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<!doctype html>
<html lang="en">
<head>
<title>COVID-19 Coronavirus Disease Spread - Investigation of Exponentially Spreading</title>
<meta charset="utf-8">
<meta name="author" content="Dr. Torben Menke">
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<h1>COVID-19 Coronavirus Disease Spread - Investigation of Exponentially Spreading</h1>
<p>This is an archived part of my <a href="/COVID-19-coronavirus/">COVID-19 disease investigations</a></p>
<h2>
<a id="ToC"></a>Table of Contents / Inhaltsverzeichnis</h2>
<ul>
<li>
<a href="#Germany">Germany / Deutschland</a>
</li>
<ul>
<li>
<a href="#Exp_Wachstum">Untersuchung der exponentiellen Zunahme der Infektionen in Deutschland</a>
</li>
<li>
<a href="#cases-de-fit-results">Vergleich der Ergebnisse</a>
</li>
</ul>
<li>
<a href="#Countries">International</a>
</li>
<ul>
<!-- <li>
<a href="#countries-fit-results">Death toll regression analyses results for selected countries</a>
</li> -->
<li>
<a href="#CountriesDeathTimeSeries">Time series, Doubling time calculation and forecast for selected
countries</a>
</li>
<li>
<a href="#DuplicationsUntilIT">Duplications until hitting Italy's deaths per capita</a>
</li>
<!-- <li>
<a href="#DaysUntilIT">Estimated days until hitting Italy's deaths per capita</a>
</li> -->
</ul>
</ul>
<h2><a id="Germany"></a>Germany / Deutschland</h2>
<h3>
<a id="Exp_Wachstum"></a> Untersuchung der exponentiellen Zunahme der Infektionen in Deutschland </h3>
<p>Dieses Kapitel ist nicht mehr relevant, da wir glücklicherweise den Bereich der exponentiellen Zunahme der
Neu-Infektionen verlassen haben. Daher werden diese Grafiken nicht mehr aktualisiert.</p>
<p> Achtung: nun wird es etwas mathematisch...<br /> Zur Untersuchung der Zunahme der Infektionen habe ich die Daten
mit einer exponentiellen Wachstumsfunktion "gefittet" (angenähert über eine <a
href="https://de.wikipedia.org/wiki/Regressionsanalyse" target="_blank">Regressionsanalyse</a>). Dabei werden 2
Parameter so ermittelt, dass die Wachstumsfunktion zu einer optimalen Übereinstimmung mit den Daten kommt. Aus den
so ermittelten Werten der Parameter lassen sich dann Aussagen zum Wachstum treffen. Dabei verwende ich für den Fit
nur die Daten der letzten 7 Tage um die ergriffenen Maßnahmen besser zu berücksichtigen. </p>
<p> Exponentielle Wachstumsfunktion:<br /> f(x) = a · exp(b · x)<br /> mit a: Skalierungsfaktor/Wert zum
Zeitpunkt (x = 0 = heute)<br /> und b: Parameter der das Wachstum beschreibt </p>
<p> Daraus lässt sich nun die "Verdopplungszeit" T berechnet über: <br /> f(T) = 2 · f(x=0) <br /> → a
· exp (b · T) = 2 · a <br /> → T = ln (2) ÷ b <br> Anmerkung: Die Verdopplungszeit
t ist unabhängig vom Skalierungsfaktor a. Daher ist es für die Bestimmung der Verdopplungszeit nicht notwendig die
Daten auf die Bevölkerung zu skalieren. </p>
<p> Diese Prozedur habe ich für jeder Tag auf der x-Achse wiederholt um einen Zeitverlauf der Verdopplungszeit zu
bestimmen. </p>
<p> So, genug der Theorie, nun zu den Ergebnissen. Zunächst für die Infektionsfälle in Deutschland, weiter unten pro
Bundesland. </p>
<a id="cases-de-fit-DE-total"></a>
<img src="plots-gnuplot/de-states/cases-de-fit-DE-total.png" alt="cases-de-fit-DE-total.png" width="640" height="480">
<img src="plots-gnuplot/de-states/cases-de-fit-DE-total-log.png" alt="cases-de-fit-DE-total-log.png" width="640"
height="480"><br />
<small>generated via Gnuplot <a
href="https://github.com/entorb/COVID-19-Coronavirus-German-Regions/tree/master/scripts-gnuplot"
target="_blank">plot-de.gp</a>, raw data can be found in <a
