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Musa SS, Zhao S, Abdulrashid I, Qureshi S, Colubri A, He D. Evaluating the spike in the symptomatic proportion of SARS-CoV-2 in China in 2022 with variolation effects: a modeling analysis. Infect Dis Model 2024; 9:601-617. [PMID: 38558958 PMCID: PMC10978539 DOI: 10.1016/j.idm.2024.02.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 02/20/2024] [Accepted: 02/24/2024] [Indexed: 04/04/2024] Open
Abstract
Despite most COVID-19 infections being asymptomatic, mainland China had a high increase in symptomatic cases at the end of 2022. In this study, we examine China's sudden COVID-19 symptomatic surge using a conceptual SIR-based model. Our model considers the epidemiological characteristics of SARS-CoV-2, particularly variolation, from non-pharmaceutical intervention (facial masking and social distance), demography, and disease mortality in mainland China. The increase in symptomatic proportions in China may be attributable to (1) higher sensitivity and vulnerability during winter and (2) enhanced viral inhalation due to spikes in SARS-CoV-2 infections (high transmissibility). These two reasons could explain China's high symptomatic proportion of COVID-19 in December 2022. Our study, therefore, can serve as a decision-support tool to enhance SARS-CoV-2 prevention and control efforts. Thus, we highlight that facemask-induced variolation could potentially reduces transmissibility rather than severity in infected individuals. However, further investigation is required to understand the variolation effect on disease severity.
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Affiliation(s)
- Salihu S. Musa
- Department of Genomics and Computational Biology, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong SAR, China
- Department of Mathematics, Aliko Dangote University of Science and Technology, Kano, Nigeria
| | - Shi Zhao
- School of Public Health, Tianjin Medical University, Tianjin, 300070, China
| | - Ismail Abdulrashid
- School of Finance and Operations Management, The University of Tulsa, 800 South Tucker Dr., Tulsa, OK, 74104, USA
| | - Sania Qureshi
- Department of Basic Sciences and Related Studies, Mehran University of Engineering and Tech., Jamshoro, Pakistan
- Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon
| | - Andrés Colubri
- Department of Genomics and Computational Biology, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA
| | - Daihai He
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong SAR, China
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Asplin P, Keeling MJ, Mancy R, Hill EM. Epidemiological and health economic implications of symptom propagation in respiratory pathogens: A mathematical modelling investigation. PLoS Comput Biol 2024; 20:e1012096. [PMID: 38701066 PMCID: PMC11095726 DOI: 10.1371/journal.pcbi.1012096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 05/15/2024] [Accepted: 04/19/2024] [Indexed: 05/05/2024] Open
Abstract
BACKGROUND Respiratory pathogens inflict a substantial burden on public health and the economy. Although the severity of symptoms caused by these pathogens can vary from asymptomatic to fatal, the factors that determine symptom severity are not fully understood. Correlations in symptoms between infector-infectee pairs, for which evidence is accumulating, can generate large-scale clusters of severe infections that could be devastating to those most at risk, whilst also conceivably leading to chains of mild or asymptomatic infections that generate widespread immunity with minimal cost to public health. Although this effect could be harnessed to amplify the impact of interventions that reduce symptom severity, the mechanistic representation of symptom propagation within mathematical and health economic modelling of respiratory diseases is understudied. METHODS AND FINDINGS We propose a novel framework for incorporating different levels of symptom propagation into models of infectious disease transmission via a single parameter, α. Varying α tunes the model from having no symptom propagation (α = 0, as typically assumed) to one where symptoms always propagate (α = 1). For parameters corresponding to three respiratory pathogens-seasonal influenza, pandemic influenza and SARS-CoV-2-we explored how symptom propagation impacted the relative epidemiological and health-economic performance of three interventions, conceptualised as vaccines with different actions: symptom-attenuating (labelled SA), infection-blocking (IB) and infection-blocking admitting only mild breakthrough infections (IB_MB). In the absence of interventions, with fixed underlying epidemiological parameters, stronger symptom propagation increased the proportion of cases that were severe. For SA and IB_MB, interventions were more effective at reducing prevalence (all infections and severe cases) for higher strengths of symptom propagation. For IB, symptom propagation had no impact on effectiveness, and for seasonal influenza this intervention type was more effective than SA at reducing severe infections for all strengths of symptom propagation. For pandemic influenza and SARS-CoV-2, at low intervention uptake, SA was more effective than IB for all levels of symptom propagation; for high uptake, SA only became more effective under strong symptom propagation. Health economic assessments found that, for SA-type interventions, the amount one could spend on control whilst maintaining a cost-effective intervention (termed threshold unit intervention cost) was very sensitive to the strength of symptom propagation. CONCLUSIONS Overall, the preferred intervention type depended on the combination of the strength of symptom propagation and uptake. Given the importance of determining robust public health responses, we highlight the need to gather further data on symptom propagation, with our modelling framework acting as a template for future analysis.
