Australia can use population-level mobility data to fight COVID-19

Lucinda Adams, Robert J Adams and Tarun Bastiampillai
Med J Aust
Published online: 13 July 2020

This is a preprint only. The final version of this article is available at:


“Stay-at-home” orders are a keystone of the COVID-19 response, but are shrouded in controversy. Apple is publishing population-level mobility throughout the pandemic. We mapped the mobility data against public health interventions worldwide. On average populations decreased movement below baseline 13 days prior to “stay-at-home” orders, in Sydney it was 8 days, in Auckland 11 days. Even in cities with minimal governments restrictions, such as Stockholm and Rio de Janeiro this was true. Worldwide this decrease in movement coincided with Lombardy’s rising COVID-19 death toll, however Hong Kong and Singapore, pandemic-experienced cities behaved differently, with earlier and ongoing decreased movement. When planning of the “second-wave” we need to be innovative and population-level mobility data can be part of the ongoing public health response.

“Stay-at-home orders”, or the lack thereof, have divided governments and the political sphere worldwide. US1 media has reported US populations decreased their movement prior to stay-at-home orders.  We looked at mobility data from major cities worldwide, including Australia, and saw the same pattern consistently replicated. Why is this so?

Apple mobility data2 was used as a measure of social mobility and was assumed to correlate with social distancing population observance. We extracted available data for twenty-two cities across nine regions from all six populated continents in a systemic manner to gain a broad cross-section. This data was then mapped against local government action taken from official websites.

We found worldwide populations reduced their movement prior to and even without stay-at-home orders (Figure 12,3,4,5,6, see sFigures 1-9 Supplementary Appendix - available in PDF). Across all cities examined, movement below baseline was seen on average 13·1 days2, with a median of 9 days, prior to government enforced stay-at-home orders. Mean reduction in walking prior to stay-at-home orders was 45·2% (range 22·0-77·2%2). For the majority of cities examined movement voluntarily decreased sharply around March 122, which in any other situation would appear coordinated and uniform to military-level precision (Figure 12,3,4,5,6, see sFigures 1-9 Supplementary Appendix - available in PDF). In Sydney, Australia’s COVID-19 hotspot, population movement dropped below baseline on 14/03/2020, prior to Prime Minister Morrison telling us to “stay-at-home”, and nine-days prior to closure of restraurants5. The consistency of this data across diverse populations and environments suggests this behaviour is cannot only be driven by strict government restrictions or political pressure within regions. Many factors may have contributed to this, including minor local public health action e.g. limiting mass gatherings, school closures. However, it also coincided with widespread reporting of sharp increases in COVID-19 deaths in the Lombardy region of Italy by March 17 (1625 reported deaths7).  This near-instantaneous mass behaviour change is significant, it took 50 years and billions of dollars to reduce smoking rates8, and John Snow9 days to garner community support to remove the Broad Street pump handle, both in the face of robust epidemiological evidence.  Why did we all just go home?

We postulate the global mass media reiterating virus seriousness, broadcasting almost real-time images of full ICUs and dying families into people’s living rooms, in conjunction with minor public health restrictions, sparked fear and drove mass behaviour change. Even in cities with minimal restrictions, such as Stockholm there was an average transit decrease of 46·7%1,4 in April (Figure 12,3,4,5,6 - available in PDF). Rio de Janeiro, a city with conflicting government advice and minimal restrictions3, across April there was an average 75·7% decrease in walking1 (Figure 12,3,4,5,6 - available in PDF). Hong Kong (HK)10, a city that did not deploy a stay-at-home advisory but with direct transport and cultural links to Wuhan and previous experience with SARS epidemics, movement dropped below baseline on January 23 (see sFigure 5 Supplementary Appendix - available in PDF), well before other cities with an ongoing reduction in transit walking of over 50%1. Singapore11,12 (see sFigure 5 Supplementary Appendix - available in PDF) with a similar history of SARS showed a parallel picture to HK suggesting past experience, like SARS, also drives risk perception and behaviour. Individual and community risk perceptions will likely determine behaviour as lockdown restrictions ease. Governments planning for re-opening or the “second wave”, need to combat complacency and “social distancing fatigue”. Behaviour in HK and Singapore suggests prior pandemic experience can lead to early voluntary social distancing. However, government restrictions may be needed for sustained social distancing. As pandemic-naïve populations with low restrictions, such as Stockholm and Atlanta13 (see sFigure 1 Supplementary Appendix - available in PDF) have up-trending mobility1. Mobility data is potentially an effective tool for monitoring population behaviour that informs public health action. As we face Australia faces the “second-wave” public health officials need to be innovative, utilising this free, publicly available, almost-real time resource could be part of the solution.

Figure 1: population-level mobility data mapped against government action3,4,5,6: (available in PDF)


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  • Lucinda Adams1
  • Robert J Adams2
  • Tarun Bastiampillai2

  • 1 South Australian Department of Health and Wellbeing
  • 2 Flinders University of South Australia



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