C. Sandeepa, C. Moremada, N. Dissanayaka, T. Gamage and M. Liyanage, "Social Interaction Tracking and Patient Prediction System for Potential COVID-19 Patients," 2020 IEEE 3rd 5G World Forum (5GWF), Bangalore, India, 2020, pp. 13-18, doi: 10.1109/5GWF49715.2020.9221268
Social interaction tracking and patient prediction system for potential COVID-19 patients
|Author:||Sandeepa, Chamara1; Moremada, Charuka1; Dissanayaka, Nadeeka1;|
1Department of Electrical and Information Engineering, University of Ruhuna, Galle, Sri Lanka
2School of Computer Science, University College Dublin, Ireland
3Centre for Wireless Communications, University of Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.6 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020111790691
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2020-11-17
Coronavirus disease 2019 (COVID-19) virus is an infectious disease which has spread globally since 2019, resulting in an ongoing pandemic. Since it is a new virus, it takes some time to develop a vaccine against it. Until then, the best way to prevent the fast spread of the virus is to enable the proper social distancing and isolation or containment to identify potential patients. Since the virus has up to 14 days of the incubation period, it is important to identify all the social interactions during this period and enforce social isolation for such potential patients. However, proper social interaction tracking methods and patient prediction methods based on such data are missing for the moment. This paper focuses on tracking the social interaction of users and predict the infection possibility based on social interactions. We first developed a BLE (Bluetooth Low Energy) and GPS based social interaction tracking system. Then, we developed an algorithm to predict the possibility of being infected with COVID-19 based on the collected data. Finally, a prototype of the system is implemented with a mobile app and a web monitoring tool. In addition, we performed a simulation of the system with a graph-based model to analyze the behaviour of the proposed algorithm and it verifies that self-isolation is important in slowing down the disease progression.
|Pages:||13 - 18|
2020 IEEE 3rd 5G World Forum (5GWF)
IEEE 5G World Forum
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work is supported by Academy of Finland in 6Genesis Flagship (grant no. 318927) and European Union in RESPONSE 5G (Grant No: 789658) project.
|Academy of Finland Grant Number:||
318927 (Academy of Finland Funding decision)
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