Pandemic Data Science



Overview

Data science has played a prominent and significant role in response to the COVID-19 pandemic. Mathematical modellers and data scientists joined virologists, public health professional and policy makers to confront the pandemic using methods that did not exist for previous generations. In recent years, the value of big-data has become increasingly appreciated across all sectors of industry, academia and society. The COVID-19 pandemic revealed to the wider world - that in the modern era - data is an exceedingly valuable commodity. Data-driven insights during the COVID-19 pandemic have played critical roles in understanding and responding to the pandemic. Moreover, data science will continue to be important beyond this pandemic in our preparedness for any future disease outbreaks. 

This course aims to introduce students to the analytical approaches, successes and challenges experienced by the data science and artificial intelligence community during the COVID-19 pandemic. Students will gain experience of how data science has impacted key decisions and policy in response to the pandemic, from epidemiological modelling through to vaccination strategies.   

Programme details

First live webinar: 24 April 2025, 4.00-5.00pm (UK time)

Please note there will be no live class on Thursday 5th June 2025.

Week 1: Pandemic preparedness and identification of “disease X”

Week 2: Mathematical modelling and prediction of infectious disease epidemiology

Week 3: Genetic and non-genetic COVID-19 risk factor identification

Week 4: COVID-19 therapeutic clinical trials

Week 5: Vaccine platform technologies and COVID-19 vaccine design

Week 6: COVID-19 vaccine efficacy studies

Week 7: Viral sequencing and variants of concern identification

Week 8: Biomarker identification in infectious diseases

Week 9: Systems immunology and systems vaccinology

Week 10: Artificial intelligence and data science in the context of COVID-19

Certification

Credit Application Transfer Scheme (CATS) points 

To earn credit (CATS points) for this course you will need to register for credit and pay an additional £30 fee. You can do this by ticking the relevant box at the bottom of the enrolment form or when enrolling online. If you do not register for credit when you enrol you have up until the course closes to enrolments to pay the £30 fee. Students who do not register for CATS points prior to the start of the course can apply retrospectively from the January 1st after the current full academic year has been completed.

See more information on CATS point

Coursework is an integral part of all online courses and everyone enrolled will be expected to do coursework, but only those who have registered for credit will be awarded CATS points for completing work at the required standard. If you are enrolled on the Certificate of Higher Education, you need to indicate this on the enrolment form but there is no additional registration fee. Students who register for CATS points will be posted a Record of CATS points on successful completion of their course assessment.

 

Digital credentials

All students who pass their final assignment, whether registered for credit or not, will be eligible for a digital Certificate of Completion. Upon successful completion, you will receive a link to download a University of Oxford digital certificate. Information on how to access this digital certificate will be emailed to you after the end of the course. The certificate will show your name, the course title and the dates of the course you attended. You will be able to download your certificate or share it on social media if you choose to do so. 

Please note that summative assignments are not graded but are marked either pass or fail. 

Fees

Description Costs
Course Fee £310.00
Take this course for CATS points £30.00

Funding

If you are in receipt of a UK state benefit, you are a full-time student in the UK or a student on a low income, you may be eligible for a reduction of 50% of tuition fees. Please see the below link for full details:

Concessionary fees for short courses

Tutor

Prof Daniel O'Connor

Daniel is an Associate Professor within the Oxford Vaccine Group, University of Oxford. Daniel's research interests relate to the analysis of contemporary, high-dimensional datasets (e.g., genomic, transcriptomic, proteomic) to elucidate the molecular determinants of immune responses to vaccines and infectious diseases.

Course aims

  • Basic understanding of current pandemic preparedness strategies.
  • Familiarity with data science, machine learning & artificial intelligence.
  • Insights into the use of data science in infectious disease and vaccine research.

Teaching methods

Learning takes place on a weekly schedule. At the start of each weekly unit, students are provided with learning materials on our online platform, including one hour of pre-recorded video, often supplemented by guided readings and educational resources. These learning materials prepare students for a one-hour live webinar with an expert tutor at the end of each weekly unit which they attend in small groups. Webinars are held on Microsoft Teams, and provide the opportunity for students to respond to discussion prompts and ask questions. The blend of weekly learning materials that can be worked through flexibly, together with a live meeting with a tutor and their peers, maximise learning and engagement through interaction in a friendly, supportive environment.

Learning outcomes

By the end of the course students will be expected to:

  • have basic understanding of how data science has impacted the response to the COVID-19 pandemic;
  • to have basic understanding of how data science will inform preparedness strategies for any future pandemics.

Assessment methods

There will be assessment options available on this course.

Please note that summative assignments are not graded but are marked either pass or fail. 

Coursework is an integral part of all weekly classes and everyone enrolled will be expected to do coursework in order to benefit fully from the course. Only those who have registered for credit will be awarded CATS points for completing work the required standard.

Students must submit a completed Declaration of Authorship form at the end of term when submitting your final piece of work. CATS points cannot be awarded without the aforementioned form - Declaration of Authorship form

Application

Please use the 'Book now' or 'Apply' button on this page. Alternatively, please complete an Enrolment Form (Word) or Enrolment Form (Pdf)

We will close to enrolments 7 days prior to the first live webinar to allow us to complete the course set up. We will email your joining instructions at that time (7 days before you first live webinar) so you can access the Canvas virtual learning environment (VLE) and watch your first pre-recorded video. Please check spam and junk folders during this period to ensure that these emails are received. 

Level and demands

The course will provide a broad and accessible content. However, an in-depth understanding of the concepts provided would require further learning.  

The Department's short online courses are taught at FHEQ Level 4, i.e. first year undergraduate level, and you will be expected to engage in private study in preparation for the classes. This may take the form, for instance, of reading and analysing set texts, responding to questions or tasks, or preparing work to present in class. FHEQ level 4 courses require approximately 10 hours study per week, therefore a total of about 100 study hours.

 

Terms & conditions for applicants and students

Information on financial support