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What-if Models (COVID-19): Results

Here are the results of the four models that I had created to get results of What-If scenarios around COVID-19 (“corona”) virus using the data on hand as published officially as of 3/20/2020. Since we don’t have all the answers or even data for many variables, I hypothesized in such instances. For explanation of my hypothesis, reasoning, and my data sources, please read my previous post What-if Models (COVID-19): Models Explained.

The Findings – Summary

These figures are for a population of 1M people each model spanning over 365 days.

As you can see without any restrictions mandated or diligently exercised otherwise, the results are impressively dismal! The number of infections, spread, and deaths can be significantly reduced even by little restrictions at place such as social and professional distancing (==total interactions per-person, per day). In the best scenario (without any restrictions in place), over 200,000 will have died! That’s a reduction of a population by 22% starting from a hypothetical area of 1M people.

In the worst-case scenario (such as a town with mostly seniors, or young people with health conditions that compromise immune systems), the entire population will be dead in just 210 days or 7 months!

In the more likely scenario (normalizing the numbers by taking into account different ages, health level, genders with latest data at time of writing), well over a half-million will have died and the country/area’s population will be reduced by 58% in just 1 year!

However, by reducing the number of interactions to just 5, the number of deaths is reduced to about 200,000 and the total loss of population will max out at 21%.

To understand the gravity of this, take a look at populations in the following cities, states, countries…

Seattle (city) 704,352
Dallas (city) 1,317,929
San Francisco (city) 883,305
Boston (city) 694,583
Minneapolis (city) 413,651
Atlanta (city) 486,290
Miami (city) 463,347
Bahamas (country) 395,361
Alaska (entire state) 739,795
New Orleans (city) 391,495
Vancouver, BC (city) 631,486
Venice, Italy (city) 260,897


The Visuals for each model

To read the number of deaths (vertical bars), use the secondary y-axis (on right). For Infected, and Remaining population use the primary y-axis (on left). Each day is shown in x-axis.

The tables setup for each model is something as below: For more details, explanation see explanation of the models.

For additional (past or future) COVID-19 related blogs (charts, metrics, statistics, analysis), please search for “COVID” on my site, or just click here for the search results.


Disclaimer: This is for hypothetical, informational purposes. However, you are encouraged to share this with whomever may benefit from this or with any data scientist/professional who may want to refute/confirm or contribute to the models.

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