CoE Analytics & Sensorics analyzes COVID-19 outbreak

April, 4th 2020 – Reading time: 8 minutes

The CoE Analytics & Sensorics analyses on a regular base available COVID-19 data to give insights into possibilities and constraints of Data Analysis

Established as Think-Tank for statistics and data analysis, the CoE AS normally enables companies to understand themselves better by analyzing their data (process, metrology, manufacturing…) to extract hidden information and parameters.

With this project we want to share our daily analysis about the actual COVID-19 outbreak and about impacts of these statistics. Everyone shall have the opportunity to understand how decisions in politics, society and in our daily life may have an impact and why data can be interpreted in such different ways from different stakeholders.

Any additional comments, adds and corrections are gratefully received.

 

COVID-19 Key Facts

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  • Current doubling time: 7.213±0.346 days
  • After March 29th the doubling time has probably increased again. This is a strong indicator for the success of government actions. We will try to confirm that on Tuesday 7th
  • The data is periodic with an interval of 7 days
  • Quarantine or lockdown does not solve the problem but delays it
  • There is an indicator that the vicinity to Italy and France might have an impact on the growth rate in Germany, however, this cannot be confirmed with the available data

Cases in Germany

The first analyses we have made are shown in Figures 1 and 2. Figure 1 shows the reported COVID-19 cases and deaths against the reporting date (note the logarithmic scale). From the 19th of March onwards, it can be seen that the growth of newly reported cases decreases. This can be taken as an indication that the measures decided and implemented by the government have already achieved initial success. However, due to the above-mentioned reporting delay, we reserve the right to revise this statement in the future.

Figure 1: (Logarithmic scale) Number of new cases and new death displayed vs. the reporting date. One can see that the growth of the number of new cases per day is getting slower after the 19th of March.
Hint: the presented figures are using logarithmic scale in contrast to the official figures presented by the Robert Koch Institute. We’ve chosen logarithmic scale, because it makes exponential growth appear linear (changes are spotted more easily) and suppresses distracting small changes. Additionally, now it’s also possible to visualize the death numbers, which are two magnitudes smaller than the number of infections.

An additional observation that we have been able to make is a periodic behaviour of the curve. The periodicity is 7 days with the lowest value on each Sunday. This is of course explained by the opening hours of the family doctors.

Figure 2 shows the accumulated cases against their respective reporting dates. Here, too, a bending of the curve can be seen from the 19th of March onwards.

Figure 2: (Logarithmic scale) Accumulated COVID-19 cases and deaths displayed vs. the reporting date. The green and grey curve are the best fits for the exponential growth. Note that the doubling time of the green curve is more than double the grey one. We also added the major government actions [4], which could influence the behavior of the curve. Additionally, we show an estimation on the number of recoveries.
(*) The estimation is based upon the WHO report on the Hubei case [1], which gives us coarse numbers on the recovery time.

This also coincides with the two adjusted exponential curves (note again the logarithmic scale, figure 3 presents the same graph using linear scale), which intersect each other on March 19th. The doubling time has doubled to 7.213 days at the transition from the grey to the green curve, which means that it takes about 7 days for the number of reported infections to double. The government wants a doubling period of 10 days until the current measures can be relaxed. So we are on the right track.

Figure 3: (Linear scale) Accumulated COVID-19 cases and deaths using a linear scale for comparison. Note that the number of deaths is small compared to the number of infections.Additionally, we show an estimation on the number of recoveries.
(*) The estimation is based upon the WHO report on the Hubei case [1], which gives us coarse numbers on the recovery time.

Due to the reporting delay and due to the periodicity of the data with the period duration of 7 days, it is not possible to make reliable statements about the current week. Even if the curves in Figure 2 for calendar week 14 seem to become flatter, this may be an effect of periodicity or statistical deviations. As soon as reliable statements are possible, we will publish them here.

Compared to the updates of the last week, starting from March 29th the growth rate has probably decreased again. However, as the data is periodic, we won’t be sure before next Tuesday, the 7th of April. If the current trend confirms, then its cause will most probably the current government actions.

Effects of the government actions

Taking into account the median incubation time of COVID-19 of 5-6 days (source), the kink in the growth rate on March 19th may be linked to the recommendation of Minister Jens Spahn to do his work in his home office. It is likely, however, that the data situation does not allow any statement about this, since the actual system is much more complex. Effects that have an influence on the propagation rate include

  • Cancellation of flights and closure of borders
  • Quarantine and rapid response by health authorities to break the chain of infection
  • Increasing uncertainty of the population and social isolation
  • The possibility to efficiently reduce the probability of infection with SARS-CoV-2 by washing hands
  • Increasing infestation of social groups

Opinions of the scientific community

FMartin Eichner, epidemiologist from Tübingen and co-responsible for the online COVID 19 simulation, was interviewed by Tagesschau.de (Interview in German). The core statement is that the current contact ban does not solve the problem in any way, but only postpones it into the future, as the central problem of missing immunity remains. The goal of the contact ban cannot be to completely survive COVID-19, but only to delay the spread of the disease until a vaccine is available. However, as this is expected to take until the end of the year, the contact ban (if continued until then) will cause considerable social, economic and financial damage, which may not be in proportion to the benefits of contact ban (the number of deaths may not necessarily be lower). Eichner recommends propagation in waves, i.e. one takes the health system to its limits and then restricts public life incomparably harshly until the situation calms down again. This is repeated until the infestation of the population has reached 70%. But even this will be a huge burden on society.

