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

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	A New Mathematical Model for Controlled Pandemics Like COVID-19 : AI Implemented Predictions
Creator	Magdon-Ismail, Malik Meshnick, Steven Dowling Jones, Liam Mersini-Houghton, Laura
source	ArXiv
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 $i(r, t)$. 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.
has issue date	2020-08-24(xsd:dateTime)
has license	arxiv
sha1sum (hex)	031d2d38775bcbb711b2006fce9234c02b23b3c2
resource representing a document's title	A New Mathematical Model for Controlled Pandemics Like COVID-19 : AI Implemented Predictions
resource representing a document's body	covid:031d2d38775bcbb711b2006fce9234c02b23b3c2#body_text
is schema:about of	named entity 'model' named entity 'best-fit' named entity 'specific' named entity 'lead' named entity 'GENERIC' »more»

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