.jpg)
Summary
During the COVID-19 pandemic, I undertook a six-month secondment at the UK Health Security Agency (UKHSA) as a data scientist. my role involved adapting COVID-19 syndromic surveillance model, developed at the University of Liverpool during my PhD, to run on the UKHSA's high-performance computing infrastructure. Syndromic surveillance is a method of analysing health-related data to detect and monitor disease outbreaks and other public health threats. this work contributed to producing consensus estimates for key public health metrics, including the R number, predicted hospital admissions, and deaths. These estimates informed public health decisions and were published on the UK government website.** I also collaborated on weekly presentations for stakeholders to communicate the latest findings.
Importance and impact
The COVID-19 pandemic demanded real-time analysis to guide public health policies. My secondment arose as part of a collaborative effort to integrate advanced epidemiological models into national-level decision-making. The University of Liverpool’s model was uniquely suited to infer syndromic trends, complementing statistical models from other institutions. By onboarding this model to the UKHSA’s high-performance computing systems, we ensured timely and robust analysis of critical metrics such as the R number and hospital admissions.
This work was crucial in generating consensus estimates by combining outputs from multiple models. The consensus estimates, which incorporated diverse methodologies, provided a more reliable basis for decision-making than any single model could offer. These metrics were used by public health officials to evaluate the trajectory of the pandemic and implement appropriate measures, such as lockdowns and vaccination strategies. Additionally, our weekly presentations were instrumental in keeping policymakers, healthcare leaders, and scientific advisors updated on the latest trends, facilitating swift and informed responses to the evolving situation.
The secondment underscored the importance of interdisciplinary collaboration and the rapid application of academic research in a public health crisis, helping to bridge the gap between research innovation and practical policymaking.
What comes next
Building on this experience, I plan to further develop and apply advanced epidemiological models for other infectious diseases. I aim to explore ways to enhance real-time syndromic surveillance, ensuring preparedness for future public health emergencies.
**Reproduction number (R) and growth rate: methodology - GOV.UK
https://royalsocietypublishing.org/doi/full/10.1098/rsta.2021.0305
Back to: Centre for Doctoral Training in Distributed Algorithms