1
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Yamaguchi D, Shimizu R, Kubota R. Development of a SARS-CoV-2 viral dynamic model for patients with COVID-19 based on the amount of viral RNA and viral titer. CPT Pharmacometrics Syst Pharmacol 2024. [PMID: 38783551 DOI: 10.1002/psp4.13164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 04/17/2024] [Accepted: 05/03/2024] [Indexed: 05/25/2024] Open
Abstract
The target-cell limited model, which is one of the mathematical modeling approaches providing a quantitative understanding of viral dynamics, has been applied to describe viral RNA profiles of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in previous studies. However, these models have been developed mainly using patient data from the early phase of the pandemic. Furthermore, no reports focused on the profiles of the viral titer. In this study, the dynamics of both viral RNA and viral titer were characterized using data reflecting the current clinical situation in which the Omicron variant has become epidemic and vaccines for SARS-CoV-2 have become available. Consecutive data for 5212 viral RNA levels and 5216 viral titers were obtained from 720 patients with coronavirus disease 2019 (COVID-19) in a phase II/III study for ensitrelvir. Our model assumed that productively infected cells would produce only infectious viruses, which could be transformed into non-infectious viruses, and has been used to describe the dynamics of both viral RNA levels and viral titer. The time from infection to symptom onset (tinf) of unvaccinated patients was estimated to be 3.0 days, which was shorter than that of the vaccinated patients. The immune-related parameter as a power function for the vaccinated patients was 1.1 times stronger than that for the unvaccinated patients. Our model allows the prediction of the viral dynamics in patients with COVID-19 from the time of infection to symptom onset. Vaccination status was identified as a factor influencing tinf and the immune function.
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Affiliation(s)
- Daichi Yamaguchi
- Clinical Pharmacology & Pharmacokinetics, Shionogi & Co., Ltd., Osaka, Japan
| | - Ryosuke Shimizu
- Clinical Pharmacology & Pharmacokinetics, Shionogi & Co., Ltd., Osaka, Japan
| | - Ryuji Kubota
- Clinical Pharmacology & Pharmacokinetics, Shionogi & Co., Ltd., Osaka, Japan
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2
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Amidei A, Dobrovolny HM. Virus-mediated cell fusion of SARS-CoV-2 variants. Math Biosci 2024; 369:109144. [PMID: 38224908 DOI: 10.1016/j.mbs.2024.109144] [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: 09/04/2023] [Revised: 11/25/2023] [Accepted: 01/12/2024] [Indexed: 01/17/2024]
Abstract
SARS-CoV-2 has the ability to form large multi-nucleated cells known as syncytia. Little is known about how syncytia affect the dynamics of the infection or severity of the disease. In this manuscript, we extend a mathematical model of cell-cell fusion assays to estimate both the syncytia formation rate and the average duration of the fusion phase for five strains of SARS-CoV-2. We find that the original Wuhan strain has the slowest rate of syncytia formation (6.4×10-4/h), but takes only 4.0 h to complete the fusion process, while the Alpha strain has the fastest rate of syncytia formation (0.36 /h), but takes 7.6 h to complete the fusion process. The Beta strain also has a fairly fast syncytia formation rate (9.7×10-2/h), and takes the longest to complete fusion (8.4 h). The D614G strain has a fairly slow syncytia formation rate (2.8×10-3/h), but completes fusion in 4.0 h. Finally, the Delta strain is in the middle with a syncytia formation rate of 3.2×10-2/h and a fusing time of 6.1 h. We note that for these SARS-CoV-2 strains, there appears to be a tradeoff between the ease of forming syncytia and the speed at which they complete the fusion process.
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Affiliation(s)
- Ava Amidei
- Department of Chemistry & Biochemistry, Texas Christian University, Fort Worth, TX, USA
| | - Hana M Dobrovolny
- Department of Physics & Astronomy, Texas Christian University, Fort Worth, TX, USA.
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3
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Gadiyar I, Dobrovolny HM. Different routes of infection of H5N1 lead to changes in infecting time. Math Biosci 2024; 367:109129. [PMID: 38101614 DOI: 10.1016/j.mbs.2023.109129] [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: 07/22/2023] [Revised: 11/15/2023] [Accepted: 12/10/2023] [Indexed: 12/17/2023]
Abstract
Influenza virus infection can result in a wide range of clinical outcomes from asymptomatic infection to severe disease and death. While there are undoubtedly many factors that contribute to the severity of disease, one possible contributing factor that needs more investigation is the route of infection. In this study, we use previously published data from cynomolgus macaques infected with A/Vietnam/1203/04 (H5N1) via either aerosol (with and without bronchoalveolar lavages (BAL)) or a combined intrabronchial, oral, and intranasal route. We fit a mathematical model of within host viral kinetics to the data and find that when the macaques are infected via the aerosol route with subsequent BAL, the infecting time is significantly lower than for the other two groups. A lower infecting time indicates that the virus spreads from cell to cell more rapidly for aerosol infection with BAL than for the combined deposition or aerosol deposition alone. This study helps elucidate the mechanism behind different infection outcomes caused by differences in routes of infection.
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Affiliation(s)
- Ishaan Gadiyar
- Department of Physics & Astronomy, Texas Christian University, Fort Worth, TX, USA; Department of Biology, Vanderbilt University, Nashville, TN, USA
| | - Hana M Dobrovolny
- Department of Physics & Astronomy, Texas Christian University, Fort Worth, TX, USA.
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4
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Noffel Z, Dobrovolny HM. Quantifying the effect of defective viral genomes in respiratory syncytial virus infections. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:12666-12681. [PMID: 37501460 DOI: 10.3934/mbe.2023564] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Defective viral genomes (DVGs) are viral genomes that contain only a partial viral RNA and so cannot replicate within cells on their own. If a cell containing DVGs is subsequently infected with a complete viral genome, the DVG can then use the missing proteins expressed by the full genome in order to replicate itself. Since the cell is producing defective genomes, it has less resources to produce fully functional virions and thus release of complete virions is often suppressed. Here, we use data from challenge studies of respiratory syncytial virus (RSV) in healthy adults to quantify the effect of DVGs. We use a mathematical model to fit the data, finding that late onset of DVGs and prolonged DVG detection are associated with lower infection rates and higher clearance rates. This result could have implications for the use of DVGs as a therapeutic.
