Mobile Technology Solution for COVID-19: Surveillance and Prevention

Ahmad, Shaban; Chitkara, Pragya; Khan, Fatima Nazish; Kishan, Avtar; Alok, V N; Ramlal, Ayyagari; Mehta, Sahil

doi:10.1007/978-981-15-8534-0_5

Cited by 32 publications

(17 citation statements)

References 26 publications

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“…Finding combating solutions for COVID-19 needs combined efforts from international cross-disciplinary collaborations to carefully identify time, course, and region-dependent clinical actions in response to COVID-19 [54]. Furthermore, the AI needs to go in hand with other digital intelligence technologies such as the wearable technology, mobile technology, the Internet of Things, big data analytics, cloud computing [55][56][57][58][59] to develop viable, thoroughly validated tools which can provide real support local healthcare providers. In addition, as discussed in the paper, AI has its applications in finding solutions for COVID-19 in many dimensions.…”

Section: Challenges and Conclusionmentioning

confidence: 99%

Leveraging Artificial Intelligence (AI) Capabilities for COVID-19 Containment

Surianarayanan

Raj

2021

New Gener. Comput.

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The Coronavirus disease (COVID-19) is an infectious disease caused by the newly discovered Severe Acute Respiratory Syndrome Coronavirus two (SARS-CoV-2). Most of the people do not have the acquired immunity to fight this virus. There is no specific treatment or medicine to cure the disease. The effects of this disease appear to vary from individual to individual, right from mild cough, fever to respiratory disease. It also leads to mortality in many people. As the virus has a very rapid transmission rate, the entire world is in distress. The control and prevention of this disease has evolved as an urgent and critical issue to be addressed through technological solutions. The Healthcare industry therefore needs support from the domain of artificial intelligence (AI). AI has the inherent capability of imitating the human brain and assisting in decision-making support by automatically learning from input data. It can process huge amounts of data quickly without getting tiresome and making errors. AI technologies and tools significantly relieve the burden of healthcare professionals. In this paper, we review the critical role of AI in responding to different research challenges around the COVID-19 crisis. A sample implementation of a powerful probabilistic machine learning (ML) algorithm for assessment of risk levels of individuals is incorporated in this paper. Other pertinent application areas such as surveillance of people and hotspots, mortality prediction, diagnosis, prognostic assistance, drug repurposing and discovery of protein structure, and vaccine are presented. The paper also describes various challenges that are associated with the implementation of AI-based tools and solutions for practical use.

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Section: Challenges and Conclusionmentioning

confidence: 99%

Leveraging Artificial Intelligence (AI) Capabilities for COVID-19 Containment

Surianarayanan

Raj

2021

New Gener. Comput.

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“…Examples of these implications include increased levels of anxiety when users receive a COVID-19 exposure notification [81][82][83]. Privacy concerns have also been raised about tracking and tracing features, specifically about "Tetamman", "Tawakkalna", and "Tabaud", similar to what has been reported by other mHealth apps [84][85][86]. The benefits and drawbacks of mHealth systems that raise issues with consumer privacy, must be examined critically by all stakeholders to ensure public by in and trust is not jeopardized.…”

Section: Major Findingsmentioning

confidence: 99%

Public Perceptions around mHealth Applications during COVID-19 Pandemic: A Network and Sentiment Analysis of Tweets in Saudi Arabia

Binkheder

Aldekhyyel

AlMogbel³

et al. 2021

IJERPH

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A series of mitigation efforts were implemented in response to the COVID-19 pandemic in Saudi Arabia, including the development of mobile health applications (mHealth apps) for the public. Assessing the acceptability of mHealth apps among the public is crucial. This study aimed to use Twitter to understand public perceptions around the use of six Saudi mHealth apps used during COVID-19: “Sehha”, “Mawid”, “Sehhaty”, “Tetamman”, “Tawakkalna”, and “Tabaud”. We used two methodological approaches: network and sentiment analysis. We retrieved Twitter data using specific mHealth apps-related keywords. After including relevant tweets, our final mHealth app networks consisted of a total of 4995 Twitter users and 8666 conversational relationships. The largest networks in size (i.e., the number of users) and volume (i.e., the conversational relationships) among all were “Tawakkalna” followed by “Tabaud”, and their conversations were led by diverse governmental accounts. In contrast, the four remaining mHealth networks were mainly led by the health sector and media. Our sentiment analysis approach included five classes and showed that most conversations were neutral, which included facts or information pieces and general inquires. For the automated sentiment classifier, we used Support Vector Machine with AraVec embeddings as it outperformed the other tested classifiers. The sentiment classifier showed an accuracy, precision, recall, and F1-score of 85%. Future studies can use social media and real-time analytics to improve mHealth apps’ services and user experience, especially during health crises.

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“…One of the successful applications in this category is the AI4COVID-19 app [ 18 ] which can detect the infection using patient cough patterns. The TraceTogether App [ 7 ] is used to track the patients whereas COCOVID [ 19 ] is developed to digitalize the mitigation efforts. MIT came up with PACT: Private Automated Contact Tracing [ 20 ] to automate the contact-tracing.…”

Section: Related Previous Work and Research Gapsmentioning

confidence: 99%

iBlock: An Intelligent Decentralised Blockchain-based Pandemic Detection and Assisting System

Egala

Pradhan

Badarla

et al. 2021

J Sign Process Syst

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The recent COVID-19 outbreak highlighted the requirement for a more sophisticated healthcare system and real-time data analytics in the pandemic mitigation process. Moreover, real-time data plays a crucial role in the detection and alerting process. Combining smart healthcare systems with accurate real-time information about medical service availability, vaccination, and how the pandemic is spreading can directly affect the quality of life and economy. The existing architecture models are become inadequate in handling the pandemic mitigation process using real-time data. The present models are server-centric and controlled by a single party, where the management of confidentiality, integrity, and availability (CIA) of data is doubtful. Therefore, a decentralised user-centric model is necessary, where the CIA of user data is assured. In this paper, we have suggested a decentralized blockchain-based pandemic detection and assistance system (iBlock). The iBlock uses robust technologies like hybrid computing and IPFS to support system functionality. A pseudo-anonymous personal identity is introduced using H-PCS and cryptography for anonymous data sharing. The distributed data management module guarantees data CIA, security, and privacy using cryptography mechanisms. Furthermore, it delivers useful intelligent information in the form of suggestions and alerts to assist the users. Finally, the iBlock reduces stress on healthcare infrastructure and workers by providing accurate predictions and early warnings using AI/ML.

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Mobile Technology Solution for COVID-19: Surveillance and Prevention

Cited by 32 publications

References 26 publications

Leveraging Artificial Intelligence (AI) Capabilities for COVID-19 Containment

Leveraging Artificial Intelligence (AI) Capabilities for COVID-19 Containment

Public Perceptions around mHealth Applications during COVID-19 Pandemic: A Network and Sentiment Analysis of Tweets in Saudi Arabia

iBlock: An Intelligent Decentralised Blockchain-based Pandemic Detection and Assisting System

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