Artist view of a feline alpha-coronavirus

Going straight to plots


SARS-COV2 have been isolated in China in December 2019. This is an new variant of the previous virus responsible of SARS an MERS outbreaks. This new virus bind with increased affinity to human cell receptors so it can infect people more efficiently. This is why this new variant is responsible of a real pandemic outbreak.

Here I provide links to plots generated from Johns Hopkins Github repository datasets. There is also a nice dashboard from Johns Hopkins University.

Detailed math is available on a Kaggle notebook

These plots are monitoring various epidemiological features:

  • Prevalence: number of infected cases over the whole population of a country.
  • Incidence: number of new cases per day over the whole population of a country.
  • Lethality or fatality rate: number of deaths over the number of confirmed cases
  • Reproduction factor:  number of people that are contaminated by a single infected person.

Access my interactive and daily updated plots:

Just click to the links below.  On descriptive epidemiology plots, you can hide some countries to focus on  what you want see. You can also zoom an area of a plot by selecting it with mouse, often we want to focus on the tail of the plot. There are also predictive models. Nice reading. Africa and South America will come soon, if the situation in these countries is getting worse.

Note about Predictive models:

These models are SIR models which admit as parameters

  • 𝛾 =  1/number of days before recovering or die from the disease (probability to recover or die)
  • 𝛽 =probability to become infectious for a susceptible person with :
  • R0 = 𝛽 / 𝛾 <=>𝛽 = R0.𝛾
  • S0 = Starting proportion of the population that is susceptible / exposed
  • I0 = Starting proportion of infectious people at the beginning of the outbreak

Current observed R0: 2.4<= R0 <=4 and

Custom adaptation to real data : So if we want to standardize observed real data we need to divide by the total population. But are the confirmed active cases speaking truth. Absolutely not, a large proportion of people will be infected but never tested thus not confirmed. We don’t know about this proportion that may dramatically vary from one country to another. So we define a true case rate (TCR). The number which multiply the confirmed cases to get the true infected (symptomatic and non symptomatic).

The tuning of these models is automated by computer to allow a daily update with new available data. But remember that models are not the truth, particularly without human supervising.


Note about mortality comparison:

The sample of European countries is split into 2 groups from the median of mortality. The first group contains those with the lower mortality rate (good performers) and the second contains the highest mortality rate (bad performers). The the 2 groups are compared for differences in public expenditures : education, ternary education, health and military. Because mortality increases with the course of the epidemic, the radius of markers allow to give less weight to countries where outbreak started later.

For the last graph, the more darker is the color, the higher is statistical significance. Trends would change with time, so this plot is also daily updated.


Current Situation in Asia

Descriptive epidemiology

Current Reproduction factor of the virus

Current Situation in Europe

Descriptive epidemiology

Current Reproduction factor of the virus

Mortality comparison

Current Situation in North America

Descriptive epidemiology

Current Reproduction factor of the virus


3 thoughts on “Monitor Covid-19 evolution through reproduction factor of SARS-COV2 coronavirus

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