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Affiliation(s)
- Zakarya Noffel
- Department of Computer Science, University of Texas at Austin, Austin, TX, US
- Department of Physics & Astronomy, Texas Christian University, Fort Worth, TX, US
| | - Hana M Dobrovolny
- Department of Physics & Astronomy, Texas Christian University, Fort Worth, TX, US
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5
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The dynamics of novel corona virus disease via stochastic epidemiological model with vaccination. Sci Rep 2023; 13:3805. [PMID: 36882515 PMCID: PMC9990022 DOI: 10.1038/s41598-023-30647-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 02/27/2023] [Indexed: 03/09/2023] Open
Abstract
During the past two years, the novel coronavirus pandemic has dramatically affected the world by producing 4.8 million deaths. Mathematical modeling is one of the useful mathematical tools which has been used frequently to investigate the dynamics of various infectious diseases. It has been observed that the nature of the novel disease of coronavirus transmission differs everywhere, implying that it is not deterministic while having stochastic nature. In this paper, a stochastic mathematical model has been investigated to study the transmission dynamics of novel coronavirus disease under the effect of fluctuated disease propagation and vaccination because effective vaccination programs and interaction of humans play a significant role in every infectious disease prevention. We develop the epidemic problem by taking into account the extended version of the susceptible-infected-recovered model and with the aid of a stochastic differential equation. We then study the fundamental axioms for existence and uniqueness to show that the problem is mathematically and biologically feasible. The extinction of novel coronavirus and persistency are examined, and sufficient conditions resulted from our investigation. In the end, some graphical representations support the analytical findings and present the effect of vaccination and fluctuated environmental variation.
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6
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Sharma S, Sarkar R, Mitra K, Giri L. Computational framework to understand the clinical stages of COVID-19 and visualization of time course for various treatment strategies. Biotechnol Bioeng 2023; 120:1640-1656. [PMID: 36810760 DOI: 10.1002/bit.28358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Revised: 12/09/2022] [Accepted: 02/13/2023] [Indexed: 02/24/2023]
Abstract
Coronavirus disease 2019 is known to be regulated by multiple factors such as delayed immune response, impaired T cell activation, and elevated levels of proinflammatory cytokines. Clinical management of the disease remains challenging due to interplay of various factors as drug candidates may elicit different responses depending on the staging of the disease. In this context, we propose a computational framework which provides insights into the interaction between viral infection and immune response in lung epithelial cells, with an aim of predicting optimal treatment strategies based on infection severity. First, we formulate the model for visualizing the nonlinear dynamics during the disease progression considering the role of T cells, macrophages and proinflammatory cytokines. Here, we show that the model is capable of emulating the dynamic and static data trends of viral load, T cell, macrophage levels, interleukin (IL)-6 and TNF-α levels. Second, we demonstrate the ability of the framework to capture the dynamics corresponding to mild, moderate, severe, and critical condition. Our result shows that, at late phase (>15 days), severity of disease is directly proportional to pro-inflammatory cytokine IL6 and tumor necrosis factor (TNF)-α levels and inversely proportional to the number of T cells. Finally, the simulation framework was used to assess the effect of drug administration time as well as efficacy of single or multiple drugs on patients. The major contribution of the proposed framework is to utilize the infection progression model for clinical management and administration of drugs inhibiting virus replication and cytokine levels as well as immunosuppressant drugs at various stages of the disease.
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Affiliation(s)
- Surbhi Sharma
- Department of Chemical Engineering, Indian Institute of Technology Hyderabad, Kandi, Sangareddy, Telangana, India
| | - Rahuldeb Sarkar
- Departments of Respiratory Medicine and Critical Care, Medway NHS Foundation Trust, Gillingham, Kent, UK.,Faculty of Life Sciences, King's College London, London, UK
| | - Kishalay Mitra
- Department of Chemical Engineering, Indian Institute of Technology Hyderabad, Kandi, Sangareddy, Telangana, India
| | - Lopamudra Giri
- Department of Chemical Engineering, Indian Institute of Technology Hyderabad, Kandi, Sangareddy, Telangana, India
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7
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Animal Models to Test SARS-CoV-2 Vaccines: Which Ones Are in Use and Future Expectations. Pathogens 2022; 12:pathogens12010020. [PMID: 36678369 PMCID: PMC9861368 DOI: 10.3390/pathogens12010020] [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: 09/22/2022] [Revised: 11/04/2022] [Accepted: 11/23/2022] [Indexed: 12/24/2022] Open
Abstract
Since late 2019 and early 2020, with the emergence of the COVID-19 pandemic, scientists are rushing to develop treatment and prevention methods to combat SARS-CoV-2. Among these are vaccines. In view of this, the use of animals as experimental models, both to investigate the immunopathology of the disease and to evaluate the efficacy and safety of vaccines, is mandatory. This work aims to describe, through recent scientific articles found in reliable databases, the animal models used for the in vivo testing of COVID-19 vaccines, demonstrating some possibilities of more advantageous/gold-standard models for SARS-CoV-2 vaccines. The majority of the studies use rodents and primates. Meanwhile, the most adequate model to be used as the gold standard for in vivo tests of COVID-19 vaccines is not yet conclusive. Promising options are being discussed as new tests are being carried out and new SARS-CoV-2 variants are emerging.
