-prediction of the COVID-19 incidence. This work includes a description of both contributions and discusses the results provided by them.