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Xu Z, Peng Q, Song J, Zhang H, Wei D, Demongeot J, Zeng Q. Bioinformatic analysis of defective viral genomes in SARS-CoV-2 and its impact on population infection characteristics. Front Immunol 2024; 15:1341906. [PMID: 38348041 PMCID: PMC10859446 DOI: 10.3389/fimmu.2024.1341906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 01/12/2024] [Indexed: 02/15/2024] Open
Abstract
DVGs (Defective Viral Genomes) are prevalent in RNA virus infections. In this investigation, we conducted an analysis of high-throughput sequencing data and observed widespread presence of DVGs in SARS-CoV-2. Comparative analysis between SARS-CoV-2 and diverse DNA viruses revealed heightened susceptibility to damage and increased sequencing sample heterogeneity within the SARS-CoV-2 genome. Whole-genome sequencing depth variability analysis exhibited a higher coefficient of variation for SARS-CoV-2, while DVG analysis indicated a significant proportion of recombination sites, signifying notable genome heterogeneity and suggesting that a large proportion of assembled virus particles contain incomplete RNA sequences. Moreover, our investigation explored the sequencing depth and DVG content differences among various strains. Our findings revealed that as the virus evolves, there is a notable increase in the proportion of intact genomes within virus particles, as evidenced by third-generation sequencing data. Specifically, the proportion of intact genome in the Omicron strain surpassed that of the Delta and Alpha strains. This observation effectively elucidates the heightened infectiousness of the Omicron strain compared to the Delta and Alpha strains. We also postulate that this improvement in completeness stems from enhanced virus assembly capacity, as the Omicron strain can promptly facilitate the binding of RNA and capsid protein, thereby reducing the exposure time of vulnerable virus RNA in the host environment and significantly mitigating its degradation. Finally, employing mathematical modeling, we simulated the impact of DVG effects under varying environmental factors on infection characteristics and population evolution. Our findings provide an explanation for the close association between symptom severity and the extent of virus invasion, as well as the substantial disparity in population infection characteristics caused by the same strain under distinct environmental conditions. This study presents a novel approach for future virus research and vaccine development.
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Affiliation(s)
- Zhaobin Xu
- Department of Life Science, Dezhou University, Dezhou, China
| | - Qingzhi Peng
- Department of Life Science, Dezhou University, Dezhou, China
| | - Jian Song
- Department of Life Science, Dezhou University, Dezhou, China
| | - Hongmei Zhang
- Department of Life Science, Dezhou University, Dezhou, China
| | - Dongqing Wei
- State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances, Shanghai Jiao Tong University, Shanghai, China
- Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- Zhongjing Research and Industrialization Institute of Chinese Medicine, Zhongguancun Scientific Park, Meixi, Nanyang, Henan, China
- Peng Cheng National Laboratory, Shenzhen, Guangdong, China
| | - Jacques Demongeot
- Laboratory AGEIS EA 7407, Team Tools for e-Gnosis Medical, Faculty of Medicine, University Grenoble Alpes (UGA), F-38700 La Tronche, France
| | - Qiangcheng Zeng
- Department of Life Science, Dezhou University, Dezhou, China
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Thakkar K, Spinardi J, Kyaw MH, Yang J, Mendoza CF, Ozbilgili E, Taysi B, Dodd J, Yarnoff B, Oh HM. Modelling the Potential Public Health Impact of Different COVID-19 Vaccination Strategies with an Adapted Vaccine in Singapore. Expert Rev Vaccines 2024; 23:16-26. [PMID: 38047434 DOI: 10.1080/14760584.2023.2290931] [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: 11/10/2023] [Accepted: 11/30/2023] [Indexed: 12/05/2023]
Abstract
BACKGROUND Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing COVID-19 has been a dynamically changing virus, requiring the development of adapted vaccines. This study estimated the potential public health impact alternative vaccination strategies for COVID-19 in Singapore. RESEARCH DESIGN AND METHODS The outcomes of alternative vaccination strategies with a future adapted vaccine were estimated using a combined Markov decision tree model. The population was stratified by high- and standard-risk. Using age-specific inputs informed by local surveillance data and published sources, the model estimated health (case numbers, hospitalizations, and deaths) and economic (medical costs and productivity losses) outcomes in different age and risk subpopulations. RESULTS Booster vaccination in only the elderly and high-risk subpopulation was estimated to avert 278,614 cases 21,558 hospitalizations, 239 deaths, Singapore dollars (SGD) 277 million in direct medical costs, and SGD 684 million in indirect medical costs. These benefits increased as vaccination was expanded to other subpopulations. Increasing the booster vaccination coverage to 75% of the standard-risk population averted more deaths (3%), hospitalizations (29%), infections (145%), direct costs (90%), and indirect costs (192%) compared to the base case. CONCLUSIONS Broader vaccination strategies using an adapted booster vaccine could have substantial public health and economic impact in Singapore.
