Lu WANG, Ryerson University, Canada
Jie YU, Ryerson University, Canada
Dongmei CHEN, Ryerson University, Canada
Lixia YANG, Ryerson University, Canada
The presentation draws from an on-going research project that explores COVID-19 outcome, neighbourhood variation and prevention behaviour in Toronto, Canada. Toronto is the most populous urban centre and the largest COVID-19 hotspot in Canada. The study focuses on the spatial and social patterning of COVID-19 in diverse neighbourhoods. Primary data (on-line survey, focus groups) collected shows evolving prevention practices at an individual level that are shaped by risk perceptions towards COVID-19, individual characteristics and public health interventions across key timelines during the pandemic. The discussions will highlight: COVID 19 pandemic data challenges, pandemic impacts on human mobility and public health responses, and implications on using a mixed-methods approach combining spatial-quantitative and qualitative analyses in health research.
Keywords: COVID-19|Neighbourhoods|Toronto|Prevention|Spatial patterns
A103792LW