What makes this map browser different
From the very beginning of the COVID-19 disease, several map browsers have appeared to illustrate the spread of the disease all around the world. COVID-19 is a complex phenomenon in space and time. Only by approaching it through several facets, from biology to geographical aspects, we will advance in a consistent way. This humble web page has the following features to contribute to the inspiration of researchers and people wishing to complete a little bit the synoptic view that humankind needs:
- We have selected a set of indicators to provide and synthetic view of the phenomenon in space and time. This includes totals, accelerations, last days trend, etc, for different groups (Confirmed, Deaths and Recovered).
- Data can be browsed by date using the drop-down menu on the left, so the dynamics can be seen at any moment of the history of the disease.
- When querying on a map symbol, a new box shows several charts with the time series for that country, and the numerical data of the chart can be copied to the clipboard for further analysis.
- Charts have colors depending on the concept they illustrate:
- Active (=Confirmed-Death-Recovered) in brown.
- Confirmed in red.
- Deaths in grey.
- Recovered in green.
- Some data are averaged every 5 days (noted “av. 5”) (date on the graph is the central day of the moving average).
- Possibility to see the detailed data from Spanish and Catalan ambits (you will understand our natural interest for our geography and the nearest colleagues and families)
Small instructions and directions
- Visual information depends on the variable selected in the menu on the left, but when querying a country or area, the new box that appears shows the information for all variables and available dates. To query a country or area first select the "Query by location button":
- As said, there is a drop-down menu at the beginning of the legend to select the date with the data to be shown on the map; after that, you may change the date simply with the keyboard arrows up and down (not necessary to drop-down and select again) creating the effect of an animation in the map. Nevertheless, charts shown after a click on the map correspond to the entire series.
- The click on the map has to be done inside the symbol (not all the country area is active).
Please click at the name of the institutions at the top of the page, or below.
How derived data is calculated
We assume that the following variables are available every day:
- Confirmed accumulated cases
- Accumulated deaths
- Accumulated recovered cases
This is true from the Johns Hopkins data source
and the Spanish Ministerio de Sanidad data (compiled in the datadista github
), but is not the case for the Generalitat de Catalunya
, where some additional work is done to transform the cases per day into accumulated counts.
With these 3 variables we automatically generate the active cases by subtracting the dead and the recovered cases form the confirmed cases. In the case of the most detailed administrative units of Catalonia, the Government is not facilitating yet the number of deaths and recovered cases on its open data portal at the current date so active cases cannot be determined.
We derive 3 indices from each of the 4 variables (confirmed, deaths, recovered and active). To explain these indices, let's adopt the following conventions:
- t2 is the value of the time considered
- t1 is the value of the day before
- t0 is the value of the day before the day before
The derived indices use the following expressions:
- Number new cases in a day: t2-t1
- Number of new cases versus the accumulated cases: (t2-t1)/t1 (in %)
- Last day acceleration (increment of new cases versus the day before). (t2-t1)-(t1-t0) or t2-2*t1+t0
Differences are very sensible to noise caused by unwanted factors (e.g. spurious difficulties in getting the data on time for accounting it in the present day report). To filter out the noise, the values used in t2, t1 and t0 are averages of five days (from two days before to two days after). This is not possible for today and yesterday (obviously, the date for tomorrow and the day after have not been released as they are in the future) and no average value is used in this case. Please note that values of the accumulate cases presented are not averages; only the derived indicators are affected by averages.
If you are curious about how this is done in practice, if you call the values used in the averages of the time t4 considered (t2, t3, t4, t5 and t6), the values used in the day before (t1, t2, t3, t4 and t5) and the averages of the day before the day before (t0, t1, t2, t3 and t4), the calculations can be simplified into the following expressions:
- Number new cases in a day: (t6-t1)/5
- Number of new cases versus the accumulated cases: (t6-t1)/(t5+t4+t3+t2+t1))
- Last day acceleration (increment of new cases versus the day before). (t6-t5+t0-t1)/5
In addition to that, we are also providing an indication of the trend of the last days. This is done by classifying the dynamics of the pandemic in phases:
- The disease has not started. There are almost no active cases.
- The active cases are increasing and accelerating
- The active cases are increasing and decelerating
- Active cases are stable at high values (local maximum or values not increasing)
- The active cases are decreasing and the decrease is accelerating
- The active cases are decreasing and the decrease is decelerating
- Active cases are low and the disease is fading away
You can see this graphically here:
If you are curious on how exactly this is done, we consider the current value (tc), the value 5 days ago (tm) and the value 10 days ago (tf) and we apply the following logic:
Is tc > (tf+tolerance)?; tolerance=(tc+tf)/2*0.06
- yes: Is tm<(tc+tf)/2-tolerance?; tolerance=(tc-tf)*0.05
- no: Is tc < (tf-tolerance)?; tolerance=(tc+tf)/2*0.06
- yes: Is tm<(tc+tf)/2+tolerance?; tolerance=(tc-tf)*0.05
- no: Is tm low enough?; (tm<700)
About the code behind the map browser
This map browser has been created with the MiraMon Map Browser of the MiraMon Remote Sensing and GIS software now celebrating its 25th anniversary. MiraMon is a project of the Grumets Research Group. Small python scripts to transform open data into the data used in the map browser can be found here.
Joan Masó and Xavier Pons