Data-driven multiscale dynamical framework to control a pandemic evolution with non-pharmaceutical interventions.
PLoS One
; 18(1): e0278882, 2023.
Artículo
en Inglés
| MEDLINE | ID: covidwho-2197059
ABSTRACT
Before the availability of vaccines, many countries have resorted multiple times to drastic social restrictions to prevent saturation of their health care system, and to regain control over an otherwise exponentially increasing COVID-19 pandemic. With the advent of data-sharing, computational approaches are key to efficiently control a pandemic with non-pharmaceutical interventions (NPIs). Here we develop a data-driven computational framework based on a time discrete and age-stratified compartmental model to control a pandemic evolution inside and outside hospitals in a constantly changing environment with NPIs. Besides the calendrical time, we introduce a second time-scale for the infection history, which allows for non-exponential transition probabilities. We develop inference methods and feedback procedures to successively recalibrate model parameters as new data becomes available. As a showcase, we calibrate the framework to study the pandemic evolution inside and outside hospitals in France until February 2021. We combine national hospitalization statistics from governmental websites with clinical data from a single hospital to calibrate hospitalization parameters. We infer changes in social contact matrices as a function of NPIs from positive testing and new hospitalization data. We use simulations to infer hidden pandemic properties such as the fraction of infected population, the hospitalisation probability, or the infection fatality ratio. We show how reproduction numbers and herd immunity levels depend on the underlying social dynamics.
Texto completo:
Disponible
Colección:
Bases de datos internacionales
Base de datos:
MEDLINE
Asunto principal:
COVID-19
Tipo de estudio:
Estudio experimental
/
Estudio observacional
/
Estudio pronóstico
Tópicos:
Vacunas
Límite:
Humanos
País/Región como asunto:
Europa
Idioma:
Inglés
Revista:
PLoS One
Asunto de la revista:
Ciencia
/
Medicina
Año:
2023
Tipo del documento:
Artículo
País de afiliación:
Journal.pone.0278882
Similares
MEDLINE
...
LILACS
LIS