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Understanding and Controlling Hospital-Acquired Influenza Through Network Modelling
Safe People
Organisation name
Imperial College London
Organisation sector
Academic Institute
Applicant name(s)
Mauricio Barahona
Funders/ Sponsors
Alison Holmes
DEA accredited researcher?
Unknown
Sub-licence arrangements (if any)?
No
Safe Projects
Project ID
NIBDAPC_2025_0043
Lay summary
Influenza, commonly known as the flu, can spread rapidly in hospitals, putting vulnerable patients at risk. This project aims to study the transmission of flu within hospitals, with a focus on how vaccinations can help reduce its spread. We will use cutting edge machine learning methods to study how patients interact with each other and predict how flu moves through a hospital. One integral part of this research is linking vaccination data from a General Practice database with hospital records in the Imperial Clinical Analytics, Research and Evaluation (iCARE) data infrastructure to understand how well flu vaccines protect patients in healthcare environments. By studying these patterns, we hope to find ways to prevent the flu from spreading in hospitals, and help hospitals improve their infection control practices (such as isolating the infected patients to avoid further spreading), ensuring they are better prepared to prevent future outbreaks of flu or similar diseases. Our findings will guide healthcare professionals on how to reduce the risk of hospital-acquired infections, especially during flu season or amid emerging respiratory viruses. Machine learning techniques are statistical methods that learn from the seen data to generate rules about the unseen data. It is particularly useful for simulating events under different scenarios (some would be counterfactual). In summary, this project aims to use advanced machine learning techniques to simulate how flu transmitted with hospitals taking into account factors like whether a patient was vaccinated, how old a patient was, which specialty a patient was admitted to, and who previously have shared ward with this patient, with the goal of showing the protective impact of vaccines, improving infection control practices and reducing the risk of hospital-acquired infections.
Public benefit statement
This research prioritises identifying the protective impact of vaccines, which directly benefits the public by informing vaccination allocation strategies for the influenza season. Vaccination remains one of the most effective tools for preventing influenza, especially among vulnerable groups such as the elderly and immunocompromised individuals. By providing patients with more information on the importance of vaccines, it highlights the critical role of vaccination in protecting their health. The detailed patient-level data in both whole systems integrated care (WSIC) and iCARE environments allows for a deeper understanding of factors influencing influenza and RSV transmission and vaccine effectiveness in real-world settings. The study is particularly important because it addresses a gap in detailed research, offering valuable insights that will help allocate resources more effectively for future flu seasons.
Request category type
Public Health Research
Other approval committees
Latest approval date
24/03/2025
Safe Data
Dataset(s) name
Linked ICHT iCARE Data Model / WSIC Data Model
Data sensitivity level
De-Personalised
Common Law Duty of Confidentiality
Not applicable
National data opt-out applied?
Not applicable
Request frequency
One-off
Safe Setting
Access type
TRE