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A probabilistic graphical model for COVID-19 infection spread through a population based on mutual contacts between pairs of individuals across time as well as test outcomes The C++/Python implementation enables full inference at the scale of millions of contacts between thousands of individuals.

License: MIT License

CMake 0.17% C++ 27.74% Python 17.66% Makefile 1.30% Jupyter Notebook 53.14%

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kashif avatar rherbrich74 avatar rvollgraf avatar

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crisp's Issues

one-off mistake initialising GibbsPIS and LBP

reported by @rherbrich74:

There must be a one-off mistake in terms of the time indices between Gibbs/fwd-sampling and LBP. See here for a marginal plot of the test_gibbs_sampler.py for only forward sampling and LBP (forward mode) with no contacts and no tests. The curves are shifted by one (this is for patient-zero=TRUE)
image

Write out world state in the Exp5.2 simulator

From Kevin Murphy: "I am looking for a good simulator, that can generate (in batch mode) a set of 'realistic' contacts for each user, plus the test diagnostic results for a subset of the population. I have looked into the Oxford OpenABM code and the MPI code (from Bernhard's team), but it seems that it might be easier to use CRISP?"

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