In this project, you will fit an SIR-type model to real Covid-19 epidemiological data, for 5-10…

In this project, you will fit an SIR-type model to real Covid-19 epidemiological data, for 5-10

regions. For each region, you will estimate the reproduction number R0 (an unfortunate symbol,

because it is not the removed population at time 0, R(0)). Python notebooks will be available

to get you started. One notebook will show you how to get data for US states. It is perfectly fine

for you to use US state data for the project, but you are welcome (and encouraged!) to do this

for other regions in the world. We’ve found resources to get data from many regions, including

China, for example.

The form of the finished product should be a Python notebook1

. Note that JupyterHub cells

can be used for text by selecting Markdown instead of Code; LaTeX can and should be used in

these blocks for math (plenty of examples in notebooks we’ve shared). The final product should


• A title and project description: regions you are analyzing and why, a description of the

data, caveats about this particular data (e.g. is the disease on the rise in these regions, or

has the curve been flattened?), etc… You should read in the data here, and include plots of

the total cases (or cases per day, or infectives), with labels and description.

• Discuss and develop the model you will fit to the data, the assumptions it makes, and the

meaning of the parameters. You may use the standard SIR model**, and in this case you

should also provide a basic fixed point/stability analysis, and how R0 is computed from its

parameters. Think carefully about the role of the total population N. Explain what outputs

from the model can be used to fit the available data.

• Include the code for computing your deterministic model output and an example computation.

• Perform fits for your data, using either the Gauss-Newton method, or another optimization

package. We will give you pointers on other options, and example Gauss-Newton code will

be provided and described in the final lecture. Provide comparison plots for the fit and the

data, for each of the regions you’ve analyzed. Extract the best fit parameters and estimate


• Conclude your work by interpreting the results. Why did different regions give different R0

values? What does this say about containment strategies? Do you believe the results? Provide any references (including basic news articles) that you use to understand and interpret

your results.

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