href="https://github.com/entorb/COVID-19-Coronavirus-German-Regions/blob/master/data/de-state-DE-total.tsv"
target="_blank">de-state-DE-total.tsv</a>
</small>
<p>Erkenntnisse</p>
<ul>
<li>Sehr gute Übereinstimmung mit dem einfachem Modell eines exponentiellen Wachstums</li>
<li>15.03.2020: Meine 1-Tages Extrapolation/Prognose vom 14.03. war nur 1% von der offiziellen Zahl des Tages
entfernt. Sehr erschreckend, hoffentlich greifen die Maßnahmen bald.</li>
<li>26.03.2020: Es ist deutlich ein Abflachen der Infektionszahlen pro Woche zu erkennen. Die Verdopplungszeit hat
sich auf 4.4 Tage erhöht. Dies deutet sehr darauf hin, dass die Maßnahmen greifen. (Sofern denn die Zahlen der
Realität entsprechen und die Dunkelziffer nicht deutlich höher ist.) </li>
</ul>
<p><small><a href="#ToC">Back to top</a></small></p>
<a id="cases-de-fit-results"></a>
<h4>Vergleich der Bundesländer</h4>
<p>Die im vorherigen Abschnitt beschriebene Methode habe ich auf die Infektionsdaten der einzelnen Bundesländer
angewandt und möchte zunächst das Ergebnis der Fits/Regressionsanalyse vorwegnehmen.</p>
<img src="plots-gnuplot/de-states/cases-de-fit-increase-1-day.png" alt="cases-de-fit-increase-1-day.png" width="640"
height="480">
<img src="plots-gnuplot/de-states/cases-de-fit-doubling-time.png" alt="cases-de-fit-doubling-time.png" width="640"
height="480"><br />
<small>generated via Gnuplot <a
href="https://github.com/entorb/COVID-19-Coronavirus-German-Regions/tree/master/scripts-gnuplot"
target="_blank">plot-de.gp</a>, raw data can be found in <a
href="https://github.com/entorb/COVID-19-Coronavirus-German-Regions/blob/master/data/cases-de-gnuplot-fit.tsv"
target="_blank">cases-de-gnuplot-fit.tsv</a>
</small>
<h4>Detailauswertung pro Bundesland </h4>
<p> Im Folgenden habe ich aus Platzgründen auf die lineare Darstellung der Daten verzichten und zeige nur die
logarithmisch skalierten Daten pro Bundesland. </p>
<div class="af_gallery_container">
<div class="af_gallery_select" id="dropdown_DE_states_fit">
<!-- <input type="text" placeholder="Filter.." id="filter" onkeyup="filter()"> -->
<p><b>Auswahl</b></p>
<a href="#cases-de-fit-BW">BW</a>
<a href="#cases-de-fit-BY">BY</a>
<a href="#cases-de-fit-BE">BE</a>
<a href="#cases-de-fit-BB">BB</a>
<a href="#cases-de-fit-HB">HB</a>
<a href="#cases-de-fit-HH">HH</a>
<a href="#cases-de-fit-HE">HE</a>
<a href="#cases-de-fit-MV">MV</a>
<a href="#cases-de-fit-NI">NI</a>
<a href="#cases-de-fit-NW">NW</a>
<a href="#cases-de-fit-RP">RP</a>
<a href="#cases-de-fit-SL">SL</a>
<a href="#cases-de-fit-SN">SN</a>
<a href="#cases-de-fit-ST">ST</a>
<a href="#cases-de-fit-SH">SH</a>
<a href="#cases-de-fit-TH">TH</a>
<a href="#cases-de-fit-DE">DE</a>
</div>
<div class="af_gallery_results" id="results_DE_states_fit" style="display: flex;flex-flow:column">
<div class="af_gallery_results_plot" id="results_DE_states_plot">
<article id="#cases-de-fit-BW">
<img src="plots-gnuplot/de-states/cases-de-fit-BW-log.png" alt="cases-de-fit-BW-log.png" width="640"
height="480" />
</article>
<article id="#cases-de-fit-BY">
<img src="plots-gnuplot/de-states/cases-de-fit-BY-log.png" alt="cases-de-fit-BY-log.png" width="640"
height="480" />
</article>
<article id="#cases-de-fit-BE">
<img src="plots-gnuplot/de-states/cases-de-fit-BE-log.png" alt="cases-de-fit-BE-log.png" width="640"
height="480" />
</article>
<article id="#cases-de-fit-BB">
<img src="plots-gnuplot/de-states/cases-de-fit-BB-log.png" alt="cases-de-fit-BB-log.png" width="640"
height="480" />
</article>
<article id="#cases-de-fit-HB">
<img src="plots-gnuplot/de-states/cases-de-fit-HB-log.png" alt="cases-de-fit-HB-log.png" width="640"