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8
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Amidei A, Dobrovolny HM. Estimation of virus-mediated cell fusion rate of SARS-CoV-2. Virology 2022; 575:91-100. [PMID: 36088794 PMCID: PMC9449781 DOI: 10.1016/j.virol.2022.08.016] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/27/2022] [Accepted: 08/28/2022] [Indexed: 12/22/2022]
Abstract
Several viruses have the ability to form large multinucleated cells known as syncytia. Many properties of syncytia and the role they play in the evolution of a viral infection are not well understood. One basic question that has not yet been answered is how quickly syncytia form. We use a novel mathematical model of cell-cell fusion assays and apply it to experimental data from SARS-CoV-2 fusion assays to provide the first estimates of virus-mediated cell fusion rate. We find that for SARS-CoV2, the fusion rate is in the range of 6 × 10−4–12×10−4/h. We also use our model to compare fusion rates when the protease TMPRSS2 is overexpressed (2–4 times larger fusion rate), when the protease furin is removed (one third the original fusion rate), and when the spike protein is altered (1/10th the original fusion rate). The use of mathematical models allows us to provide additional quantitative information about syncytia formation.
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Affiliation(s)
- Ava Amidei
- Department of Chemistry & Biochemistry, Texas Christian University, Fort Worth, TX, USA
| | - Hana M Dobrovolny
- Department of Physics & Astronomy, Texas Christian University, Fort Worth, TX, USA.
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9
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Khan T, Ullah R, Alwan BA, El-Khatib Y, Zaman G. Correlated stochastic epidemic model for the dynamics of SARS-CoV-2 with vaccination. Sci Rep 2022; 12:16105. [PMID: 36168022 PMCID: PMC9514201 DOI: 10.1038/s41598-022-20059-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 09/08/2022] [Indexed: 11/09/2022] Open
Abstract
In this paper, we propose a mathematical model to describe the influence of the SARS-CoV-2 virus with correlated sources of randomness and with vaccination. The total human population is divided into three groups susceptible, infected, and recovered. Each population group of the model is assumed to be subject to various types of randomness. We develop the correlated stochastic model by considering correlated Brownian motions for the population groups. As the environmental reservoir plays a weighty role in the transmission of the SARS-CoV-2 virus, our model encompasses a fourth stochastic differential equation representing the reservoir. Moreover, the vaccination of susceptible is also considered. Once the correlated stochastic model, the existence and uniqueness of a positive solution are discussed to show the problem’s feasibility. The SARS-CoV-2 extinction, as well as persistency, are also examined, and sufficient conditions resulted from our investigation. The theoretical results are supported through numerical/graphical findings.
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Affiliation(s)
- Tahir Khan
- Department of Computing, Muscat College, Muscat, Oman
| | - Roman Ullah
- Department of Computing, Muscat College, Muscat, Oman
| | - Basem Al Alwan
- Chemical Engineering Department, College of Engineering, King Khalid University, 61411, Abha, Saudi Arabia
| | - Youssef El-Khatib
- Department of Mathematical Sciences, UAE University, P.O. Box 15551, Al-Ain, United Arab Emirates.
| | - Gul Zaman
- Department of Mathematics, University of Malakand, Chakdara, Dir (Lower), Khyber Pakhtunkhawa, Pakistan
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10
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Goyal A, Duke ER, Cardozo-Ojeda EF, Schiffer JT. Modeling explains prolonged SARS-CoV-2 nasal shedding relative to lung shedding in remdesivir treated rhesus macaques. iScience 2022; 25:104448. [PMID: 35634576 PMCID: PMC9130309 DOI: 10.1016/j.isci.2022.104448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 04/19/2022] [Accepted: 05/16/2022] [Indexed: 12/12/2022] Open
Abstract
In clinical trials, remdesivir decreased recovery time in hospitalized patients with SARS- CoV-2 and prevented hospitalization when given early during infection, despite not reducing nasal viral loads. In rhesus macaques, early remdesivir prevented pneumonia and lowered lung viral loads, but viral loads increased in nasal passages after five days. We developed mathematical models to explain these results. Our model raises the hypotheses that: 1) in contrast to nasal passages viral load monotonically decreases in lungs during therapy because of infection-dependent generation of refractory cells, 2) slight reduction in lung viral loads with an imperfect agent may result in a substantial decrease in lung damage, and 3) increases in nasal viral load may occur due to a blunting of peak viral load which decreases the intensity of the innate immune response. We demonstrate that a higher potency drug could lower viral loads in nasal passages and lung.
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Affiliation(s)
- Ashish Goyal
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center
| | - Elizabeth R Duke
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center.,Department of Medicine, University of Washington, Seattle
| | | | - Joshua T Schiffer
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center.,Department of Medicine, University of Washington, Seattle.,Clinical Research Division, Fred Hutchinson Cancer Research Center
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11
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Multiscale Model of Antiviral Timing, Potency, and Heterogeneity Effects on an Epithelial Tissue Patch Infected by SARS-CoV-2. Viruses 2022; 14:v14030605. [PMID: 35337012 PMCID: PMC8953050 DOI: 10.3390/v14030605] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 03/08/2022] [Accepted: 03/10/2022] [Indexed: 02/06/2023] Open
Abstract
We extend our established agent-based multiscale computational model of infection of lung tissue by SARS-CoV-2 to include pharmacokinetic and pharmacodynamic models of remdesivir. We model remdesivir treatment for COVID-19; however, our methods are general to other viral infections and antiviral therapies. We investigate the effects of drug potency, drug dosing frequency, treatment initiation delay, antiviral half-life, and variability in cellular uptake and metabolism of remdesivir and its active metabolite on treatment outcomes in a simulated patch of infected epithelial tissue. Non-spatial deterministic population models which treat all cells of a given class as identical can clarify how treatment dosage and timing influence treatment efficacy. However, they do not reveal how cell-to-cell variability affects treatment outcomes. Our simulations suggest that for a given treatment regime, including cell-to-cell variation in drug uptake, permeability and metabolism increase the likelihood of uncontrolled infection as the cells with the lowest internal levels of antiviral act as super-spreaders within the tissue. The model predicts substantial variability in infection outcomes between similar tissue patches for different treatment options. In models with cellular metabolic variability, antiviral doses have to be increased significantly (>50% depending on simulation parameters) to achieve the same treatment results as with the homogeneous cellular metabolism.
