  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 a flexible function class to learn from data compared to traditional
mechanistic models. Although the model is non-mechanistic, we show that it
arises as the natural outcome of a stochastic process based on a networked SIR
framework. This allows learned parameters to take on a more meaningful
interpretation compared to similar non-mechanistic models, and we validate the
interpretations using auxiliary mobility data collected during the COVID-19
pandemic. We provide a simple learning algorithm to identify model parameters
and establish theoretical results which show the model can be efficiently
learned from data. Empirically, we find the model to have low prediction error.
The model is available live at covidpredictions.mit.edu. Ultimately, this
allows us to systematically understand the impacts of interventions on
COVID-19, which is critical in developing data-driven solutions to controlling
epidemics.
