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TJMusiitwa/The-African-Covid-19-Dashboard

Domain:

healthcare

Record type:

software
Creator:
TJM
Host:
A Python dashboard visualising the impact of the coronavirus pandemic on the African continent. ## The African Covid-19 Dashboard ### Foreword Hello and welcome, you might be wondering "oh great" another dashboard and yes you are right it is however, the prime focus of this dashboard is as stated in the title to mainly visualize an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic as it relates to the African continent. This dashboard was built with Python using Dash, with charts made in Plotly and the Flatly theme of the app provided by Bootswatch. This dashboard is set up to update the data daily from the Johns Hopkins Center for Systems Science and Engineering Coronavirus repository. ## To-Do While a lot has already been done; there still remains a few things in my opinion would add greater functionality to the dashboard in general. - [ ] Make the web app phone responsive - [ ] Make into a PWA (Progressive Web App) - [ ] Add insight into the data with SIR/SEIR models and timeline predictions on the data ## Home Page ### Graph Indicators The statistic card indicators showcase the overall number of respective cases on the African continent. The green percentage marks show the delta in terms of increase in the number of cases from the previous day. ### African Maps The African maps show markers for each region is relative to the square root of the cases within that country. The size of the marker is a measure of how many people have caught the virus within that country since the outbreak began and the color is a measure of how active the virus currently is, with darker colors indicating the virus has had a greater impact and lighter shades indicating that it has caused less of an impact. ### Overall Trend The trend chart displays the totals for `CONFIRMED`, `ACTIVE`, `RECOVERED`, and `DEATHS` for Africa, by date. Hovering the mouse over the chart will reveal the counts for each of these measures on the specific date. Using the mouse, you can zoom in and out or click and drag to select a box to zoom in on. ## Data The data tab …

Visit

github.com

Tags

coronavirusdashdata-visualizationplotly-dashpython

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