"The recent COVID-19 global pandemic exemplifies the need for efficient, reliable, and real-time tools and technology for forecasting and predicting healthcare disasters as well as for helping to restrict subsequent spread and fatality of deadly diseases. This new book discusses many of the innovative and state-of-the-art tools and technology that can help meet the challenges of predicting such disasters. The chapters offer a plethora of useful information for designing healthcare disaster management systems that can be dynamically configurable with implementation of today's modern technology, such as cloud computing, artificial intelligence, IoT, data analytics, and machine learning. These can increase effectiveness in remote sensing technologies, data analytics, data storage, communication networks, geographic information system (GIS), and global positioning System (GPS), to name a few. The book discusses mathematical models using graph-based approaches for analyzing dynamic, heterogeneous, and unstructured data for applications in epidemiology. The authors also address the use of mobile applications for communication efforts and remote monitoring for gauging health and the effectiveness of preventive healthcare measures. The chapters discuss influencing factors that directly or indirectly target public health infrastructure that can lead to or exacerbate global health crises, such as extreme climate changes, refugee health crises, terrorism and cyberterrorism, and technology-related incidents. The book further looks at efficient methods to analyze disasters and how to deliver healthcare in areas of conflict and crisis. This important volume, Global Healthcare Disasters: Predicting the Unpredictable with Emerging Technologies, provides a bounty of useful information for health professionals, academicians, researchers, governmental agencies, and policymakers across the world to predict, mitigate, and manage global health disaster with emerging technologies"--
Contributors from management, information technology, and computer science explore the potential of using advanced technology to predict and manage global health disasters, focusing on gathering data then storing it where it can be accessed and analyzed. Their topics include the role of knowledge graphs in analyzing epidemics and health disasters, using artificial intelligence to improve hospital management during the era of COVID-19, the effectiveness of Aarogya Setu mobile application during COVID-19 healthcare management: a technology acceptance model-based approach, COVID-19 disaster healthcare management systems in rural areas, and design schema to offer security and confidentiality to healthcare data in a cloud environment. Annotation ©2022 Ringgold, Inc., Portland, OR (protoview.com)
The recent COVID-19 global pandemic exemplifies the need for efficient, reliable, and real-time tools and technology for forecasting and predicting healthcare disasters as well as for helping to restrict subsequent spread and fatality of deadly diseases. This new book discusses many of the innovative and state-of-the-art tools and technology that can help meet the challenges of predicting such disasters. The chapters offer a plethora of useful information for designing healthcare disaster management systems that can be dynamically configurable with implementation of today’s modern technology, such as cloud computing, artificial intelligence, IoT, data analytics, and machine learning. These can increase effectiveness in remote sensing technologies, data analytics, data storage, communication networks, geographic information system (GIS), and global positioning System (GPS), to name a few.
The book discusses mathematical models using graph-based approaches for analyzing dynamic, heterogeneous, and unstructured data for applications in epidemiology. The authors also address the use of mobile applications for communication efforts and remote monitoring for gauging health and the effectiveness of preventive healthcare measures. The chapters discuss influencing factors that directly or indirectly target public health infrastructure that can lead to or exacerbate global health crises, such as extreme climate changes, refugee health crises, terrorism and cyberterrorism, and technology-related incidents. The book further looks at efficient methods to analyze disasters and how to deliver healthcare in areas of conflict and crisis.
This important volume, Global Healthcare Disasters: Predicting the Unpredictable with Emerging Technologies, provides a bounty of useful information for health professionals, academicians, researchers, governmental agencies, and policymakers across the world to predict, mitigate, and manage global health disaster with emerging technologies.
Discusses state-of-the-art technology for predicting disasters by designing healthcare disaster management systems that use modern technology—cloud computing, artificial intelligence, IoT, data analytics, machine learning—to increase effectiveness in remote sensing technologies, data analytics, data storage, GIS, GPS, etc.
