1
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Appanna N, Mew R, Williams S, Starkey T, Patel G, Hudson L, Burke E, Aquilina F, Harnett C, Boult H, Greig W, Ubsdell D, Crouch S, Smith P, Jiskrova K, Vallance G, Nallamilli S, Burnett A, Clark J, Khan S, Little M, Liu J, Panneerselvam H, Patel V, Platt J, Tilby M, Watts I, Harper Wynne C, Lee L. Safe prescribing in cancer patients during the COVID-19 pandemic and outcomes following restart of cancer care following SARS-CoV-2 infection: The COV-SPOT initiative. Int J Cancer 2025; 156:2087-2093. [PMID: 40062998 DOI: 10.1002/ijc.35377] [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: 03/22/2024] [Revised: 01/16/2025] [Accepted: 02/05/2025] [Indexed: 04/05/2025]
Abstract
SARS-CoV-2 continues to spread across the world as a highly transmissible endemic disease. For many cancer patients, SARS-CoV-2 infection is unavoidable. It continues to disrupt cancer care, causing treatment delays and major psycho-socio-medical issues. At present, there is limited evidence on safe prescribing of anti-cancer therapy, and safe treatment restart following SARS-CoV-2 infection. We conducted a prospective cohort study involving 406 COVID-19-positive cancer patients across five UK cancer centres and collected data on delay durations, COVID-19 symptoms and mortality, to ascertain the effect of treatment interruptions. Patients were studied between May 2022 and March 2023, during which Omicron variants of SARS-CoV-2 were predominant. Mean treatment interruption was 12.7 days (standard deviation 47.3 days). Upon resuming anti-cancer therapy, 8.5% experienced COVID-19 symptom progression, and 1.2% succumbed to COVID-19-related mortality. Patients with haematological cancers had a 3.4-fold increased risk of severe symptoms at 4 weeks compared to solid tumour patients. Higher symptom burden at COVID-19 diagnosis was associated with a 3.0-fold increase in symptom severity at 4 weeks following treatment restart. At 8 weeks following restart, 2.1% had increased morbidity or mortality. We highlight the ongoing impact of COVID-19 on patients and cancer care, and the risk of resuming cancer treatments in patients with symptomatic COVID-19. Although the risk of mortality is relatively low upon treatment resumption, personalised approaches assessing cancer diagnosis and SARS-CoV-2 status are crucial. Treatments are also stopped due to other infectious conditions and our results could be reviewed in the context of yearly influenza pandemics.
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Affiliation(s)
- Nathan Appanna
- Derriford Hospital, University Hospitals Plymouth NHS Trust, Plymouth, UK
- Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Rosie Mew
- Royal Devon and Exeter Hospital, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK
| | - Sophie Williams
- Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
- Weston Park Cancer Centre, Sheffield, UK
| | - Thomas Starkey
- Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK
| | - Grisma Patel
- University College London Cancer Institute, London, UK
| | - Laura Hudson
- Royal Devon and Exeter Hospital, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK
| | - Emma Burke
- John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Francesca Aquilina
- Royal Devon and Exeter Hospital, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK
| | - Caroline Harnett
- Royal Devon and Exeter Hospital, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK
| | - Harrison Boult
- Royal Devon and Exeter Hospital, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK
| | | | - Daisy Ubsdell
- Royal Devon and Exeter Hospital, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK
| | - Shannon Crouch
- Royal Devon and Exeter Hospital, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK
| | - Philippa Smith
- Royal Devon and Exeter Hospital, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK
| | - Katerina Jiskrova
- Basingstoke and North Hampshire Hospital, Hampshire Hospitals NHS Foundation Trust, Winchester, UK
| | - Grant Vallance
- John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | | | | | - James Clark
- Department of Medicine, Imperial College London, London, UK
| | - Sam Khan
- Department of Genetics and Genome Biology, University of Leicester, Leicester, UK
| | - Martin Little
- John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Justin Liu
- Leeds Institute of Cancer and Pathology, Leeds, UK
| | | | - Vijay Patel
- NHS England and NHS Improvement London, London, UK
| | - James Platt
- Faculty of Medicine and Health, University of Leeds, Leeds, UK
| | - Michael Tilby
- Birmingham Medical School, University of Birmingham, Birmingham, UK
| | - Isabella Watts
- Royal Free Hospital, Royal Free London NHS Foundation Trust, London, UK
| | | | - Lennard Lee
- Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
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2
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Chen X, Zhang S, Xu J. A modelling approach to characterise the interaction between behavioral response and epidemics: A study based on COVID-19. Infect Dis Model 2025; 10:477-492. [PMID: 39845730 PMCID: PMC11750544 DOI: 10.1016/j.idm.2024.12.013] [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: 09/25/2024] [Revised: 11/24/2024] [Accepted: 12/17/2024] [Indexed: 01/24/2025] Open
Abstract
During epidemic outbreaks, human behavior is highly influential on the disease transmission and hence affects the course, duration and outcome of the epidemics. In order to examine the feedback effect between the dynamics of the behavioral response and disease outbreak, a simple SIR-β type model is established by introducing the independent variable β of effective contact rate, characterizing how human behavior interacts with disease transmission dynamics and allowing for the feedback changing over time along the progress of epidemic and population's perception of risk. By a particle swarm optimization algorithm in the solution procedures and time series of COVID-19 data with different shapes of infection peaks, we show that the proposed model, together with such behavioral change mechanism, is capable of capturing the trend of the selected data and can give rise to oscillatory prevalence of different magnitude over time, revealing how different levels of behavioral response affect the waves of infection as well as the evolution of the disease.
