SARPPIC: Exploiting COVID-19 Contact Tracing Recommendation through Social Awareness

Abstract

Globally, the current coronavirus disease 2019 (COVID-19) pandemic is resulting in high fatality rates. Consequently, the prevention of further transmission is very vital. Until vaccines are widely available, the only available infection prevention methods include the following: contact tracing, case isolation and quarantine, social (physical) distancing, and hygiene measures (washing of hands with soap and water and using alcohol-based hand sanitizers). Contact tracing, which is key in preventing the spread of COVID-19, refers to the process of finding unreported people who maybe infected by using a verified case to trace back possible infections of contacts. Consequently, the wide and fast spread of COVID-19 requires computational approaches which utilize innovative algorithms that build a memory of proximity contacts of cases that are positive. In this paper, a recommender algorithm called socially aware recommendation of people probably infected with COVID-19 (SARPPIC) is proposed. SARPPIC initially utilizes betweenness centrality in a social network to measure the number of target contact points (nodes/users) who have come into contact with an infected contact point (COVID-19 patient). Then, using contact durations and contact frequencies, tie strengths of the same contact points above are also computed. Finally, the above algorithmic computations are hybridized through profile integration to generate results for effective contact tracing recommendations of possible COVID-19-infected patients who will require testing in a healthcare facility. Benchmarking experimental results in the paper demonstrate that, using two interconnected relevant real-world datasets, SARPPIC outperforms other relevant methods in terms of suitable evaluation metrics such as precision, recall, and F-measure.

A knowledge graph-based method for epidemic contact tracing in public transportation

“…Most applications of digital contact tracing focus on identifying close contacts through detected cases, and then isolating them to reduce further transmission ( Abeler et al, 2020 , Keeling et al, 2020 , Robert et al, 2020 ). Asabere et al (2020) applied betweenness centralities and tie strengths in a social network to recommend probable infectors. Keeling et al (2020) predicted that approximately 36 persons must be traced per individual infected case to reduce further infections by more the 5/6.…”

Recommender System for the Efficient Treatment of COVID-19 Using a Convolutional Neural Network Model and Image Similarity

“…RS has also been useful in combating the COVID-19 pandemic by making recommendations such as medical therapies for self-care [ 46 ], wearable gadgets to prevent the COVID-19 outbreak [ 47 ], and unreported people to reduce infection rates by contact tracing [ 48 ], among others. An RS based on image content was proposed in ref.…”