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Information and Communication Technologies (ICT) are becoming indispensable nowadays for the healthcare industry. The utilization of ICT in healthcare services has accelerated even faster after the commencement of the COVID-19 outbreak. This study aims to perform a scientometric analysis of scholarly literature on airborne diseases in the discipline of science and technology. It explores the recent advancement of internet technologies in healthcare to control the prevalence of deadly airborne illnesses by applying analytical approaches. It presents publication trends, citation structure, influential sources, co-citation, and cooccurrence network analysis using the CiteSpace tool. It identifies the important research topics, current research hotspots, most active research areas, and leading technologies in this scientific knowledge domain. It inferred significant results fromanalyses thatwill benefit researchers and the academic fraternity across the globe to understand the evolving paths and recent scientific progress of ICT in airborne disease management. [ABSTRACT FROM AUTHOR]

Disasters around the world have adversely affected every aspect of life and panic-health of stranded persons is one such category. An effective and on-time evacuation from disaster-affected areas can avoid any panic-related health problems of the stranded persons. Although the nature of disasters differ in terms of how they occur, the evacuation of stranded persons faces approximately same set of issues related to the communication, time-sensitive computation and energy efficiency of the devices operated in the disaster-affected areas. In this paper, a cyber physical system (CPS) is proposed that takes into account various challenges of the disaster evacuation, so an efficient on-time and orderly evacuation of stranded panicked persons could be realized. The system employs fog-assisted mobile and UAV devices for time-sensitive computation services, data relaying and energy-aware computation. The system uses a fog-assisted two-factor energy-aware computation approach using data reduction, which enables the energy-efficient data reception and transmission (DRecTrans) operations at the fog nodes and compensates to extend the period for other functionalities. The data reduction at fog devices employs Novel Events Identification (NEI) and Principal Component Analysis (PCA) for detecting consecutive duplicate traffic and data summarization of high dimensional data, respectively. The proposed system operates in two spaces: physical and cyber. Physical space facilitates real-world data acquisition and information sharing with the concerned stakeholders (stranded persons, evacuation teams and medical professionals). The cyber space houses various data-analytics layers and comprises of two subspaces: fog and cloud. The fog space helps in providing real-time panic-health diagnostic and alert services and enables the optimized energy consumption of devices operate in disaster-affected areas, whereas the cloud space facilitates the monitoring and prediction of panic severity of the stranded persons, using a conditional probabilistic model and seasonal auto regression integrated moving average (SARIMA), respectively. Cloud space also facilitates the disaster mapping for converging the evacuation map to the actual situation of the disaster-affected area, and geographical population analysis (GPA) for the identification of the panic severity-based critical regions. The performance evaluation of the proposed CPS acknowledges its Logistic Regression-based panic-well being determination and real-time alert generation efficiency. The simulated implementation of NEI and PCA depicts the fog-assisted energy efficiency of the DRecTrans operations of the fog nodes. The performance evaluation of the proposed CPS also acknowledges the prediction efficiency of the SARIMA and disaster mapping accuracy through GPA. The proposed system also discusses a case study related to the pandemic disaster of coronavirus disease 2019 (COVID-19), where the system can help in panic-based selective testing of the persons, and preventing panic due to distressing period of COVID-19 outbreak. [ABSTRACT FROM AUTHOR]

The biggest challenges for several universities and educational institutions throughout the COVID‐19 pandemic are online and e‐learning infrastructure availability. The incorporation of Information and Communication Technologies in the domain of disaster management is one such dimension, which focuses on the sustainability of human beings regarding the handling of unexpected pandemics. Therefore, it is important to evaluate the panic well‐being of the student in this situation. Moreover, virtual reality technology provides a virtual classroom environment that improves the skill and knowledge of the students at remote sites. In this paper, a fog‐assisted cyber physical system is proposed that deals with the various aspects of the panic well‐being of the student, including the virtual reality platform for remote learning. The proposed system utilizes the concepts of physical and cyberspace. The physical space facilitates real‐time data acquisition, and cyberspace determines and predicts the panic well‐being of the student. The performance assessment of the proposed model acknowledges the efficiency of the virtual learning system and panic well‐being determination and prediction. The proposed system also discussed a virtual learning system that provides a virtual classroom environment to the students at remote sites and reduces the panic due to stressful times during the COVID‐19 pandemic. [ABSTRACT FROM AUTHOR]

The past five decades have witnessed the unprecedented contribution of arboviral diseases towards global morbidity and disability. It is primarily attributed due to unplanned urbanization, population explosion and globalization. Out of these, dengue is considered the most important arboviral disease because of its predominant growth in the past. The presented study explores the immense potential of Internet of things (IoT), fog and cloud computing for providing technology-based healthcare solutions for dengue virus (DENV) infection. In this paper, a hierarchical healthcare computing system for controlling DENV infection using fog–cloud-assisted IoT is proposed. This system provides a real-time remote diagnosis of DENV infection in individuals and monitors and predicts their health sensitivity during its infection period. The system uses fog computing to diagnose the DENV infection status of the individuals using |$k$| -means clustering and generates immediate diagnostic alerts to individuals, at the fog layer. Furthermore, the system uses cloud computing to monitor and predict the probabilistic health sensitivity of the DENV-infected individuals using Bayesian belief network and artificial neural network, respectively, at the cloud layer. The prediction of health sensitivity in the proposed system helps the infected individuals and healthcare agencies in determining the health vulnerability of DENV-infected individuals and preventing severe or permanent health losses in the future. The proposed system is experimentally evaluated using well-defined approaches, which conform to its validity and applicability. The results obtained from the experimental evaluations of the proposed system acknowledge the performance superiority and high efficiency of the system in delivering DENV-related healthcare services in real time. [ABSTRACT FROM AUTHOR]