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Affiliation(s)
- Xinyu Chen
- School of Science, Xi'an University of Technology, Xi'an, 710048, PR China
| | - Suxia Zhang
- School of Science, Xi'an University of Technology, Xi'an, 710048, PR China
| | - Jinhu Xu
- School of Science, Xi'an University of Technology, Xi'an, 710048, PR China
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3
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Deng Q, Woldegerima WA, Zhang W, Asgary A, Kong JD, Flicker S, Ogden NH, Orbinski J, Bragazzi NL, Wu J. Uncovering the impact of infection routes on within-host MPXV dynamics: Insights from a mathematical modeling study. PLoS Comput Biol 2025; 21:e1013073. [PMID: 40388391 PMCID: PMC12088049 DOI: 10.1371/journal.pcbi.1013073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2024] [Accepted: 04/21/2025] [Indexed: 05/21/2025] Open
Abstract
The unprecedented mpox outbreak in non-endemic regions during 2022-2023, which has seen a recent resurgence in late 2023-2024, poses a significant public health threat. Despite its global spread, the viral dynamics of mpox infection and the specific characteristics driving these outbreaks remain insufficiently explored. We develop mathematical models to examine the interactions between host immune responses and the virus across three distinct infection routes (intravenous, intradermal, and intrarectal). The models are calibrated using viral load data from macaques infected through each of these three infection routes. Subsequently, we calculate the infectiousness of each infected macaque, finding that the proportion of presymptomatic infectiousness is highest in those infected via sexual contact, followed by skin-to-skin contact. These observations demonstrate that close contact during sexual activity is a significant route of viral transmission, with presymptomatic spread playing a crucial role in the 2022-2023 multi-country outbreak and potentially also in the 2023-2024 multi-source outbreak. Leveraging model predictions and infectiousness data, we assess the impact of antiviral drugs on interventions against mpox infection. Model simulations suggest that early administration of antiviral drugs can reduce peak viral loads, even in individuals with compromised immunity, particularly in cases of infection through skin-to-skin and sexual contact. These results underscore the importance of initiating antiviral treatment as early as possible for mpox-infected patients with compromised immune systems, such as those who are HIV-positive.
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Affiliation(s)
- Qi Deng
- Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada
| | | | - Wenjing Zhang
- Department of Mathematics and Statistics, Texas Tech University, Lubbock, Texas, United States of America
| | - Ali Asgary
- Global South Artificial Intelligence for Pandemic and Epidemic Preparedness and Response Network, York University, Toronto, Ontario, Canada
| | - Jude Dzevela Kong
- Artificial Intelligence and Mathematical Modelling Lab, University of Toronto, Toronto, Ontario, Canada
| | - Sarah Flicker
- Department Environmental Studies, York University, Toronto, Ontario, Canada
| | - Nicholas H. Ogden
- National Microbiology Laboratory, Public Health Agency of Canada, Saint-Hyacinthe, Québec, Canada
| | - James Orbinski
- The Dahdaleh Institute for Global Health Research, York University, Toronto, Ontario, Canada
| | | | - Jianhong Wu
- Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada
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4
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Sun X, Zhou W, Ruan Y, Lan G, Zhu Q, Xiao Y. Perceived risk induced multiscale model: Coupled within-host and between-host dynamics and behavioral dynamics. J Theor Biol 2025; 599:111998. [PMID: 39667577 DOI: 10.1016/j.jtbi.2024.111998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2024] [Revised: 11/07/2024] [Accepted: 11/13/2024] [Indexed: 12/14/2024]
Abstract
A novel multiscale model is formulated to examine the co-evolution among behavioral dynamics, disease transmission dynamics and viral dynamics, in which perceived risk act as a bridge for realizing the bidirectional coupling of between-host dynamics and within-host dynamics. The model is validated by real data and exhibits rich dynamic behaviors including the periodic oscillations of the solutions, the discordance of transmission dynamics and viral dynamics. It is observed that new infections may increase with improving treatment efficacy, which may reveal the hidden mechanisms why it is hard to eliminate HIV/AIDS infection only with the strategy of treatment. If increasing treatment efficacy but without improving diagnosis rate, "nearly elimination" phenomenon may happen when the risk threshold for behavior changes is low, in which the number of new infections may drop to a relatively low level but increase again to a relatively high level after a period of time as people may hardly keep their awareness and increase their high risk behaviors. The findings indicate that the intervention measures should be implemented both at individual level and population level to realize "ending the AIDS".
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Affiliation(s)
- Xiaodan Sun
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, PR China
| | - Weike Zhou
- School of Mathematics, Northwest University, Xi'an, 710127, PR China
| | - Yuhua Ruan
- State Key Laboratory of Infectious Disease Prevention and Control (SKLID), National Center for AIDS/STD Control and Prevention (NCAIDS), Chinese Center for Disease Control and Prevention (China CDC), Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Beijing, 102206, PR China
| | - Guanghua Lan
- Guangxi Key Laboratory of Major Infectious Disease Prevention Control and Biosafety Emergency Response, Guangxi Center for Disease Control and Prevention, Nanning, 530028, PR China
| | - Qiuying Zhu
- Guangxi Key Laboratory of Major Infectious Disease Prevention Control and Biosafety Emergency Response, Guangxi Center for Disease Control and Prevention, Nanning, 530028, PR China
| | - Yanni Xiao
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, PR China.
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5
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Osi A, Ghaffarzadegan N. A simultaneous simulation of human behavior dynamics and epidemic spread: A multi-country study amidst the COVID-19 pandemic. Math Biosci 2025; 380:109368. [PMID: 39681158 DOI: 10.1016/j.mbs.2024.109368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Revised: 12/05/2024] [Accepted: 12/13/2024] [Indexed: 12/18/2024]
Abstract
The transmission dynamics of infectious diseases and human responses are intertwined, forming complex feedback loops. However, many epidemic models fail to endogenously represent human behavior change. In this study, we introduce a novel behavioral epidemic model that incorporates various behavioral phenomena into SEIR models, including risk-response dynamics, shifts in containment policies, adherence fatigue, and societal learning, alongside disease transmission dynamics. By testing our model against data from 8 countries, where extensive behavioral data were available, we simultaneously replicate death rates, mobility trends, fatigue levels, and policy changes, both in-sample and out-of-sample. Our model offers a comprehensive depiction of changes in multiple behavioral measures along with the spread of the disease. We assess the explanatory power of each model mechanism in capturing data variability. Our findings demonstrate that the comprehensive model that includes all mechanisms provides the most insightful perspective for understanding the influence of human behavior during pandemics.
