  We introduce a minimalist outbreak forecasting model that combines
data-driven parameter estimation with variational data assimilation. By
focusing on the fundamental components of nonlinear disease transmission and
representing data in a domain where model stochasticity simplifies into a
process with independent increments, we design an approach that only requires
four core parameters to be estimated. We illustrate this novel methodology on
COVID-19 forecasts. Results include case count and deaths predictions for the
US and all of its 50 states, the District of Columbia, and Puerto Rico. The
method is computationally efficient and is not disease- or location-specific.
It may therefore be applied to other outbreaks or other countries, provided
case counts and/or deaths data are available.
