Estimating the time-varying effective reproduction number via Cycle Threshold-based Transformer.
Monitoring the spread of infectious disease is essential to design and adjust the interventions timely for the prevention of the epidemic outbreak and safeguarding the public health. The governments have generally adopted the incidence-based statistical method to estimate the time-varying effective...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-12-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012694 |
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| _version_ | 1841533224730230784 |
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| author | Xin-Yu Zhang Lan-Lan Yu Wei-Yi Wang Gui-Quan Sun Jian-Cheng Lv Tao Zhou Quan-Hui Liu |
| author_facet | Xin-Yu Zhang Lan-Lan Yu Wei-Yi Wang Gui-Quan Sun Jian-Cheng Lv Tao Zhou Quan-Hui Liu |
| author_sort | Xin-Yu Zhang |
| collection | DOAJ |
| description | Monitoring the spread of infectious disease is essential to design and adjust the interventions timely for the prevention of the epidemic outbreak and safeguarding the public health. The governments have generally adopted the incidence-based statistical method to estimate the time-varying effective reproduction number Rt and evaluate the transmission ability of epidemics. However, this method exhibits biases arising from the reported incidence data and assumes the generation interval distribution which is not available at the early stage of epidemic. Recent studies showed that the viral loads characterized by cycle threshold (Ct) of the infected populations evolving throughout the course of epidemic and providing a possibility to infer the epidemic trajectory. In this work, we propose the Cycle Threshold-based Transformer (Ct-Transformer) to estimate Rt. We find the supervised learning of Ct-Transformer outperforms the traditional incidence-based statistic and Ct-based Rt estimating methods, and more importantly Ct-Transformer is robust to the detection resources. Further, we apply the proposed model to self-supervised pre-training tasks and obtain excellent fine-tuned performance, which attains comparable performance with the supervised Ct-Transformer, verified by both the synthetic and real-world datasets. We demonstrate that the Ct-based deep learning method can improve the real-time estimates of Rt, enabling more easily adapted to the track of the newly emerged epidemic. |
| format | Article |
| id | doaj-art-3ad7d2b2907340da90a4406eb7a34287 |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-3ad7d2b2907340da90a4406eb7a342872025-01-17T05:30:56ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582024-12-012012e101269410.1371/journal.pcbi.1012694Estimating the time-varying effective reproduction number via Cycle Threshold-based Transformer.Xin-Yu ZhangLan-Lan YuWei-Yi WangGui-Quan SunJian-Cheng LvTao ZhouQuan-Hui LiuMonitoring the spread of infectious disease is essential to design and adjust the interventions timely for the prevention of the epidemic outbreak and safeguarding the public health. The governments have generally adopted the incidence-based statistical method to estimate the time-varying effective reproduction number Rt and evaluate the transmission ability of epidemics. However, this method exhibits biases arising from the reported incidence data and assumes the generation interval distribution which is not available at the early stage of epidemic. Recent studies showed that the viral loads characterized by cycle threshold (Ct) of the infected populations evolving throughout the course of epidemic and providing a possibility to infer the epidemic trajectory. In this work, we propose the Cycle Threshold-based Transformer (Ct-Transformer) to estimate Rt. We find the supervised learning of Ct-Transformer outperforms the traditional incidence-based statistic and Ct-based Rt estimating methods, and more importantly Ct-Transformer is robust to the detection resources. Further, we apply the proposed model to self-supervised pre-training tasks and obtain excellent fine-tuned performance, which attains comparable performance with the supervised Ct-Transformer, verified by both the synthetic and real-world datasets. We demonstrate that the Ct-based deep learning method can improve the real-time estimates of Rt, enabling more easily adapted to the track of the newly emerged epidemic.https://doi.org/10.1371/journal.pcbi.1012694 |
| spellingShingle | Xin-Yu Zhang Lan-Lan Yu Wei-Yi Wang Gui-Quan Sun Jian-Cheng Lv Tao Zhou Quan-Hui Liu Estimating the time-varying effective reproduction number via Cycle Threshold-based Transformer. PLoS Computational Biology |
| title | Estimating the time-varying effective reproduction number via Cycle Threshold-based Transformer. |
| title_full | Estimating the time-varying effective reproduction number via Cycle Threshold-based Transformer. |
| title_fullStr | Estimating the time-varying effective reproduction number via Cycle Threshold-based Transformer. |
| title_full_unstemmed | Estimating the time-varying effective reproduction number via Cycle Threshold-based Transformer. |
| title_short | Estimating the time-varying effective reproduction number via Cycle Threshold-based Transformer. |
| title_sort | estimating the time varying effective reproduction number via cycle threshold based transformer |
| url | https://doi.org/10.1371/journal.pcbi.1012694 |
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