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Affiliation(s)
- Ann Osi
- Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, USA.
| | - Navid Ghaffarzadegan
- Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, USA
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6
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Tang B, Ma K, Liu Y, Wang X, Tang S, Xiao Y, Cheke RA. Managing spatio-temporal heterogeneity of susceptibles by embedding it into an homogeneous model: A mechanistic and deep learning study. PLoS Comput Biol 2024; 20:e1012497. [PMID: 39348420 PMCID: PMC11476686 DOI: 10.1371/journal.pcbi.1012497] [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: 04/16/2024] [Revised: 10/10/2024] [Accepted: 09/17/2024] [Indexed: 10/02/2024] Open
Abstract
Accurate prediction of epidemics is pivotal for making well-informed decisions for the control of infectious diseases, but addressing heterogeneity in the system poses a challenge. In this study, we propose a novel modelling framework integrating the spatio-temporal heterogeneity of susceptible individuals into homogeneous models, by introducing a continuous recruitment process for the susceptibles. A neural network approximates the recruitment rate to develop a Universal Differential Equations (UDE) model. Simultaneously, we pre-set a specific form for the recruitment rate and develop a mechanistic model. Data from a COVID Omicron variant outbreak in Shanghai are used to train the UDE model using deep learning methods and to calibrate the mechanistic model using MCMC methods. Subsequently, we project the attack rate and peak of new infections for the first Omicron wave in China after the adjustment of the dynamic zero-COVID policy. Our projections indicate an attack rate and a peak of new infections of 80.06% and 3.17% of the population, respectively, compared with the homogeneous model's projections of 99.97% and 32.78%, thus providing an 18.6% improvement in the prediction accuracy based on the actual data. Our simulations demonstrate that heterogeneity in the susceptibles decreases herd immunity for ~37.36% of the population and prolongs the outbreak period from ~30 days to ~70 days, also aligning with the real case. We consider that this study lays the groundwork for the development of a new class of models and new insights for modelling heterogeneity.
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Affiliation(s)
- Biao Tang
- School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, People’s Republic of China
- The Interdisciplinary Research Center for Mathematics and Life Sciences, Xi’an Jiaotong University, Xi’an, People’s Republic of China
| | - Kexin Ma
- School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, People’s Republic of China
| | - Yan Liu
- School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, People’s Republic of China
| | - Xia Wang
- School of Mathematics and Statistics, Shaanxi Normal University, Xi’an, People’s Republic of China
| | - Sanyi Tang
- School of Mathematics and Statistics, Shaanxi Normal University, Xi’an, People’s Republic of China
| | - Yanni Xiao
- School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, People’s Republic of China
- The Interdisciplinary Research Center for Mathematics and Life Sciences, Xi’an Jiaotong University, Xi’an, People’s Republic of China
| | - Robert A. Cheke
- Natural Resources Institute, University of Greenwich at Medway, Central Avenue, Chatham Maritime, Kent, United Kingdom
- Department of Infectious Disease Epidemiology, Imperial College London, School of Public Health, White City Campus, London, United Kingdom
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7
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Li T, Xiao Y, Heffernan J. Linking Spontaneous Behavioral Changes to Disease Transmission Dynamics: Behavior Change Includes Periodic Oscillation. Bull Math Biol 2024; 86:73. [PMID: 38739351 DOI: 10.1007/s11538-024-01298-w] [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: 07/16/2023] [Accepted: 04/08/2024] [Indexed: 05/14/2024]
Abstract
Behavior change significantly influences the transmission of diseases during outbreaks. To incorporate spontaneous preventive measures, we propose a model that integrates behavior change with disease transmission. The model represents behavior change through an imitation process, wherein players exclusively adopt the behavior associated with higher payoff. We find that relying solely on spontaneous behavior change is insufficient for eradicating the disease. The dynamics of behavior change are contingent on the basic reproduction number R a corresponding to the scenario where all players adopt non-pharmaceutical interventions (NPIs). WhenR a < 1 , partial adherence to NPIs remains consistently feasible. We can ensure that the disease stays at a low level or maintains minor fluctuations around a lower value by increasing sensitivity to perceived infection. In cases where oscillations occur, a further reduction in the maximum prevalence of infection over a cycle can be achieved by increasing the rate of behavior change. WhenR a > 1 , almost all players consistently adopt NPIs if they are highly sensitive to perceived infection. Further consideration of saturated recovery leads to saddle-node homoclinic and Bogdanov-Takens bifurcations, emphasizing the adverse impact of limited medical resources on controlling the scale of infection. Finally, we parameterize our model with COVID-19 data and Tokyo subway ridership, enabling us to illustrate the disease spread co-evolving with behavior change dynamics. We further demonstrate that an increase in sensitivity to perceived infection can accelerate the peak time and reduce the peak size of infection prevalence in the initial wave.
