epymorph turns spatial epidemic model comparisons into controlled forecasting experiments

Metapopulation models are used widely to understand and forecast epidemics, but they are often built as complex, custom code in which assumptions about transmission, movement and geography are intertwined and obscure, making these models hard to construct and reproduce, and the effect of any single component difficult to isolate. Here we present epymorph, a Python package that implements a novel conception of metapopulation disease modeling centered around standardization, modularity, and interchangeable components. epymorph constructs spatial models from separately specified transmission, mov

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