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12
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Wang B, Mondal J, Samui P, Chatterjee AN, Yusuf A. Effect of an antiviral drug control and its variable order fractional network in host COVID-19 kinetics. THE EUROPEAN PHYSICAL JOURNAL. SPECIAL TOPICS 2022; 231:1915-1929. [PMID: 35126876 PMCID: PMC8803578 DOI: 10.1140/epjs/s11734-022-00454-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Accepted: 01/13/2022] [Indexed: 05/17/2023]
Abstract
In December 2019, a novel coronavirus disease (COVID-19) appeared in Wuhan, China. After that, it spread rapidly all over the world. Novel coronavirus belongs to the family of Coronaviridae and this new strain is called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epithelial cells of our throat and lungs are the main target area of the SARS-CoV-2 virus which leads to COVID-19 disease. In this article, we propose a mathematical model for examining the effects of antiviral treatment over viral mutation to control disease transmission. We have considered here three populations namely uninfected epithelial cells, infected epithelial cells, and SARS-CoV-2 virus. To explore the model in light of the optimal control-theoretic strategy, we use Pontryagin's maximum principle. We also illustrate the existence of the optimal control and the effectiveness of the optimal control is studied here. Cost-effectiveness and efficiency analysis confirms that time-dependent antiviral controlled drug therapy can reduce the viral load and infection process at a low cost. Numerical simulations have been done to illustrate our analytical findings. In addition, a new variable-order fractional model is proposed to investigate the effect of antiviral treatment over viral mutation to control disease transmission. Considering the superiority of fractional order calculus in the modeling of systems and processes, the proposed variable-order fractional model can provide more accurate insight for the modeling of the disease. Then through the genetic algorithm, optimal treatment is presented, and its numerical simulations are illustrated.
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Affiliation(s)
- Bo Wang
- School of Electronic Information and Automation, Aba Teachers University, Wenchuan, 623002 China
- School of Applied Mathematics, University Electronic Science and Technology of China, Chengdu, 610054 China
| | - Jayanta Mondal
- Department of Mathematics, Diamond Harbour Women’s University, Sarisha, West Bengal 743368 India
| | - Piu Samui
- Department of Mathematics, Diamond Harbour Women’s University, Sarisha, West Bengal 743368 India
| | | | - Abdullahi Yusuf
- Department of Computer Engineering, Biruni University, 34010 Istanbul, Turkey
- Department of Mathematics, Federal University Dutse, Dutse, 7156 Jigawa Nigeria
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13
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Zhang L, Li R, Song G, Scholes GD, She ZS. Impairment of T cells' antiviral and anti-inflammation immunities may be critical to death from COVID-19. ROYAL SOCIETY OPEN SCIENCE 2021; 8:211606. [PMID: 34950497 PMCID: PMC8692966 DOI: 10.1098/rsos.211606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 11/25/2021] [Indexed: 05/02/2023]
Abstract
Clarifying dominant factors determining the immune heterogeneity from non-survivors to survivors is crucial for developing therapeutics and vaccines against COVID-19. The main difficulty is quantitatively analysing the multi-level clinical data, including viral dynamics, immune response and tissue damages. Here, we adopt a top-down modelling approach to quantify key functional aspects and their dynamical interplay in the battle between the virus and the immune system, yielding an accurate description of real-time clinical data involving hundreds of patients for the first time. The quantification of antiviral responses gives that, compared to antibodies, T cells play a more dominant role in virus clearance, especially for mild patients (96.5%). Moreover, the anti-inflammatory responses, namely the cytokine inhibition and tissue repair rates, also positively correlate with T cell number and are significantly suppressed in non-survivors. Simulations show that the lack of T cells can lead to more significant inflammation, proposing an explanation for the monotonic increase of COVID-19 mortality with age and higher mortality for males. We propose that T cells play a crucial role in the immunity against COVID-19, which provides a new direction-improvement of T cell number for advancing current prevention and treatment.
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Affiliation(s)
- Luhao Zhang
- Institute of Health System Engineering, College of Engineering, Peking University, Beijing 100871, People's Republic of China
- Department of Chemistry, Princeton University, Princeton, NJ 08540, USA
| | - Rong Li
- Institute of Health System Engineering, College of Engineering, Peking University, Beijing 100871, People's Republic of China
- State Key Laboratory for Turbulence and Complex Systems, Peking University, Beijing 100871, People's Republic of China
| | - Gang Song
- Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, People's Republic of China
| | | | - Zhen-Su She
- Institute of Health System Engineering, College of Engineering, Peking University, Beijing 100871, People's Republic of China
- State Key Laboratory for Turbulence and Complex Systems, Peking University, Beijing 100871, People's Republic of China
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14
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Rodriguez T, Dobrovolny HM. Estimation of viral kinetics model parameters in young and aged SARS-CoV-2 infected macaques. ROYAL SOCIETY OPEN SCIENCE 2021; 8:202345. [PMID: 34804559 PMCID: PMC8595996 DOI: 10.1098/rsos.202345] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 10/25/2021] [Indexed: 06/13/2023]
Abstract
The SARS-CoV-2 virus disproportionately causes serious illness and death in older individuals. In order to have the greatest impact in decreasing the human toll caused by the virus, antiviral treatment should be targeted to older patients. For this, we need a better understanding of the differences in viral dynamics between SARS-CoV-2 infection in younger and older adults. In this study, we use previously published averaged viral titre measurements from the nose and throat of SARS-CoV-2 infection in young and aged cynomolgus macaques to parametrize a viral kinetics model. We find that all viral kinetics parameters differ between young and aged macaques in the nasal passages, but that there are fewer differences in parameter estimates from the throat. We further use our parametrized model to study the antiviral treatment of young and aged animals, finding that early antiviral treatment is more likely to lead to a lengthening of the infection in aged animals, but not in young animals.
