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Jeong YD, Ejima K, Kim KS, Joohyeon W, Iwanami S, Fujita Y, Jung IH, Aihara K, Shibuya K, Iwami S, Bento AI, Ajelli M. Designing isolation guidelines for COVID-19 patients with rapid antigen tests. Nat Commun 2022; 13:4910. [PMID: 35987759 PMCID: PMC9392070 DOI: 10.1038/s41467-022-32663-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 08/10/2022] [Indexed: 12/18/2022] Open
Abstract
Appropriate isolation guidelines for COVID-19 patients are warranted. Currently, isolating for fixed time is adopted in most countries. However, given the variability in viral dynamics between patients, some patients may no longer be infectious by the end of isolation, whereas others may still be infectious. Utilizing viral test results to determine isolation length would minimize both the risk of prematurely ending isolation of infectious patients and the unnecessary individual burden of redundant isolation of noninfectious patients. In this study, we develop a data-driven computational framework to compute the population-level risk and the burden of different isolation guidelines with rapid antigen tests (i.e., lateral flow tests). Here, we show that when the detection limit is higher than the infectiousness threshold values, additional consecutive negative results are needed to ascertain infectiousness status. Further, rapid antigen tests should be designed to have lower detection limits than infectiousness threshold values to minimize the length of prolonged isolation.
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Affiliation(s)
- Yong Dam Jeong
- interdisciplinary Biology Laboratory (iBLab), Division of Biological Science, Graduate School of Science, Nagoya University, Nagoya, Japan
- Department of Mathematics, Pusan National University, Busan, South Korea
| | - Keisuke Ejima
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, IN, USA.
- The Tokyo Foundation for Policy Research, Tokyo, Japan.
| | - Kwang Su Kim
- interdisciplinary Biology Laboratory (iBLab), Division of Biological Science, Graduate School of Science, Nagoya University, Nagoya, Japan
- Department of Scientific computing, Pukyong National University, Busan, South Korea
| | - Woo Joohyeon
- interdisciplinary Biology Laboratory (iBLab), Division of Biological Science, Graduate School of Science, Nagoya University, Nagoya, Japan
| | - Shoya Iwanami
- interdisciplinary Biology Laboratory (iBLab), Division of Biological Science, Graduate School of Science, Nagoya University, Nagoya, Japan
| | - Yasuhisa Fujita
- interdisciplinary Biology Laboratory (iBLab), Division of Biological Science, Graduate School of Science, Nagoya University, Nagoya, Japan
| | - Il Hyo Jung
- Department of Mathematics, Pusan National University, Busan, South Korea
| | - Kazuyuki Aihara
- International Research Center for Neurointelligence, The University of Tokyo, Tokyo, Japan
| | - Kenji Shibuya
- The Tokyo Foundation for Policy Research, Tokyo, Japan
| | - Shingo Iwami
- interdisciplinary Biology Laboratory (iBLab), Division of Biological Science, Graduate School of Science, Nagoya University, Nagoya, Japan.
- Institute of Mathematics for Industry, Kyushu University, Fukuoka, Japan.
- Institute for the Advanced Study of Human Biology (ASHBi), Kyoto University, Kyoto, Japan.
- NEXT-Ganken Program, Japanese Foundation for Cancer Research (JFCR), Tokyo, Japan.
- Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS), RIKEN, Saitama, Japan.
- Science Groove Inc, Fukuoka, Japan.
| | - Ana I Bento
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, IN, USA
| | - Marco Ajelli
- Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, IN, USA
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Prague M, Alexandre M, Thiébaut R, Guedj J. Within-host models of SARS-CoV-2: What can it teach us on the biological factors driving virus pathogenesis and transmission? Anaesth Crit Care Pain Med 2022; 41:101055. [PMID: 35247638 PMCID: PMC8889677 DOI: 10.1016/j.accpm.2022.101055] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Affiliation(s)
- Mélanie Prague
- Univ. Bordeaux, Department of Public Health, INSERM UMR 1219 Bordeaux Population Health Research Centre, Inria SISTM, Bordeaux, France; Vaccine Research Institute, Créteil, France.
| | - Marie Alexandre
- Univ. Bordeaux, Department of Public Health, INSERM UMR 1219 Bordeaux Population Health Research Centre, Inria SISTM, Bordeaux, France; Vaccine Research Institute, Créteil, France
| | - Rodolphe Thiébaut
- Univ. Bordeaux, Department of Public Health, INSERM UMR 1219 Bordeaux Population Health Research Centre, Inria SISTM, Bordeaux, France; Vaccine Research Institute, Créteil, France
| | - Jérémie Guedj
- Université de Paris, IAME, INSERM, F-75018 Paris, France
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Park H, Woo JH, Iwanami S, Iwami S. [Digital transformation of COVID-19 research]. Uirusu 2022; 72:39-46. [PMID: 37899228 DOI: 10.2222/jsv.72.39] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2023]
Abstract
In a current life sciences research, we are in an era in which advanced technology emerging and utilize big data. Data-driven approaches such as machine learnings play an important role to analyze these datasets. However, limited clinical (time-course) datasets are available for infectious diseases, cancer, and other diseases. Especially in the case of emerging infectious disease outbreaks, clinical data obtained from a limited number of cases must be used to develop treatment strategies and public health policies. This means that many clinical data are not big data, which often makes the application of data-driven approaches difficult. In this paper, we mainly apply a mathematical model-based approach to the clinical data of COVID-19 and discuss how biologically important information can be extracted from the limited data and how they can benefit society.
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Affiliation(s)
- Hyeongki Park
- interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University
| | - Joo Hyeon Woo
- interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University
| | - Shoya Iwanami
- interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University
| | - Shingo Iwami
- interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University
- Institute of Mathematics for Industry, Kyushu University
- Institute for the Advanced Study of Human Biology (ASHBi), Kyoto University
- NEXT-Ganken Program, Japanese Foundation for Cancer Research (JFCR)
- Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS), RIKEN
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