SPATIAL MODELLING OF COVID-19 INCIDENCE RATE IN CANADA
The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences
; XLIII-B4-2022:111-116, 2022.
Article
in English
| ProQuest Central | ID: covidwho-1870915
ABSTRACT
The COVID-19 was first declared by World Health Organization (WHO) as global pandemic on March 11th 2020. While most of COVID-related studies have focused on epidemiological perspective, the spatial analysis of disease outbreak is also important to provide perceptions of transmission rates. Therefore, this paper attempts to identify the potential factors contributing to the COVID-19 incidence rate at provincial-level in Canada. Three statistical regression models, ordinary least squares (OLS), spatial error model, and spatial lag model (SLM) were applied to 14 independent variables including socio-demographic, economic, weather, health and facilities related factors. The results indicated that three factors including median income, diabetes and unemployment significantly affected the COVID-19 rates in Canada. Among three global models, the SLM performed the best to explain the key variables and spatial variability of disease incidence with a R2 value of 61%. However, in this study, the application of local regression models such as geographically weighted regression (GWR) and multiscale GWR (MGWR) have not been considered and this could be a scope for the future research.
Geography; Pandemics; Regressions; Transmission; Spatial analysis; Demographic variables; Spatial variations; Economics; Diabetes mellitus; Regression models; Sociological aspects; Economic models; Independent variables; Viral diseases; Statistical analysis; Coronaviruses; Disease transmission; COVID-19; Canada
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Collection:
Databases of international organizations
Database:
ProQuest Central
Type of study:
Observational study
Language:
English
Journal:
The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences
Year:
2022
Document Type:
Article
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