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spatialEpisim: an open-source R Shiny app for tracking COVID-19 in low- and middle-income (LMIC) countries

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spatialEpisim: an open-source R Shiny app for tracking COVID-19 in low- and middle-income (LMIC) countries
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351
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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.
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Production Year2022

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Abstract
It is essential to understand what future epidemic trends will be, as well as the effectiveness and potential impact of public health intervention measures. The goal of this research is to provide insight that would support public health officials towards informed, data-driven decision making. We present spatialEpisim, an R Shiny app (github.com/ashokkrish/spatialEpisim) that integrates mathematical modelling and open-source tools for tracking the spatial spread of COVID-19 in low- and middle-income (LMIC) countries. We present spatial compartmental models of epidemiology (ex: SEIR, SEIRD, SVEIRD) to capture the transmission dynamics of the spread of COVID-19. Our interactive app can be used to output and visualize how COVID-19 spreads across a large geographical area. The rate of spread of the disease is influenced by changing the model parameters and human mobility patterns. First, we run the spatial simulations under the worst-case scenario, in which there are no major public health interventions. Next, we account for mitigation efforts including strict mask wearing and social distancing mandates, targeted lockdowns, and widespread vaccine rollout to vaccinate priority groups. As a test case Nigeria is selected and the projected number of newly infected and death cases are estimated and presented. Projections for disease prevalence with and without mitigation efforts are presented via time-series graphs for the epidemic compartments. Predicting the transmission dynamics of COVID-19 is challenging and comes with a lot of uncertainty. In this research we seek primarily to clarify mathematical ideas, rather than to offer definitive medical answers. Our analyses may shed light more broadly on how COVID-19 spreads in a large geographical area with places where no empirical data is recorded or observed.
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Source codeMathematicsLink (knot theory)Open setSimulationParameter (computer programming)Computer simulationImage resolutionCellular automatonFreewareNumberRaster graphicsFile formatApproximationWordComputer networkUniverse (mathematics)Total S.A.Mathematical modelAxonometric projectionMathematical analysisPersonal digital assistantVideoconferencingTemporal logicPresentation of a groupAsynchronous Transfer ModeCountingPresentation of a groupComputer simulationServer (computing)Revision controlMobile appIncidence algebraSimulationProjective planeLevel (video gaming)Mathematical analysisSoftware testingDeterminismCellular automatonCASE <Informatik>Line (geometry)Open sourceEstimatorWeb browserTraffic reportingParameter (computer programming)Moving averagePoint (geometry)CumulantComputer animationDiagram
Transcript: English(auto-generated)
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,
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.
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
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.
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
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
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
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.
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
is that governments intervene and restrictions during pandemics. So here, I'll be showing a deterministic model simulation using Nigeria's data.
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.
And I'd like to acknowledge the team behind this dashboard. And yeah, thank you for attending.