interdisciplinary Biology Laboratory (iBLab), Division of Biological Science, Graduate School of Science, Nagoya University, Nagoya, Japan; Department of Mathematics, Pusan National University, Busan, Republic of Korea
Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, United States
Kwang Su Kim
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
Ana I Bento
Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, United States
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, Republic of Korea
Kazuyuki Aihara
International Research Center for Neurointelligence, The University of Tokyo, Tokyo, Japan
Koichi Watashi
Department of Virology II, National Institute of Infectious Diseases, Tokyo, Japan; Research Center for Drug and Vaccine Development, National Institute of Infectious Diseases, Tokyo, Japan; Department of Applied Biological Science, Tokyo University of Science, Noda, Japan
Taiga Miyazaki
Department of Infectious Diseases, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan; Division of Respirology, Rheumatology, Infectious Diseases, and Neurology, Department of Internal Medicine, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan
Takaji Wakita
Department of Virology II, National Institute of Infectious Diseases, Tokyo, Japan
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; Science Groove Inc, Fukuoka, Japan
Marco Ajelli
Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, United States; Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, United States
Since the start of the COVID-19 pandemic, two mainstream guidelines for defining when to end the isolation of SARS-CoV-2-infected individuals have been in use: the one-size-fits-all approach (i.e. patients are isolated for a fixed number of days) and the personalized approach (i.e. based on repeated testing of isolated patients). We use a mathematical framework to model within-host viral dynamics and test different criteria for ending isolation. By considering a fixed time of 10 days since symptom onset as the criterion for ending isolation, we estimated that the risk of releasing an individual who is still infectious is low (0–6.6%). However, this policy entails lengthy unnecessary isolations (4.8–8.3 days). In contrast, by using a personalized strategy, similar low risks can be reached with shorter prolonged isolations. The obtained findings provide a scientific rationale for policies on ending the isolation of SARS-CoV-2-infected individuals.