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Affiliation(s)
- Tangjuan Li
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China
| | - Yanni Xiao
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China.
| | - Jane Heffernan
- York Research Chair, Modelling Infection and Immunity Lab, Centre for Disease Modelling, Mathematics and Statistics, York University, Toronto, M3J 1P3, Canada
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8
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Zhang F, Zhang J, Li M, Jin Z, Wen Y. Assessing the impact of different contact patterns on disease transmission: Taking COVID-19 as a case. PLoS One 2024; 19:e0300884. [PMID: 38603698 PMCID: PMC11008907 DOI: 10.1371/journal.pone.0300884] [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: 06/30/2023] [Accepted: 03/02/2024] [Indexed: 04/13/2024] Open
Abstract
Human-to-human contact plays a leading role in the transmission of infectious diseases, and the contact pattern between individuals has an important influence on the intensity and trend of disease transmission. In this paper, we define regular contacts and random contacts. Then, taking the COVID-19 outbreak in Yangzhou City, China as an example, we consider age heterogeneity, household structure and two contact patterns to establish discrete dynamic models with switching between daytime and nighttime to depict the transmission mechanism of COVID-19 in population. We studied the changes in the reproduction number with different age groups and household sizes at different stages. The effects of the proportion of two contacts patterns on reproduction number were also studied. Furthermore, taking the final size, the peak value of infected individuals in community and the peak value of quarantine infected individuals and nucleic acid test positive individuals as indicators, we evaluate the impact of the number of random contacts, the duration of the free transmission stage and summer vacation on the spread of the disease. The results show that a series of prevention and control measures taken by the Chinese government in response to the epidemic situation are reasonable and effective, and the young and middle-aged adults (aged 18-59) with household size of 6 have the strongest transmission ability. In addition, the results also indicate that increasing the proportion of random contact is beneficial to the control of the infectious disease in the phase with interventions. This work enriches the content of infectious disease modeling and provides theoretical guidance for the prevention and control of follow-up major infectious diseases.
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Affiliation(s)
- Fenfen Zhang
- College of Mathematics and Statistics, Taiyuan Normal University, Jinzhong, Shanxi, China
- Shanxi College of Technology, Shuozhou, Shanxi, China
- Complex Systems Research Center, Shanxi University, Taiyuan, Shanxi, China
- Shanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention, Shanxi University, Taiyuan, Shanxi, China
- Key Laboratory of Complex Systems and Data Science of Ministry of Education, Shanxi University, Taiyuan, Shanxi, China
| | - Juan Zhang
- Complex Systems Research Center, Shanxi University, Taiyuan, Shanxi, China
- Shanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention, Shanxi University, Taiyuan, Shanxi, China
- Key Laboratory of Complex Systems and Data Science of Ministry of Education, Shanxi University, Taiyuan, Shanxi, China
| | - Mingtao Li
- School of Mathematics, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Zhen Jin
- Complex Systems Research Center, Shanxi University, Taiyuan, Shanxi, China
- Shanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention, Shanxi University, Taiyuan, Shanxi, China
- Key Laboratory of Complex Systems and Data Science of Ministry of Education, Shanxi University, Taiyuan, Shanxi, China
| | - Yuqi Wen
- School of Materials Science & Engineering, Beijing Institute of Technology, Beijing, China
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9
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Wang JL, Xiao XL, Zhang FF, Pei X, Li MT, Zhang JP, Zhang J, Sun GQ. Forecast of peak infection and estimate of excess deaths in COVID-19 transmission and prevalence in Taiyuan City, 2022 to 2023. Infect Dis Model 2024; 9:56-69. [PMID: 38130878 PMCID: PMC10733700 DOI: 10.1016/j.idm.2023.11.005] [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: 08/17/2023] [Revised: 11/03/2023] [Accepted: 11/10/2023] [Indexed: 12/23/2023] Open
Abstract
In this paper, with the method of epidemic dynamics, we assess the spread and prevalence of COVID-19 after the policy adjustment of prevention and control measure in December 2022 in Taiyuan City in China, and estimate the excess population deaths caused by COVID-19. Based on the transmission mechanism of COVID-19 among individuals, a dynamic model with heterogeneous contacts is established to describe the change of control measures and the population's social behavior in Taiyuan city. The model is verified and simulated by basing on reported case data from November 8th to December 5th, 2022 in Taiyuan city and the statistical data of the questionnaire survey from December 1st to 23rd, 2022 in Neijiang city. Combining with reported numbers of permanent residents and deaths from 2017 to 2021 in Taiyuan city, we apply the dynamic model to estimate theoretical population of 2022 under the assumption that there is no effect of COVID-19. In addition, we carry out sensitivity analysis to determine the propagation character of the Omicron strain and the effect of the control measures. As a result of the study, it is concluded that after adjusting the epidemic policy on December 6th, 2022, three peaks of infection in Taiyuan are estimated to be from December 22nd to 31st, 2022, from May 10th to June 1st, 2023, and from September 5th to October 13th, 2023, and the corresponding daily peaks of new cases can reach 400 000, 44 000 and 22 000, respectively. By the end of 2022, excess deaths can range from 887 to 4887, and excess mortality rate can range from 3.06% to 14.82%. The threshold of the infectivity of the COVID-19 variant is estimated 0.0353, that is if the strain infectivity is above it, the epidemic cannot be control with the previous normalization measures.
