Antibiotic use and lower respiratory tract infections burden in children during Coronavirus (COVID-19)

The project will assess antibiotic use and lower respiratory tract infections in children before and during COVID-19 pandemic. Machine learning algorithms will be developed to predict infection disease patterns during the COVID-19 pandemic using a national database. A global systematic review and meta-analysis study will be conducted to compare antibiotic prescribing patterns in paediatric populations before and during COVID-19.  

The student will receive training on quantitative methods, pharmaco-epidemiological methods, generic skills in writing and presentations skills, critical thinking, and project management. Training will also be provided on computer programming using statistical software (STATA, Rstudio). The student will work with a large interdisciplinary team which is highly experienced in such studies.

The findings for this project will develop a risk prediction model which can be applied to UK national database. The training provided to the project will also provide an excellent grounding for a career in medical research or information technology.

Applicants should have a 1st or 2.1 honours degree (or equivalent) in a relevant subject. Relevant subjects include Pharmacy, Pharmaceutical Sciences, Epidemiology, Public Health, Biochemistry, Biological/Biomedical Sciences, Chemistry, Engineering, or a closely related discipline. Students who have a 2.2 honours degree and a Master’s degree may also be considered, but the School reserves the right to shortlist for interview only those applicants who have demonstrated high academic attainment to date.

Applicants should apply through the University's Direct Application Portal: https://dap.qub.ac.uk/portal/user/u_login.php