An AI-empowered indoor digital contact tracing system for COVID-19 outbreaks in residential care homes

Jiahui Meng; Justina Yat Wa Liu; Lin Yang; Man Sing Wong; Hilda Tsang; Boyu Yu; Jincheng Yu; Freddy Man-Hin Lam; Daihai He; Lei Yang; Yan Li; Gilman Kit-Hang Siu; Stefanos Tyrovolas; Yao Jie Xie; David Man; David H K Shum

doi:10.1016/j.idm.2024.02.002

An AI-empowered indoor digital contact tracing system for COVID-19 outbreaks in residential care homes

Infect Dis Model. 2024 Feb 10;9(2):474-482. doi: 10.1016/j.idm.2024.02.002. eCollection 2024 Jun.

Authors

Jiahui Meng^{1

2}, Justina Yat Wa Liu¹, Lin Yang^{1

3}, Man Sing Wong⁴, Hilda Tsang¹, Boyu Yu⁵, Jincheng Yu⁶, Freddy Man-Hin Lam⁷, Daihai He⁵, Lei Yang⁸, Yan Li¹, Gilman Kit-Hang Siu⁹, Stefanos Tyrovolas^{1

10}, Yao Jie Xie¹, David Man^{11

12}, David H K Shum⁷

Affiliations

¹ School of Nursing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.
² Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.
³ Research Centre of Textiles for Future Fashion, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.
⁴ Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.
⁵ Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.
⁶ The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China.
⁷ Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.
⁸ Department of Computing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.
⁹ Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.
¹⁰ Department of Nutrition and Food Studies, George Mason University, USA.
¹¹ Tung Wah College, Hong Kong Special Administrative Region, China.
¹² Mental Health Research Centre, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.

Abstract

An AI-empowered indoor digital contact-tracing system was developed using a centralized architecture and advanced low-energy Bluetooth technologies for indoor positioning, with careful preservation of privacy and data security. We analyzed the contact pattern data from two RCHs and investigated a COVID-19 outbreak in one study site. To evaluate the effectiveness of the system in containing outbreaks with minimal contacts under quarantine, a simulation study was conducted to compare the impact of different quarantine strategies on outbreak containment within RCHs. The significant difference in contact hours between weekdays and weekends was observed for some pairs of RCH residents and staff during the two-week data collection period. No significant difference between secondary cases and uninfected contacts was observed in a COVID-19 outbreak in terms of their demographics and contact patterns. Simulation results based on the collected contact data indicated that a threshold of accumulative contact hours one or two days prior to diagnosis of the index case could dramatically increase the efficiency of outbreak containment within RCHs by targeted isolation of the close contacts. This study demonstrated the feasibility and efficiency of employing an AI-empowered system in indoor digital contact tracing of outbreaks in RCHs in the post-pandemic era.

Keywords: Artificial intelligence; COVID-19; Contact pattern; Indoor contact tracing; Outbreak containment.