When Ontario implemented targeted closures at the regional level during the height of the COVID-19 pandemic, a common presumption was that travel from very restricted areas to those with low levels of restriction would increase, negating the intention closures. Now, new research shows that wasn’t necessarily the case.
Researchers, led by Jed Long of Western University, used anonymized and aggregated network mobility data to determine the effectiveness of targeted lockdowns at the Ontario regional level in reducing movement during the pandemic. Research found that the closures did not significantly reduce mobility from one public health region to another.
Long and his research team measured interregional mobility (egress) between public health regions. They used mobility data from TELUS ‘Data for Good program network through its Insights platform, a privacy-preserving system to analyze patterns of mass mobility in Canada. Device locations were aggregated by determining which cell phone towers people are most often connected to, and were divided into Aggregated Scattered Areas (ADAs), which have populations of around 5,000 to 15,000 each.
In a paper, Do regionally targeted lockdowns alter movement to unlocked regions? Testimonial from Ontario, Canada, Long, with Milad Malekzadeh and Ben Klar from Western University and Gina Martin from Athabasca University, focused on two specific intervention dates to determine the effectiveness of targeted blockages at the regional level.
Until 2020, the Ontario government has put in place travel restrictions for citizens, based on regional health unit boundaries. The researchers focused on the week before and after July 17, 2020 and the week before and after November 23, 2020, when significant parts of the province had different levels of travel restrictions in place. In July, most of Ontario moved to what were then called Stage 3 restrictions, while a number of regions like Windsor, Toronto and Peel maintained higher levels of restrictions. . In November, Toronto and Peel reinstated lockdown restrictions, while the rest of Ontario was not quarantined until December 26.
“One hypothesis was that there would be an increase in the number of people leaving the targeted area for shopping and other activities. The media have said this is happening, but the data does not confirm it, ”said Long, a professor in the Department of Geography and the Environment. “An alternative hypothesis was that people would see responsibility and not leave their area. In the end, we didn’t see a lot of change.
In a second project, Associations between mobility and socio-economic indicators vary according to the chronology of the Covid-19 pandemic, Long and Chang Ren, also from Western, examined how socio-economic factors were associated with mobility patterns and how this relationship changed until 2020. They found that those in the most disadvantaged neighborhoods, as determined by census data had not changed. their mobility patterns as much as those of the most economically well-off areas. Long said it is important to note the socio-economic consequences of the lockdown measures and to understand that different areas have been affected differently.
“At the start, at the onset of the pandemic, people in more economically well-off areas could further reduce their mobility because they had the option of working from home,” Long said. “In areas where people had to go to work, who couldn’t work from home; these areas have retained their original mobility patterns. The strong association between economic deprivation and relative changes in mobility levels was one of the only consistent results we observed throughout 2020. ”
The researchers also found that these associations had changed during the pandemic and depended on how they measured mobility. “During the first wave of the pandemic in the spring of 2020, we found that those in more urban / densely populated areas did not change the variety of places they visited as much as those in less densely populated areas,” he said. said Long.
Further research is underway using anonymized and aggregated network mobility data to determine whether restrictions would be more effective if targeted at smaller regions or boundaries rather than using public health regions. .
“With targeted interventions, you are trying to eliminate the spread within and between subpopulations,” Long said. “Maybe the subpopulations can be determined based on where people are traveling as opposed to the public health regions of the province. If we ever have to implement these kinds of restrictions again, this might be a better approach. “