Abstract
Containment measures in high-risk closed settings, like migrant worker (MW) dormitories, are critical for mitigating emerging infectious disease outbreaks and protecting potentially vulnerable populations in outbreaks such as coronavirus disease 2019 (COVID-19). The direct impact of social distancing measures can be assessed through wearable contact tracing devices. Here, we developed an individual-based model using data collected through a Bluetooth wearable device that collected 33.6M and 52.8M contact events in two dormitories in Singapore, one apartment style and the other a barrack style, to assess the impact of measures to reduce the social contact of cases and their contacts. The simulation of highly detailed contact networks accounts for different infrastructural levels, including room, floor, block, and dormitory, and intensity in terms of being regular or transient. Via a branching process model, we then simulated outbreaks that matched the prevalence during the COVID-19 outbreak in the two dormitories and explored alternative scenarios for control. We found that strict isolation of all cases and quarantine of all contacts would lead to very low prevalence but that quarantining only regular contacts would lead to only marginally higher prevalence but substantially fewer total man-hours lost in quarantine. Reducing the density of contacts by 30% through the construction of additional dormitories was modelled to reduce the prevalence by 14 and 9% under smaller and larger outbreaks, respectively. Wearable contact tracing devices may be used not just for contact tracing efforts but also to inform alternative containment measures in high-risk closed settings.
| Original language | English |
|---|---|
| Journal | Digital Health |
| Volume | 9 |
| DOIs | |
| State | Published - 1 Jan 2023 |
Bibliographical note
Funding Information:The research was supported by the help and technical training of Bhagya Gunaearchrathne Perera of D'Crypt, who facilitated data extraction from the BluePass server. Data collection was possible thanks to the efforts of Amanda Low, Aysha Farwin, Crystal Chua, Haoyang Sun, Jie Sun, Rayner Tan, Sharon Quaye, Ligo Val Alvern Cueco, Yinan Mao, and Zitong Zeng from Saw Swee Hock School of Public Health, NUS. We are also grateful to Zaw Myo Tun, Shweta Rajkumar Singh, and Kyin New Moong Don for their help with translation. Finally, we would like to thank the staff in the dormitories, especially Shahul Hameed and Richard Lim, and their colleagues, for helping to facilitate the data collection process, and the participants themselves for their kind forbearance. The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by Temasek Foundation and iHealthtech at the National University of Singapore.
Funding Information:
The research was supported by the help and technical training of Bhagya Gunaearchrathne Perera of D'Crypt, who facilitated data extraction from the BluePass server. Data collection was possible thanks to the efforts of Amanda Low, Aysha Farwin, Crystal Chua, Haoyang Sun, Jie Sun, Rayner Tan, Sharon Quaye, Ligo Val Alvern Cueco, Yinan Mao, and Zitong Zeng from Saw Swee Hock School of Public Health, NUS. We are also grateful to Zaw Myo Tun, Shweta Rajkumar Singh, and Kyin New Moong Don for their help with translation. Finally, we would like to thank the staff in the dormitories, especially Shahul Hameed and Richard Lim, and their colleagues, for helping to facilitate the data collection process, and the participants themselves for their kind forbearance.
Publisher Copyright:
© The Author(s) 2023.
Keywords
- Bluetooth digital contact tracing tools
- branching process model
- contact network
- COVID-19
- high-risk closed setting
- migrant worker dormitory
- simulation study
