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Location
London, Ontario
Website
https://westernu.maps.arcgis.com/apps/dashboards/61234059488042ccb4ad3b9583e03dee
Start Date
18-11-2021 2:00 PM
End Date
18-11-2021 3:00 PM
Description
We present an interactive R Shiny app called “COVIDEpisim” to capture the spatiotemporal transmission dynamics of the spread of COVID-19 in Low- and middle-income (LMIC) countries. Our app uses open-source GIS tools and freely available population count data downloaded as a gridded raster map at the 30-arc second resolution from WorldPop (www.worldpop.org) to assess the geographical spread of COVID-19. We consider Nigeria as a test case and use the daily COVID-19 incidence, prevalence, vaccination and death data retrieved from the Nigerian Centers for Disease Control (NCDC) webpage. We implement a SVEIRD (which stands for Susceptible, Vaccinated, Exposed, Infectious, Recovered and Dead compartments) epidemic model. Our simulation provides insight that would support the Public Health officials towards informed, data-driven decision making and more broadly on how COVID-19 spreads in a large geographical area with places where no empirical data is recorded or observed.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Tracking COVID-19 in Low- and middle-income (LMIC) countries using open source GIS tools, Demonstration (20 min)
London, Ontario
We present an interactive R Shiny app called “COVIDEpisim” to capture the spatiotemporal transmission dynamics of the spread of COVID-19 in Low- and middle-income (LMIC) countries. Our app uses open-source GIS tools and freely available population count data downloaded as a gridded raster map at the 30-arc second resolution from WorldPop (www.worldpop.org) to assess the geographical spread of COVID-19. We consider Nigeria as a test case and use the daily COVID-19 incidence, prevalence, vaccination and death data retrieved from the Nigerian Centers for Disease Control (NCDC) webpage. We implement a SVEIRD (which stands for Susceptible, Vaccinated, Exposed, Infectious, Recovered and Dead compartments) epidemic model. Our simulation provides insight that would support the Public Health officials towards informed, data-driven decision making and more broadly on how COVID-19 spreads in a large geographical area with places where no empirical data is recorded or observed.
https://ir.lib.uwo.ca/wlgisday/2021/demonstrations/7
Comments
Peoples Choice Award Winner
SRT file available upon request, contact the GIS team via https://guides.lib.uwo.ca/gis/support.