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Controlling Epidemics With Mathematical and Machine Learning Models

Controlling Epidemics With Mathematical and Machine Learning Models
Author(s)/Editor(s): Abraham Varghese (Higher College of Technology, Oman), Eduardo M. Lacap, Jr. (Higher College of Technology, Oman), Ibrahim Sajath (Higher College of Technology, Oman), Kamal Kumar (Higher College of Technology, Oman) and Shajidmon Kolamban (Higher College of Technology, Oman)
Copyright: ©2023
DOI: 10.4018/978-1-7998-8343-2
ISBN13: 9781799883432
ISBN10: 1799883434
EISBN13: 9781799883449

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Description

Communicable diseases have been an important part of human history. Epidemics afflicted populations, causing many deaths before gradually fading away and emerging again years after. Epidemics of infectious diseases are occurring more often, and spreading faster and further than ever, in many different regions of the world. The scientific community, in addition to its accelerated efforts to develop an effective treatment and vaccination, is also playing an important role in advising policymakers on possible non-pharmacological approaches to limit the catastrophic impact of epidemics using mathematical and machine learning models.

Controlling Epidemics With Mathematical and Machine Learning Models provides mathematical and machine learning models for epidemical diseases, with special attention given to the COVID-19 pandemic. It gives mathematical proof of the stability and size of diseases. Covering topics such as compartmental models, reproduction number, and SIR model simulation, this premier reference source is an essential resource for statisticians, government officials, health professionals, epidemiologists, sociologists, students and educators of higher education, librarians, researchers, and academicians.



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