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Affiliation(s)
- Jia-Lin Wang
- Complex Systems Research Center, Shanxi University, Taiyuan, 030006, China
- School of Mathematical Sciences, Shanxi University, Taiyuan, 030006, China
| | - Xin-Long Xiao
- Complex Systems Research Center, Shanxi University, Taiyuan, 030006, China
- School of Mathematical Sciences, Shanxi University, Taiyuan, 030006, China
| | - Fen-Fen Zhang
- School of Mathematics, North University of China, Taiyuan, 030051, China
| | - Xin Pei
- College of Mathematics, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Ming-Tao Li
- College of Mathematics, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Ju-Ping Zhang
- Complex Systems Research Center, Shanxi University, Taiyuan, 030006, China
- Complex Systems and Data Science Key Laboratory of Ministry of Education, Taiyuan, 030006, China
| | - Juan Zhang
- Complex Systems Research Center, Shanxi University, Taiyuan, 030006, China
- Complex Systems and Data Science Key Laboratory of Ministry of Education, Taiyuan, 030006, China
| | - Gui-Quan Sun
- School of Mathematics, North University of China, Taiyuan, 030051, China
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10
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Zhao W, Wang X, Tang B. The impacts of spatial-temporal heterogeneity of human-to-human contacts on the extinction probability of infectious disease from branching process model. J Theor Biol 2024; 579:111703. [PMID: 38096979 DOI: 10.1016/j.jtbi.2023.111703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 11/26/2023] [Accepted: 12/07/2023] [Indexed: 12/18/2023]
Abstract
In this study, we focus on the impacts of spatial-temporal heterogeneity of human-to-human contacts on the spread of infectious diseases and develop a multi-type branching process model by introducing random human-to-human contact mode into a structured population. We provide the general formulas of the generation size, extinction probability, and basic reproduction number of the proposed branching process model. The result shows that the natural temporal heterogeneity (i.e. random contacts over time) can lead to a higher extinction probability while remains the same basic reproduction number and generation size. This is also numerically verified by choosing the real contact distributions from different circumstances of four countries. In addition, we observe a non-monotonic pattern of the differences, against the transmission probability and the mean contact rate, between the extinction probabilities under the constant and random contact patterns. Given the spatial heterogeneity, we show that it can contribute to the increase of basic reproduction number, but also increase the extinction probability of the infectious disease. This study adds novel insights to the course of the impact of heterogeneity on the transmission dynamics and also provides additional evidence for the limited role of reproduction numbers.
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Affiliation(s)
- Wuqiong Zhao
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049, PR China.
| | - Xia Wang
- School of Mathematics and Statistics, Shaanxi Normal University, Xi'an 710119, PR China.
| | - Biao Tang
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049, PR China.
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11
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Espinosa O, Mora L, Sanabria C, Ramos A, Rincón D, Bejarano V, Rodríguez J, Barrera N, Álvarez-Moreno C, Cortés J, Saavedra C, Robayo A, Franco OH. Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review. Syst Rev 2024; 13:30. [PMID: 38229123 PMCID: PMC10790449 DOI: 10.1186/s13643-023-02411-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 12/04/2023] [Indexed: 01/18/2024] Open
Abstract
BACKGROUND The interaction between modelers and policymakers is becoming more common due to the increase in computing speed seen in recent decades. The recent pandemic caused by the SARS-CoV-2 virus was no exception. Thus, this study aims to identify and assess epidemiological mathematical models of SARS-CoV-2 applied to real-world data, including immunization for coronavirus 2019 (COVID-19). METHODOLOGY PubMed, JSTOR, medRxiv, LILACS, EconLit, and other databases were searched for studies employing epidemiological mathematical models of SARS-CoV-2 applied to real-world data. We summarized the information qualitatively, and each article included was assessed for bias risk using the Joanna Briggs Institute (JBI) and PROBAST checklist tool. The PROSPERO registration number is CRD42022344542. FINDINGS In total, 5646 articles were retrieved, of which 411 were included. Most of the information was published in 2021. The countries with the highest number of studies were the United States, Canada, China, and the United Kingdom; no studies were found in low-income countries. The SEIR model (susceptible, exposed, infectious, and recovered) was the most frequently used approach, followed by agent-based modeling. Moreover, the most commonly used software were R, Matlab, and Python, with the most recurring health outcomes being death and recovery. According to the JBI assessment, 61.4% of articles were considered to have a low risk of bias. INTERPRETATION The utilization of mathematical models increased following the onset of the SARS-CoV-2 pandemic. Stakeholders have begun to incorporate these analytical tools more extensively into public policy, enabling the construction of various scenarios for public health. This contribution adds value to informed decision-making. Therefore, understanding their advancements, strengths, and limitations is essential.
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Affiliation(s)
- Oscar Espinosa
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS) & Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de Colombia, Bogotá, D.C., Colombia.
| | - Laura Mora
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | - Cristian Sanabria
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | - Antonio Ramos
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS) & Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Duván Rincón
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | - Valeria Bejarano
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS) & Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Jhonathan Rodríguez
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS) & Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Nicolás Barrera
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | | | - Jorge Cortés
- Faculty of Medicine, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Carlos Saavedra
- Faculty of Medicine, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Adriana Robayo
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | - Oscar H Franco
- University Medical Center Utrecht, Utrecht University & Harvard T.H. Chan School of Public Health, Harvard University, Cambridge, USA
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12
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Walkowiak MP, Domaradzki J, Walkowiak D. Unmasking the COVID-19 pandemic prevention gains: excess mortality reversal in 2022. Public Health 2023; 223:193-201. [PMID: 37672832 DOI: 10.1016/j.puhe.2023.08.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 07/11/2023] [Accepted: 08/02/2023] [Indexed: 09/08/2023]
Abstract
OBJECTIVES The purpose of this study was to assess the long-term effectiveness of COVID-19 pandemic prevention measures in saving lives after European governments began to lift restrictions. STUDY DESIGN Excess mortality interrupted time series. METHODS Country-level weekly data on deaths were fitted to the Poisson mixed linear model to estimate excess deaths. Based on this estimate, the percentage of excess deaths above the baseline during the pandemic (week 11 in 2020 to week 15 in 2022) (when public health interventions were in place) and during the post-pandemic period (week 16 in 2022 to week 52 in 2022) were calculated. These results were fitted to the linear regression model to determine any potential relationship between mortality during these two periods. RESULTS The model used in this study had high predictive value (adjusted R2 = 59.4%). Mortality during the endemic (post-pandemic) period alone increased by 7.2% (95% confidence interval [CI]: 5.7, 8.6) above baseline, while each percentage increase in mortality during the pandemic corresponded to a 0.357% reduction (95% CI: 0.243, 0.471) in mortality during the post-pandemic period. CONCLUSIONS The most successful countries in terms of protective measures also experienced the highest mortality rates after restrictions were lifted. The model used in this study clearly shows a measure of bidirectional mortality displacement that is sufficiently clear to mask any impact of long COVID on overall mortality. Results from this study also seriously impact previous cost-benefit analyses of pandemic prevention measures, since, according to the current model, 12.2% (95% CI: 8.3, 16.1) of the gains achieved in pandemic containment were lost after restrictions were lifted.
