COVID-19 and Data Visualization
How to visualise and understand the pandemic — graphs by Dr. AZM
Caught up by the new rules limiting our freedom of movement and shedding a new light on the value of having an efficient healthcare system, it is easy to lose the overview of what is a global viral spread.
What do we know about COVID-19?
Coronavirus COVID-19 emerged in late 2019 in Wuhan, a city of more than 6 million inhabitants in Eastern China. Italy is currently the focal point of the pandemic with more than 1K new cases each day and more than 2K deceased, as writing.
Coronaviruses are particles with a spherical shape containing one filament of (single-stranded positive-sense) RNA. The viral envelope is decorated with sugar chains that confer a visible halo, or corona, around it when visualised under the electron microscope. One image of representative coronavirus particles is available here.
This new strain of coronavirus has been spotted as the cause behind a new series of pneumonia cases recorded in China in late 2019. Coronaviruses are known to infect mammals and birds, and they can acquire the genetic material to cross species and infect humans. In this case, pangolins have been suggested as the animal carrying the COVID-19 to humans but the finding is not definitive.
What are scientists doing?
Scientists have been struggling to gather all the relevant data and thus provide a model able to predict the spreading of the COVID-19 coronavirus.
Scientific magazines and portals have made the research freshly produced, freely available to the public and can be accessed also through a specialised website.
Using the that is made data freely available on Wikipedia, we tried to give an overview of the situation in three graphs.
It is uncertain what the amount of unreported cases is, even for countries with the lowest reported death rate, like South Korea or Germany. Basic epidemiological considerations tell that the reported deaths illustrate the state of the pandemic at the moment of the contagion, which for COVID-19 is around two weeks before the death. Back then, the number of detected cases was significantly lower (the number of cases can duplicate every around 6 days). On the other hand, the fraction of hidden cases (unreported or people with mild symptoms that go undetected) can be as high as 10–100% of the reported cases. Since both effects are large but they drive the death rate in opposite direction, it is difficult to decide which can be the real figure for the death rate. However, it looks like the number of cases that go undetected or unreported may dominate the final value. If this assumption is correct, the reported death rates are high estimates of the real death rate.