spatialEpisim: an open-source R Shiny app for tracking COVID-19 in low- and middle-income (LMIC) countries
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Number of Parts | 351 | |
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License | CC Attribution 3.0 Unported: You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor. | |
Identifiers | 10.5446/68970 (DOI) | |
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Production Year | 2022 |
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Transcript: English(auto-generated)
00:00
Hi, everyone. My name is Crystal Wei, and I'm from Mount Royal University. And today, I will be showing you an open source R Shiny app that will be demonstrating for low and middle income countries tracking COVID-19. So the presentation will be available upon request,
00:22
but there is a deployed version on the Shiny app server as well we have our GitHub available. So what our key goal behind this project is that we want to be able to track COVID-19, but it is adaptable to any infectious disease.
00:41
And our primary emphasis is in low and middle income countries, however, because of the socioeconomic situation and their limited resources, but it is applicable to all countries. So first, I will address the three simple steps
01:02
that users will need to access our app. So you can access our app via browser or from our GitHub, and then you'll upload your seed data, and then you'll set the appropriate parameters and model, and you'll be able to run your simulation.
01:23
So here, we're using Nigeria as a test case where we have about 1.6 million grid cells containing 210 million citizens. So here, we have the simulations
01:42
able to do two aspects, a retrospective analysis and a projection into the future. So our dates will be September 2020 to December 2020, and then June 2022 and October 2022. So here, the blue line represents the actual reported
02:02
deaths by the Nigerian Center of Disease Control. As you can see that there were daily reports until eventually they were only reporting about once a week, and that's why we have these aggregated cumulative data points. And then the red and green lines
02:21
represent scenario one and two where with and without restrictions. So we can see that the model overestimated the amount of deaths, but if you're able to adequately play around the parameters, you're able to get a more accurate estimation.
02:42
So in conclusion, when there are no restrictions or interventions, Nigeria experienced about nearly twice as many deaths, and the vaccine rollout would not be as nearly effective in 2021. So we want to emphasize how important it
03:01
is that governments intervene and restrictions during pandemics. So here, I'll be showing a deterministic model simulation using Nigeria's data.
03:25
So here, we can see that Lagos is experiencing most of the level of incidence as well as to other states, but as we near October, we only have a few liter of incidence in Lagos.
03:46
And I'd like to acknowledge the team behind this dashboard. And yeah, thank you for attending.