The advancements in information and communication technologies have propelled the efforts of various nations toward a disaster-ready society. This progression has even conceived a new term called as smart disaster management (SDM). This profound interest and popularity of SDM among the nations in recent years has led to a considerable number of research publications in this domain. Henceforth, the high relevance and growth of research require the overall study of the structure and development in this field, which can be well understood through quantitative approaches. This article presents a bidimensional scientometric study of SDM research from the perspective of various categories of disasters and research areas. This study is based on an extensive bibliographic data of the period 2009–2018 retrieved from Scopus. It exhibits various empirical approaches to study the evolution, status quo, and output of SDM research. This article provides extensive insights into the publication patterns, citation patterns, and keywords co-occurrence analysis for technological trends of the SDM research. This scientometric study identifies significant categories, research areas, nations, and institutions for SDM research from various perspectives. In whole, this article presents an overall and better understanding of various patterns, trends, and other factors as the foundation for the future research directives and collaborations in the domain of SDM research. [ABSTRACT FROM AUTHOR]

Wildfires are catastrophic disasters. They pose a fatal threat not only to the forest resources but also to the entire regime of flora and fauna, gravely disturbing the bio-diversity and ecology of the region. The frequency and severity of wildfires are expected to grow, owing to global warming. Therefore, it is essential to adopt a comprehensive, multifaceted approach that enables the real-time monitoring of forest terrains and prompt responsiveness. The Internet of Things (IoT) technology has grown exponentially in recent years, with IoT sensors being deployed to monitor and collect time critical data. This research proposes an integrated IoT-Fog-Cloud energy-efficient framework for wildfire prediction and forecasting. Initially, analysis of variance and Tukey’s post hoc test-based energy conserving mechanism ensures the enhanced lifetime of resource-constrained sensors by adapting the sampling rate of wildfire influent parameters (WIPs) at fog layer. Principal component analysis (PCA) is employed for WIPs’ reduction. Wildfire vulnerability level of a forest terrain is predicted and forecasted using Naïve Bayes (NB) classifier and seasonal auto regressive integrated moving average model, respectively, at cloud layer. Burnt forest area is also predicted using support vector regression. The implementation results of the proposed framework prove its efficiency in predicting and forecasting wildfires. [ABSTRACT FROM AUTHOR]

Dengue fever (DENF) is considered as the world's fastest-growing vector-borne disease. Its timely and proper treatment possesses great significance, as it affects vital body organs and causes morbidity and mortality. In this paper, a dew-cloud assisted DENF-based cyber physical system (CPS) is proposed. This system diagnoses DENF, and monitors the effects of DENF infection on vital body organs. This system uses Linear Discriminant Analysis-Adaptive Neuro Fuzzy Inference System (LDA-ANFIS) classification at the dew space to diagnose the DENF, and monitor the coronary heart disease (CHD) risk of the DENF infected users in real-time. Based on the real-time diagnosis and monitoring, the system immediately alerts the concerned stakeholders and provides timely medical support in case of any dengue-related emergency. The proposed CPS also assesses the Temporal Network Analysis (TNA)-based DENF outbreak risk, at the cloud space to control and prevent the outbreak of DENF by identifying the critical areas and individuals, which contributes in spreading DENF infection. The experimental evaluation of the proposed system acknowledges performance efficiency and utilization through various experimental and statistical approaches. [ABSTRACT FROM AUTHOR]

A correction is presented to the article "A Scientometric Review of Global Research on Smart Disaster Management" published in 2020 issue of the periodical.

In the current scenario of the COVID-19 pandemic and worldwide health emergency, one of the major challenges is to identify and predict the panic health of persons. The management of panic health and on-time evacuation prevents COVID-19 infection incidences in educational institutions and public places. Therefore, a system is required to predict the infection and suggests a safe evacuation path to people that control panic scenarios with mortality. In this paper, a fog-assisted cyber physical system is introduced to control panic attacks and COVID-19 infection risk in public places. The proposed model uses the concept of physical and cyber space. The physical space helps in real time data collection and transmission of the alert generation to the stakeholders. Cyberspace consists of two spaces, fog space, and cloud-space. The fog-space facilitates panic health and COVID-19 symptoms determination with alert generation for risk-affected areas. Cloud space monitors and predicts the person's panic health and symptoms using the SARIMA model. Furthermore, it also identifies risk-prone regions in the affected place using Geographical Population Analysis. The performance evaluation acknowledges the efficiency related to panic health determination and prediction based on the SARIMA with risks mapping accuracy. The proposed system provides an efficient on time evacuation with priority from risk-affected places that protect people from attacks due to panic and infection caused by COVID-19., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 Elsevier B.V. All rights reserved.)