The goal of this dashboard is to provide an epidemiological overview of the COVID-19 pandemic, incorporating data from multiple sources.
It is being actively developed by the data science team at Epicentre MSF.
The tool is built using the R programming language and the shiny web framework. Packages used include the tidyverse, leaflet and highcharter.
If you are part of the MSF network, you can access further Epicentre COVID-19 content on our Sharepoint site.
Trends are estimated daily on the daily number of cases and deaths in JHU CSSE data observed over a period of 12 days. The most recent 2 days of data are not considered, as counts may not yet be complete due to reporting delays. For example, if data is available as of the 14th of a given month, trends will be calculated on the period from 1st-12th of that month.
On the 12-day time-series we first carry a smoothing using a 3-day moving average. As a result, we obtain 10-day smoothed values for which we run a linear regression of the values in the natural logarithm scale using the following formula:
lm(ln(smoothed values) ~ 10 days)
The standard error of the model was used to calculate the confidence intervals.
Trends presented in the report were defined using the coefficients of the linear regression as follows:
To ensure reliable estimates we estimate the trend only if the cumulative number cases (or deaths) during the 12-day period was higher than 50.
We opted to model the slope on window of 12 days because it includes 3 generation time of 4 days.