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Agent-Based Network Models for COVID-19

Posted 4 years ago

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18 Replies
Posted 4 months ago

Dear Christopher,

Congratulations on the development of a very useful contagion network model. I have been working along the same issues using production networks (in which economic shocks are considered as disease propagation), which are structurally different from your contagion model based on undirected graphs. I have changed few properties of your model to experiment with directed production graphs.

My question: except a change in "agentNeighborhood" code to "Catenate[{#1 -> #2}", is there any other place that needs to be adjusted to accommodate directed networks? If so, could you please let me know which parts of your code needs revising to also study shock propagation in directed networks?

Furthermore, reading the comments on your code, I wonder whether there is a fundamental difference between simple and efficient implementations. I thought they would both produce the same results, but apparently experiments by others in this forum indicate that they are different in several respects like peak infection and speed of infection. Am I right?

Look forward to hearing from you.

Tugrul Temel

POSTED BY: Tugrul Temel
Posted 4 years ago

Thanks for sharing this incredible project, Christopher. I used it for a for a project with my kids this morning. They are in 8th and 10th grade and your work really made the high level ideas that go into virus modeling accessible to them:

POSTED BY: Mike Lawler
POSTED BY: Barry Wardell

Ah, that's a good catch. I'll try to add that change to the notebook and edit the post.

I made that change and recomputed everything. None of the results look significantly affected, but thanks for finding that bug. The main post has been updated to show results from the new version.

Great, thank you for checking.

Do you understand why the two implementations might still differ? They are in much closer agreement now, but there still seems to be a small, but statistically significant difference (as measured by, e.g. the time and height of the peak infection).

POSTED BY: Barry Wardell

Thanks for this very interesting post.

I did some experiments with your two different implementations and found that they behave differently in a statistically significant sense. To verify this, I ran your simple example a large number of times (1000 simulations) using both implementations and extracted the time and magnitude of the infection peak. The attached histograms show the results. Orange is the "Efficient implementation" and blue is the other implementation. You can see that the infection peak is earlier and higher when run with the efficient implementation, suggesting that it has a higher effective transmission rate.

I thought I understood both implementations, but there must be something I am missing as I can't see why they should behave differently. Can anyone explain why I am finding differences?

Time of peak Infection peak

POSTED BY: Barry Wardell

Thanks. Very interesting. As a clinician in, so far, lucky New Zealand, I was interested in how increasing number of degrees of association in networks might affect transmission. I'm watching how Denmark reopening schools will, to my mind, likely create a very large network of associations very quickly.

Also found this paper, which appears to introduce heterogeneity of transmission as a concept. I suspect this is tautologous to how one might model death likelihoods in differing age/risk groups.

Maths so far a bit beyond my weekend explorer level, but the concepts seem useful.

[Percolation on heterogeneous networks as a model for epidemics][1]

POSTED BY: Matt Brook
Posted 4 years ago

Nice post, Christopher, thanks. It shows the power of Mathematica to generate and manipulate efficiently highly structured systems. We're working on similar structured-contact covid model for local settings (e.g. hospitals, city districts et al). Would be glad to share it, when ready

POSTED BY: David Gurarie

Thank you Shenghui! I ran successfully the notebook covid.nb in 2292 seconds. Thank you all!

Dear Christopher, Thank you for your prompt reply! I believe I did not run out of memory. I am trying again, using ParallelTable.

Aristeidis, I can give you a rough benchmark: I use a desktop with Xeon E2176M 6 core + 16GB RAM to run the whole notebook. It consumes 10 GB (maximum resource I have besides OS resources) and takes 3655 seconds to complete (using ParallelTable). Attached is a notebook with only input code from Christopher's article and my timing result.

POSTED BY: Shenghui Yang

Impressive project! Congratulations! I wonder what is the minimum configuration of a Windows 10 Pro workstation to evaluate the notebook, as I was not able to evaluate superGraphSimsByConnectivity, using a workstation with an Intel Core i9-9900K @ 3.60 GHz CPU, 32.0 GB RAM, and Mathematica 12.1.

Hmm. I ran this notebook on my laptop, so it should work on your much higher-spec computer. When I ran that particular line originally, however, I used ParallelTable, but that shouldn't change anything.

In what way did it fail? Did it run out of memory?

Posted 4 years ago

Excellent post @Christopher Wolfram , I was working along the same lines. I'll switch the focus to cover other interesting aspects of using this type of models.

POSTED BY: Diego Zviovich

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POSTED BY: Moderation Team

Outstanding post, Christopher!

It's intriguing that the shape of the infected curve in the Simple Example section is fairly symmetrical and does not have the long exponential tail.

Generating the phase plane analysis from the simulations is amazing. I've seen (and done) these analyses using the traditional calculus-based approach, but it's wonderful that you can do the same with this model and show that many of the same principles apply.

POSTED BY: Robert Nachbar
Posted 4 years ago

Does this model enable to evaluate a number of infected people, using very long tail(s) of the confirmed cases (for example, using published data from China)? My thought was - the number of reported can be lower than the real/infected cases, but to make a justified decision on quarantine stop, one has to know both.

POSTED BY: Michael D.
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