Incidences vs. case density

To make the case numbers of the federal states comparable, the so-called incidence is used. The incidence represents the number of cases per 100,000 inhabitants (see Figure 4).

Figure 4: Calculated indicences for each German federal state. Incidences (cases per 100,000 inhabitants) are a possibility to make the different federal states comparable.

The population figures of the Länder (in 100,000 inhabitants) are shown in Table 1, middle column.

Table 1: Populations and population densities (in units of Berlin) of the German federal states. The population density correlates with the infection probability. Although we will face some deaths, these numbers will not change significantly over time.

Looking at the incidences of the individual federal states, three main groups emerge over time:

  • High incidence: Hamburg, Baden-Württemberg and Bavaria
  • Medium incidence: Saarland, North Rhine-Westphalia, Berlin and Rhineland-Palatinate
  • Low incidence: all other federal states

This suggests that Hamburg, Baden-Württemberg and Bavaria are particularly affected by COVID-19 and have a higher rate of spread than the other German states.

What the incidence does not take into account, however, is the fact that the probability of infection is not the same for the different federal states. Since a distance of more than 2 m provides significant protection against infection, the probability of infection must be higher for more densely populated areas than for sparsely populated regions. If the cases are presented in relation to population density (simplified called „case density“ and standardized to the population density of Berlin), a different picture emerges (Figure 5):

  • Bavaria now has the highest case density, followed by Baden-Württemberg
  • North Rhine-Westphalia and Lower Saxony will follow later
  • Hamburg (previously particularly affected) and all other federal states have relatively low case densities

Figure 4: Calculated indicences for each German federal state. Incidences (cases per 100,000 inhabitants) are a possibility to make the different federal states comparable.

The spread of COVID-19 is therefore particularly strong in Bavaria and to a lesser extent in Baden-Württemberg. The proximity to Italy and France could have a significant influence on this rate. However, additional influences are also the settlement of large corporations in these regions (Bosch, Zeiss, BMW, Audi, Daimler, etc.), which have many plants in the Asian countries and thus could have favoured the probability of transmission at the beginning. However, it is not possible to make reliable statements on these contributions on the basis of the data.

A research of the Bavarian Broadcasting Corporation attests the ski tourism to Ischgl/Tyrol a contribution not to be underestimated. In comparison to the calculated case densities, it should follow that the number of unrecorded cases (see BR’s visualisation) must have been particularly high in Bavaria. This is, however, statistically very unlikely, so the Ischgl case cannot be central to the significantly higher spread in Bavaria.

Author

Erik Hänel
Erik HänelHead of Analytics & Sensorics

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Center of Excellence Analytics & Sensorics

  • Core Competences
    • Modelling & Simulation
    • Measurement & Sensoric
    • Statistics & Data Analysis
    • Predictive Maintenance

Disclaimer

The presented analysis is based upon the data provided by the Robert Koch Institute. This data is updated daily (see status). The statements, which we derived today, may already be invalidated through new data provided tomorrow. We try to keep this analysis as up-to-date as possible.

Because not every health department updates their data during the weekends, data updates provided by the Robert Koch Institute on Sun- and Monday cannot be trusted and will be ignored for our analysis. The next update will be performed on Tuesday.

Change history

2020-04-04:

  • Doubling time: 7.213±0.346 days
  • Highlighted the COVID-19 key facts

2020-04-03:

  • Doubling time: 6.939±0.345 days

2020-04-02:

  • Doubling time: 6.623±0.417 days
  • Added estimated number of recoveries based upon WHO estimation
  • Added a literature collection

2020-04-01:

  • Doubling time: 6.231±0.274 days
  • Added population and population density of the German federal states
  • Added incidences and case densities of the German federal states
  • Added an initial analysis on the spread in the federal states
  • Added section headings for easier navigation

2020-03-31:

  • Doubling time: 5.849±0.172 days
  • Changed time axes to use dates instead days
  • Added grid for enhanced readability
  • Added major government actions
  • Last two days are now ignored for fit, because their data is quite incomplete
  • Added initial analysis about government actions

2020-03-28:

  • Doubling time: 6.182 days
  • Initial version

Further Reading

  1. WHO. Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19). Report. World Health Organization (WHO); 2020 16-24.02.2020.
  2. RKI. COVID-19 Dashboard. Robert Koch Institute; 2020
  3. RKI. SARS-CoV2-Steckbrief. Robert Koch Institute; 2020
  4. Finn Bauer et al. CovidCountries. 2020. Data source for government actions
  5. RKI. COVID-19 data set. NPGEO Corona; 2020. Main data source
  6. RKI. Federal state data set. NPGEO Corona; 2020
  7. Tagesschau.de. Interview with Martin Eichner. Tagesschau.de; 2020
  8. BR. Recherche zum Casus Ischgl. Tagesschau.de; 2020
  9. Martin Eichner et al. COVIDsim. 2020
  10. DIVI. Momentane Auslastung der Intensivbetten in Deutschland. Deutsche Interdisziplinäre Vereinigung für Intensiv- und Notfallmedizin; 2020

Data reference

Reference: Robert Koch-Institut (RKI), dl-de/by-2-0

The data are the „Case Figures in Germany“ of the Robert Koch Institute (RKI) and are available under the Open Data Data License Germany – Attribution – Version 2.0.

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INVENSITY Competencies

CONSULTING

Accelerate your development

Career

Let’s make things better

© Copyright 2007 – 2020
All Rights Reserved