Who are the 'silent spreaders'?: Contact tracing in spatio-temporal memory models
The COVID-19 epidemic has swept the world for over two years. However, a large number of infectious asymptomatic COVID-19 cases (ACCs) are still making the breaking up of the transmission chains very difficult. Efforts by epidemiological researchers in many countries have thrown light on the clinica...
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sg-smu-ink.sis_research-84262022-10-13T05:48:06Z Who are the 'silent spreaders'?: Contact tracing in spatio-temporal memory models HU, Yue SUBAGDJA, Budhitama TAN, Ah-hwee QUEK, Chai YIN, Quanjun The COVID-19 epidemic has swept the world for over two years. However, a large number of infectious asymptomatic COVID-19 cases (ACCs) are still making the breaking up of the transmission chains very difficult. Efforts by epidemiological researchers in many countries have thrown light on the clinical features of ACCs, but there is still a lack of practical approaches to detect ACCs so as to help contain the pandemic. To address the issue of ACCs, this paper presents a neural network model called Spatio-Temporal Episodic Memory for COVID-19 (STEM-COVID) to identify ACCs from contact tracing data. Based on the fusion Adaptive Resonance Theory (ART), the model encodes a collective spatio-temporal episodic memory of individuals and incorporates an effective mechanism of parallel searches for ACCs. Specifically, the episodic traces of the identified positive cases are used to map out the episodic traces of suspected ACCs using a weighted evidence pooling method. To evaluate the efficacy of STEM-COVID, a realistic agent-based simulation model for COVID-19 spreading is implemented based on the recent epidemiological findings on ACCs. The experiments based on rigorous simulation scenarios, manifesting the current situation of COVID-19 spread, show that the STEM-COVID model with weighted evidence pooling has a higher level of accuracy and efficiency for identifying ACCs when compared with several baselines. Moreover, the model displays strong robustness against noisy data and different ACC proportions, which partially reflects the effect of breakthrough infections after vaccination on the virus transmission. 2022-05-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/7423 info:doi/10.1007/s00521-022-07210-8 https://ink.library.smu.edu.sg/context/sis_research/article/8426/viewcontent/Hu2022_Article_WhoAreTheSilentSpreaders_pvoa.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Asymptomatic coronavirus carriers ART-based spatio-temporal episodic memory Weighted evidence pooling COVID-19 simulation Realistic scenarios Public Health Software Engineering |
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Asymptomatic coronavirus carriers ART-based spatio-temporal episodic memory Weighted evidence pooling COVID-19 simulation Realistic scenarios Public Health Software Engineering HU, Yue SUBAGDJA, Budhitama TAN, Ah-hwee QUEK, Chai YIN, Quanjun Who are the 'silent spreaders'?: Contact tracing in spatio-temporal memory models |
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The COVID-19 epidemic has swept the world for over two years. However, a large number of infectious asymptomatic COVID-19 cases (ACCs) are still making the breaking up of the transmission chains very difficult. Efforts by epidemiological researchers in many countries have thrown light on the clinical features of ACCs, but there is still a lack of practical approaches to detect ACCs so as to help contain the pandemic. To address the issue of ACCs, this paper presents a neural network model called Spatio-Temporal Episodic Memory for COVID-19 (STEM-COVID) to identify ACCs from contact tracing data. Based on the fusion Adaptive Resonance Theory (ART), the model encodes a collective spatio-temporal episodic memory of individuals and incorporates an effective mechanism of parallel searches for ACCs. Specifically, the episodic traces of the identified positive cases are used to map out the episodic traces of suspected ACCs using a weighted evidence pooling method. To evaluate the efficacy of STEM-COVID, a realistic agent-based simulation model for COVID-19 spreading is implemented based on the recent epidemiological findings on ACCs. The experiments based on rigorous simulation scenarios, manifesting the current situation of COVID-19 spread, show that the STEM-COVID model with weighted evidence pooling has a higher level of accuracy and efficiency for identifying ACCs when compared with several baselines. Moreover, the model displays strong robustness against noisy data and different ACC proportions, which partially reflects the effect of breakthrough infections after vaccination on the virus transmission. |
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HU, Yue SUBAGDJA, Budhitama TAN, Ah-hwee QUEK, Chai YIN, Quanjun |
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HU, Yue SUBAGDJA, Budhitama TAN, Ah-hwee QUEK, Chai YIN, Quanjun |
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HU, Yue |
title |
Who are the 'silent spreaders'?: Contact tracing in spatio-temporal memory models |
title_short |
Who are the 'silent spreaders'?: Contact tracing in spatio-temporal memory models |
title_full |
Who are the 'silent spreaders'?: Contact tracing in spatio-temporal memory models |
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Who are the 'silent spreaders'?: Contact tracing in spatio-temporal memory models |
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Who are the 'silent spreaders'?: Contact tracing in spatio-temporal memory models |
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who are the 'silent spreaders'?: contact tracing in spatio-temporal memory models |
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Institutional Knowledge at Singapore Management University |
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2022 |
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https://ink.library.smu.edu.sg/sis_research/7423 https://ink.library.smu.edu.sg/context/sis_research/article/8426/viewcontent/Hu2022_Article_WhoAreTheSilentSpreaders_pvoa.pdf |
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