Simulating an epidemic

But the ones that have recovered can get infected again after a while. Why dont consider this?

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After watching this video again, I am curious how the regulations of the government (in the Netherlands, for example) would look like in these models. as an example: that you start with the standard situation and then implement hygiene and social distancing. then the quarantine, vaccine and reopening of social destinations. all in the order of the regulation that took place and then put that model next to the observed data.

would it be possible to show me how you coded these?

Once I get the chance I will make a game out of this idea, so you can play as a suspectible person and try and avoid the virus or be an infectious one and try to spread the disease

I am curious what would happen in the simulation if the Removed units became Susceptible again after a certain period of time, as is what happened in the real world. I had covid twice in a row with a break in between. Whole family was bouncing it around while we were isolated. I bet it would just exaggerate or drag on the curves we saw in this video, except in the simulations with proper contact tracing and isolation.

this has aged really, really poorly lmao

Thank you very much. 😊

China took some inspiration

This video inspired me to make my own infection code on Khan Academy.

I guess removed can mean two things, you got immune or you are dead. And that is solely up to the type of virus and how it works. Great explanation!

Could you do a follow-up that includes animal reservoirs as a method of incubation and its possible effect on proliferation? Or the odds that a portion of the population would not be immune after contracting the virus, therefore not placed into the "recovered/removed" category? Mutations that make immune people susceptible again? My gut feeling is that those factors would effectively result in an inability to ever fully eradicate it.

5,065,282nd view.

A virus is God's way of telling you idiots that you're going about your business wrong. You reap what you sow including the irony of not being able to recognize the solution.

Nice model. It is overly simplistic and incomplete. It applies to every virus regardless of severity. It doesn't take into account the way our immune systems actually work or the way viral load factors into something being an outbreak vs an endemic spread of a mild contagion, and it doesn't model how a relatively mild virus spreads in population of people with robust immune systems who aren't afraid of germs. Using models like this to justify infringing on people's rights is a dangerous game and I hate everything about it.

Gotta ask, what say you now? Bahahaha

Solution: Replace your assumed value (= guess) for P (probability of infection by being in a certain radius of a so-called 'infectious' person ), by my assumed value, 0 (zero)

It's too bad people aren't exactly the same and have different immune systems to handle the problem

Gotta love the people in the comment section that now think that mathematics itself is a Jewish conspiracy just so they can hold onto their crazy beliefs. Either these people don't remember what they say in-between clauses, or they don't care about truth in the slightest.

Anyway, really interesting vid.

i love how the 50 percent social distancing is literally just america

looking through this video and i see all the things we did wrong. but couldn't prevent. 1. not every social distanced. 2. people need food and water thus stores had to stay open. (yes we had rules in effect to try and slow/prevent infection but that doesn't mean much when an infected person walks in coughing up a storm without any prevenitive measure in place) 3. Testing was to broad you could have been infected and already passed the effects but still test positive from it. (i heard it was 9 months) and alot of other things as the news got old and people just stopped caring.

There needs to be a component where during social distancing the rate of other diseases increases and so does suicide so that is relevant to the removed or dead component .

Australia saw the isolation box, and was like, hold my beer.

Uh

Thanks for this video!

I hate how generalized this algorithm is. Each action is infinitely complex, having variable we do not know. I wish you would have done a better job of demonstrating the actions one takes to infect, remove, or move around.

I remember watching this two years ago and getting to the part about how letting up quarantine too early just causes a second, possibly larger, wave and thinking "Oh. That sounds horrible." As it turns out (two years later) I was right. That is completely horrible.

2022 and MANY people cheated on lockdowns. Very similar to your simulation. :(

covid won.

Alterenate title: What NOT to do when playing Plauge Inc.

Did anyone include the virtual pandemic of World of Warcraft?

Very nice, love your videos,
BUT here these models are just false for covid: experimentally it has been observed that the WAVES WERE CAUSED BY VARIANTS/mutants and not at all or just marginally because of social distancing stopping. In 2022 we now observed that immunity is lost very fast, and that cross immunity is weak from one variant to the next.