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Affiliation(s)
- Thalia Rodriguez
- Department of Physics and Astronomy, Texas Christian University, Fort Worth, TX, USA
| | - Hana M. Dobrovolny
- Department of Physics and Astronomy, Texas Christian University, Fort Worth, TX, USA
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15
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Valdebenito S, Bessis S, Annane D, Lorin de la Grandmaison G, Cramer–Bordé E, Prideaux B, Eugenin EA, Bomsel M. COVID-19 Lung Pathogenesis in SARS-CoV-2 Autopsy Cases. Front Immunol 2021; 12:735922. [PMID: 34671353 PMCID: PMC8521087 DOI: 10.3389/fimmu.2021.735922] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Accepted: 09/06/2021] [Indexed: 12/13/2022] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a major public health issue. COVID-19 is considered an airway/multi-systemic disease, and demise has been associated with an uncontrolled immune response and a cytokine storm in response to the virus. However, the lung pathology, immune response, and tissue damage associated with COVID-19 demise are poorly described and understood due to safety concerns. Using post-mortem lung tissues from uninfected and COVID-19 deadly cases as well as an unbiased combined analysis of histology, multi-viral and host markers staining, correlative microscopy, confocal, and image analysis, we identified three distinct phenotypes of COVID-19-induced lung damage. First, a COVID-19-induced hemorrhage characterized by minimal immune infiltration and large thrombus; Second, a COVID-19-induced immune infiltration with excessive immune cell infiltration but no hemorrhagic events. The third phenotype correspond to the combination of the two previous ones. We observed the loss of alveolar wall integrity, detachment of lung tissue pieces, fibroblast proliferation, and extensive fibrosis in all three phenotypes. Although lung tissues studied were from lethal COVID-19, a strong immune response was observed in all cases analyzed with significant B cell and poor T cell infiltrations, suggesting an exhausted or compromised immune cellular response in these patients. Overall, our data show that SARS-CoV-2-induced lung damage is highly heterogeneous. These individual differences need to be considered to understand the acute and long-term COVID-19 consequences.
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Affiliation(s)
- Silvana Valdebenito
- Department of Neuroscience, Cell Biology and Anatomy, University of Texas Medical Branch (UTMB), Galveston, TX, United States
| | - Simon Bessis
- Service des Maladies Infectieuses, Centre Hospitalier Universitaire Raymond Poincaré, AP-HP, Garches, France
| | - Djillali Annane
- Intensive Care Unit, Raymond Poincaré Hospital (AP-HP), Paris, France
- Simone Veil School of Medicine, Université of Versailles, Versailles, France
- University Paris Saclay, Garches, France
| | - Geoffroy Lorin de la Grandmaison
- Department of Forensic Medicine and Pathology, Versailles Saint-Quentin Université, AP-HP, Raymond Poincaré Hospital, Garches, France
| | | | - Brendan Prideaux
- Department of Neuroscience, Cell Biology and Anatomy, University of Texas Medical Branch (UTMB), Galveston, TX, United States
| | - Eliseo A. Eugenin
- Department of Neuroscience, Cell Biology and Anatomy, University of Texas Medical Branch (UTMB), Galveston, TX, United States
| | - Morgane Bomsel
- Laboratory of Mucosal Entry of HIV-1 and Mucosal Immunity, Department of Infection, Immunity, and Inflammation, Institute Cochin, CNRS UMR 8104, INSERM U1016, University of Paris, Paris, France
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16
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Kim KS, Iwanami S, Oda T, Fujita Y, Kuba K, Miyazaki T, Ejima K, Iwami S. Incomplete antiviral treatment may induce longer durations of viral shedding during SARS-CoV-2 infection. Life Sci Alliance 2021; 4:e202101049. [PMID: 34344719 PMCID: PMC8340032 DOI: 10.26508/lsa.202101049] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 07/23/2021] [Accepted: 07/23/2021] [Indexed: 12/15/2022] Open
Abstract
The duration of viral shedding is determined by a balance between de novo infection and removal of infected cells. That is, if infection is completely blocked with antiviral drugs (100% inhibition), the duration of viral shedding is minimal and is determined by the length of virus production. However, some mathematical models predict that if infected individuals are treated with antiviral drugs with efficacy below 100%, viral shedding may last longer than without treatment because further de novo infections are driven by entry of the virus into partially protected, uninfected cells at a slower rate. Using a simple mathematical model, we quantified SARS-CoV-2 infection dynamics in non-human primates and characterized the kinetics of viral shedding. We counterintuitively found that treatments initiated early, such as 0.5 d after virus inoculation, with intermediate to relatively high efficacy (30-70% inhibition of virus replication) yield a prolonged duration of viral shedding (by about 6.0 d) compared with no treatment.
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Affiliation(s)
- Kwang Su Kim
- Interdisciplinary Biology Laboratory (iBLab), Division of Biological Science, Graduate School of Science, Nagoya University, Nagoya, Japan
| | - Shoya Iwanami
- Interdisciplinary Biology Laboratory (iBLab), Division of Biological Science, Graduate School of Science, Nagoya University, Nagoya, Japan
| | - Takafumi Oda
- Department of Biology, Faculty of Sciences, Kyushu University, Fukuoka, Japan
| | - Yasuhisa Fujita
- Interdisciplinary Biology Laboratory (iBLab), Division of Biological Science, Graduate School of Science, Nagoya University, Nagoya, Japan
| | - Keiji Kuba
- Department of Biochemistry and Metabolic Science, Akita University Graduate School of Medicine, Akita, Japan
| | - Taiga Miyazaki
- Department of Infectious Diseases, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Keisuke Ejima
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, IN, USA
| | - Shingo Iwami
- Interdisciplinary Biology Laboratory (iBLab), Division of Biological Science, Graduate School of Science, Nagoya University, Nagoya, Japan
- Institute of Mathematics for Industry, Kyushu University, Fukuoka, Japan
- Institute for the Advanced Study of Human Biology (ASHBi), Kyoto University, Kyoto, Japan
- NEXT-Ganken Program, Japanese Foundation for Cancer Research, Tokyo, Japan
- Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS), RIKEN, Saitama, Japan
- Science Groove Inc., Fukuoka, Japan
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17
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Trichel AM. Overview of Nonhuman Primate Models of SARS-CoV-2 Infection. Comp Med 2021; 71:411-432. [PMID: 34548126 PMCID: PMC8594265 DOI: 10.30802/aalas-cm-20-000119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Revised: 02/04/2021] [Accepted: 04/19/2021] [Indexed: 12/28/2022]
Abstract
COVID-19, the disease caused by the SARS-CoV-2 betacoronavirus, was declared a pandemic by the World Health Organization on March 11, 2020. Since then, SARS-CoV-2 has triggered a devastating global health and economic emergency. In response, a broad range of preclinical animal models have been used to identify effective therapies and vaccines. Current animal models do not express the full spectrum of human COVID-19 disease and pathology, with most exhibiting mild to moderate disease without mortality. NHPs are physiologically, genetically, and immunologically more closely related to humans than other animal species; thus, they provide a relevant model for SARS-CoV-2 investigations. This overview summarizes NHP models of SARS-CoV-2 and their role in vaccine and therapeutic development.
