Aligning policy to effects of discretionary behaviour change accurately
Aligning policy to effects of discretionary behaviour change accurately
Summary
Models of infectious disease transmission have been instrumental in providing evidence to inform COVID-19 response policies. One factor that is poorly captured by existing models is the discretionary response of individuals to behavioural interventions designed to reduce physical contact and transmission. Quantifying changes in discretionary (i.e., not work-related) social behaviour is challenging as it requires a detailed understanding of how people make decisions about their social behaviour. This study will treat the COVID-19 crisis as a natural experiment in which discretionary behaviour was altered, and contribute towards the development of policy decision support models that accurately account for the effects of discretionary behaviour change.
Aim
This project aims to shed light on discretionary social activity changes during COVID-19 in Australia by
analysing detailed mobility data over selected periods of the pandemic. Mobility indicators computed at
appropriate levels of spatial and temporal aggregation will enable robust analysis of the socioeconomic correlates of discretionary behavioural change.
Objectives
Differentiate between discretionary and non-discretionary travel (mobility data). Generate indicators of
discretionary social activity change, across periods with and without lockdown mandates. This study will
be the first to analyse a new, detailed mobility dataset generated by the Australian startup Pathzz, generated from hundreds of different mobile device applications reporting GPS tracking data. Pathzz has generously committed to providing access to their anonymised aggregated dataset for research purposes.
Methodologies
Statistical analysis of changes in mobility trends, stratified by socioeconomic indicators.
Progress to date
We are currently composing an open-access publication describing the methods used for aggregation and analysis of mobility data, and demonstrating the trends in mobility patterns stratified by socioeconomic (SEIFA) covariates produced by the ABS.
Researchers
Chief Investigator
- Dr Cameron Zachreson, Research Fellow In Modelling & Simulation
School of Computing and Information System
Faculty of Engineering and Information Technology - Ms Erika Martino, Research Fellow In Healthy Housing
Melbourne School of Population and Global Health
Faculty of Medicine, Dentistry and Health Sciences - A/Prof Martin Tomko, Associate Professor in Spatial Information
Department of Infrastructure Engineering
Faculty of Engineering and Information Technology - Prof Rebecca Bentley, Professor, Health Inequalities
Melbourne School of Population and Global Health
Faculty of Medicine, Dentistry and Health Science - A/Prof Nic Geard, Associate Professor, Computational Simulation and Modelling
School of Computing and Information Systems
Faculty of Engineering and Information Technology