Research Interests
My research focuses on how the current pandemic is changing Canadian life. Previously this year, I was working with my team on the study SPATIAL MODELLING OF COVID-19 INCIDENCE RATE IN CANADA, addressing the key factors of COVID-19 bumping among provinces in Canada, now published on ISPRS. The top three strongest effects point to provincial median income, percentage of diabetes and unemployment rate, supported by regression models, ordinary least squares (OLS), spatial error model, and spatial lag model (SLM). My current project aims at detecting, monitoring and analyzing mental health evolution, utilizing geospatial sentiment analysis powered by social media contents, through the Canadian COVID-19 timeline. Some of the questions that I aim to address using both geospatial techniques and NLP analysis include:
How does the mental health trend among Twitter users evolve along the Canadian pandemic timeline?
What are the critical factors behind the trends? Are there significant correlations between the trends and the publishing of new pandemic policy? Or does it correlate with vaccine availability, COVID-19 cases or hospitalizations?
Is there an observable geospatial difference in public mental health state during the same period? If so, what can be the key factors behind these variations across different regions?
How we can boost/maintain public confidence over the pandemic and government policy and reconstruct mental health resilience, in the light of the three questions above?
Education
- B.Sc., Major in Statistics Economics, Minor in Mathematics, University of Toronto, 2020
Publications
- Fatholahi, S. N., Pan, C. Z., Wang, L., & Li, J. (2022). SPATIAL MODELLING OF COVID-19 INCIDENCE RATE IN CANADA. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 111-116.