height="480" />
</article>
<article id="#cases-de-fit-HH">
<img src="plots-gnuplot/de-states/cases-de-fit-HH-log.png" alt="cases-de-fit-HH-log.png" width="640"
height="480" />
</article>
<article id="#cases-de-fit-HE">
<img src="plots-gnuplot/de-states/cases-de-fit-HE-log.png" alt="cases-de-fit-HE-log.png" width="640"
height="480" />
</article>
<article id="#cases-de-fit-MV">
<img src="plots-gnuplot/de-states/cases-de-fit-MV-log.png" alt="cases-de-fit-MV-log.png" width="640"
height="480" />
</article>
<article id="#cases-de-fit-NI">
<img src="plots-gnuplot/de-states/cases-de-fit-NI-log.png" alt="cases-de-fit-NI-log.png" width="640"
height="480" />
</article>
<article id="#cases-de-fit-NW">
<img src="plots-gnuplot/de-states/cases-de-fit-NW-log.png" alt="cases-de-fit-NW-log.png" width="640"
height="480" />
</article>
<article id="#cases-de-fit-RP">
<img src="plots-gnuplot/de-states/cases-de-fit-RP-log.png" alt="cases-de-fit-RP-log.png" width="640"
height="480" />
</article>
<article id="#cases-de-fit-SL">
<img src="plots-gnuplot/de-states/cases-de-fit-SL-log.png" alt="cases-de-fit-SL-log.png" width="640"
height="480" />
</article>
<article id="#cases-de-fit-SN">
<img src="plots-gnuplot/de-states/cases-de-fit-SN-log.png" alt="cases-de-fit-SN-log.png" width="640"
height="480" />
</article>
<article id="#cases-de-fit-ST">
<img src="plots-gnuplot/de-states/cases-de-fit-ST-log.png" alt="cases-de-fit-ST-log.png" width="640"
height="480" />
</article>
<article id="#cases-de-fit-SH">
<img src="plots-gnuplot/de-states/cases-de-fit-SH-log.png" alt="cases-de-fit-SH-log.png" width="640"
height="480" />
</article>
<article id="#cases-de-fit-TH">
<img src="plots-gnuplot/de-states/cases-de-fit-TH-log.png" alt="cases-de-fit-TH-log.png" width="640"
height="480" />
</article>
<article id="#cases-de-fit-DE">
<img src="plots-gnuplot/de-states/cases-de-fit-DE-total-log.png" alt="cases-de-fit-DE-total-log.png"
width="640" height="480" />
</article>
</div>
<div class="af_gallery_results_footer" id="results_DE_states_footer">
<small> generated via Gnuplot <a
href="https://github.com/entorb/COVID-19-Coronavirus-German-Regions/tree/master/scripts-gnuplot"
target="_blank">plot-de.gp</a>, raw data can be found in <a
href="https://github.com/entorb/COVID-19-Coronavirus-German-Regions/blob/master/data/de-state/"
target="_blank">de-state-XX.tsv</a>
<br /> Many thanks to Alex for writing this gallery feature!</small>
</div>
</div>
</div>
<p><small><a href="#ToC">Back to top</a></small></p>
<h2>
<a id="Countries"></a>Comparing Countries</h2>
<h3>
<a id="CountriesDeathTimeSeries"></a>Time series, Doubling time calculation and forecast for selected countries
</h3>
<p>As in all countries the exponentially increase of the death toll is stopped, this chapter is not relevant any more,
and will not be updated.</p>
<p> Now comes the math part... <br /> As we saw above, the death toll growths exponentially with time for most
countries. This can be "fitted" (=performing <a href="https://en.wikipedia.org/wiki/Regression_analysis"
target="_blank">regression analysis</a>) by scaling a model function for exponential growth to fit the data. From
this fitting/scaling we can obtain information about the growth rate. I restricted the fit to data of the last 7
days to take recent actions into account and to obtain a more reliable forecast.<br />
</p>
<p> Model used for fitting of data:<br /> f(x) = a · exp(b · x)<br /> where a is the value at x=0 (here
I chose 0 to be the date of the latest reported data) <br />and b defines the rate of increase. </p>
<p> From b we can derive the time it take to reach a duplication of the value as x=0, the so called the doubling time
T:<br /> f(T) = 2 · f(x=0) <br /> → a · exp (b · T) = 2 · a <br /> → T = ln
(2) ÷ b <br> Note: the doubling time T is independant of the scaling factor a. Therefore, it is save to use
data of absolute numbers in the fitting without the need to re-scale by the population. </p>
<p> I repeated this procedure for each day on the x-axis to derive a trend for the doubling time. </p>
<div class="af_gallery_container">
<div class="af_gallery_select" id="dropdown_int_countries_fit">
<!-- <input type="text" placeholder="Filter.." id="filter" onkeyup="filter()"> -->
<p><b>Select</b></p>
<a href="#deaths-AT-fit">AT</a>
<a href="#deaths-BE-fit">BE</a>
<a href="#deaths-CA-fit">CA</a>
<a href="#deaths-CZ-fit">CZ</a>
<a href="#deaths-DK-fit">DK</a>
<a href="#deaths-FI-fit">FI</a>
<a href="#deaths-FR-fit">FR</a>
<a href="#deaths-DE-fit">DE</a>
<a href="#deaths-GB-fit">GB</a>
<a href="#deaths-GR-fit">GR</a>
<a href="#deaths-HU-fit">HU</a>
<a href="#deaths-IR-fit">IR</a>
<a href="#deaths-IT-fit">IT</a>
<a href="#deaths-JP-fit">JP</a>
<a href="#deaths-KR-fit">KR</a>
<a href="#deaths-NL-fit">NL</a>
<a href="#deaths-PT-fit">PT</a>
<a href="#deaths-ES-fit">ES</a>
<a href="#deaths-SE-fit">SE</a>
<a href="#deaths-CH-fit">CH</a>
<a href="#deaths-TR-fit">TR</a>
<a href="#deaths-US-fit">US</a>
</div>
<div class="af_gallery_results" id="results_int_countries_fit" style="display: flex;flex-flow:column">
<div class="af_gallery_results_plot" id="results_int_countries_plot">
<article id="#deaths-AT-fit">
<img src="plots-gnuplot/int/deaths-AT-fit.png" alt="deaths-AT-fit.png" width="640" height="480" />
<img src="plots-gnuplot/int/deaths-AT-fit-log.png" alt="deaths-AT-fit-log.png" width="640" height="480" />
</article>
<article id="#deaths-BE-fit">
<img src="plots-gnuplot/int/deaths-BE-fit.png" alt="deaths-BE-fit.png" width="640" height="480" />
<img src="plots-gnuplot/int/deaths-BE-fit-log.png" alt="deaths-BE-fit-log.png" width="640" height="480" />
</article>
<article id="#deaths-CA-fit">
<img src="plots-gnuplot/int/deaths-CA-fit.png" alt="deaths-CA-fit.png" width="640" height="480" />
<img src="plots-gnuplot/int/deaths-CA-fit-log.png" alt="deaths-CA-fit-log.png" width="640" height="480" />
</article>
<article id="#deaths-CZ-fit">
<img src="plots-gnuplot/int/deaths-CZ-fit.png" alt="deaths-CZ-fit.png" width="640" height="480" />
<img src="plots-gnuplot/int/deaths-CZ-fit-log.png" alt="deaths-CZ-fit-log.png" width="640" height="480" />
</article>
<article id="#deaths-DK-fit">
<img src="plots-gnuplot/int/deaths-DK-fit.png" alt="deaths-DK-fit.png" width="640" height="480" />
<img src="plots-gnuplot/int/deaths-DK-fit-log.png" alt="deaths-DK-fit-log.png" width="640" height="480" />
</article>
<article id="#deaths-FI-fit">
<img src="plots-gnuplot/int/deaths-FI-fit.png" alt="deaths-FI-fit.png" width="640" height="480" />
<img src="plots-gnuplot/int/deaths-FI-fit-log.png" alt="deaths-FI-fit-log.png" width="640" height="480" />
</article>
<article id="#deaths-FR-fit">
<img src="plots-gnuplot/int/deaths-FR-fit.png" alt="deaths-FR-fit.png" width="640" height="480" />
<img src="plots-gnuplot/int/deaths-FR-fit-log.png" alt="deaths-FR-fit-log.png" width="640" height="480" />
</article>
<article id="#deaths-DE-fit">
<img src="plots-gnuplot/int/deaths-DE-fit.png" alt="deaths-DE-fit.png" width="640" height="480" />
<img src="plots-gnuplot/int/deaths-DE-fit-log.png" alt="deaths-DE-fit-log.png" width="640" height="480" />
</article>
<article id="#deaths-GB-fit">
<img src="plots-gnuplot/int/deaths-GB-fit.png" alt="deaths-GB-fit.png" width="640" height="480" />
<img src="plots-gnuplot/int/deaths-GB-fit-log.png" alt="deaths-GB-fit-log.png" width="640" height="480" />
</article>
<article id="#deaths-GR-fit">
<img src="plots-gnuplot/int/deaths-GR-fit.png" alt="deaths-GR-fit.png" width="640" height="480" />
<img src="plots-gnuplot/int/deaths-GR-fit-log.png" alt="deaths-GR-fit-log.png" width="640" height="480" />
</article>
<article id="#deaths-HU-fit">
<img src="plots-gnuplot/int/deaths-HU-fit.png" alt="deaths-HU-fit.png" width="640" height="480" />
<img src="plots-gnuplot/int/deaths-HU-fit-log.png" alt="deaths-HU-fit-log.png" width="640" height="480" />
</article>
<article id="#deaths-IR-fit">
<img src="plots-gnuplot/int/deaths-IR-fit.png" alt="deaths-IR-fit.png" width="640" height="480" />
<img src="plots-gnuplot/int/deaths-IR-fit-log.png" alt="deaths-IR-fit-log.png" width="640" height="480" />
</article>
<article id="#deaths-IT-fit">
<img src="plots-gnuplot/int/deaths-IT-fit.png" alt="deaths-IT-fit.png" width="640" height="480" />
<img src="plots-gnuplot/int/deaths-IT-fit-log.png" alt="deaths-IT-fit-log.png" width="640" height="480" />
</article>
<article id="#deaths-JP-fit">
<img src="plots-gnuplot/int/deaths-JP-fit.png" alt="deaths-JP-fit.png" width="640" height="480" />
<img src="plots-gnuplot/int/deaths-JP-fit-log.png" alt="deaths-JP-fit-log.png" width="640" height="480" />
</article>
<article id="#deaths-KR-fit">
<img src="plots-gnuplot/int/deaths-KR-fit.png" alt="deaths-KR-fit.png" width="640" height="480" />
<img src="plots-gnuplot/int/deaths-KR-fit-log.png" alt="deaths-KR-fit-log.png" width="640" height="480" />
</article>
<article id="#deaths-NL-fit">
<img src="plots-gnuplot/int/deaths-NL-fit.png" alt="deaths-NL-fit.png" width="640" height="480" />
<img src="plots-gnuplot/int/deaths-NL-fit-log.png" alt="deaths-NL-fit-log.png" width="640" height="480" />
</article>
<article id="#deaths-PT-fit">
<img src="plots-gnuplot/int/deaths-PT-fit.png" alt="deaths-PT-fit.png" width="640" height="480" />
<img src="plots-gnuplot/int/deaths-PT-fit-log.png" alt="deaths-PT-fit-log.png" width="640" height="480" />
</article>
<article id="#deaths-ES-fit">
<img src="plots-gnuplot/int/deaths-ES-fit.png" alt="deaths-ES-fit.png" width="640" height="480" />
<img src="plots-gnuplot/int/deaths-ES-fit-log.png" alt="deaths-ES-fit-log.png" width="640" height="480" />
</article>
<article id="#deaths-SE-fit">
<img src="plots-gnuplot/int/deaths-SE-fit.png" alt="deaths-SE-fit.png" width="640" height="480" />
<img src="plots-gnuplot/int/deaths-SE-fit-log.png" alt="deaths-SE-fit-log.png" width="640" height="480" />
</article>
<article id="#deaths-CH-fit">
<img src="plots-gnuplot/int/deaths-CH-fit.png" alt="deaths-CH-fit.png" width="640" height="480" />
<img src="plots-gnuplot/int/deaths-CH-fit-log.png" alt="deaths-CH-fit-log.png" width="640" height="480" />
</article>
<article id="#deaths-TR-fit">
<img src="plots-gnuplot/int/deaths-TR-fit.png" alt="deaths-TR-fit.png" width="640" height="480" />
<img src="plots-gnuplot/int/deaths-TR-fit-log.png" alt="deaths-TR-fit-log.png" width="640" height="480" />
</article>
<article id="#deaths-US-fit">
<img src="plots-gnuplot/int/deaths-US-fit.png" alt="deaths-US-fit.png" width="640" height="480" />
<img src="plots-gnuplot/int/deaths-US-fit-log.png" alt="deaths-US-fit-log.png" width="640" height="480" />
</article>
</div>
<div class="af_gallery_results_footer" id="results_int_countries_footer">
<small> generated via Gnuplot <a
href="https://github.com/entorb/COVID-19-Coronavirus-German-Regions/tree/master/scripts-gnuplot"