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Affiliation(s)
| | - Julia Spinardi
- Medical and Scientific Affairs, Pfizer Inc, New York, NY, USA
| | - Moe H Kyaw
- Medical and Scientific Affairs, Pfizer Inc, New York, NY, USA
| | - Jingyan Yang
- Value and Evidence, Pfizer Inc, New York, NY, USA
| | | | | | - Bulent Taysi
- Asia Medical Affairs, Pfizer Inc, New York, NY, USA
| | - Josie Dodd
- Modeling and Simulation, Evidera Inc, Bethesda, MD, USA
| | - Ben Yarnoff
- Modeling and Simulation, Evidera Inc, Bethesda, MD, USA
| | - Helen M Oh
- Department of Infectious Disease, Changi General Hospital, Simei, Singapore
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Xu Z, Song J, Liu W, Wei D. An agent-based model with antibody dynamics information in COVID-19 epidemic simulation. Infect Dis Model 2023; 8:1151-1168. [PMID: 38033394 PMCID: PMC10685381 DOI: 10.1016/j.idm.2023.11.001] [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: 05/11/2023] [Revised: 11/01/2023] [Accepted: 11/03/2023] [Indexed: 12/02/2023] Open
Abstract
Accurate prediction of the temporal and spatial characteristics of COVID-19 infection is of paramount importance for effective epidemic prevention and control. In order to accomplish this objective, we incorporated individual antibody dynamics into an agent-based model and devised a methodology that encompasses the dynamic behaviors of each individual, thereby explicitly capturing the count and spatial distribution of infected individuals with varying symptoms at distinct time points. Our model also permits the evaluation of diverse prevention and control measures. Based on our findings, the widespread employment of nucleic acid testing and the implementation of quarantine measures for positive cases and their close contacts in China have yielded remarkable outcomes in curtailing a less transmissible yet more virulent strain; however, they may prove inadequate against highly transmissible and less virulent variants. Additionally, our model excels in its ability to trace back to the initial infected case (patient zero) through early epidemic patterns. Ultimately, our model extends the frontiers of traditional epidemiological simulation methodologies and offers an alternative approach to epidemic modeling.
