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

NCT06070896 | Completed

• 1 other identifier: 2020111078
• Study Type: observational
• Target: 715,206
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

COVID-19

Keywords

COVID-19; Mathematical Model; Public Health

Outcome Measures

Primary Outcomes (3)

• COVID19 positive cases

  Number of estimated positive COVID19 cases by age and day.

  Daily

• COVID19 hospital admissions

  Number of estimated COVID19 hospital admissions by age and day.

  Daily

• Deaths due to COVID19

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

  Daily

Secondary Outcomes (1)

• ICU admissions

  Daily

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Information collected on daily incidence data aggregated by age and sex for: positive cases, hospital admissions and ICU admissions by SARS-CoV-2, hospital discharges and ICU discharges, recovered and mortality (in ICU, in hospital or in the community) of individuals with COVID19).

You may qualify if:

• To be a positive 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:
• Consider different episodes as a single admission when it comes to transfers from one center to another.
• Exclusively admissions due to COVID-19.

You may not qualify if:

• Patients admitted for other reasons who have developed the disease during their hospital stay.

Sponsors & Collaborators

• Hospital Galdakao-Usansolo: lead
• Basque Government Department of Public Health: collaborator
• University of the Basque Country (UPV/EHU): collaborator
• BCAM (Basque Center for Applied Mathematics): collaborator
• Osakidetza: collaborator

Study Sites (1)

Hospital Galdakao-Usansolo

Galdakao, Bizkaia, 48960, Spain

Location

MeSH Terms

Conditions

COVID-19

Condition Hierarchy (Ancestors)

Pneumonia, Viral; Pneumonia; Respiratory Tract Infections; Infections; Virus Diseases; Coronavirus Infections; Coronaviridae Infections; Nidovirales Infections; RNA Virus Infections; Lung Diseases; Respiratory Tract Diseases

Study Officials

• Inmaculada Arostegui, PhD

  Basque University

  STUDY CHAIR

• Dae Jin Lee, PhD

  BCAM

  STUDY CHAIR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER GOV
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: MD, PhD

Study Record Dates

• First Submitted: October 5, 2023
• First Posted: October 6, 2023
• Study Start: March 1, 2020
• Primary Completion: September 30, 2022
• Study Completion: December 31, 2023
• Last Updated: February 28, 2024

Record last verified: 2023-10

Data Sharing

• IPD Sharing: Will not share

Locations

ES (1)