Droplet-Transmitted Infection Risk Ranking Based on Close Proximity Interaction

Guo, Shihui; Yu, Jubo; Shi, Xinyu; Wang, Hongran; Xie, Feibin; Gao, Xing; Jiang, Min

doi:10.3389/fnbot.2019.00113

Cited by 9 publications

(7 citation statements)

References 45 publications

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“…To sense mobile social interactions, Banerjee et al [44] proposed virtual compass, which effectively perceives the mutual distance between users, but cannot obtain direction information. Guo et al [45] and Rezaei et al [46] proposed an automatic infection risk assessment method that utilizes captured surveillance videos to identify potentially infected person by droplet-transmitted model. At the national level, many countries have developed contact tracking systems.…”

Section: Contact Tracingmentioning

confidence: 99%

A smartphone-based zero-effort method for mitigating epidemic propagation

Wang

et al. 2023

EURASIP J. Adv. Signal Process.

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A large number of epidemics, including COVID-19 and SARS, quickly swept the world and claimed the precious lives of large numbers of people. Due to the concealment and rapid spread of the virus, it is difficult to track down individuals with mild or asymptomatic symptoms with limited human resources. Building a low-cost and real-time epidemic early warning system to identify individuals who have been in contact with infected individuals and determine whether they need to be quarantined is an effective means to mitigate the spread of the epidemic. In this paper, we propose a smartphone-based zero-effort epidemic warning method for mitigating epidemic propagation. Firstly, we recognize epidemic-related voice activity relevant to epidemics spread by hierarchical attention mechanism and temporal convolutional network. Subsequently, we estimate the social distance between users through sensors built-in smartphone. Furthermore, we combine Wi-Fi network logs and social distance to comprehensively judge whether there is spatiotemporal contact between users and determine the duration of contact. Finally, we estimate infection risk based on epidemic-related vocal activity, social distance, and contact time. We conduct a large number of well-designed experiments in typical scenarios to fully verify the proposed method. The proposed method does not rely on any additional infrastructure and historical training data, which is conducive to integration with epidemic prevention and control systems and large-scale applications.

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Section: Contact Tracingmentioning

confidence: 99%

A smartphone-based zero-effort method for mitigating epidemic propagation

Wang

et al. 2023

EURASIP J. Adv. Signal Process.

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show abstract

“…To sense mobile social interactions, Banerjee et al [47] proposed virtual compass , which effectively perceives the mutual distance between users, but cannot obtain direction information. Guo et al [48] and Rezaei et al [49] proposed an automatic infection risk assessment method that utilizes captured surveillance videos to identify potentially infected person by droplet-transmitted model.…”

Section: Contact Tracingmentioning

confidence: 99%

A Smartphone-based Zero-Effort Method for Mitigating Epidemic Propagation

Wang

et al. 2023

Preprint

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A large number of epidemics, including COVID-19 and SARS, spread through droplets, quickly swept the world and claimed the precious lives of large numbers of people. Building a low-cost and real-time epidemic early warning system to identify individuals who have been in contact with infected individuals and determine whether they need to be quarantined is an effective means to mitigate the spread of the epidemic. In this paper, we propose a smartphone-based zero-effort epidemic warning method for mitigating epidemic propagation. Firstly, we recognize epidemic-related voice activity relevant to epidemics spread by hierarchical attention mechanism and temporal convolutional network. The hierarchical attention mechanism is used to find local important information through global scanning, then enhances useful information and suppresses useless information. Subsequently, we estimate the social distance between users through sensors built-in smartphone. Furthermore, we combine Wi-Fi network logs and social distance to comprehensively judge whether there is spatiotemporal contact between users and determine the duration of contact. Finally, we estimate infection risk based on epidemic-related vocal activity, social distance, and contact time. We conduct a large number of well-designed experiments in typical scenarios to fully verify the proposed method. The proposed method does not rely on any additional infrastructure and historical training data, which is conducive to integration with epidemic prevention and control systems and large-scale applications.

show abstract

“…Recent advancements in information and communication technologies have made it possible to collect empirical data relevant to human behavior and social contact in real-world conditions instead of relying on survey data. Solutions such as tracking WiFi and Bluetooth traces ( Faggian et al, 2020 ; Berke et al, 2020 ), using smartphone applications ( Cho et al, 2020 ; Inn, 2020 ; Udugama et al, 2020 ), and active radio frequency identifications devices (RFID) or other wireless sensors ( Guo et al, 2020 ; Zhang et al, 2020 ; Vanhems et al, 2013 , A. Barrat et al, 2014 ) have been recently employed to collect data on proximity of human-to-human interactions.…”

Section: Literature Reviewmentioning

confidence: 99%