Название: Mathematical Modeling and Soft Computing in Epidemiology
Автор: Jyoti Mishra, Ritu Agarwal
Издательство: CRC Press
Серия: Information Technology, Management and Operations Research Practices
Год: 2021
Страниц: 441
Язык: английский
Формат: pdf (true)
Размер: 22.5 MB

This book describes the uses of different mathematical modeling and soft computing techniques used in epidemiology for experiential research in projects such as how infectious diseases progress to show the likely outcome of an epidemic, and to contribute to public health interventions. This book covers mathematical modeling and soft computing techniques used to study the spread of diseases, predict the future course of an outbreak, and evaluate epidemic control strategies. This book explores the applications covering numerical and analytical solutions, presents basic and advanced concepts for beginners and industry professionals, and incorporates the latest methodologies and challenges using mathematical modeling and soft computing techniques in epidemiology.

Cloud computing is a way to access data and storage resources without clear control and active resource management. Therefore, purchasing, maintaining, and updating systems may be a massive investment of time, and resources in today’s world computing and storage demands are very diverse. Cloud computing provides various facilities and amenities over the Internet, such as databases, servers, storage, and applications. Rather than storing data on a hard disk in local storage, cloud computing allows us to store and save data on a centralized database. Cloud computing provides the computer we use has Internet access; it will also have access to the data. Cloud computing essentially outsources computer programs a bit. Such computer programs are managed by an outside party and are in the cloud. Because of this, users have no worries about storage and power, and can be at ease when it comes to their data.

Data science plays an important part in healthcare data mining and data science. Although clinical data is imprecise and very information rich, this data can be useful, but also needless failure to obtain information. Rich abstraction of real data progressively replaced health data and result generated rendering the prediction disease. Information mining is used for research and develops clinical data which helps systematically determine the relationship. The first draft was prepared and predicted secure health system using conventional security systems. Take maximum storage and keep the data protection and privacy layer mix. Disguise encryption, security mechanisms, and granular access control; allow data encryption and the injection point for authentication, and various processes are integrated.

Despite the variety of Artificial Intelligence (AI) applications in clinical trials and healthcare facilities, they fall into two main categories: structured data analysis (including images, genes, and biomarkers) and unstructured data analysis (such as records, medical reports, or patient surveys to supplement structured data). The former method is driven by Machine Learning (ML) and Deep Learning (DL) algorithms, while the latter is based on the practice of advanced natural language processing (NLP).

Primary users of this book include researchers, academicians, postgraduate students, and specialists.