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Affiliation(s)
- Zhaobin Xu
- Department of Life Science, Dezhou University, Shandong, 253023, China
| | - Jian Song
- Department of Life Science, Dezhou University, Shandong, 253023, China
| | - Weidong Liu
- Department of Physical Education, Dezhou University, Shandong, 253023, China
| | - Dongqing Wei
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200030, China
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Xu Z, Wei D, Zhang H, Demongeot J. A Novel Mathematical Model That Predicts the Protection Time of SARS-CoV-2 Antibodies. Viruses 2023; 15:v15020586. [PMID: 36851801 PMCID: PMC9962246 DOI: 10.3390/v15020586] [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: 01/30/2023] [Revised: 02/10/2023] [Accepted: 02/17/2023] [Indexed: 02/25/2023] Open
Abstract
Infectious diseases such as SARS-CoV-2 pose a considerable threat to public health. Constructing a reliable mathematical model helps us quantitatively explain the kinetic characteristics of antibody-virus interactions. A novel and robust model is developed to integrate antibody dynamics with virus dynamics based on a comprehensive understanding of immunology principles. This model explicitly formulizes the pernicious effect of the antibody, together with a positive feedback stimulation of the virus-antibody complex on the antibody regeneration. Besides providing quantitative insights into antibody and virus dynamics, it demonstrates good adaptivity in recapturing the virus-antibody interaction. It is proposed that the environmental antigenic substances help maintain the memory cell level and the corresponding neutralizing antibodies secreted by those memory cells. A broader application is also visualized in predicting the antibody protection time caused by a natural infection. Suitable binding antibodies and the presence of massive environmental antigenic substances would prolong the protection time against breakthrough infection. The model also displays excellent fitness and provides good explanations for antibody selection, antibody interference, and self-reinfection. It helps elucidate how our immune system efficiently develops neutralizing antibodies with good binding kinetics. It provides a reasonable explanation for the lower SARS-CoV-2 mortality in the population that was vaccinated with other vaccines. It is inferred that the best strategy for prolonging the vaccine protection time is not repeated inoculation but a directed induction of fast-binding antibodies. Eventually, this model will inform the future construction of an optimal mathematical model and help us fight against those infectious diseases.
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Affiliation(s)
- Zhaobin Xu
- Department of Life Science, Dezhou University, Dezhou 253023, China
- Correspondence: (Z.X.); (J.D.)
| | - Dongqing Wei
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Hongmei Zhang
- Department of Life Science, Dezhou University, Dezhou 253023, China
| | - Jacques Demongeot
- Laboratory AGEIS EA 7407, Team Tools for e-Gnosis Medical, Faculty of Medicine, University Grenoble Alpes (UGA), 38700 La Tronche, France
- Correspondence: (Z.X.); (J.D.)
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Thakkar K, Spinardi J, Kyaw MH, Yang J, Mendoza CF, Ozbilgili E, Dodd J, Yarnoff B, Punrin S. Modelling the potential public health impact of different vaccination strategies with an omicron-adapted bivalent vaccine in Thailand. Expert Rev Vaccines 2023; 22:860-870. [PMID: 37779484 DOI: 10.1080/14760584.2023.2265460] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 09/27/2023] [Indexed: 10/03/2023]
Abstract
BACKGROUND Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing COVID-19 has continuously evolved, requiring the development of adapted vaccines. This study estimated the impact of the introduction and increased coverage of an Omicron-adapted bivalent booster vaccine in Thailand. RESEARCH DESIGN AND METHODS The outcomes of booster vaccination with an Omicron-adapted bivalent vaccine versus no booster vaccination were estimated using a combined cohort Markov decision tree model. The population was stratified into high- and standard-risk subpopulations. Using age-specific inputs informed by published sources, the model estimated health (case numbers, hospitalizations, and deaths) and economic (medical costs and productivity losses) outcomes in different age and risk subpopulations. RESULTS Booster vaccination in only the elderly and high-risk subpopulation was estimated to avert 97,596 cases 36,578 hospitalizations, 903 deaths, THB 3,119 million in direct medical costs, and THB 10,589 million in indirect medical costs. These benefits increased as vaccination was expanded to other subpopulations. Increasing the booster vaccination coverage to 75% of the standard-risk population averted more deaths (95%), hospitalizations (512%), infections (782%), direct costs (550%), and indirect costs (687%) compared to the base case. CONCLUSIONS Broader vaccination with an Omicron-adapted bivalent booster vaccine could have significant public health and economic benefits in Thailand.
