Data Access Register - full project summary
| Application ID | llc_0039 |
|---|---|
| Project Title | The Risk Prediction on Winter Respiratory Diseases and the Burden on Healthcare Systems: Pre-, During and Post-COVID-19 Pandemic |
| Lead Applicant | Ruonan Pei |
| Organisation(s) Name(s) | The University of Edinburgh |
| Approval Date | 09/06/2025 |
| Application Status | Approved |
| Lay Summary | Winter respiratory diseases, such as COVID-19, flu, and respiratory infections, cause a significant number of hospital visits and deaths each year, placing immense pressure on the National Health Service (NHS), especially during the COVID-19 pandemic. This project aims to improve how we predict and prevent these diseases and identify the most at-risk residents and apply early actions to protect them. The project will investigate patterns of respiratory illnesses pre-, during and post-COVID-19 pandemic to understand how risk factors, such as age, smoking status, existing health conditions, vaccination history, and environmental factors (e.g., air pollution), affect hospitalisation rates. We will develop a prediction system by advanced machine learning algorithms to monitor patients who most likely require hospital care, emergency treatment, or facing serious health outcomes. With the assistant of this project identifying those most at risk, healthcare providers can offer earlier interventions, such as vaccinations, medication, and lifestyle advice, to help prevent severe illness. This research will also assist policymakers in making decisions about how to allocate healthcare resources efficiently, particularly in winter when hospitals are under the most strain. In summary, this project will help improve public health, reduce hospital admissions, and support a stronger, more resilient NHS. |
| Datasets Requested | LPS Data:
Linked Data Requested:
|
| Results & Impact |