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  • Project No: NDORMS-2021/1
  • Intake: 2021


Secondary use of routine observational healthcare (“real world”) data provides a rich source of clinical information to understand the real time coding of disease, and the impact of public health interventions on provision of care and overall morbidity and mortality.

Real world data inherently reflect the system to which they are captured from administrative claims, electronic health records, disease-specific registries and similar sources. Due to the ‘live’ nature of these data, they constitute a unique yet underutilised asset for viral outbreak vigilance. Methods to identify anomalies in routine care delivery and deviations from ‘normal’ can be used to detect viral outbreaks well before a trend is clinically identified. This has been shown recently by comparing the 2019-2020 seasonal influenza vs previous epidemiological curves, to identify the onset of the SARS-COV-2 pandemic in Europe, well before officially reported [Coma E et al.]


1. To develop strategies for using time-series analyses to detect and evaluate data anomalies in observational healthcare data and flag potential disease misclassification.

2. To develop and validate natural experiment methods for the early identification of global viral outbreaks (pandemics) from real world data globally, and to apply them prospectively for the identification of potential future outbreaks of COVID19 or other influenza-like viral disease.

3. To test the use of time-series analyses to measure the impact of public health strategies (lockdown, vaccination campaigns) on the spread of COVID19 disease globally

4. To utilise natural experiment methods to assess the impact of COVID19 on non-COVID morbidity (e.g. diabetes complications), healthcare provision (e.g. elective surgery) and mortality (e.g. cardiovascular or cancer-related mortality).

To achieve these, multinational healthcare data will be standardised to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (OMOP-CDM) to account for semantic variation in clinical systems. The project will analyse historical seasonal influenza data to learn on best methods for the identification of viral outbreaks, and use the 2020 COVID19 outbreak in Europe, South Korea and the US for validation of the chosen modelling strategy.

Researchers will collaborate with the European EHDEN ( and global OHDSI ( open science communities.



The DPhil will be jointly supervised by Professor Prieto-Alhambra, Dr Sara Khalid, Dr Victoria Strauss and Dr Antonella Delmestri, who are all based at the Oxford Centre for Statistics in Medicine (CSM), part of NDORMS, Medical Sciences Division at University of Oxford.

Professor Daniel Prieto-Alhambra has published extensively in the field of real world epidemiology, and is recognized internationally as an authority on use of routine data for musculoskeletal pharmaco- and device epidemiology.

Dr Sara Khalid is a senior research associate in biomedical data science and leads the machine learning and big data research of the Pharmaco-epidemiology group in NDORMS. She will provide expertise in the methods used in this project, in particular data-driven analyses and methods.

Dr Victoria Strauss is a senior statistician with great expertise in the analysis of real world data and an interest in longitudinal methods. She will provide oversight and statistical input.

Dr Antonella Delmestri is a senior health data scientist with a background in computer science and software engineering. She is an expert in EHR and has been involved in the mapping of the Clinical Practice Research Datalink (CPRD GOLD to OMOP-CDM). She will provide support in understanding the OMOP-CDM structure and interpreting data.

Current DPhil  (PhD) Students within the group: 7



The Centre for Statistics in Medicine (CSM) is located in the Botnar Research Centre (NDORMS). CSM has more than 20 years’ experience in medical statistics, has participated in more than 80 trials, and is the home of the department’s Big Health Data Research, the Oxford Clinical Trials Research Unit, and the EQUATOR Centre. The proposed project would be part of the work of the Big Health Data Research group.

Training will be provided in relevant related research methodology, including the handling and analysis of large datasets, and advanced statistical techniques. Attendance at formal training courses will be encouraged, and will include the "Real World Epidemiology Oxford Summer School" and advanced statistics courses.

In addition, courses from the Medical Sciences Divisional Skills Training and the Oxford University Computer Sciences will be available on key skills for the completion of a successful DPhil thesis. Additional on the job training opportunities will arise, and the supervisors will encourage the student to pursue such opportunities.

All research students will be required to attend a number of core curriculum of lectures organised by NDORMS during the first term to provide a solid foundation in a broad range of subjects including epidemiology, health economics and data analysis as well as more general site-specific topics.

Students will be expected to present data regularly to the research group and at various departmental seminars, as well as attending various external relevant conferences to present their findings globally.



Prof D Prieto-Alhambra:



The department accepts applications throughout the year but it is recommended that, in the first instance, you contact the relevant supervisor(s) or the Graduate Studies Officer ( who will be able to advise you of the essential requirements.

Interested applicants should have, or expect to obtain a first or upper second-class BSc degree or equivalent, and will also need to provide evidence of English language competence. The University requires candidates to formally apply online and for their referees to submit online references via the online application system.

The application guidelines and relevant forms can be found online for starting in October 2021.

When completing the online application, please read the University Guide.