Design. Retrospective observational study. The modeling will be based on the SARS-CoV-2
pandemic that started at the beginning of 2020.

Subjects of the study. Information will be collected on daily incidence data aggregated
by age and sex for: tests performed, positive cases, hospital admissions and ICU
admissions for SARS-CoV-2, hospital discharges and ICU discharges, recovered and
mortality (in ICU, in hospital or in the community) of individuals with Coronavirus
Disease of 2019 (COVID 19).

Criteria for inclusion. Of positive cases: Having a SARS-CoV-2 infection
laboratory-confirmed by a positive result on the reverse transcriptase-polymerase chain
reaction assay for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or a
positive antigen test from March 1, 2020 to January 9, 2022.

For hospital admissions: Hospital admissions since the start of the pandemic. Considering
different episodes as a single admission when it comes to transfers from one center to
another. Consider exclusively income due to the COVID19.

Exclusion criteria: Patients admitted for other reasons who have developed the disease
during their hospital stay.

Variables. The data to be collected is aggregated data in the form of incidents. The
population will be stratified into ten age groups (0 - 9, 10 - 19, ..., 70 - 79, 80 - 89,
90+) and by sex. Variables:

- Individuals in the study population by age.

- Number of new confirmed positive cases of COVID19 by age and day.

- Number of new hospital admissions due to COVID19 by age and day. Number of ICU
admissions due to COVID19

- Number of total deaths from COVID19 by age and day.

- Number of hospital discharges (live patients) of patients who have been hospitalized
for COVID19 by age and day (excluding transfers).

- Number of deaths in hospital due to COVID19 by age and day.

- Number of deaths in the ICU due to COVID19 by age and day.

The outcome variables that will be obtained from the proposed modeling are:

- Number of estimated positive COVID19 cases by age and day.

- Number of estimated COVID19 hospital admissions by age and day.

- Number of estimated total deaths due to COVID19 estimated by age and day.

- Number of estimated ICU admissions due to COVID19 estimated by age and day.

Analysis of data. The investigators will use P-splines and Negative Binomial
Distribution. P-splines, or penalized splines, are a powerful tool for modeling nonlinear
relationships in temporal data. By combining them with the negative binomial
distribution, a model is obtained that is especially suitable for counting data with
over-dispersion, as is the case with pandemic data.

Procedure:

- Data Collection: Daily data on positive cases, hospital admissions and ICU
admissions will be obtained from the beginning of the pandemic until september 2022.

- Modeling: A P-splines model based on the negative binomial distribution will be
fitted to the data. This model will be designed to capture temporal trends and
seasonal patterns, as well as to handle the over-dispersion present in the data.

- Model with Random Effect for Day of the Week: Specifically for the prediction of
hospital admissions, a random effect for the day of the week will be incorporated.
This adjustment will be made because a systematic variability in income was
identified depending on the day of the week. Incorporating this random effect
significantly will improve the accuracy of the model for this variable.

- Prediction: Predictions will be made for two time horizons: short term (1 and 2
days) and medium term (5 days). These predictions will allow us to anticipate the
evolution of the pandemic and make informed decisions.

Validation of Predictions: To validate the accuracy and robustness of the predictions, a
retrospective analysis will be carried out at different times (or waves) of the pandemic.
Model predictions will be compared to actual observed data, and error metrics will be
calculated to evaluate model performance.

Limitations. One of the limitations of the study is the possible loss of hospitalizations
due to the disease considered and death (or recovery) in individuals whose temporal
sequence of testing, admission and death (or recovery) has not followed the sequence used
in searches carried out.

Ethical aspects. This study uses only anonymized information to meet its objectives.
There is no data available to identify a patient.

The processing, communication and transfer of personal data of all participating persons
complies with the provisions of the European Data Protection Regulation (EU2016/679)
regarding the protection of natural persons with regard to processing. of personal data
and the free circulation of these data and Organic Law 3/2018, of December 5, on the
Protection of Personal Data and guarantee of digital rights. Virtually all of the data
necessary for this study is aggregated data that in no case can be associated with
individuals. All information will be treated absolutely confidentially.

Regarding obtaining informed consent from the patient, this research team proposes
carrying out the study without asking the patient for informed consent. The reasons why
this proposal is made are based on article 58 of Law 14/2007, of July 3, on Biomedical
Research (""..exceptionally, coded or identified samples may be treated for the purposes
of biomedical research without the consent of the source subject, when obtaining said
consent is not possible or represents an unreasonable effort. In these cases, the
favorable opinion of the corresponding Research Ethics Committee will be required. ")