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Affiliation(s)
| | - Julia Spinardi
- Medical and Scientific Affairs, Pfizer Inc, New York, NY, USA
| | - Moe H Kyaw
- Medical and Scientific Affairs, Pfizer Inc, New York, NY, USA
| | - Jingyan Yang
- Value and Evidence, Pfizer Inc, New York, NY, USA
| | | | | | - Josie Dodd
- Model and Simulation, Evidera Inc, London, UK of Great Britain and UK
| | - Ben Yarnoff
- Model and Simulation, Evidera Inc, London, UK of Great Britain and UK
| | - Suda Punrin
- Queen Saovabha Memorial Institute, Thai Red Cross Society, Bangkok, Thailand
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Xu Z, Yang D, Wang L, Demongeot J. Statistical analysis supports UTR (untranslated region) deletion theory in SARS-CoV-2. Virulence 2022; 13:1772-1789. [PMID: 36217240 PMCID: PMC9553139 DOI: 10.1080/21505594.2022.2132059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
It was noticed that the mortality rate of SARS-CoV-2 infection experienced a significant declination in the early stage of the epidemic. We suspect that the sharp deterioration of virus toxicity is related to the deletion of the untranslated region (UTR) of the virus genome. It was found that the genome length of SARS-CoV-2 engaged a significant truncation due to UTR deletion after a mega-sequence analysis. Sequence similarity analysis further indicated that short UTR strains originated from its long UTR ancestors after an irreversible deletion. A good correlation was discovered between genome length and mortality, which demonstrated that the deletion of the virus UTR significantly affected the toxicity of the virus. This correlation was further confirmed in a significance analysis of the genetic influence on the clinical outcomes. The viral genome length of hospitalized patients was significantly more extensive than that of asymptomatic patients. In contrast, the viral genome length of asymptomatic was considerably longer than that of ordinary patients with symptoms. A genome-level mutation scanning was performed to systematically evaluate the influence of mutations at each position on virulence. The results indicated that UTR deletion was the primary driving force in alternating virus virulence in the early evolution. In the end, we proposed a mathematical model to explain why this UTR deletion was not continuous.
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Affiliation(s)
- Zhaobin Xu
- Department of Life Science, Dezhou University, Dezhou, China
| | - Dongying Yang
- Department of Medicine, Dezhou University, Dezhou, China
| | - Liyan Wang
- Department of Life Science, Dezhou University, Dezhou, China
| | - Jacques Demongeot
- Laboratory AGEIS EA 7407, Team Tools for e-Gnosis Medical, Faculty of Medicine, University Grenoble Alpes (UGA), La Tronche, France,CONTACT Jacques Demongeot
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A Mathematical Model of Vaccinations Using New Fractional Order Derivative. Vaccines (Basel) 2022; 10:vaccines10121980. [PMID: 36560391 PMCID: PMC9785217 DOI: 10.3390/vaccines10121980] [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: 10/19/2022] [Revised: 11/09/2022] [Accepted: 11/10/2022] [Indexed: 11/24/2022] Open
Abstract
Purpose: This paper studies a simple SVIR (susceptible, vaccinated, infected, recovered) type of model to investigate the coronavirus’s dynamics in Saudi Arabia with the recent cases of the coronavirus. Our purpose is to investigate coronavirus cases in Saudi Arabia and to predict the early eliminations as well as future case predictions. The impact of vaccinations on COVID-19 is also analyzed. Methods: We consider the recently introduced fractional derivative known as the generalized Hattaf fractional derivative to extend our COVID-19 model. To obtain the fitted and estimated values of the parameters, we consider the nonlinear least square fitting method. We present the numerical scheme using the newly introduced fractional operator for the graphical solution of the generalized fractional differential equation in the sense of the Hattaf fractional derivative. Mathematical as well as numerical aspects of the model are investigated. Results: The local stability of the model at disease-free equilibrium is shown. Further, we consider real cases from Saudi Arabia since 1 May−4 August 2022, to parameterize the model and obtain the basic reproduction number R0v≈2.92. Further, we find the equilibrium point of the endemic state and observe the possibility of the backward bifurcation for the model and present their results. We present the global stability of the model at the endemic case, which we found to be globally asymptotically stable when R0v>1. Conclusion: The simulation results using the recently introduced scheme are obtained and discussed in detail. We present graphical results with different fractional orders and found that when the order is decreased, the number of cases decreases. The sensitive parameters indicate that future infected cases decrease faster if face masks, social distancing, vaccination, etc., are effective.