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Affiliation(s)
- Phoebe Asplin
- EPSRC & MRC Centre for Doctoral Training in Mathematics for Real-World Systems, University of Warwick, Coventry, United Kingdom
- Mathematics Institute, University of Warwick, Coventry, United Kingdom
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom
| | - Matt J. Keeling
- Mathematics Institute, University of Warwick, Coventry, United Kingdom
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
| | - Rebecca Mancy
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, United Kingdom
- MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow, United Kingdom
| | - Edward M. Hill
- Mathematics Institute, University of Warwick, Coventry, United Kingdom
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom
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Parsons TL, Bolker BM, Dushoff J, Earn DJD. The probability of epidemic burnout in the stochastic SIR model with vital dynamics. Proc Natl Acad Sci U S A 2024; 121:e2313708120. [PMID: 38277438 PMCID: PMC10835029 DOI: 10.1073/pnas.2313708120] [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: 08/09/2023] [Accepted: 11/17/2023] [Indexed: 01/28/2024] Open
Abstract
We present an approach to computing the probability of epidemic "burnout," i.e., the probability that a newly emergent pathogen will go extinct after a major epidemic. Our analysis is based on the standard stochastic formulation of the Susceptible-Infectious-Removed (SIR) epidemic model including host demography (births and deaths) and corresponds to the standard SIR ordinary differential equations (ODEs) in the infinite population limit. Exploiting a boundary layer approximation to the ODEs and a birth-death process approximation to the stochastic dynamics within the boundary layer, we derive convenient, fully analytical approximations for the burnout probability. We demonstrate-by comparing with computationally demanding individual-based stochastic simulations and with semi-analytical approximations derived previously-that our fully analytical approximations are highly accurate for biologically plausible parameters. We show that the probability of burnout always decreases with increased mean infectious period. However, for typical biological parameters, there is a relevant local minimum in the probability of persistence as a function of the basic reproduction number [Formula: see text]. For the shortest infectious periods, persistence is least likely if [Formula: see text]; for longer infectious periods, the minimum point decreases to [Formula: see text]. For typical acute immunizing infections in human populations of realistic size, our analysis of the SIR model shows that burnout is almost certain in a well-mixed population, implying that susceptible recruitment through births is insufficient on its own to explain disease persistence.
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Affiliation(s)
- Todd L. Parsons
- Laboratoire de Probabilités, Statistique et Modélisation, Sorbonne Université, CNRS UMR 8001, Paris75005, France
| | - Benjamin M. Bolker
- Department of Biology, McMaster University, Hamilton, OntarioL8S 4K1, Canada
- Department of Mathematics & Statistics, McMaster University, Hamilton, OntarioL8S 4K1, Canada
| | - Jonathan Dushoff
- Department of Biology, McMaster University, Hamilton, OntarioL8S 4K1, Canada
- Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, OntarioL8S 4K1, Canada
| | - David J. D. Earn
- Department of Mathematics & Statistics, McMaster University, Hamilton, OntarioL8S 4K1, Canada
- Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, OntarioL8S 4K1, Canada
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Nishimura M, Asai K, Tabuchi T, Toyokura E, Kawai T, Miyamoto A, Watanabe T, Kawaguchi T. Association of combustible cigarettes and heated tobacco products use with SARS-CoV-2 infection and severe COVID-19 in Japan: a JASTIS 2022 cross-sectional study. Sci Rep 2023; 13:1120. [PMID: 36732559 PMCID: PMC9894839 DOI: 10.1038/s41598-023-28006-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 01/11/2023] [Indexed: 02/04/2023] Open
Abstract
Insufficient evidence has been accumulated regarding associations of heated tobacco products (HTPs) use with coronavirus infection and severity of coronavirus disease 2019 (COVID-19), an ongoing pandemic. We conducted a cross-sectional study using data from an internet questionnaire administered in February 2022 to 30,130 individuals from the general Japanese population (age range, 16-81 years). Single users of HTPs and dual users of combustible cigarettes and HTPs comprised 5.2% and 7.3% of respondents, and 6.7% and 38.0% of those infected (n = 1117). Approximately 70% of infected dual users experienced severe disease. Single users of HTPs and dual users were more likely to be infected with coronavirus than never-users (adjusted odds ratio [aOR] = 1.65/4.66; 95% confidence interval [CI] 1.26-2.15/3.89-5.58). Regarding severity, former and current tobacco users (former/combustible cigarettes/HTPs: aOR = 1.88/3.17/1.90; 95%CI 1.11-3.19/1.77-5.67/1.01-3.59) were more likely to be administered oxygen than never-users, and dual users required oxygen administration the most (aOR = 4.15, 95%CI 2.70-6.36). Use of HTPs may increase risks of coronavirus infection and severe COVID-19. Our results provide an opportunity to consider the safety of tobacco products use, including HTPs, during the COVID-19 pandemic.
