Using data to improve public health: COVID-19 secondment

The COVID-19 pandemic has seen the importance of using data to understand the spread of the Coronavirus, predict future trends of the pandemic in space and time, forecast challenges for healthcare delivery and inform government management policies. The availability of national electronic health records and longitudinal cohort studies provides unique opportunities for using big data to better understand the impact of COVID-19 on health, society and economics.
The aim of this fellowship is to develop well-designed statistical analyses which make the best use of data from multiple sources in order to understand the impacts and management of the COVID-19 pandemic to improve public health.
The objectives of this fellowship are to:
1. Identify risk factors associated with Long COVID.
2. Evaluate the effectiveness and safety of vaccines.
3. Quantify the healthcare disruptions during different waves of the pandemic.
4. Assess the effects of COVID-19 infection on other health outcomes.
A key focus of this fellowship will be for me to advance my research career from an independent researcher to become a leading expert in big data enabled health research. The proposed research will allow me to:
a) Conduct COVID-19 research through engaging in the Longitudinal Health and Wellbeing National Core Study.
The fellowship will allow me to apply and advance my expertise in big data, statistical modelling and epidemiology to develop statistical analyses for addressing important research questions which will have an impact on public health.
b) Grow a research team for COVID-19 research and health data science.
This will be achieved through bringing together researchers to support this fellowship, and by making funding applications to support the team's activities and to train early-career researchers beyond this fellowship.
c) Advance my expertise in using big data for health research.
I have an intrinsic passion for, and continually develop my experience in, using real-world big data to address important research questions, especially within the clinical decision-making framework. This fellowship will allow me to extend my expertise in this exciting area that has a great potential to influence healthcare policy leading to better population health.
d) Disseminate research outputs widely.
I will disseminate the outputs from this research in peer-reviewed journal publications and conference presentations. Furthermore, I will create a website to communicate the key findings of my work to a wider audience including the general public. I will also host seminars or a stakeholder workshop related to COVID-19, to generate further interests, collaborations and impact of the research.
Achieving these fellowship objectives will enable me to reach my career goal to be a leading expert in big data approaches to health, heading up a research team that develops quantitative methods to address research questions related to COVID-19 public health and beyond. Big data approaches will allow us to provide better insights, more robust conclusions, and better recommendations for health policy, which will ultimately benefit the population health.

The fellowship will focus on making the best use of data from multiple sources to better understand the impact of COVID-19 on population health in the UK.
The proposed research includes,
1. Identifying risk factors associated with Long COVID.
o Use data from multiple sources, such as longitudinal cohort studies and/or electronic health records, to develop a prediction model to classify patients into groups with and without Long COVID.
o Use unsupervised learning to further identify and define the subgroups of patients with Long COVID.
2. Evaluating vaccine effectiveness and safety using real-world (out of trial) data.
o Evaluate and compare the effectiveness of different methods of vaccine offering: one dose, two doses of the same brand, and mixed brands of vaccine.
o Evaluate vaccine safety, such as possible blood clots and heart inflammation, by using regression models for rare adverse events to better identify subgroups of patients at high risk.
3. Quantifying healthcare disruptions during different waves of the pandemic.
o Analyse the data related to COVID-19 healthcare activity, to identify how the healthcare burdens change in different waves of the pandemic.
o Investigate what factors are associated with healthcare burdens, what are the contributing factors in heterogeneity, and identify good practices in mitigating healthcare disruptions.
4. Assessing the effects of COVID-19 infection on other health outcomes.
o Use electronic health records and/or multiple longitudinal cohort studies, to examine how COVID-19 infection impacts on other health outcomes.
o Quantify how the effects of COVID-19 infection on health outcomes change with time.
o Identify subgroups of the population who are more vulnerable to adverse health outcomes associated with COVID-19 infection.
Well documented statistical code will be produced to allow for future updated analysis when new data become available, and for reproducible research.