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Huang Z, Shu X, Xuan Q, Ruan Z. Epidemic spreading under game-based self-quarantine behaviors: The different effects of local and global information. CHAOS (WOODBURY, N.Y.) 2024; 34:013112. [PMID: 38198677 DOI: 10.1063/5.0180484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 12/12/2023] [Indexed: 01/12/2024]
Abstract
During the outbreak of an epidemic, individuals may modify their behaviors in response to external (including local and global) infection-related information. However, the difference between local and global information in influencing the spread of diseases remains inadequately explored. Here, we study a simple epidemic model that incorporates the game-based self-quarantine behavior of individuals, taking into account the influence of local infection status, global disease prevalence, and node heterogeneity (non-identical degree distribution). Our findings reveal that local information can effectively contain an epidemic, even with only a small proportion of individuals opting for self-quarantine. On the other hand, global information can cause infection evolution curves shaking during the declining phase of an epidemic, owing to the synchronous release of nodes with the same degree from the quarantined state. In contrast, the releasing pattern under the local information appears to be more random. This shaking phenomenon can be observed in various types of networks associated with different characteristics. Moreover, it is found that under the proposed game-epidemic framework, a disease is more difficult to spread in heterogeneous networks than in homogeneous networks, which differs from conventional epidemic models.
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Affiliation(s)
- Zegang Huang
- Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou 310023, China
- Binjiang Cyberspace Security Institute of ZJUT, Hangzhou 310051, China
| | - Xincheng Shu
- Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou 310023, China
- Binjiang Cyberspace Security Institute of ZJUT, Hangzhou 310051, China
| | - Qi Xuan
- Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou 310023, China
- Binjiang Cyberspace Security Institute of ZJUT, Hangzhou 310051, China
| | - Zhongyuan Ruan
- Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou 310023, China
- Binjiang Cyberspace Security Institute of ZJUT, Hangzhou 310051, China
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Sunagawa J, Park H, Kim KS, Komorizono R, Choi S, Ramirez Torres L, Woo J, Jeong YD, Hart WS, Thompson RN, Aihara K, Iwami S, Yamaguchi R. Isolation may select for earlier and higher peak viral load but shorter duration in SARS-CoV-2 evolution. Nat Commun 2023; 14:7395. [PMID: 37989736 PMCID: PMC10663562 DOI: 10.1038/s41467-023-43043-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Accepted: 10/30/2023] [Indexed: 11/23/2023] Open
Abstract
During the COVID-19 pandemic, human behavior change as a result of nonpharmaceutical interventions such as isolation may have induced directional selection for viral evolution. By combining previously published empirical clinical data analysis and multi-level mathematical modeling, we find that the SARS-CoV-2 variants selected for as the virus evolved from the pre-Alpha to the Delta variant had earlier and higher peak in viral load dynamics but a shorter duration of infection. Selection for increased transmissibility shapes the viral load dynamics, and the isolation measure is likely to be a driver of these evolutionary transitions. In addition, we show that a decreased incubation period and an increased proportion of asymptomatic infection are also positively selected for as SARS-CoV-2 mutated to adapt to human behavior (i.e., Omicron variants). The quantitative information and predictions we present here can guide future responses in the potential arms race between pandemic interventions and viral evolution.
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Affiliation(s)
- Junya Sunagawa
- Department of Advanced Transdisciplinary Sciences, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Hyeongki Park
- interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan
| | - Kwang Su Kim
- interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan
- Department of Scientific Computing, Pukyong National University, Busan, South Korea
- Department of Mathematics, Pusan National University, Busan, South Korea
| | - Ryo Komorizono
- Laboratory of RNA Viruses, Department of Virus Research, Institute for Life and Medical Sciences (LiMe), Kyoto University, Kyoto, Japan
| | - Sooyoun Choi
- interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan
- Department of Mathematics, Pusan National University, Busan, South Korea
| | - Lucia Ramirez Torres
- interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan
| | - Joohyeon Woo
- interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan
| | - Yong Dam Jeong
- interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan
- Department of Mathematics, Pusan National University, Busan, South Korea
| | - William S Hart
- Mathematical Institute, University of Oxford, Oxford, UK
| | - Robin N Thompson
- Mathematical Institute, University of Oxford, Oxford, UK
- Mathematics Institute, University of Warwick, Coventry, UK
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK
| | - Kazuyuki Aihara
- International Research Center for Neurointelligence, The University of Tokyo Institutes for Advanced Study, The University of Tokyo, Tokyo, Japan
| | - Shingo Iwami
- interdisciplinary Biology Laboratory (iBLab), Division of Natural 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.
- Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS), RIKEN, Saitama, Japan.
- NEXT-Ganken Program, Japanese Foundation for Cancer Research (JFCR), Tokyo, Japan.
- Science Groove Inc, Fukuoka, Japan.
| | - Ryo Yamaguchi
- Department of Advanced Transdisciplinary Sciences, Hokkaido University, Sapporo, Hokkaido, Japan.
- Department of Zoology & Biodiversity Research Centre, University of British Columbia, Vancouver, BC, Canada.
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Adewole MO, Faniran TS, Abdullah FA, Ali MKM. COVID-19 dynamics and immune response: Linking within-host and between-host dynamics. CHAOS, SOLITONS, AND FRACTALS 2023; 173:113722. [PMID: 38620099 PMCID: PMC10291298 DOI: 10.1016/j.chaos.2023.113722] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 04/26/2023] [Accepted: 06/13/2023] [Indexed: 11/04/2023]
Abstract
The global impact of COVID-19 has led to the development of numerous mathematical models to understand and control the pandemic. However, these models have not fully captured how the disease's dynamics are influenced by both within-host and between-host factors. To address this, a new mathematical model is proposed that links these dynamics and incorporates immune response. The model is compartmentalized with a fractional derivative in the sense of Caputo-Fabrizio, and its properties are studied to show a unique solution. Parameter estimation is carried out by fitting real-life data, and sensitivity analysis is conducted using various methods. The model is then numerically implemented to demonstrate how the dynamics within infected hosts drive human-to-human transmission, and various intervention strategies are compared based on the percentage of averted deaths. The simulations suggest that a combination of medication to boost the immune system, prevent infected cells from producing the virus, and adherence to COVID-19 protocols is necessary to control the spread of the virus since no single intervention strategy is sufficient.
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Affiliation(s)
- Matthew O Adewole
- School of Mathematical Sciences, Universiti Sains Malaysia, Malaysia
- Department of Computer Science and Mathematics, Mountain Top University, Prayer City, Ogun State, Nigeria
| | - Taye Samuel Faniran
- Laboratory de Mathematiques de Besancon, University of Franche-Comte, France
- Department of Computer Science, Lead City University, Ibadan, Nigeria
| | - Farah A Abdullah
- School of Mathematical Sciences, Universiti Sains Malaysia, Malaysia
| | - Majid K M Ali
- School of Mathematical Sciences, Universiti Sains Malaysia, Malaysia
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