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Epidemic Modeling for Everyone

Multipopulation model

Repository to accompany the blog series: Epidemic Modeling

These blog posts were recently featured in the Data Exchange Podcast by Ben Lorica in two episodes:

Background Information

  1. CoVID-19: Everything you need to know

  2. CoVID-19: The first truly global event


  1. Epidemiology001.ipynb - Visualizing the spread of CoVID-19

  2. Epidemiology002.ipynb - Visualizing individual CoVID-19 patient data

  3. Epidemiology003.ipynb - CoVID-19: Forecasting the death toll

Compartmental Models

  1. Epidemiology101.ipynb - Epidemic Modeling 101: Or why your CoVID19 exponential fits are wrong

  2. Epidemiology102.ipynb - Epidemic Modeling 102: All CoVID-19 models are wrong, but some are useful

  3. Epidemiology103.ipynb - Epidemic Modeling 103: Adding confidence intervals and stochastic effects to your CoVID-19 Models

  4. Epidemiology104.ipynb - Epidemic Modeling 104: Impact of Seasonal effects on CoVID-19

  5. Epidemiology105.ipynb - Epidemic Modeling 105: Competing CoVID-19 Strains

Network models

  1. Epidemiology 201.ipynb - Epidemiology 201: Network Structure, Super-spreaders and Contact Tracing\

  2. Epidemiology 202.ipynb - Epidemiology 202: Network Models, the effect of degree correlations


  1. Epidemiology 301.ipynb - Epidemiology 301: How to model the effects of vaccination


  • Jupyter notebook includes all the code used to generate the figures in the blog posts. Run the code online using Binder

  • - Python module encapsulates all the code necessary to numerically implement arbitrary compartmental epidemic models

  • - Python module encapsulates all the code necessary to numerically implement arbitrary epidemic models on networks

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