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Li XP, Alrihieli HF, Algehyne EA, Khan MA, Alshahrani MY, Alraey Y, Riaz MB. Application of piecewise fractional differential equation to COVID-19 infection dynamics. RESULTS IN PHYSICS 2022; 39:105685. [PMID: 35694036 PMCID: PMC9167048 DOI: 10.1016/j.rinp.2022.105685] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 05/31/2022] [Accepted: 05/31/2022] [Indexed: 05/04/2023]
Abstract
We proposed a new mathematical model to study the COVID-19 infection in piecewise fractional differential equations. The model was initially designed using the classical differential equations and later we extend it to the fractional case. We consider the infected cases generated at health care and formulate the model first in integer order. We extend the model into Caputo fractional differential equation and study its background mathematical results. We show that the fractional model is locally asymptotically stable when R 0 < 1 at the disease-free case. For R 0 ≤ 1 , we show the global asymptotical stability of the model. We consider the infected cases in Saudi Arabia and determine the parameters of the model. We show that for the real cases, the basic reproduction is R 0 ≈ 1 . 7372 . We further extend the Caputo model into piecewise stochastic fractional differential equations and discuss the procedure for its numerical simulation. Numerical simulations for the Caputo case and piecewise models are shown in detail.
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Affiliation(s)
- Xiao-Ping Li
- School of Mathematics and Information Science, Xiangnan University, Chenzhou, 423000, Hunan, PR China
| | - Haifaa F Alrihieli
- Department of Mathematics, Faculty of Science, University of Tabuk, P.O. Box 741, Tabuk 71491, Saudi Arabia
| | - Ebrahem A Algehyne
- Department of Mathematics, Faculty of Science, University of Tabuk, P.O. Box 741, Tabuk 71491, Saudi Arabia
| | - Muhammad Altaf Khan
- Institute for Ground Water Studies, Faculty of Natural and Agricultural Sciences, University of the Free State, South Africa
| | - Mohammad Y Alshahrani
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Khalid University, P.O. Box 61413, Abha, 9088, Saudi Arabia
| | - Yasser Alraey
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Khalid University, P.O. Box 61413, Abha, 9088, Saudi Arabia
| | - Muhammad Bilal Riaz
- Department of Automation, Biomechanics and Mechatronics, Lodz University of Technology, 1/15 Stefanowskiego St., 90-924 Lodz, Poland
- Department of Mathematics, University of Management and Technology, 54770, Lahore, Pakistan
- Institute for Ground Water Studies, Faculty of Natural and Agricultural Sciences, University of the Free State, South Africa
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Comparison of Conventional Modeling Techniques with the Neural Network Autoregressive Model (NNAR): Application to COVID-19 Data. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:4802743. [PMID: 35747601 PMCID: PMC9213132 DOI: 10.1155/2022/4802743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 05/12/2022] [Accepted: 05/19/2022] [Indexed: 11/17/2022]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic continues to destroy human life around the world. Almost every country throughout the globe suffered from this pandemic, forcing various governments to apply different restrictions to reduce its impact. In this study, we compare different time-series models with the neural network autoregressive model (NNAR). The study used COVID-19 data in Pakistan from February 26, 2020, to February 18, 2022, as a training and testing data set for modeling. Different models were applied and estimated on the training data set, and these models were assessed on the testing data set. Based on the mean absolute scaled error (MAE) and root mean square error (RMSE) for the training and testing data sets, the NNAR model outperformed the autoregressive integrated moving average (ARIMA) model and other competing models indicating that the NNAR model is the most appropriate for forecasting. Forecasts from the NNAR model showed that the cumulative confirmed COVID-19 cases will be 1,597,180 and cumulative confirmed COVID-19 deaths will be 32,628 on April 18, 2022. We encourage the Pakistan Government to boost its immunization policy.
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