Case Study • Jillur Quddus

Joint Biosecurity Centre

Modelling the transmission of COVID-19 and simulating the impact of non-pharmaceutical interventions (NPIs).

Joint Biosecurity Centre

Case Study Summary

The Challenge

Protect public health by breaking the chains of COVID-19 transmission as part of the UK's health protection ecosystem.

What We Did

We built bleeding-edge AI-driven disease modelling systems capable of tracking & predicting COVID-19 transmission.

Key Outcomes

Data products and simulations directly consumed by SAGE to inform policy-making to better protect public health.

Introduction

The Joint Biosecurity Centre (JBC) was established in May 2020 to provide evidence-based, objective analysis, assessment and advice to inform local and national decision-making in response to the COVID-19 pandemic. Its immediate term objective was to break the chains of COVID-19 transmission to protect public health as part of the evolving health protection ecosystem in the UK. The JBC brings together and combines internationally-leading epidemiological expertise with data science to provide analysis and insights on the drivers and risk factors of virus transmission.

Joint Biosecurity Centre
Joint Biosecurity Centre Logo

The Challenge

Working with internationally-leading partners (including mathematicians from CERN, AI researchers from The Alan Turing Institute, leading epidemiologists from academia, genomic sequencing SMEs, the NHS, the Office for National Statistics, and major public cloud providers), the JBC sought to provide insights into the factors that affect the spread of COVID-19, to identify the most significant drivers of transmission, and to understand the factors behind localised increases in infection rates and the potential consequences for local health care systems.

COVID-19 surveillance and immunity studies
COVID-19 virus

What We Did

Our founder Jillur Quddus led an expert team of globally-leading mathematicians, epidemiologists, AI research fellows and technology professionals, responsible for rapidly developing bleeding-edge disease modelling systems, utilising the latest research in machine learning and artificial intelligence, capable of tracking & predicting the transmission of COVID-19 in real-time, and simulating the impact of non-pharmaceutical interventions (NPIs) such as lockdown events.

Enduring SARS-CoV-2 prevalence risk factors
Map of global COVID-19 cases

Key Outcomes

Our innovative AI-powered disease modelling systems enabled the Scientfic Advisory Group for Emergencies (SAGE) to identify both known and previously unknown clusters of COVID-19 outbreaks, coupled with the ability to understand the key drivers of transmission within and between localised clusters, and the likely impact of NPIs. Furthermore, our solutions were guided by the architectural principles of reusability and interoperability, meaning that they can be easily reused by the UK's Health Security Agency (UKHSA) in response to future pandemics.

Scientific Advisory Group for Emergencies
COVID-19 public messaging