Associations between mobility and socio-economic indicators vary across the timeline of the Covid-19 pandemic

Abstract

Covid-19 interventions are greatly affecting patterns of human mobility. Changes in mobility during Covid-19 have differed across socio-economic gradients during the first wave. We use fine-scale network mobility data in Ontario, Canada to study the association between three different mobility measures and four socio-economic indicators throughout the first and second wave of Covid-19 (January to December 2020). We find strong associations between mobility and the socio-economic indicators and that relationships between mobility and other socio-economic indicators vary over time. We further demonstrate that understanding how mobility has changed in response to Covid-19 varies considerably depending on how mobility is measured. Our findings have important implications for understanding how mobility data should be used to study interventions across space and time. Our results support that Covid-19 non-pharmaceutical interventions have resulted in geographically disparate responses to mobility and quantifying mobility changes at fine geographical scales is crucial to understanding the impacts of Covid-19.

Does the COVID-19 Pandemic Change Human Mobility Equally Worldwide? Cross-Country Cluster Analysis

“…Moreover, the effect of the analysed socio-economic determinants on human mobility varied throughout the pandemic periods. This is in line with Long and Ren (2022), who show that human mobility was associated with socio-economic indicators and changed throughout 2020. Our results also correspond to Gunawan (2021), who reveals a significant relationship between human mobility changes and macroeconomic indicators, i.e., the HDI and labour force participation.…”

“…Surprisingly, Maloney and Taskin (2020) suggest that social distancing measured by human mobility changes is independent of the stringency level of anti-COVID-19 government interventions and restrictions. Long and Ren (2022), based on the first year of COVID-19, found that human mobility was associated with socio-economic indicators, and changed throughout the year 2020. W. D. Lee et al (2021), based on the early stages of England's COVID-19 pandemic, confirm that socioeconomic status was strongly related to human mobility reductions, but varied across England.…”

Legal Restrictions and Mitigation Strategies Amongst People with Disabilities During COVID-19

“…Specifically, individuals who were higher in socio-economic status tended to visit other people less. This finding is in accordance with a Canadian study showing that individuals who were socio-economically deprived were likely to move around more in periods of restrictions (Long and Ren, 2022). Individuals who were higher in socio-economic status were perhaps more able than others to work from home (Moehring et al, 2021), and also the use the Internet and technology to maintain social relationships, in lieu of in-person visitation.…”

SARS-CoV-2 Transmission Risk Model in an Urban Area of Mexico, Based on GIS Analysis and Viral Load

“…Since the first cases of SARS-CoV-2 were detected in Tepic, Nayarit (urban area studied in this research) around April 2020 [ 16 ], up to 26 January 2022, Nayarit has had 46,664 confirmed cases and this Mexican state has faced a four-pattern epidemiological peak with highest peaks registered in July 2020, the beginning of January 2021, August 2021, and January 2022 [ 6 ]. Many measures had been undertaken to mitigate the adverse effects of the COVID-19 pandemic; restrictions on mobility and social distancing, the generalized increase in work at home, the closure of educational and recreational centers, as well as the reduction of close contacts with other people, especially those outside the household [ 17 , 18 ]. The lack of planning, the high cost of PCR analysis, plus the specific characteristics of the pandemic having such as long incubation period, asymptomatic infection, and high false-negative rate diagnosis, have hindered the mitigation of the contagion [ 19 ].…”

“…These zones were red spots in the risk map during the four maximum peaks of the pandemic. Mobility and socio-economic indicators are closely related as relevant factors in this pandemic as they link human flows and virus transmission rates [ 18 , 19 ]. A blind spot in this study is that economic factors were not used to feed the GIS, this represents an opportunity for future research in the area.…”