SMS scnews item created by Munir Hiabu at Wed 3 Jun 2020 1433
Type: Seminar
Modified: Wed 10 Jun 2020 0936
Distribution: World
Expiry: 17 Jun 2020
Calendar1: 12 Jun 2020 1400-1500
CalLoc1: https://uni-sydney.zoom.us/j/97443063685
CalTitle1: Bayesian modelling of complex trajectories: a case study of covid-19
Auth: munir@119-18-2-42.771202.syd.nbn.aussiebb.net (mhia8050) in SMS-WASM

# Statistics Across Campuses: Kerrie Mengersen -- Bayesian modelling of complex trajectories: a case study of covid-19

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

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.