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Key Words
- ace2, angiotensin l converting enzyme 2
- ade, antibody dependent enhancement
- agm, african green monkey
- ards, acute respiratory distress syndrome
- balf, bronchoalveolar lavage fluid
- cj, conjunctival
- cm, cynomolgus macaque
- covid-19, coronavirus disease 19
- cp, convalescent plasma
- dad, diffuse alveolar damage
- dpc, days post challenge
- dpi, days post infection
- ggos, ground glass opacities
- grna, genomic ribonucleic acid
- hcq, hydroxychloroquine
- it, intratracheal
- nab, neutralizing antibodies
- ptm, pigtail macaque
- rbd, receptor binding domain
- rm, rhesus macaque
- s, spike
- sgrna, subgenomic ribonucleic acid
- th1, type 1 t helper cell
- vrna, viral ribonucleic acid
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Affiliation(s)
- Anita M Trichel
- Division of Laboratory Animal Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
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18
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Contreras C, Newby JM, Hillen T. Personalized Virus Load Curves for Acute Viral Infections. Viruses 2021; 13:1815. [PMID: 34578396 PMCID: PMC8472998 DOI: 10.3390/v13091815] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 07/09/2021] [Accepted: 09/03/2021] [Indexed: 12/11/2022] Open
Abstract
We introduce an explicit function that describes virus-load curves on a patient-specific level. This function is based on simple and intuitive model parameters. It allows virus load analysis of acute viral infections without solving a full virus load dynamic model. We validate our model on data from mice influenza A, human rhinovirus data, human influenza A data, and monkey and human SARS-CoV-2 data. We find wide distributions for the model parameters, reflecting large variability in the disease outcomes between individuals. Further, we compare the virus load function to an established target model of virus dynamics, and we provide a new way to estimate the exponential growth rates of the corresponding infection phases. The virus load function, the target model, and the exponential approximations show excellent fits for the data considered. Our virus-load function offers a new way to analyze patient-specific virus load data, and it can be used as input for higher level models for the physiological effects of a virus infection, for models of tissue damage, and to estimate patient risks.
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Affiliation(s)
- Carlos Contreras
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G 2R3, Canada; (C.C.); (J.M.N.)
- Collaborative Mathematical Biology Group, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Jay M. Newby
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G 2R3, Canada; (C.C.); (J.M.N.)
- Collaborative Mathematical Biology Group, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Thomas Hillen
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G 2R3, Canada; (C.C.); (J.M.N.)
- Collaborative Mathematical Biology Group, University of Alberta, Edmonton, AB T6G 2R3, Canada
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19
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Pagliano P, Sellitto C, Scarpati G, Ascione T, Conti V, Franci G, Piazza O, Filippelli A. An overview of the preclinical discovery and development of remdesivir for the treatment of coronavirus disease 2019 (COVID-19). Expert Opin Drug Discov 2021; 17:9-18. [PMID: 34412564 PMCID: PMC8425432 DOI: 10.1080/17460441.2021.1970743] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Introduction Remdesivir (RDV) is an inhibitor of the viral RNA-dependent RNA polymerases that are active in some RNA viruses, including the Ebola virus and zoonotic coronaviruses. When severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) was identified as the etiologic agent of the coronavirus disease 2019 (COVID-19), several investigations have assessed the potential activity of RDV in inhibiting viral replication, giving rise to hope for an effective treatment. Areas covered In this review, the authors describe the main investigations leading to the discovery of RDV and its subsequent development as an antiviral agent, focusing on the main clinical trials investigating its efficacy in terms of symptom resolution and mortality reduction. Expert opinion RDV is the most widely investigated antiviral drug for the treatment of COVID-19. This attention on RDV activity against SARS-CoV-2 is justified by promising in vitro studies, which demonstrated that RDV was able to suppress viral replication without significant toxicity. Such activity was confirmed by an investigation in an animal model and by the results of preliminary clinical investigations. Nevertheless, the efficacy of RDV in reducing mortality has not been clearly demonstrated.
