Constructing an Integrated Platform that Maps Neighborhood COVID-19 Cases with Census and other Open Data

This project proposes to optimize a map-based web application, which integrates numerous Open Data sets with data on the number of daily cases of COVID-19 in each public health region in Ontario. The platform will be able to merge data from the province’s Integrated Public Health Information System (iPHIS) and offer real time information to government on possible local determinants of the growth in incidence of COVID-19 infections as well as what policies should be employed in arresting the spread of the disease. This will be driven by AI methods that are able to link COVID-19 cases with neighborhood characteristics such as age distribution, population density, occupational distribution, household income, commuting patterns, and density of retail businesses, as well with the presence of lockdown policies and corresponding levels of social mobility captured through Google data. The proposed platform has the potential to make significant policy contributions given the absence of a comparable market product and the current lack of understanding on how COVID-19 is spread within and across communities.The platform is especially relevant given the demand for data on local infections for hotspots and a need for understanding the impact of important determinants.

Faculty Supervisor:

Anindya Sen


Gursimran Singh;Shahzeb Naveed


Evantage Media




Professional, scientific and technical services


University of Waterloo



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