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Affiliation(s)
- Misako Nishimura
- Department of Respiratory Medicine, Graduate School of Medicine, Osaka City University, 1-4-3, Asahimachi, Abeno-Ku, Osaka, 545-8585, Japan
| | - Kazuhisa Asai
- Department of Respiratory Medicine, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3, Asahimachi, Abeno-Ku, Osaka, 545-8585, Japan.
| | - Takahiro Tabuchi
- Cancer Control Center, Osaka International Cancer Institute, 1-69, Ohtemae 3-Chome, Chuo-Ku, Osaka, 541-8567, Japan
| | - Erika Toyokura
- Department of Respiratory Medicine, Graduate School of Medicine, Osaka City University, 1-4-3, Asahimachi, Abeno-Ku, Osaka, 545-8585, Japan
| | - Takahiro Kawai
- Department of Respiratory Medicine, Graduate School of Medicine, Osaka City University, 1-4-3, Asahimachi, Abeno-Ku, Osaka, 545-8585, Japan
| | - Atsushi Miyamoto
- Department of Respiratory Medicine, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3, Asahimachi, Abeno-Ku, Osaka, 545-8585, Japan
| | - Tetsuya Watanabe
- Department of Respiratory Medicine, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3, Asahimachi, Abeno-Ku, Osaka, 545-8585, Japan
| | - Tomoya Kawaguchi
- Department of Respiratory Medicine, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3, Asahimachi, Abeno-Ku, Osaka, 545-8585, Japan
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Egbert ER, Xiao S, Prochaska E, Ali SO, Colantuoni E, Gadala A, Koontz D, Zhong D, Schumacher CM, Sick-Samuels AC, Debes AK, Milstone AM. Association of healthcare worker behaviors with coronavirus disease 2019 (COVID-19) risk during four pandemic periods and characteristics associated with high-risk behaviors. ANTIMICROBIAL STEWARDSHIP & HEALTHCARE EPIDEMIOLOGY : ASHE 2023; 3:e16. [PMID: 36714294 PMCID: PMC9879855 DOI: 10.1017/ash.2022.371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 12/02/2022] [Accepted: 12/02/2022] [Indexed: 01/18/2023]
Abstract
In a large healthcare worker cohort, we quantified the association between behaviors and risk of coronavirus disease 2019 (COVID-19) during different pandemic phases, adjusting for prior infection and vaccination. Individual characteristics, including personal concerns, were associated with these behaviors. Public health messaging should target high-risk populations and behaviors as the pandemic evolves.
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Affiliation(s)
- Emily R. Egbert
- Division of Infectious Diseases, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Shaoming Xiao
- Division of Infectious Diseases, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Erica Prochaska
- Division of Infectious Diseases, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - S. Omar Ali
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Elizabeth Colantuoni
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Avi Gadala
- Johns Hopkins Health System, Baltimore, Maryland
| | - Danielle Koontz
- Division of Infectious Diseases, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Diana Zhong
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Christina M. Schumacher
- Division of General Pediatrics, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Anna C. Sick-Samuels
- Division of Infectious Diseases, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Amanda K. Debes
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Aaron M. Milstone
- Division of Infectious Diseases, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland
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Dynamic analysis and optimal control considering cross transmission and variation of information. Sci Rep 2022; 12:18104. [PMID: 36302934 PMCID: PMC9610354 DOI: 10.1038/s41598-022-21774-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 10/04/2022] [Indexed: 12/30/2022] Open
Abstract
Cross-transmission of information has a profound influence on the progress of science and technology and the discipline integration in the field of education. In this work, knowledge gained from the viral recombination and variation in COVID-19 transmission is applied to information transmission. Virus recombination and virus variation are similar to the crossing and information fusion phenomena in information transmission. An S2I4MR model with information crossing and variation is constructed. Then, the local and global asymptotic stabilities of the information-free equilibrium and information-existence equilibrium are analyzed. Additionally, the basic reproduction number [Formula: see text] of the model is calculated. As such, an optimal control strategy is hereby proposed to promote the cross-transmission of information and generate variant information. The numerical simulations support the results of the theoretical analysis and the sensitivity of the system towards certain control parameters. In particular, the results show that strengthening information crossing promotes the generation of variant information. Furthermore, encouraging information exchange and enhancing education improve the generation of information crossing and information variation.
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