Uploaded December 2020 | Updated September 2026, 2 weeks ago
This IMA event explored the mathematical challenges and opportunities that the COVID-19 epidemic has highlighted, that will go on to be important into the future. Sam Tickle (University of Bristol) was one of the invited speakers at this event and presented a talk on 'Detecting Local and Universal Changes in Big Data: from Global Terrorism to COVID-19.' His full abstract is below:
At many points during the year, life has changed incredibly rapidly. Aside from the global changes that almost all of us have experienced, there has been wide interest in the dichotomy between, for instance, abrupt surges in case of COVID-19 in one locality versus another, for reasons of pandemic and policy. This simultaneous consideration of global and more local-scale changes motivates the use of existing techniques in changepoint detection.
I’ll discuss a new technique for detecting local and global changepoints in data intensive settings, as well as the original motivating example giving rise to this method: that of detecting changes in the probability of a terrorist attack using the Global Terrorism Database. Subsequently, this new method has now been successfully used in a project reporting to the Cabinet Office on the economic impact of Non-Pharmaceutical Interventions (NPIs) introduced by the UK government to combat the spread of COVID-19. I shall discuss the use of novel changepoint detection techniques and other approaches that we took during this project, as well as some pertinent outcomes.
This IMA event explored the mathematical challenges and opportunities that the COVID-19 epidemic has highlighted, that will go on to be important into the future. Sam Tickle (University of Bristol) was one of the invited speakers at this event and presented a talk on 'Detecting Local and Universal Changes in Big Data: from Global Terrorism to COVID-19.' His full abstract is below:
At many points during the year, life has changed incredibly rapidly. Aside from the global changes that almost all of us have experienced, there has been wide interest in the dichotomy between, for instance, abrupt surges in case of COVID-19 in one locality versus another, for reasons of pandemic and policy. This simultaneous consideration of global and more local-scale changes motivates the use of existing techniques in changepoint detection.
I’ll discuss a new technique for detecting local and global changepoints in data intensive settings, as well as the original motivating example giving rise to this method: that of detecting changes in the probability of a terrorist attack using the Global Terrorism Database. Subsequently, this new method has now been successfully used in a project reporting to the Cabinet Office on the economic impact of Non-Pharmaceutical Interventions (NPIs) introduced by the UK government to combat the spread of COVID-19. I shall discuss the use of novel changepoint detection techniques and other approaches that we took during this project, as well as some pertinent outcomes.
