Predicting consequences of COVID-19 control measure de-escalation on nosocomial transmission and mortality: a modelling study in a French rehabilitation hospital

Smith, D.R.M.; Duval, A.; Grant, R.; Abbas, M.; Harbarth, S.; Opatowski, L.; Temime, L.

doi:10.1016/j.jhin.2024.02.020

Journal of Hospital Infection

2024

DOI: 10.1016/j.jhin.2024.02.020

|View full text |Cite

Predicting consequences of COVID-19 control measure de-escalation on nosocomial transmission and mortality: a modelling study in a French rehabilitation hospital

D.R.M. Smith,

A. Duval,

R. Grant

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Expanding the use of mathematical modeling in healthcare epidemiology and infection prevention and control

Grant,

Rubin,

Abbas

et al. 2024

Infect. Control Hosp. Epidemiol.

View full text Add to dashboard Cite

During the coronavirus disease 2019 pandemic, mathematical modeling has been widely used to understand epidemiological burden, trends, and transmission dynamics, to facilitate policy decisions, and, to a lesser extent, to evaluate infection prevention and control (IPC) measures. This review highlights the added value of using conventional epidemiology and modeling approaches to address the complexity of healthcare-associated infections (HAI) and antimicrobial resistance. It demonstrates how epidemiological surveillance data and modeling can be used to infer transmission dynamics in healthcare settings and to forecast healthcare impact, how modeling can be used to improve the validity of interpretation of epidemiological surveillance data, how modeling can be used to estimate the impact of IPC interventions, and how modeling can be used to guide IPC and antimicrobial treatment and stewardship decision-making. There are several priority areas for expanding the use of modeling in healthcare epidemiology and IPC. Importantly, modeling should be viewed as complementary to conventional healthcare epidemiological approaches, and this requires collaboration and active coordination between IPC, healthcare epidemiology, and mathematical modeling groups.

show abstract

Expanding the use of mathematical modeling in healthcare epidemiology and infection prevention and control

Grant,

Rubin,

Abbas

et al. 2024

Infect. Control Hosp. Epidemiol.

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Predicting consequences of COVID-19 control measure de-escalation on nosocomial transmission and mortality: a modelling study in a French rehabilitation hospital

Cited by 1 publication

References 28 publications

Expanding the use of mathematical modeling in healthcare epidemiology and infection prevention and control

Expanding the use of mathematical modeling in healthcare epidemiology and infection prevention and control

Contact Info

Product

Resources

About