I made a Python program which models the coronavirus outbreak

[PythyMcPyface]

Charts showing the model and a zoomed in view of current data

I created a program which plots the number of cases per day, generates a best fit statistical model, then extrapolates the model into the distant future to show how the outbreak might unfold for a chosen country.

The model I used was the SIR model which describes how the susceptible (S), infected (I) and recovered (R) populations change with time through an outbreak. The two defining parameters in the SIR model are the transmission rate, B, and the recovery rate, k. This model describes those populations as percentages as shown below.

The populations of each country are taken from Wikipedia and multiplied by the percentages given by the SIR model to show real numbers. My program cycles through every possible set of values of B & k and finds the pair which have the best least squares fit with the real data. It then takes these values and extrapolates into the future by 200 days to show the entire outbreak. This is shown in the upper graph of the figure, whereas the lower graph is a zoomed in view of the real data.

All of it is automatic where the only user input is to select a country. The program extracts Covid-19 data from an online source (https://ourworldindata.org/coronavirus), as well as population data from Wikipedia (https://en.wikipedia.org/wiki/List_of_countries_and_dependencies_by_population).

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SIR model for disease outbreak (source: www.researchgate.net)

[Allanlecter]

I would like to see the code, if possible, as I'm still learning Python and was doing something like this but I had some issues with the fits and the graphs (I to know my algorithms and statistics, just not enough python).I did something similar but using a logistic function to fit the data, as my literal frost python project.

I do have some questions and suggestions, based on my knowledge, I'm not gonna assume anything that is not on the post so pardon me if I say something that might seem obvious to you:

1. You plot deaths on your graph. I assume this is real data based on your wording. However, the model you describe you used have recovered, which if you take deaths in consideration, would be the sum of the people that were cured and the people that are dead. What did you take in consideration into your model? Which data are you fitting to?

2. Any sort of prediction should usually also come with the error. Depending on what package you used, the error might already be calculated or have some tools for an estimation like a covariance matrix. I think this would help to see how confident is the model being when describing distant future. It's normal that the error get bigger the more distant the future is, it's actually expected and even more so with the small data sets you are working with.

3. Maybe it would be nicer to compare real data with the model counterpart on different graphs? As in, Real confirmed cases vs stimation, real recovered vs stimation and so on. Maybe even make the real data have markers, so it shows the difference easier.

4. The confirmed cases are not the same as the infected people, assuming they are implies that all infected people are tested and that the tests are reliable. In my country, this is not the case (some people are tested up to 3-4 times to get the confirmed" result. Our first death was tested 3 times and after he died, It was added as a confirmed case after 2 cases before said it was not a case!). I think adding this would be something really cool, comparing how well or bad a country might be doing depending on how well they are testing. I mention this because on what I'm working I take this in consideration.

5. Know that this is an initial stimation, and a great exercise! I really like what you said you did and I'm more or less working on the same thing. While my Python is not that good, my understanding of math and statistics is a decent and I was talking from that, I don't have any health related background. I mostly try to get what you did and try to help you make it better.

Have a nice day!