Covid19, Disease Spread, Neural Network, Pandemic, Respiratory Disease, Spatial Visualization
Conditions
Brief summary
One of the major problems in suppressing the spreading of an epidemic resides in understanding and monitoring its propagation patterns, and in evaluating how these are modified by enforced policies. The standard solution requires detailed information at the microscopic scales, e.g. how infected people have moved and whom they came in contact with, which is hardly ever available. The researchers propose a novel approach to the study of the propagation of COVID-19, in which a proxy of this information is derived at macroscopic scales. This will be based on two ingredients: the spatiotemporal study in shiny with mathematical models with aggregated or non aggregated data and the reconstruction of functional networks of spreading patterns, and the development of a supporting software.
Interventions
OTHERno intervention
just descriptive analysis of clinical data
Sponsors
University of Valencia
Hospital General Universitario de Valencia
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 100 Years
Healthy volunteers
No
Inclusion criteria
* positive test (PCR or serology) for SARS-CoV-2 * outpatient follow-up
Exclusion criteria
* hospitalisation in ward or ICU
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| spatiotemporal spread | February 1, 2020 to September 30, 2020 | spatiotemporal spread of COVID-19 patient in our hospital |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| classification score | February 1, 2020 to September 30, 2020 | risk classification score of each patients with clinical and analytical variables |
Countries
Spain
Outcome results
None listed