Bayesian modelling of complex trajectories: a case study of covid-19
Date: 12 June 2020, Friday
Time: 2 pm
Speaker: Prof. Kerrie Mengersen (Queensland University of Technology)
Abstract: Since the initial outbreak in Wuhan (Hubei, China) in December 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for coronavirus disease 2019 (COVID-19), has rapidly spread to cause one of the most pressing challenges facing our world today: the COVID-19 pandemic. Within four months of the first reported cases, more than two and a half million cases were confirmed with over two hundred thousand deaths globally, and many countries had taken extreme measures to stop the spread. Although Bayesian models of epidemics are well known in the literature, modelling COVID-19 has been problematic because of the complexity of control responses that were implemented to contain the spread of the disease in different countries. In this presentation, I will describe a novel stochastic epidemiological model that was developed by our team* to analyse the response to the COVID-19 outbreak for 103 countries over the period 22 January to 13 April 2020. The model includes a regulatory mechanism that captures the level of tolerance to rising confirmed cases within the response behaviour. Using approximate Bayesian computation, we characterise countries with respect to this tolerance and also identify the impact of incomplete information. In addition to analysing each country separately, the model was embedded in a network structure informed by transport data. This enabled evaluation of the additional impact of connectivity between countries. The model also allows for the evaluation of 'what if' scenarios, importantly to provide forward projections of the impact of relaxing certain restrictions in individual countries and globally.
*This work was led by David Warne and is joint with Anthony Ebert, Christopher Drovandi and Antonietta Mira, medrxiv-link
About the speaker: Distinguished Professor Kerrie Mengersen is a statistician and Director of the Centre for Data Science at QUT. She is an elected Fellow of the Australian Academy of Science, the Australian Social Sciences Academy, and the Queensland Academy of Arts and Sciences. As an ARC Laureate Fellow, she works on the development and application of methods for using diverse types of data to learn about complex systems and problems in health, the environment and industry. She is currently working with international teams to use data science to gain insights into national and international dynamics of covid-19.
Zoom Link: https://uni-sydney.zoom.us/j/97443063685
Video:
Date: 12 June 2020, Friday
Time: 2 pm
Speaker: Prof. Kerrie Mengersen (Queensland University of Technology)
Abstract: Since the initial outbreak in Wuhan (Hubei, China) in December 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for coronavirus disease 2019 (COVID-19), has rapidly spread to cause one of the most pressing challenges facing our world today: the COVID-19 pandemic. Within four months of the first reported cases, more than two and a half million cases were confirmed with over two hundred thousand deaths globally, and many countries had taken extreme measures to stop the spread. Although Bayesian models of epidemics are well known in the literature, modelling COVID-19 has been problematic because of the complexity of control responses that were implemented to contain the spread of the disease in different countries. In this presentation, I will describe a novel stochastic epidemiological model that was developed by our team* to analyse the response to the COVID-19 outbreak for 103 countries over the period 22 January to 13 April 2020. The model includes a regulatory mechanism that captures the level of tolerance to rising confirmed cases within the response behaviour. Using approximate Bayesian computation, we characterise countries with respect to this tolerance and also identify the impact of incomplete information. In addition to analysing each country separately, the model was embedded in a network structure informed by transport data. This enabled evaluation of the additional impact of connectivity between countries. The model also allows for the evaluation of 'what if' scenarios, importantly to provide forward projections of the impact of relaxing certain restrictions in individual countries and globally.
*This work was led by David Warne and is joint with Anthony Ebert, Christopher Drovandi and Antonietta Mira, medrxiv-link
About the speaker: Distinguished Professor Kerrie Mengersen is a statistician and Director of the Centre for Data Science at QUT. She is an elected Fellow of the Australian Academy of Science, the Australian Social Sciences Academy, and the Queensland Academy of Arts and Sciences. As an ARC Laureate Fellow, she works on the development and application of methods for using diverse types of data to learn about complex systems and problems in health, the environment and industry. She is currently working with international teams to use data science to gain insights into national and international dynamics of covid-19.
Zoom Link: https://uni-sydney.zoom.us/j/97443063685
Video: