Corona virus (COVID-19) - a system dynamics simulation model using not just the bass diffusion

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Description

Link to corresponding paper:

https://www.consideo.com/papers-33.html (English)

https://www.consideo.de/papers.html (German)

Summary - it really is severe

The model shows how crucial the availability of a cure and vaccine has become. The death toll is directly related to the intensive care capacities that are totally unrealistic to keep up with any slow down of infections. The flu has a toll of 0.1 percent, Covid-19 allegedly a toll of 1.1 percent though some areas like Italy already show a rise close to 10 percent once the limits of the capacities are reached.

What is included with the model

It startet as just a small general model on tipping points (using the Bass function that can be found e.g. in Sterman's 'Business Dynamics'). It showed the nice bell shaped curves we also find in media and it showed the effects of delayed action or the potential for flattening the bell curve in order to win some time to develop capacities and even the vaccine and the cure. However, wondering why the German government on one hand claims to already have a lot capacities and on the other hand taking more drastic measures and announcing the increase of capacities I enhanced the model with some more parameters.
The model simulates 700 days now that there seems to be no natural slow down of infections during summertime. It uses figures from Germany.
Now it features:
  • Parameters to play with the assumptions on the effectiveness of social distancing
  • A possible delay of these distancing actions to show the importance of immediate action
  • The number of days after which the distancing cannot be uphold anymore
  • The number of days until a vaccine/cure becomes available
  • The possibility to assume additional caring capacities
  • A parameter to vary the proportion of untreated cases (the effect of more testing)

The results

The social distancing is crucial and for the unrealistic assumption that it could be uphold it would take almost two years until the whole population has become effected. One doesn't recognize a bell shaped curve anymore, but rather a constant rate of cases, of healing and dying. Even with this slow continuation of infections and even with an upscaling of capacities we would not have enough capacities and face more than 1.1 percent as a death toll. But still we would have way more without effective distancing and of course we all hope that it doesn't take two years until we got the cure and vaccine for this.
The model has a switch to assume that we give up social distancing at one point of time, maybe because the governments decide so or because of some scary social chaos. Well, and that would indeed lead to a scary scenario.

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Comments (27)

Kai Neumann

Kai Neumann

Just added a factor "7day incidence rate" to show how to use the delay(1, ...) + delay (2, ...) etc. construct in the formula.
Reply
d3dhemmer

d3dhemmer

Long-Haulers Are Redefining COVID-19
Without understanding the lingering illness that some patients experience, we can’t understand the pandemic.
Reply
Kai Neumann

Kai Neumann

Not good - maybe just 60 percent become immune after they went through the disease: https://www.scinexx.de/news/medizin/corona-nicht-jeder-entwickelt-schuetzende-antikoerper/
Let's wait for confirmation from other studies and then adapt the model to this new insight.
Reply
d3dhemmer

d3dhemmer

Nun das Verdienen geht weiter, sehr Berechtig für Alle welche arbeiten und Gotteslohn erhalten.
Steuern werden benötigt um die Gemeinschaft zu finanzieren, nicht die Lobbyisten, Politiker und weitere „Gewählte“. Leider lassen wir alle es zu das die „Großen“ nicht in unsere Gemenschaft einzahlen. https://www.heise.de/news/Amazon-verdoppelt-Quartalsgewinn-dank-Online-Shopping-Boom-4859784.html

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Kai Neumann

Kai Neumann replies Kai Neumann

I have just added two factors to include the possibility of loss of immunity.
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Kai Neumann

Kai Neumann

If immunity disappears I have to adapt the model to a new situation. Here is the news from Technology Review:

"The lowdown: Immunity to covid-19 may be short-lived, according to a new longitudinal study of people who have caught the disease and recovered.

The study: Researchers at King’s College London repeatedly tested 96 patients and health-care workers at Guy’s and St Thomas’ NHS Foundation Trust for antibodies between March and June. All the participants were confirmed to have had covid-19, either via a PCR test or a positive antibody test. The researchers found that levels of virus-fighting antibodies peaked about three weeks after symptoms started and then rapidly fell away. Although 60% of participants produced a “potent” antibody response while they had covid-19, only 17% had the same level of potency at the end of the three-month testing period. Antibody levels were higher and longer-lasting in people who had had more severe cases of covid-19. For some milder cases, it was impossible to detect any antibodies at all at the end of the three months. The research is published in a preprint paper in medRxiv, which means the findings have yet to be subjected to peer review.

What it means: The study raises the prospect that, like other coronaviruses, covid-19 could reinfect people repeatedly. If that’s the case, “herd immunity” may never arrive, either through a one-shot vaccine or through community spread of the virus, as any protective antibodies would wane with time. However, antibodies are not the only way people can fight off covid-19. T cells, which seek and destroy cells infected with SARS-CoV-2, could also provide some protection. In short, we have not yet generated enough data from patients to be able to draw conclusions on immunity with a high degree of certainty. There have been anecdotal reports of people catching covid-19 for a second time, but none have been confirmed."

Reply
Kai Neumann

Kai Neumann

Fareed Zakaria suggests in today's newsletter ( https://view.newsletters.cnn.com/messages/1586557993073d7f0b74e14eb/raw?utm_term=1586557993073d7f0b7... ) that new data could lead to different death tolls and thus different decisions. Here is my reply:

"Good morning

As always important topics, questions and answers - thank you very much for that. Exactly these questions we have already some time ago systemically investigated and answered: https://www.know-why.net/model/Cln3x63jKV3jfSzGwAFZdzw
Our simulation model seems to me to be the most advanced at present, as it does not show the simple bell-shaped curves, but rather assumes dynamic parameters - including a mortality rate depending on the availability of intensive care capacities.
I therefore think it is wrong and dangerous to now read a lower mortality rate from the rough models and the new data and to want to re-evaluate the situation - which many people naturally want to hear and which puts unnecessary pressure on governments that appear to be acting correctly. Mortality is made up of two factors - the proportion of those who need treatment and the proportion of those for whom sufficient capacity is then available. The latter makes it possible to distinguish between avoidable and unavoidable deaths - if we rule out waiting more than 400 days for the availability of cures and vaccines. So the absolute number of deaths may fall, but as long as we still have avoidable deaths, we must act ethically.
If you have any questions about the systemic connections, we are happy to help.

Best thanks like greetings from Europe

Kai "
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Kai Neumann

Kai Neumann

Now we have added a third model ( https://www.know-why.net/model/Cln3x63jKV3jfSzGwAFZdzw) to the paper exploring the possible world after corona. It suggests the economy will not recover but nevertheless societies could gain something from it.
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Kai Neumann

Kai Neumann

Just added an important chapter to the paper explaining why there can't be any valid models on this topic, why we can only falsify models, and which parameters are the reason why the corona epidemic in particular remains unpredictable in absolute numbers: https://www.consideo.com/papers-33.html
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Kai Neumann

Kai Neumann replies Kai Neumann

Here's the link onto the qualitative model on further arguments: https://www.know-why.net/model/CEXPi9s5zeEtEwHpUBslo3w
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Kai Neumann

Kai Neumann

For those who don't find the resources to play with the model we have written a short paper on its use and interpretation: https://www.consideo.com/papers-33.html (the German site features a German version of the paper). Let's see how things turn out and what additional factors become crucial.
Reply
Kai Neumann

Kai Neumann replies d3dhemmer

What is the connection of these comments to the model? I think they'd deserve another, probably qualitative model. Btw. many other parts of Europe are catching up without the background of a China connection. I look forward for additional models - one should come by me.
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d3dhemmer

d3dhemmer

Warum der Corona-Virus besonders in Italien wütet?
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d3dhemmer

d3dhemmer

https://de.wikipedia.org/wiki/Prato_(Toskana)#Chinatown
Hier sollte besonders genau hingeschaut (abgeschaut) werden.
Von 200.000 Einwohnern sind 50.000 Chinesen, nicht gezählt sind mehrere Zehntausende illegale Chinesen, die Prato zur drittgrößten Stadt Mittelitaliens anwachsen ließen. 5000 chinesische Betriebe sind gemeldet. Bei einer Gewerbeanmeldung wird der Wohnsitz der Eigentümerin nicht überprüft.

Enrico Rossi, der Präsident der Region Toskana, möchte die Sklaverei beenden und sagt, dass „nirgendwo in Mittel- und Norditalien und vielleicht sogar nirgendwo in Europa mehr schwarzgearbeitet wird“

In der Stadt hat sich eine Chinatown gebildet.

Per Money Transfer werden täglich bis 1,5 Mio. Euro nach China überwiesen.

Reply
d3dhemmer

d3dhemmer

The Importance of Simulation Training for Professionals in Preparation for Epidemics
Posted on March 17, 2020 by Eric Howard https://www.simio.com/blog/2020/03/
The Great Influenza Epidemic or Spanish flu in 1918, did not announce itself with fanfare. It simply spread from one region to another with traveling individuals which led to a global economic depression within 1918 and 1920. As with the Spanish flu, Covid-19 is been spread through travel and from all indications, it has the ability to slow down economic activities across the world. But thankfully, this is not 1918 and we have experienced the effects and benefits of digital transformation technologies.
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Kai Neumann

Kai Neumann

Here's a German newspaper article commenting on the Neil Ferguson model and its interpretations. https://www.welt.de/gesundheit/article206665229/Coronavirus-Diese-Studie-zeigt-was-uns-bevorstehen-k....
Interestingly, it could be describing this model.
Reply
d3dhemmer

d3dhemmer

compare - results from FT please have a look.
how todo it by imodeler?
https://www.ft.com/content/a26fbf7e-48f8-11ea-aeb3-955839e06441
Reply
Kai Neumann

Kai Neumann

MAJOR UPDATE: just added a lot more details to the model that now doesn't produce the classic flattened curves anymore assuming that the social distancing is extremely effective. However, still then it would mean that there are not enough intensive care capacities thus I added a factor to add to the capacities!
Also I added a way to define the arrival of a vaccine and the day when the social distancing will be abandoned.
I am eager to discuss the many assumptions and to try different scenarios since my first runs were all frightening.
Reply
Kai Neumann

Kai Neumann replies d3dhemmer

Nice link, thanks. That and today's news inspired me to insert a range() function into the model to show the effect of delayed action compared to immediate action. Cool thing so easily match reality's data with such a small model.
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Louis replies Kai Neumann

Hi Kai,
I've adjusted the book keeping... There was an error in one delay for the factor "Zunahme an Todesfällen" . That's why the "Gesamtbevölkerung nach Epidemie" showed a small peak arount the peak of the epidemic. It was basically the offset of casualties for two days. Furthermore the "Start Input" had not been subtracted from the initial susceptible population. Which was responsible for a steady offset of "Start Input" people.
As for the Alps. They are still somewhat winterly. However here at Bern it's currently more like April...
I will translate the model to English (or is the majority german speaking?) and the put it on KNOW-WHY.net.
See you
Louis
Reply
Kai Neumann

Kai Neumann replies Louis

Hi Louis
Cool model. "Gesamtbevölkerung nach Epidemie" is a dummy factor, isn't it? Otherwise I'd expected it either being constant or diminished by the death toll. You haven't published it on KNOW-WHY.net before, have you? There it would be more prominent than just as a comment.
Grüezi to the winterly(?) alps.
Kai
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Louis

Hallo Kai,
I agree with your conclusions. I myself have recycled my old Consideo-Modeler model about the pig flu from 2009 and added a couple of factors to consider the effects of social distancing und den quarantine measures (both quite effective). In the factor "Erkrankte Personen" there is a quick and dirty dashboard. To all readers: There is no guarantee, that the epidemic of the virus behaves like the model. It's just what it is: A model.
Grüsse in den Norden. Louis

Reply
Kai Neumann

Kai Neumann

It remains the gib question whether healed patients become immune or if they can even still carry the virus and infect others: https://www.scinexx.de/news/medizin/coronavirus-covid-19-und-danach/ .... the model allows to play with these kind of assumptions.
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Kai Neumann

Kai Neumann

The model now includes a lever to assume a natural slow down of the virus through summer warmth.
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