Controlling Epidemics With Mathematical and Machine Learning Models - Couverture rigide

Varghese, Abraham; Jr., Eduardo M. Lacap; Sajath, Ibrahim; Kumar, Kamal; Kolamban, Shajidmon

 
9781799883432: Controlling Epidemics With Mathematical and Machine Learning Models

Synopsis

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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À propos de l?auteur

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

Shajidmon Kolamban, Higher College of Technology, Oman

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Autres éditions populaires du même titre

9781668478844: Controlling Epidemics With Mathematical and Machine Learning Models

Edition présentée

ISBN 10 :  1668478846 ISBN 13 :  9781668478844
Editeur : Engineering Science Reference, 2022
Couverture souple