Advanced Modeling of the Evolution of the Epidemiological Outbreak of SARS-CoV-2 Pandemic

The various epidemics that health systems periodically suffers require having valid and detailed information on its evolution and predictions in the short, medium and long term in real time to allow the health system to organize itself in advance to be able to address the health and sanitary problem that this entails.The objectives of this proposal are: to study the usefulness of the health system's information and data storage system as a source for quickly and efficiently obtaining data necessary for modeling an epidemiological outbreak; its modeling in order to predict its evolution and the presentation of results to help in decision making. The investigatorswill rely on the experience obtained so far during the Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, to define semi-automatic and flexible criteria for searching, extracting, cleaning and aggregating data. Predictions of incidence, number of hospital and ICU admissions, and number of deaths will be made at the Basque Country level.Within the analysis of temporal data, especially in the context of the pandemic, it is essential to have robust tools that allow accurate predictions. In this study, the investigators employed P-splines based on the negative binomial distribution to predict pandemic-related positive cases, hospital admissions, and ICU admissions.