HughMurrell/CoVmodel

SIR model for COVID-19 breakout in South Africa # CoVmodel ## Simple SIR models for COVID-19 breakout In this repository we present Julia notebooks for modeling the COVID-19 breakout in South Africa and outher countries using case counts from open data sets. ### `RtLive` This directory contains a selection of Jupyter notebooks with Julia code for estimating Rt in real time. The code uses maximum likelyhood to estimate the "most likely" R value for a population at time t. Version 1 of the code is a direct translation from Kevin Systrom's Python code discribed at rt.live and made available to the public as a Jupyter notebook with accompanying explanations of the methods employed. Version 2 and 3 are modifications of that code with version 3 implementing a simple fast discrete estimation of Rt from daily case counts. #### Visualisation Below is a visualisation of Rt over time for the COVID-19 South African case counts. This graphic can be generated using the `RtLiveZA.ipynb` notebook from the `RtLive` directory. ### `SIRjulia` This directory contains a Jupyter notebook that uses `julia` to perform discrete simulation and optimisation to fit a simple piecewise SIR model to John Hopkins case count data. The piecewise nature of the model allows the user to observe the effect of government interventions. A spreadsheet is also provided to create simple SIR simulations. #### Visualisation Below is a visualisation of the early breakout of COVID-19 in South Africa. This graphic can be generated using the `covid_sir.ipynb` notebook from the `SIRjulia` directory.