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.

The book examines, contemporary globalisation that signifies a growing interconnectedness between people and societies across the world through increasing flows of people, goods, services, finance and ideas across the borders. The concept of globalisation and its meaning is discussed through insights from scholars such as David Held, Anthony Giddens, David Harvey, Arjun Appadurai, Ulrich Beck, Manuel Castells, Saskia Sassen and many other scholars to explain divergent perspectives of globalisation. The book also studies threats like nuclear weapons proliferation, global terrorism, environmental security issues, global justice, poverty, migration, and global shifts. It aims to generate readers’ interest in understanding globalisation and analysing the changing dynamics of world politics by studying it from diverse viewpoints. The emergence of Covid-19 and resurgence of great power politics has given rise to the debate of de-globalisation and return of a new kind of ‘cold war’. In this context, this volume will also help readers to understand globalisation in the present changing world order. The book will be useful to the departments of Political Science, International Relations, and other interdisciplinary Social Sciences subjects like Political Economy and Global/International Politics.

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.