An integrated framework for building trustworthy data-driven epidemiological models: Application to the COVID-19 outbreak in New York City

An integrated framework for building trustworthy data-driven epidemiological models: Application to the COVID-19 outbreak in New York City

Epidemiological models can provide the dynamic evolution of a pandemic but they are based on many assumptions and parameters that have to be adjusted over the time when the pandemic lasts. However, often the available data are not sufficient to identify the model parameters and hence infer the unobs...

Full description

Bibliographic Details
Main Authors: Karniadakis, G. (Author), Lin, G. (Author), Ponce, J. (Author), Zhang, S. (Author), Zhang, Z. (Author)
Format: Article
Language:English
Published: Public Library of Science 2021
Subjects:
Article
biology
calibration
communicable disease control
Communicable Disease Control
Computational Biology
coronavirus disease 2019
COVID-19
Disease Outbreaks
disease transmission
epidemic
epidemiology
government
health care system
hospitalization
human
Humans
mathematical analysis
Models, Statistical
mortality rate
New York
New York City
population size
prevention and control
SARS-CoV-2
sensitivity analysis
statistical model
time series analysis
uncertainty
vaccination
virus transmission
workflow
Online Access:View Fulltext in Publisher

Internet

View Fulltext in Publisher

Similar Items