Controlling Epidemics With Mathematical and Machine Learning Models eBook

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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.

This is a digital product.

Controlling Epidemics With Mathematical and Machine Learning Models is written by Abraham Varghese; Eduardo M. Lacap, Jr.; Ibrahim Sajath; M. Kamal Kumar; Shajidmon Kolamban and published by Engineering Science Reference. The Digital and eTextbook ISBNs for Controlling Epidemics With Mathematical and Machine Learning Models are 9781799883456, 1799883450 and the print ISBNs are 9781799883432, 1799883434. Additional ISBNs for this eTextbook include 9781799883449.