Mobility changes during the COVID-19 pandemic

Introduction

The COVID-19 pandemic has led to significant changes in the movement of people, either through regulated or voluntary social distancing; in a global effort to reduce or eliminate transmission.

Here, we present how these changes have occurred for a selection of countries, based on Google’s COVID-19 Community Mobility Report (CMR) data. The CMR data aims to

…provide insights into what has changed in response to policies aimed at combating COVID-19. The reports chart movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.

To create the CMR, Google uses anonymous mobility data from those users that have activated ‘Location History’ on their mobile devices.

International Comparison

Presented below is the percentage change in activity relative to the baseline period of January 3rd – February 6th, 2020, for a selection of countries that were comparable to Australia and New Zealand, as this is where CAST is based. Mobility for China would also have been interesting to show, but is unavailable in the CMR dataset.

The CMR was generated on 21st of May, with data ending on 16 May 2020.

NB: Countries can be toggled off/on by clicking the legend lines to the right of the charts. Moving the cursor over the lines will display the underlying values.

Several interesting observations can be seen above:

• Across all countries, there has been a marked reduction in the number of visits to retail & recreation, transit stations (i.e. public transport) and workplace locations, and this still remains below baseline levels.

• Time spent at ‘Residential’ locations has increased as a result, and again remains higher than the baseline period. [1]

• Italy underwent a much earlier (and mostly deeper) reduction in mobile activity than elsewhere, reflecting the earlier spread and impact that COVID-19 had there.

• The relative reduction in mobility unsuprisingly mirrors the strictness of social-distancing / lockdown rules introduced by each country.

• Sweden, with the most relaxed rules, has the smallest reduction in normal activity. The large increase for Swedish park visits is probably due to warmer temperatures relative to the baseline period, given that January temperatures for Sweden typically hover around freezing point.

• Next in mobility reduction is the United States and Australia, where movement rules vary across States for both countries, but national lockdown rules have not been introduced. Both have very similar patterns, after taking into consideration baseline seasonality (see below).

• The countries that introduced national lockdowns - Italy, the United Kingdom and New Zealand - all reached similar decreases in ‘Grocery and Pharmacy’, ‘Retail and Recreation’, and ‘Workplace’ visits.

• Australia and New Zealand’s workplace patterns are likely too high, by approximately 15%, as the baseline period overlaps with the summer holiday period in the southern hemisphere when most people are away from work.

• New Zealand has seen a big jump in mobility from the 14th of May, corresponding to the second relaxation of lockdown rules. New Zealand’s workplace and transit station activity is now ~10% ahead of Australia, with other forms of mobility now mostly on par with Australia. However, workplace visits are probably still 27% below normal, after applying the 15% baseline correction described above.

Australia

Australian mobility change patterns by State are shown below, again as percentage change relative to the baseline period of January 3rd – February 6th, 2020.

Items of interest include:

• The rush on supermarket and pharmacy supplies during the 16th-18th of March can clearly be seen in the ‘Grocery and Pharmacy’ category, with the largest being a 45% increase observed in Victoria.
• The ongoing spikes in ‘Parks’ activity for the Australian Capital Territory, may be partly due to reduced park activity during the baseline period, as this coincided with several bushfires that prevented many outdoor activities because of smoke.
• The Northern Territory consistently had the smallest reduction in activity for ‘Retail and Recreation’, ‘Transit Stations’ and ‘Workplace’ visits, with workplace activity appearing to return to baseline levels from April 20th onwards.

New Zealand

Mobility change patterns for New Zealand provinces are shown below. Elements of interest include:

• All provinces demonstrated very similar reductions in behaviour across all mobility categories, and similarly commensurate increase in residential duration.
• Most provinces exhibited a double peak of increased grocery and pharmacy visits as the NZ Government signalled upcoming lockdown restrictions, spiking first on the 19th of March, then again on the 23rd of March.
  
• The noticeable ramp-up in mobility matches the increased relaxation of full lockdown restrictions, effective on 28th April and 14th of May.
• The Bay of Plenty region is closest to returning to normal workplace activity, at -1% of baseline (or -16% after baseline seasonal correction).
  
• The noticeable reduction in ‘Transit Station’ visits prior to lockdown for Northland, and to a lesser extent Marlborough, is probably due to increased seasonal visitors during the summer baseline period.
  
• The big drop for Taranaki ‘Workplace’ visits is caused by the Taranaki Anniversary Day on March 9th.
• Wellington’s ‘Parks’ activity appears higher than the rest of the country, but may be due to an unusual baseline period.

Summary

The COVID-19 pandemic has resulted in enormous loss of life and disruption throughout the world. The results presented above from Google’s mobility data provides a small glimpse of the extent and depth of this upheaval, which is still far from over.

We have deliberately avoided presenting any COVID-19 cases or mortality statistics, to avoid the temptation of exploring any causal relationships with mobility behaviour. That is not our place. There are better equipped teams, with access to far richer data than us, that will examine this pandemic from every angle. We hope that the outcome of their work will stem the current spread, and to identify better, evidence-based, control measures to prevent future pandemics. We wish them good fortune.

Stay safe and be kind everybody.