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Affiliation(s)
- Pasquale Pagliano
- Department of Medicine, Surgery and Dentistry, "Scuola Medica Salernitana", Unit of Infectious Diseases, University of Salerno, Baronissi, Italy
| | - Carmine Sellitto
- Department of Medicine, Surgery and Dentistry, "Scuola Medica Salernitana", Unit of Pharmacology, University of Salerno, Baronissi, Italy
| | - Giuliana Scarpati
- Department of Medicine, Surgery and Dentistry, "Scuola Medica Salernitana", Unit of Anesthesiology, University of Salerno, Baronissi, Italy
| | - Tiziana Ascione
- Department of Medicine, Service of Infectious Diseases, Cardarelli Hospital, Naples, Italy
| | - Valeria Conti
- Department of Medicine, Surgery and Dentistry, "Scuola Medica Salernitana", Unit of Pharmacology, University of Salerno, Baronissi, Italy
| | - Gianluigi Franci
- Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", Unit of Microbiology, University of Salerno, Baronissi, Italy
| | - Ornella Piazza
- Department of Medicine, Surgery and Dentistry, "Scuola Medica Salernitana", Unit of Anesthesiology, University of Salerno, Baronissi, Italy
| | - Amelia Filippelli
- Department of Medicine, Surgery and Dentistry, "Scuola Medica Salernitana", Unit of Pharmacology, University of Salerno, Baronissi, Italy
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20
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Khan T, Ullah R, Zaman G, Alzabut J. A mathematical model for the dynamics of SARS-CoV-2 virus using the Caputo-Fabrizio operator. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:6095-6116. [PMID: 34517525 DOI: 10.3934/mbe.2021305] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The pandemic of SARS-CoV-2 virus remains a pressing issue with unpredictable characteristics which spread worldwide through human interactions. The current study is focusing on the investigation and analysis of a fractional-order epidemic model that discusses the temporal dynamics of the SARS-CoV-2 virus in a community. It is well known that symptomatic and asymptomatic individuals have a major effect on the dynamics of the SARS-CoV-2 virus therefore, we divide the total population into susceptible, asymptomatic, symptomatic, and recovered groups of the population. Further, we assume that the vaccine confers permanent immunity because multiple vaccinations have commenced across the globe. The new fractional-order model for the transmission dynamics of SARS-CoV-2 virus is formulated via the Caputo-Fabrizio fractional-order approach with the maintenance of dimension during the process of fractionalization. The theory of fixed point will be used to show that the proposed model possesses a unique solution whereas the well-posedness (bounded-ness and positivity) of the fractional-order model solutions are discussed. The steady states of the model are analyzed and the sensitivity analysis of the basic reproductive number is explored. Moreover to parameterize the model a real data of SARS-CoV-2 virus reported in the Sultanate of Oman from January 1st, 2021 to May 23rd, 2021 are used. We then perform the large scale numerical findings to show the validity of the analytical work.
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Affiliation(s)
- Tahir Khan
- Department of Mathematics, University of Malakand Chakdara, Dir (L), Pakhtunkhwa, Pakistan
- Department of Computing, Muscat College, Muscat Oman
| | - Roman Ullah
- Department of Computing, Muscat College, Muscat Oman
| | - Gul Zaman
- Department of Mathematics, University of Malakand Chakdara, Dir (L), Pakhtunkhwa, Pakistan
| | - Jehad Alzabut
- Department of Mathematics and General Sciences, Prince Sultan, University, Riyadh, Saudi Arabia
- Department of Industrial Engineering, OSTIM Technical University, Ankara 06374, Turkey
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21
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Martínez-Rodríguez D, Gonzalez-Parra G, Villanueva RJ. Analysis of Key Factors of a SARS-CoV-2 Vaccination Program: A Mathematical Modeling Approach. EPIDEMIOLOGIA 2021; 2:140-161. [PMID: 35141702 PMCID: PMC8824484 DOI: 10.3390/epidemiologia2020012] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 03/25/2021] [Indexed: 02/07/2023] Open
Abstract
The administration of vaccines against the coronavirus disease 2019 (COVID-19) started in early December of 2020. Currently, there are only a few approved vaccines, each with different efficacies and mechanisms of action. Moreover, vaccination programs in different regions may vary due to differences in implementation, for instance, simply the availability of the vaccine. In this article, we study the impact of the pace of vaccination and the intrinsic efficacy of the vaccine on prevalence, hospitalizations, and deaths related to the SARS-CoV-2 virus. Then we study different potential scenarios regarding the burden of the COVID-19 pandemic in the near future. We construct a compartmental mathematical model and use computational methodologies to study these different scenarios. Thus, we are able to identify some key factors to reach the aims of the vaccination programs. We use some metrics related to the outcomes of the COVID-19 pandemic in order to assess the impact of the efficacy of the vaccine and the pace of the vaccine inoculation. We found that both factors have a high impact on the outcomes. However, the rate of vaccine administration has a higher impact in reducing the burden of the COVID-19 pandemic. This result shows that health institutions need to focus on increasing the vaccine inoculation pace and create awareness in the population about the importance of COVID-19 vaccines.
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Affiliation(s)
- David Martínez-Rodríguez
- Insituto Universitario de Matemática Multidisciplinar, Universitat Politècnica de València, 46022 Valencia, Spain; (D.M.-R.); (R.-J.V.)
| | | | - Rafael-J. Villanueva
- Insituto Universitario de Matemática Multidisciplinar, Universitat Politècnica de València, 46022 Valencia, Spain; (D.M.-R.); (R.-J.V.)
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22
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Winkler MS, Skirecki T, Brunkhorst FM, Cajander S, Cavaillon JM, Ferrer R, Flohé SB, García-Salido A, Giamarellos-Bourboulis EJ, Girardis M, Kox M, Lachmann G, Martin-Loeches I, Netea MG, Spinetti T, Schefold JC, Torres A, Uhle F, Venet F, Weis S, Scherag A, Rubio I, Osuchowski MF. Bridging animal and clinical research during SARS-CoV-2 pandemic: A new-old challenge. EBioMedicine 2021; 66:103291. [PMID: 33813139 PMCID: PMC8016444 DOI: 10.1016/j.ebiom.2021.103291] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Revised: 02/22/2021] [Accepted: 03/05/2021] [Indexed: 02/07/2023] Open
Abstract
Many milestones in medical history rest on animal modeling of human diseases. The SARS-CoV-2 pandemic has evoked a tremendous investigative effort primarily centered on clinical studies. However, several animal SARS-CoV-2/COVID-19 models have been developed and pre-clinical findings aimed at supporting clinical evidence rapidly emerge. In this review, we characterize the existing animal models exposing their relevance and limitations as well as outline their utility in COVID-19 drug and vaccine development. Concurrently, we summarize the status of clinical trial research and discuss the novel tactics utilized in the largest multi-center trials aiming to accelerate generation of reliable results that may subsequently shape COVID-19 clinical treatment practices. We also highlight areas of improvement for animal studies in order to elevate their translational utility. In pandemics, to optimize the use of strained resources in a short time-frame, optimizing and strengthening the synergy between the preclinical and clinical domains is pivotal.
