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We present a new mathematical model to explicitly capture the effects thatthe three restriction measures: the lockdown date and duration, socialdistancing and masks, and, schools and border closing, have in controlling thespread of COVID-19 infections $i(r, t)$. Before restrictions were introduced,the random spread of infections as described by the SEIR model grewexponentially. The addition of control measures introduces a mixing of orderand disorder in the system's evolution which fall under a differentmathematical class of models that can eventually lead to critical phenomena. Ageneric analytical solution is hard to obtain. We use machine learning to solvethe 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 functionof the strength of the specific measure taken and their duration. The machineis 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-fitvalues 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 thatmay be added or relaxed anywhere. We hope this interdisciplinary effort, a newmathematical model that predicts the impact of each measure in slowing downinfection spread combined with the solving power of machine learning, is auseful tool in the fight against the current pandemic and potentially futureones.

A New Mathematical Model for Controlled Pandemics Like COVID-19 : AI Implemented Predictions