Unifying Epidemic Models with Mixtures. (arXiv:2201.04960v1 [stat.ML])

The COVID-19 pandemic has emphasized the need for a robust understanding of
epidemic models. Current models of epidemics are classified as either
mechanistic or non-mechanistic: mechanistic models make explicit assumptions on
the dynamics of disease, whereas non-mechanistic models make assumptions on the
form of observed time series. Here, we introduce a simple mixture-based model
which bridges the two approaches while retaining benefits of both. The model
represents time series of cases and fatalities as a mixture of Gaussian curves,
providing …

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