Internet of Things for Epidemic Detection: A Critical Review

Kaushalya, S.; Kulawansa, Kadt; Firdhous, Mohamed

doi:10.1007/978-981-13-6861-5_42

Cited by 6 publications

(5 citation statements)

References 29 publications

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“…The traditional modelbased approaches and simulations, though extensively developed, cannot adequately contain the real time nature of epidemics. These methods may lack timely data and may provide inaccurate predictions since it depends highly on assumptions on the behaviour of a new virus [101]. There is no doubt that modern hospital equipment are science-fictionlike to people living in 100 years ago.…”

Section: Discussionmentioning

confidence: 99%

Wearables for the Next Pandemic

Hedayatipour

McFarlane

2020

IEEE Access

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This paper reviews the current state of the art in wearable sensors, including current challenges, that can alleviate the loads on hospitals and medical centers. During the COVID-19 Pandemic in 2020, healthcare systems were overwhelmed by people with mild to severe symptoms needing care. A careful study of pandemics and their symptoms in the past 100 years reveals common traits that should be monitored for managing the health and economic costs. Cheap, low power, and portable multi-modalsensors that detect the common symptoms can be stockpiled and ready for the next pandemic. These sensors include temperature sensors for fever monitoring, pulse oximetry sensors for blood oxygen levels, impedance sensors for thoracic impedance, and other state sensors that can be integrated into a single system and connected to a smartphone or data center. Both research and commercial medically approved devices are reviewed with an emphasis on the electronics required to realize the sensing. The performance characteristics, such as accuracy, power, resolution, and size of each sensor modality are critically examined. A discussion of the characteristics, research challenges, and features of an ideal integrated wearable system is also presented.

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Section: Discussionmentioning

confidence: 99%

Wearables for the Next Pandemic

Hedayatipour

McFarlane

2020

IEEE Access

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“…-Citizen collaboration and participation in the city management with their governments (e.g. creating collaborative and participative environments) (Kaushalya et al 2019). -Improving information transparency and disclosure (Ibid).…”

Section: Challenges and Windup Considerationsmentioning

confidence: 99%

“…In this respect, some contextualization of the broader institutional constraints over urban planning and urban management should be considered as follow: Citizen collaboration and participation in the city management with their governments (e.g. creating collaborative and participative environments) (Kaushalya et al 2019 ). Improving information transparency and disclosure (Ibid).…”

Section: Challenges and Windup Considerationsmentioning

confidence: 99%

“…One of the most technical and smart solutions today is developing the potentialities to use big data analysis. In this respect, crowdsourcing approach provides “creative solutions” (Howe 2008 , p. 19), and the internet of things and its application in healthcare for epidemic detection, which depends on advanced computing technologies (Kaushalya et al 2019 ).…”

Section: Research Directions To Follow After the Pandemic Recedesmentioning

confidence: 99%

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COVID-19 Challenge, Information Technologies, and Smart Cities: Considerations for Well-Being

Abusaada

Elshater

2020

Int. Journal of Com. WB

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Coronavirus (COVID-19) raises an essential debate about implementing the ideas and insights of smart technology in the fields of urban planning and design. This commentary sheds light on considerations and challenges in the area of knowledge in these fields as consequences of the recent pandemic. The concluded remarks cover issues with a specific focus on accelerating the digital transformation in education and a typo-morphological analysis that ends with revisiting the norms and standards of social distancing. Besides, this commentary recommends research directions to follow after the pandemic recedes, tackling the multidisciplinarity between fields of specialisation.

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“…Some researchers further extend this approach to benefit from Internet of Things (IoT) technologies 6 . In addition, recent advances cover the analysis of real-time data or even the sentiment of the data found inside social media 7 , such as Twitter.…”

mentioning

confidence: 99%

Comparing Social media and Google to detect and predict severe epidemics

Samaras

García‐Barriocanal

Sicilia

2020

Sci Rep

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internet technologies have demonstrated their value for the early detection and prediction of epidemics. in diverse cases, electronic surveillance systems can be created by obtaining and analyzing on-line data, complementing other existing monitoring resources. this paper reports the feasibility of building such a system with search engine and social network data. concretely, this study aims at gathering evidence on which kind of data source leads to better results. Data have been acquired from the Internet by means of a system which gathered real-time data for 23 weeks. Data on influenza in Greece have been collected from Google and Twitter and they have been compared to influenza data from the official authority of Europe. The data were analyzed by using two models: the ARIMA model computed estimations based on weekly sums and a customized approximate model which uses daily sums. Results indicate that influenza was successfully monitored during the test period. Google data show a high pearson correlation and a relatively low Mean Absolute percentage error (R = 0.933, MAPE = 21.358). Twitter results are slightly better (R = 0.943, MAPE = 18.742). The alternative model is slightly worse than the ARIMA(X) (R = 0.863, MAPE = 22.614), but with a higher mean deviation (abs. mean dev: 5.99% vs 4.74%).

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Internet of Things for Epidemic Detection: A Critical Review

Cited by 6 publications

References 29 publications

Wearables for the Next Pandemic

Wearables for the Next Pandemic

COVID-19 Challenge, Information Technologies, and Smart Cities: Considerations for Well-Being

Comparing Social media and Google to detect and predict severe epidemics

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