Disaster and Pandemic Management Using Machine Learning: A Survey

Chamola, Vinay; Hassija, Vikas; Gupta, Sakshi; Goyal, Adit; Guizani, Mohsen; Sikdar, Biplab

doi:10.1109/jiot.2020.3044966

Cited by 76 publications

(45 citation statements)

References 164 publications

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“…The work carried out by Rezaei and Azarmi [20] have developed a framework which can perform assessment of risk associated with COVID-19 using a new idea of social distancing. A closer look into various attempted approach of social distancing shows the adoption of artificial intelligence [21]- [24], machine learning [25], [26], game theory [27], [28], different variants of neural network [29], [30] when it comes to controlling the pandemic situation control.…”

Section: Related Workmentioning

confidence: 99%

A novel predictive model for capturing threats for facilitating effective social distancing in COVID-19

Firdose

Kumar²,

Meegama

2022

IJECE

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Social distancing is one of the simple and effective shields for every individual to control spreading of virus in present scenario of pandemic coronavirus disease (COVID-19). However, existing application of social distancing is a basic model and it is also characterized by various pitfalls in case of dynamic monitoring of infected individual accurately. Review of existing literature shows that there has been various dedicated research attempt towards social distancing using available technologies, however, there are further scope of improvement too. This paper has introduced a novel framework which is capable of computing the level of threat with much higher degree of accuracy using distance and duration of stay as elementary parameters. Finally, the model can successfully classify the level of threats using deep learning. The study outcome shows that proposed system offers better predictive performance in contrast to other approaches.

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Section: Related Workmentioning

confidence: 99%

A novel predictive model for capturing threats for facilitating effective social distancing in COVID-19

Firdose

Kumar²,

Meegama

2022

IJECE

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“…The history of human development has proven that medicine and healthcare are the main driving forces for the development of science and technology [ 35 , 36 ]. Cloud-based medical applications have grown robustly in recent years [ 37 , 38 ].…”

Section: Survey Of Recent Iot Healthcare Applicationsmentioning

confidence: 99%

Smart healthcare IoT applications based on fog computing: architecture, applications and challenges

Quy

Nguyen

Anh

et al. 2021

Complex Intell. Syst.

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The history of human development has proven that medical and healthcare applications for humanity always are the main driving force behind the development of science and technology. The advent of Cloud technology for the first time allows providing systems infrastructure as a service, platform as a service and software as a service. Cloud technology has dominated healthcare information systems for decades now. However, one limitation of cloud-based applications is the high service response time. In some emergency scenarios, the control and monitoring of patient status, decision-making with related resources are limited such as hospital, ambulance, doctor, medical conditions in seconds and has a direct impact on the life of patients. To solve these challenges, optimal computing technologies have been proposed such as cloud computing, edge computing, and fog computing technologies. In this article, we make a comparison between computing technologies. Then, we present a common architectural framework based on fog computing for Internet of Health Things (Fog-IoHT) applications. Besides, we also indicate possible applications and challenges in integrating fog computing into IoT Healthcare applications. The analysis results indicated that there is huge potential for IoHT applications based on fog computing. We hope, this study will be an important guide for the future development of fog-based Healthcare IoT applications.

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“…Regarding machine learning applications, Chamola et al [16] provided a review of several machine learning algorithms that can be adopted in disaster and pandemic management, also presenting a tutorial on machine learning algorithms. Mathematical models, including compartment, statistical and machine learning models for COVID-19 transmission and diagnosis were also reviewed in a work presented by John et al [17].…”

Section: Background and Related Workmentioning

confidence: 99%

Applications of Technological Solutions in Primary Ways of Preventing Transmission of Respiratory Infectious Diseases—A Systematic Literature Review

Leite

Albuquerque

Pinheiro

2021

IJERPH

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With the growing concern about the spread of new respiratory infectious diseases, several studies involving the application of technology in the prevention of these diseases have been carried out. Among these studies, it is worth highlighting the importance of those focused on the primary forms of prevention, such as social distancing, mask usage, quarantine, among others. This importance arises because, from the emergence of a new disease to the production of immunizers, preventive actions must be taken to reduce contamination and fatalities rates. Despite the considerable number of studies, no records of works aimed at the identification, registration, selection, and rigorous analysis and synthesis of the literature were found. For this purpose, this paper presents a systematic review of the literature on the application of technological solutions in the primary ways of respiratory infectious diseases transmission prevention. From the 1139 initially retrieved, 219 papers were selected for data extraction, analysis, and synthesis according to predefined inclusion and exclusion criteria. Results enabled the identification of a general categorization of application domains, as well as mapping of the adopted support mechanisms. Findings showed a greater trend in studies related to pandemic planning and, among the support mechanisms adopted, data and mathematical application-related solutions received greater attention. Topics for further research and improvement were also identified such as the need for a better description of data analysis and evidence.

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Disaster and Pandemic Management Using Machine Learning: A Survey

Cited by 76 publications

References 164 publications

A novel predictive model for capturing threats for facilitating effective social distancing in COVID-19

A novel predictive model for capturing threats for facilitating effective social distancing in COVID-19

Smart healthcare IoT applications based on fog computing: architecture, applications and challenges

Applications of Technological Solutions in Primary Ways of Preventing Transmission of Respiratory Infectious Diseases—A Systematic Literature Review

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