Fighting Pandemics with Physical Distancing Management Technologies

Lukyanenko, Roman; Grangé, Camille

doi:10.4018/jdm.305731

Cited by 3 publications

(1 citation statement)

References 81 publications

(5 reference statements)

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“…It utilizes information in the smartphone to identify and notify the individuals who have been in close contact with confirmed patients. Keeping a safe distance from people who may have been exposed to the virus is a key challenge related to physical containment (Storey et al, 2022). Abeler et al (2020) proposed a contact tracing method by periodically receiving temporary IDs of nearby people via Bluetooth.…”

Section: Contact Tracingmentioning

confidence: 99%

Privacy-Preserving Contact Tracing for Curbing the Spread of Infectious Disease

Zhu

2023

Journal of Database Management

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The world is facing one of the greatest public health threats in modern history. Various techniques based on contact tracing have been developed to support non-pharmaceutical interventions. The growing evidence shows that app-based contact tracing can reduce the spread of COVID-19 if a certain proportion of the population uses the apps. However, the risk of privacy breaches that comes with such apps has long been a public concern which may hinder the uptake of the apps. In this paper, the authors attempt to find a solution to complete the spatiotemporal intersection computation without exposing the infected patient location and the user location to one another. The authors implement the solution in the WeChat applet to aid the local health center. This study conducts experiments for six scenarios to justify the applicability of the applet. Experiment results indicate that the applet is a promising non-pharmaceutical tool for curbing the spread of COVID-19.

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Section: Contact Tracingmentioning

confidence: 99%

Privacy-Preserving Contact Tracing for Curbing the Spread of Infectious Disease

Zhu

2023

Journal of Database Management

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Text Analysis of Evolving Emotions and Sentiments in COVID-19 Twitter Communication

O’Leary

2022

Cogn Comput

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Scientists and regular citizens alike search for ways to manage the widespread effects of the COVID-19 pandemic. While scientists are busy in their labs, other citizens often turn to online sources to report their experiences and concerns and to seek and share knowledge of the virus. The text generated by those users in online social media platforms can provide valuable insights about evolving users’ opinions and attitudes. The objective of this research is to analyze text of such user disclosures to study human communication during a pandemic in four primary ways. First, we analyze Twitter tweet information, generated throughout the pandemic, to understand users’ communications concerning COVID-19 and how those communications have evolved during the pandemic. Second, we analyze linguistic sentiment concepts (analytic, authentic, clout, and tone concepts) in different Twitter settings (sentiment in tweets with pictures or no pictures and tweets versus retweets). Third, we investigate the relationship between Twitter tweets with additional forms of internet activity, namely, Google searches and Wikipedia page views. Finally, we create and use a dictionary of specific COVID-19-related concepts (e.g., symptom of lost taste) to assess how the use of those concepts in tweets are related to the spread of information and the resulting influence of Twitter users. The analysis showed a surprisingly lack of emotion in the initial phases of the pandemic as people were information seeking. As time progressed, there were more expressions of sentiment, including anger. Further, tweets with and without pictures and/or video had statistically significant differences in text sentiment characteristics. Similarly, there were differences between the sentiment in tweets and retweets and tweets. We also found that Google and Wikipedia searches were predictive of sentiment in the tweets. Finally, a variable representing a dictionary of COVID-related concepts was statistically significant when related to users’ Twitter influence score and number of retweets, illustrating the general impact of COVID-19 on Twitter and human communication. Overall, the results provide insights into human communication as well as models of human internet and social media use. These findings could be useful for the management of global challenges beyond, or different from, a pandemic.

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System: A core conceptual modeling construct for capturing complexity

Lukyanenko

Pastor

2022

Data & Knowledge Engineering

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Fighting Pandemics with Physical Distancing Management Technologies

Cited by 3 publications

References 81 publications

Privacy-Preserving Contact Tracing for Curbing the Spread of Infectious Disease

Privacy-Preserving Contact Tracing for Curbing the Spread of Infectious Disease

Text Analysis of Evolving Emotions and Sentiments in COVID-19 Twitter Communication

System: A core conceptual modeling construct for capturing complexity

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