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Affiliation(s)
- M P Walkowiak
- Department of Preventive Medicine, Poznan University of Medical Sciences, Poznań, Poland.
| | - J Domaradzki
- Department of Social Sciences and Humanities, Poznan University of Medical Sciences, Poznań, Poland.
| | - D Walkowiak
- Department of Organization and Management in Health Care, Poznan University of Medical Sciences, Poznań, Poland.
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13
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He M, Tang S, Xiao Y. Combining the dynamic model and deep neural networks to identify the intensity of interventions during COVID-19 pandemic. PLoS Comput Biol 2023; 19:e1011535. [PMID: 37851640 PMCID: PMC10584194 DOI: 10.1371/journal.pcbi.1011535] [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: 04/06/2023] [Accepted: 09/20/2023] [Indexed: 10/20/2023] Open
Abstract
During the COVID-19 pandemic, control measures, especially massive contact tracing following prompt quarantine and isolation, play an important role in mitigating the disease spread, and quantifying the dynamic contact rate and quarantine rate and estimate their impacts remain challenging. To precisely quantify the intensity of interventions, we develop the mechanism of physics-informed neural network (PINN) to propose the extended transmission-dynamics-informed neural network (TDINN) algorithm by combining scattered observational data with deep learning and epidemic models. The TDINN algorithm can not only avoid assuming the specific rate functions in advance but also make neural networks follow the rules of epidemic systems in the process of learning. We show that the proposed algorithm can fit the multi-source epidemic data in Xi'an, Guangzhou and Yangzhou cities well, and moreover reconstruct the epidemic development trend in Hainan and Xinjiang with incomplete reported data. We inferred the temporal evolution patterns of contact/quarantine rates, selected the best combination from the family of functions to accurately simulate the contact/quarantine time series learned by TDINN algorithm, and consequently reconstructed the epidemic process. The selected rate functions based on the time series inferred by deep learning have epidemiologically reasonable meanings. In addition, the proposed TDINN algorithm has also been verified by COVID-19 epidemic data with multiple waves in Liaoning province and shows good performance. We find the significant fluctuations in estimated contact/quarantine rates, and a feedback loop between the strengthening/relaxation of intervention strategies and the recurrence of the outbreaks. Moreover, the findings show that there is diversity in the shape of the temporal evolution curves of the inferred contact/quarantine rates in the considered regions, which indicates variation in the intensity of control strategies adopted in various regions.
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Affiliation(s)
- Mengqi He
- School of Mathematics and Statistics, Shaanxi Normal University, Xi’an, China
| | - Sanyi Tang
- School of Mathematics and Statistics, Shaanxi Normal University, Xi’an, China
| | - Yanni Xiao
- School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, China
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14
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He M, Tang B, Xiao Y, Tang S. Transmission dynamics informed neural network with application to COVID-19 infections. Comput Biol Med 2023; 165:107431. [PMID: 37696183 DOI: 10.1016/j.compbiomed.2023.107431] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 07/26/2023] [Accepted: 08/28/2023] [Indexed: 09/13/2023]
Abstract
Since the end of 2019 the COVID-19 repeatedly surges with most countries/territories experiencing multiple waves, and mechanism-based epidemic models played important roles in understanding the transmission mechanism of multiple epidemic waves. However, capturing temporal changes of the transmissibility of COVID-19 during the multiple waves keeps ill-posed problem for traditional mechanism-based epidemic compartment models, because that the transmission rate is usually assumed to be specific piecewise functions and more parameters are added to the model once multiple epidemic waves involved, which poses a huge challenge to parameter estimation. Meanwhile, data-driven deep neural networks fail to discover the driving factors of repeated outbreaks and lack interpretability. In this study, aiming at developing a data-driven method to project time-dependent parameters but also merging the advantage of mechanism-based models, we propose a transmission dynamics informed neural network (TDINN) by encoding the SEIRD compartment model into deep neural networks. We show that the proposed TDINN algorithm performs very well when fitting the COVID-19 epidemic data with multiple waves, where the epidemics in the United States, Italy, South Africa, and Kenya, and several outbreaks the Omicron variant in China are taken as examples. In addition, the numerical simulation shows that the trained TDINN can also perform as a predictive model to capture the future development of COVID-19 epidemic. We find that the transmission rate inferred by the TDINN frequently fluctuates, and a feedback loop between the epidemic shifting and the changes of transmissibility drives the occurrence of multiple waves. We observe a long response delay to the implementation of control interventions in the four countries, while the decline of the transmission rate in the outbreaks in China usually happens once the implementation of control interventions. The further simulation show that 17 days' delay of the response to the implementation of control interventions lead to a roughly four-fold increase in daily reported cases in one epidemic wave in Italy, which suggest that a rapid response to policies that strengthen control interventions can be effective in flattening the epidemic curve or avoiding subsequent epidemic waves. We observe that the transmission rate in the outbreaks in China is already decreasing before enhancing control interventions, providing the evidence that the increasing of the epidemics can drive self-conscious behavioural changes to protect against infections.
