An operationally implementable model for predicting the effects of an infectious disease on a comprehensive regional healthcare system

Autor: Nirav Shah, Lakshmi Halasyamani, Ernest Wang, Daniel L. Chertok, Anthony Solomonides, Kamaljit Singh, Loretta Au, Chad Konchak, Brian Murray, Jared Hammernik
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Viral Diseases
Epidemiology
Medical Conditions
Mathematical and Statistical Techniques
Pandemic
Medicine and Health Sciences
Public and Occupational Health
Duration (project management)
Multidisciplinary
Communicable disease
Statistics
Vaccination and Immunization
Laboratory Equipment
Infectious Diseases
Research Design
Physical Sciences
Engineering and Technology
Medicine
Comprehensive Health Care
Research Article
Healthcare system
Census
medicine.medical_specialty
Coronavirus disease 2019 (COVID-19)
Death Rates
Supply chain
Science
Immunology
Ventilators
Equipment
Surgical and Invasive Medical Procedures
Research and Analysis Methods
Population Metrics
medicine
Humans
Operations management
Statistical Methods
Intensive care medicine
Pandemics
Survey Research
Population Biology
SARS-CoV-2
business.industry
COVID-19
Biology and Life Sciences
Covid 19
Models
Theoretical
Patient population
Infectious disease (medical specialty)
Preventive Medicine
Intubation
business
Mathematics
Forecasting
Zdroj: PLoS ONE, Vol 16, Iss 10, p e0258710 (2021)
PLoS ONE, Vol 16, Iss 10 (2021)
PLoS ONE
ISSN: 1932-6203
Popis: An operationally implementable predictive model has been developed to forecast the number of COVID-19 infections in the patient population, hospital floor and ICU censuses, ventilator and related supply chain demand. The model is intended for clinical, operational, financial and supply chain leaders and executives of a comprehensive healthcare system responsible for making decisions that depend on epidemiological contingencies. This paper describes the model that was implemented at NorthShore University HealthSystem and is applicable to any communicable disease whose risk of reinfection for the duration of the pandemic is negligible.
Databáze: OpenAIRE
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