  We present a new mathematical model to explicitly capture the effects that
the three restriction measures: the lockdown date and duration, social
distancing and masks, and, schools and border closing, have in controlling the
spread of COVID-19 infections $i(r, t)$. Before restrictions were introduced,
the random spread of infections as described by the SEIR model grew
exponentially. The addition of control measures introduces a mixing of order
and disorder in the system's evolution which fall under a different
mathematical class of models that can eventually lead to critical phenomena. A
generic analytical solution is hard to obtain. We use machine learning to solve
the new equations for $i(r,t)$, the infections $i$ in any region $r$ at time
$t$ and derive predictions for the spread of infections over time as a function
of the strength of the specific measure taken and their duration. The machine
is trained in all of the COVID-19 published data for each region, county,
state, and country in the world. It utilizes optimization to learn the best-fit
values of the model's parameters from past data in each region in the world,
and it updates the predicted infections curves for any future restrictions that
may be added or relaxed anywhere. We hope this interdisciplinary effort, a new
mathematical model that predicts the impact of each measure in slowing down
infection spread combined with the solving power of machine learning, is a
useful tool in the fight against the current pandemic and potentially future
ones.