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Affiliation(s)
- Mengqi He
- School of Mathematics and Statistics, Shaanxi Normal University, Xi'an, China
| | - Biao Tang
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China.
| | - Yanni Xiao
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China
| | - Sanyi Tang
- School of Mathematics and Statistics, Shaanxi Normal University, Xi'an, China
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15
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Walkowiak MP, Walkowiak D, Walkowiak J. To vaccinate or to isolate? Establishing which intervention leads to measurable mortality reduction during the COVID-19 Delta wave in Poland. Front Public Health 2023; 11:1221964. [PMID: 37744498 PMCID: PMC10513426 DOI: 10.3389/fpubh.2023.1221964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Accepted: 08/21/2023] [Indexed: 09/26/2023] Open
Abstract
Background During the Delta variant COVID-19 wave in Poland there were serious regional differences in vaccination rates and discrepancies in the enforcement of pandemic preventive measures, which allowed us to assess the relative effectiveness of the policies implemented. Methods Creating a model that would predict mortality based on vaccination rates among the most vulnerable groups and the timing of the wave peak enabled us to calculate to what extent flattening the curve reduced mortality. Subsequently, a model was created to assess which preventive measures delayed the peak of infection waves. Combining those two models allowed us to estimate the relative effectiveness of those measures. Results Flattening the infection curve worked: according to our model, each week of postponing the peak of the wave reduced excess deaths by 1.79%. Saving a single life during the Delta wave required one of the following: either the vaccination of 57 high-risk people, or 1,258 low-risk people to build herd immunity, or the isolation of 334 infected individuals for a cumulative period of 10.1 years, or finally quarantining 782 contacts for a cumulative period of 19.3 years. Conclusions Except for the most disciplined societies, vaccination of high-risk individuals followed by vaccinating low-risk groups should have been the top priority instead of relying on isolation and quarantine measures which can incur disproportionately higher social costs. Our study demonstrates that even in a country with uniform policies, implementation outcomes varied, highlighting the importance of fine-tuning policies to regional specificity.
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Affiliation(s)
- Marcin Piotr Walkowiak
- Department of Preventive Medicine, Poznan University of Medical Sciences, Poznań, Poland
| | - Dariusz Walkowiak
- Department of Organization and Management in Health Care, Poznan University of Medical Sciences, Poznań, Poland
| | - Jarosław Walkowiak
- Department of Pediatric Gastroenterology and Metabolic Diseases, Poznan University of Medical Sciences, Poznań, Poland
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16
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Jing S, Milne R, Wang H, Xue L. Vaccine hesitancy promotes emergence of new SARS-CoV-2 variants. J Theor Biol 2023; 570:111522. [PMID: 37210068 PMCID: PMC10193816 DOI: 10.1016/j.jtbi.2023.111522] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 04/30/2023] [Accepted: 05/03/2023] [Indexed: 05/22/2023]
Abstract
The successive emergence of SARS-CoV-2 mutations has led to an unprecedented increase in COVID-19 incidence worldwide. Currently, vaccination is considered to be the best available solution to control the ongoing COVID-19 pandemic. However, public opposition to vaccination persists in many countries, which can lead to increased COVID-19 caseloads and hence greater opportunities for vaccine-evasive mutant strains to arise. To determine the extent that public opinion regarding vaccination can induce or hamper the emergence of new variants, we develop a model that couples a compartmental disease transmission framework featuring two strains of SARS-CoV-2 with game theoretical dynamics on whether or not to vaccinate. We combine semi-stochastic and deterministic simulations to explore the effect of mutation probability, perceived cost of receiving vaccines, and perceived risks of infection on the emergence and spread of mutant SARS-CoV-2 strains. We find that decreasing the perceived costs of being vaccinated and increasing the perceived risks of infection (that is, decreasing vaccine hesitation) will decrease the possibility of vaccine-resistant mutant strains becoming established by about fourfold for intermediate mutation rates. Conversely, we find increasing vaccine hesitation to cause both higher probability of mutant strains emerging and more wild-type cases after the mutant strain has appeared. We also find that once a new variant has emerged, perceived risk of being infected by the original variant plays a much larger role than perceptions of the new variant in determining future outbreak characteristics. Furthermore, we find that rapid vaccination under non-pharmaceutical interventions is a highly effective strategy for preventing new variant emergence, due to interaction effects between non-pharmaceutical interventions and public support for vaccination. Our findings indicate that policies that combine combating vaccine-related misinformation with non-pharmaceutical interventions (such as reducing social contact) will be the most effective for avoiding the establishment of harmful new variants.
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Affiliation(s)
- Shuanglin Jing
- College of Mathematical Sciences, Harbin Engineering University, Harbin, Heilongjiang, 150001, China
| | - Russell Milne
- Department of Mathematical and Statistical Sciences & Interdisciplinary Lab for Mathematical Ecology and Epidemiology, University of Alberta, Edmonton, Alberta T6G 2R3, Canada
| | - Hao Wang
- Department of Mathematical and Statistical Sciences & Interdisciplinary Lab for Mathematical Ecology and Epidemiology, University of Alberta, Edmonton, Alberta T6G 2R3, Canada.
| | - Ling Xue
- College of Mathematical Sciences, Harbin Engineering University, Harbin, Heilongjiang, 150001, China
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17
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Kamal M, Atchadé MN, Sokadjo YM, Siddiqui SA, Riad FH, El-Raouf MMA, Aldallal R, Hussam E, Alshanbari HM, Alsuhabi H, Gemeay AM. Influence of COVID-19 vaccination on the dynamics of new infected cases in the world. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:3324-3341. [PMID: 36899583 DOI: 10.3934/mbe.2023156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
The initial COVID-19 vaccinations were created and distributed to the general population in 2020 thanks to emergency authorization and conditional approval. Consequently, numerous countries followed the process that is currently a global campaign. Taking into account the fact that people are being vaccinated, there are concerns about the effectiveness of that medical solution. Actually, this study is the first one focusing on how the number of vaccinated people might influence the spread of the pandemic in the world. From the Global Change Data Lab "Our World in Data", we were able to get data sets about the number of new cases and vaccinated people. This study is a longitudinal one from 14/12/2020 to 21/03/2021. In addition, we computed Generalized log-Linear Model on count time series (Negative Binomial distribution due to over dispersion in data) and implemented validation tests to confirm the robustness of our results. The findings revealed that when the number of vaccinated people increases by one new vaccination on a given day, the number of new cases decreases significantly two days after by one. The influence is not notable on the same day of vaccination. Authorities should increase the vaccination campaign to control well the pandemic. That solution has effectively started to reduce the spread of COVID-19 in the world.