The real mechanism for a covid wave extinction is still not a scientific consensus: the heard immunity, either natural or by vaccine is not the only candidate to explain the wave stopping.
Very detailled PCR observations over the last 2 years are strongly suggesting that a variant is genetically weakened during its propagation/replication until it is unable to propagate anymore, thus stopping the wave. And also that eventually new variants comes from recombinations, not really mutations.

And also that animals such as furets and felines are now endemic reservoirs. All those new experimental facts are game changer for this kind of mathematical models interpretation.
The parameters of those SEIR models are very off, the models are forced to fit the real epidemics plot but is is just a false positive result. This is well know issue in chemical kinetics or plasma kinetics models.
Sad that the majority of epidemilogists still refuse to admit their errors.
Thanks again for your videos.

I'd be interested to see how re-infection and increased contagiousness of variants impact these models

The Deep State has been telling you what they were going to do the whole time. You just weren't paying attention.

I think an important take away from this is that these measures, at the rates they can feasibly be implemented without destroying more lives than the virus itself, aren't as effective as advertised...

50% social distancing has almost the exact same effect as 90% social distancing. It is unbearable societal change for exponentially diminishing returns... it would explain why neighboring cities with radically different social distancing and lockdown policies showed almost no difference in contagion rates... we blew up whole economies for basically nothing.

This was very cool, there are other factors that I wish were tested, like a vaccine, or people more vulnerable/resistant to the virus, or even the virus mutating over time. These factors would make things much more complex, but would be cool to test.

I like your toy model. Ideas I would suggest: Splitting the R group into a green Recovered group and a gray Deceased group. Green is temporary and wears off after some time. When a green person gets pinged by a red or yellow person, their timer resets. If they don't get pinged in long enough time, they go back to being blue. This represents novel strains evolving. Someone constantly exposed to the disease through the evolution of the novel strain maintains their immunity to the evolving strain, while someone who hasn't been exposed in a while becomes susceptible to those changes. And any yellow people will of course become green instead of gray when the infection runs its course, since not having symptoms means they won't die. And you could make the rate at which people turn green or gray after being red depend on how full the hospitals are at any moment. Red people who are at risk of dying turn purple, and if they can get into a hospital they have a very good chance to turn green and leave the hospital. But if they can't then they have a very high risk to turn gray. This makes it actually matter if there's a long tail or not, and can help compare what approaches are best given that the goal is to minimize the total number of times that people turn purple. It may turn out that various means of "slowing the spread" end up being detrimental in the long run because you're allowing the strain to avoid burning itself out for longer, so people recover and survive long enough to lose their immunity and get infected again.

Thanks!

이제 오미크론 덕분(?)에 감염자가 많아지고 치명률은 낮아졌네요. 빨리 이 코로나 사태가 종식 되는 날이 오길...

From Belgium we had really poor result against covid and it's easy to explain why :
- A tiny area with many people.
- Old population.
- A lot of obesity.
- Extremely good transports, you can reach all key cities by trains (and it's quick because most cities merge to eachothers).
- Many neighboors from France, Germany, Netherlands, Luxembourg that work or study in Belgium.
- Students use to come back to their familly during weekends.
- Social distance was almost impossible for students that lives in the same house, they only have 1 kitchen or 1 toilet for 3 or 4 student.
- Cold country and the covid arrived during the first warm week of the year.
- Many covid cases in the cold season, which mean 80% of the year when people doesn't see the sun and get cold even without covid.
- When it's cold this is hard to open windows
- Chrismast is something important.
- Europe center, people from everywhere travel and cross the country with trucks and cars, Brussels airport is also a big point.
We tried everything possible but the stats are terrible, in fact it was impossible to do better, because there is no way to close a country like that, Belgium isn't self sufficent and the geography doesn't help, there is no mountains or anything, also Anvers is the second biggest port of Europe after Amsterdam, both a close to eachothers, the covid was there for sure