A New Mathematical Model for Controlled Pandemics Like COVID-19 : AI Implemented Predictions
Liam Dowling Jones, Malik Magdon-Ismail, Laura Mersini-Houghton and, Steven Meshnick

TL;DR
This paper introduces a new mathematical model combined with machine learning to predict COVID-19 infection spread under various control measures, enabling region-specific forecasts and policy evaluation.
Contribution
It develops a novel mathematical framework for modeling pandemic control measures and employs machine learning to fit and predict infection dynamics globally.
Findings
Model captures effects of lockdowns, social distancing, masks, and closures.
Machine learning accurately fits past COVID-19 data across regions.
Predicts future infection trends based on policy changes.
Abstract
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 . 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 , the infections in any region at time and derive predictions for the spread of infections over time as a function of the strength of the specific measure taken and their duration.…
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Taxonomy
TopicsCOVID-19 epidemiological studies · COVID-19 diagnosis using AI · Complex Systems and Time Series Analysis