target="_blank">plot-countries.gp</a>, raw data can be found in <a
href="https://github.com/entorb/COVID-19-Coronavirus-German-Regions/blob/master/data/int/"
target="_blank">countries-timeseries-XX.tsv</a>
<br /> Many thanks to Alex for writing this gallery feature!</small>
</div>
</div>
</div>
<!-- <p>Findings</p>
<ul>
<li>In Italy at around 06.03.2020 the slope decreased from around a doubling time of 2 days to 3
days</li>
<li>17.03.2020: the increase of deaths in Italy is quite similar to the slope of infections in
Germany,
both having a doubling time of 3 days.</li>
<li>22.03.2020: Italy today has 50% more casualties than China</li>
</ul> -->
<p><small>
<a href="#ToC">Back to top</a></small></p>
<h3>
<a id="DuplicationsUntilIT"></a>Duplications until hitting Italy's deaths per capita</h3>
<p> As Italy is very badly hit, I decided to calculate how many duplications of deaths tool other countries are "away"
from their situation. The result is a kind of time scale, showing if you have much or little time to prepare. </p>
<img src="plots-gnuplot/int/countries-duplications-until-IT-level-of-deaths.png"
alt="countries-duplications-until-IT-level-of-deaths.png" width="640" height="480"><br />
<small> generated via Gnuplot <a
href="https://github.com/entorb/COVID-19-Coronavirus-German-Regions/tree/master/scripts-gnuplot"
target="_blank">plot-countries.gp</a>, raw data can be found in <a
href="https://github.com/entorb/COVID-19-Coronavirus-German-Regions/blob/master/data/countries-latest-selected.tsv"
target="_blank">countries-latest-selected.tsv</a>
</small>
<p> Findings</p>
<ul>
<li>Using deaths per capita instead of absolute number to be able to compare small and big countries</li>
<li>Less is worse, of course</li>
<li>18.03.2020: Very scary plot. In most countries currently the number deaths double every 2 till 3 days (see other
plots). This allows for an estimation of when we will get hit by Corona as hard as Italy is today... </li>
<li>18.03.2020: Spain is only 2 duplications (probably equaling 4-6 days) behind Italy's current situation. Germany
is 7 duplications (probably 2-3 weeks) behind Italy's current situation. </li>
</ul>
<p><small>
<a href="#ToC">Back to top</a></small></p>
<!--
<h3>
<a id="DaysUntilIT"></a>Estimated days until hitting Italy's deaths per capita</h3>
<p> This plot combines the fit result for the deaths doubling time and the number of duplications until hitting
Italy's deaths per capita. It therefore is a rough estimation. Nevertheless in my opinion other country's
inhabitants should check how Italy is doing today and via social distancing buy their hospitals as much time as
possible for preparation. </p>
<img src="plots-gnuplot/int/countries-days-until-IT-level-of-deaths.png"
alt="countries-days-until-IT-level-of-deaths.png" width="640" height="480"><br />
<small>generated via Gnuplot <a
href="https://github.com/entorb/COVID-19-Coronavirus-German-Regions/tree/master/scripts-gnuplot"
target="_blank">plot-countries.gp</a>, raw data can be found in <a
href="https://github.com/entorb/COVID-19-Coronavirus-German-Regions/blob/master/data/countries-joined_selected_and_gnuplot_fit.tsv"
target="_blank">countries-joined_selected_and_gnuplot_fit.tsv</a>
</small>
<p>Findings</p>
<ul>
<li>22.03.2020: <1 week away are: Belgium, France, Netherlands, Portugal, Spain, Switzerland<br /> <2 weeks
away are: Germany, Hungary, Iran, Sweden, UK <br /> Japan and South Korea seem to have slowed down the virus
spread successfully.</li>
</ul>
<p><small>
<a href="#ToC">Back to top</a></small></p>
-->
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