About this project¶

We are a multidisciplinary team of researchers from different areas, with the aim to develop better models to understand the spread of the novel SARS-CoV-2 (COVID-19) coronavirus. This project was motivated by a previous study where we highlighted some of the limitations of current mathematical models in the context of COVID-19:

The main goal of this project is to develop a probabilistic model that can accurately estimate the true levels of infections (or the seroprevalence) of the population, using only data from the registered deaths caused by COVID-19. The model can be adapted as well to predict the evolution of the virus.

The principal contributors of this project are:

• Marcos Matabuena: B.Sc. in Mathematics, M.Sc. in Statistics (University of Santiago de Compostela, USC). Researcher at the Clinical Epidemiological Unit, Hospital Clínico Universitario (Santiago de Compostela) and at the Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS, USC).
• Carlos Meijide García: Graduate student in Mathematics & Physics, USC.
• Pablo Rodríguez-Mier: B.Sc. in Computer Science, M.Sc. in Artificial Intelligence, PhD in Computer Science & AI (CiTIUS, USC). Postdoctoral researcher in Bioinformatics & Systems Biology at INRAE Toxalim, Toulouse, France.
• Victor Leboran: B.Sc. in Physics, PhD in Computer Vision (CiTIUS, USC). Researcher at Computational Neuroscience Laboratory (LANCON, USC) 2005-2010. Researcher at Center for Research in Biological Chemistry and Molecular Materials (CIQUS) (2010-Present).

A detailed description about the model can be found in the following preprint:

Contributions & Acknowledgements¶

Translations¶

• French
  • Loic Le Sciellour