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Affiliation(s)
- Mustafa Kamal
- Department of Basic Sciences, College of Science and Theoretical Studies, Saudi Electronic University, Dammam 32256, Saudi Arabia
| | - Mintodê Nicodème Atchadé
- National Higher School of Mathematics Genius and Modelization, National University of Sciences, Technologies, Engineering and Mathematics, Abomey, Benin Republic
- University of Abomey-Calavi/International Chair in Mathematical Physics and Applications (ICMPA: UNESCO-Chair), 072 BP 50 Cotonou, Rep. Benin
| | - Yves Morel Sokadjo
- University of Abomey-Calavi/International Chair in Mathematical Physics and Applications (ICMPA: UNESCO-Chair), 072 BP 50 Cotonou, Rep. Benin
| | - Sabir Ali Siddiqui
- Department of Mathematics and Sciences, College of Arts and Applied Sciences, Dhofar University, Salalah, Oman
| | - Fathy H Riad
- Mathematics Department, College of Science, Jouf University, P.O. Box 2014, Sakaka, Saudi Arabia
| | - M M Abd El-Raouf
- Basic and Applied Science Institute, Arab Academy for Science, Technology and Maritime Transport (AASTMT), Alexandria, Egypt
| | - Ramy Aldallal
- Department of Accounting, College of Business Administration in Hawtat Bani Tamim, Prince Sattam Abdulaziz University, Saudi Arabia
| | - Eslam Hussam
- Department of Mathematics, Faculty of Science, Helwan University, Cairo, Egypt
| | - Huda M Alshanbari
- Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Hassan Alsuhabi
- Department of Mathematics, Al-Qunfudah University College, Umm Al-Qura University, Mecca, Saudi Arabia
| | - Ahmed M Gemeay
- Department of Mathematics, Faculty of Science, Tanta University, Tanta 31527, Egypt
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18
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Ma J, Ma S. Dynamics of a stochastic hepatitis B virus transmission model with media coverage and a case study of China. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:3070-3098. [PMID: 36899572 DOI: 10.3934/mbe.2023145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Hepatitis B virus (HBV) infection is a global public health problem and there are 257 million people living with chronic HBV infection throughout the world. In this paper, we investigate the dynamics of a stochastic HBV transmission model with media coverage and saturated incidence rate. Firstly, we prove the existence and uniqueness of positive solution for the stochastic model. Then the condition on the extinction of HBV infection is obtained, which implies that media coverage helps to control the disease spread and the noise intensities on the acute and chronic HBV infection play a key role in disease eradication. Furthermore, we verify that the system has a unique stationary distribution under certain conditions, and the disease will prevail from the biological perspective. Numerical simulations are conducted to illustrate our theoretical results intuitively. As a case study, we fit our model to the available hepatitis B data of mainland China from 2005 to 2021.
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Affiliation(s)
- Jiying Ma
- College of Science, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Shasha Ma
- College of Science, University of Shanghai for Science and Technology, Shanghai 200093, China
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19
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Deng Y, Zhao Y. Mathematical modeling for COVID-19 with focus on intervention strategies and cost-effectiveness analysis. NONLINEAR DYNAMICS 2022; 110:3893-3919. [PMID: 36060281 PMCID: PMC9419650 DOI: 10.1007/s11071-022-07777-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 07/09/2022] [Indexed: 06/15/2023]
Abstract
The realistic assessments of public health intervention strategies are of great significance to effectively combat the COVID-19 epidemic and the formation of intervention policy. In this paper, an extended COVID-19 epidemic model is devised to assess the severity of the pandemic and explore effective control strategies. The model is characterized by ordinary differential equations with seven-state variables, and it incorporates some parameters associated with the interventions (i.e., media publicity, home isolation, vaccination and face-mask wearing) to investigate the impacts of these interventions on the spread of the COVID-19 epidemic. Some dynamic behaviors of the model, such as forward and backward bifurcation, are analyzed. Specifically, we calibrate the model parameters using actual COVID-19 infected data in Brazil by Markov Chain Monte Carlo algorithm such that we can study the effects of interventions on a practical case. Through a comprehensive exploration of model design and analysis, model calibration, sensitivity analysis, implementation of optimal control problems and cost-effectiveness analysis, the rationality of our model is verified, and the effective strategies to combat the epidemic in Brazil are revealed. The results show that the asymptomatic infected individuals are the main drivers of COVID-19 transmission, and rapid detection of asymptomatic infections is critical to combat the COVID-19 epidemic in Brazil. Interestingly, the effect of the vaccination rate associated with pharmaceutical intervention on the basic reproduction number is much lower than that of non-pharmaceutical interventions (NPIs). Our study also highlights the importance of media publicity. To reduce the infected individuals, the multi-pronged NPIs have considerable positive effects on controlling the outbreak of COVID-19. The infections are significantly decreased by the early implementation of media publicity complemented with home isolation and face-mask wearing strategy. When the cost of implementation is taken into account, the early implementation of media publicity complemented with a face-mask wearing strategy can significantly mitigate the second wave of the epidemic in Brazil. These results provide some management implications for controlling COVID-19.
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Affiliation(s)
- Yang Deng
- School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, 518055 China
| | - Yi Zhao
- School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, 518055 China
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