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Affiliation(s)
- Martin S Winkler
- Department of Anesthesiology, Emergency and Intensive Care Medicine, University of Göttingen, Göttingen, Robert-Koch-Str. 40, 37085 Göttingen, Germany
| | - Tomasz Skirecki
- Laboratory of Flow Cytometry, Centre of Postgraduate Medical Education, Warsaw, Poland
| | - Frank M Brunkhorst
- Dept. of Anesthesiology and Intensive Care Medicine & Center for Sepsis Control and Care (CSCC), Jena University Hospital-Friedrich Schiller University, Am Klinikum 1, 07747 Jena, Germany; Center for Clinical Studies, Jena University Hospital, 07747 Jena, Germany
| | - Sara Cajander
- Department of Infectious Diseases, Faculty of Medicine and Health, Örebro University, Sweden
| | | | - Ricard Ferrer
- Intensive Care Department and Shock, Organ Dysfunction and Resuscitation Research Group, Vall d'Hebron University Hospital, Vall d'Hebron Barcelona Hospital Campus, Passeig Vall d'Hebron 119-129, Barcelona, 08035, Spain; Centro de Investigación Biomedica En Red-Enfermedades Respiratorias (CibeRes, CB06/06/0028), Instituto de salud Carlos III (ISCIII), Av. de Monforte de Lemos, 5, 28029 Madrid, Spain
| | - Stefanie B Flohé
- Department of Trauma, Hand, and Reconstructive Surgery, University Hospital Essen, University Duisburg-Essen, Hufelandstr. 55, 45147 Essen, Germany
| | - Alberto García-Salido
- Pediatric Critical Care Unit, Hospital Infantil Universitario Niño Jesús, Madrid, Spain
| | | | - Massimo Girardis
- Department of Anesthesia and Intensive Care, University Hospital of Modena, Italy
| | - Matthijs Kox
- Department of Intensive Care Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, The Netherlands
| | - Gunnar Lachmann
- Department of Anesthesiology and Operative Intensive Care Medicine (CCM, CVK), Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany; Berlin Institute of Health (BIH), Anna-Louisa-Karsch-Straße 2, 10178 Berlin, Germany
| | - Ignacio Martin-Loeches
- Multidisciplinary Intensive Care Research Organization (MICRO), St. James's Hospital, James's St N, Ushers, Dublin, D03 VX82, Ireland
| | - Mihai G Netea
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Thibaud Spinetti
- Department of Intensive Care Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010 Bern, Switzerland
| | - Joerg C Schefold
- Department of Intensive Care Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010 Bern, Switzerland
| | - Antoni Torres
- Pneumology Department, Respiratory Institute (ICR), Hospital Clinic of Barcelona - Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) - University of Barcelona (UB), Spain
| | - Florian Uhle
- Department of Anesthesiology, Heidelberg University Hospital, Im Neuenheimer Feld 110, 69120 Heidelberg, Germany
| | - Fabienne Venet
- Hospices Civils de Lyon, Immunology Laboratory, Edouard Herriot Hospital, 5 Place d'Arsonval, 69003 Lyon, France; EA 7426 "Pathophysiology of Injury-Induced Immunosuppression - PI3", Université Claude Bernard Lyon 1/bioMérieux/Hospices Civils de Lyon, Edouard Herriot Hospital, 5 Place d'Arsonval, 69003 Lyon, France
| | - Sebastian Weis
- Dept. of Anesthesiology and Intensive Care Medicine & Center for Sepsis Control and Care (CSCC), Jena University Hospital-Friedrich Schiller University, Am Klinikum 1, 07747 Jena, Germany; Institute for Infectious Disease and Infection Control, Jena University Hospital-Friedrich Schiller University, Am Klinikum 1, 07747 Jena, Germany
| | - André Scherag
- Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital-Friedrich Schiller University, Bachstrasse 18, 07743 Jena, Germany
| | - Ignacio Rubio
- Dept. of Anesthesiology and Intensive Care Medicine & Center for Sepsis Control and Care (CSCC), Jena University Hospital-Friedrich Schiller University, Am Klinikum 1, 07747 Jena, Germany
| | - Marcin F Osuchowski
- Ludwig Boltzmann Institute for Experimental and Clinical Traumatology in the AUVA Research Center, Donaueschingenstrasse 13, 1200, Vienna, Austria.
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23
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Impact of a New SARS-CoV-2 Variant on the Population: A Mathematical Modeling Approach. MATHEMATICAL AND COMPUTATIONAL APPLICATIONS 2021. [DOI: 10.3390/mca26020025] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Several SARS-CoV-2 variants have emerged around the world, and the appearance of other variants depends on many factors. These new variants might have different characteristics that can affect the transmissibility and death rate. The administration of vaccines against the coronavirus disease 2019 (COVID-19) started in early December of 2020 and in some countries the vaccines will not soon be widely available. For this article, we studied the impact of a new more transmissible SARS-CoV-2 strain on prevalence, hospitalizations, and deaths related to the SARS-CoV-2 virus. We studied different scenarios regarding the transmissibility in order to provide a scientific support for public health policies and bring awareness of potential future situations related to the COVID-19 pandemic. We constructed a compartmental mathematical model based on differential equations to study these different scenarios. In this way, we are able to understand how a new, more infectious strain of the virus can impact the dynamics of the COVID-19 pandemic. We studied several metrics related to the possible outcomes of the COVID-19 pandemic in order to assess the impact of a higher transmissibility of a new SARS-CoV-2 strain on these metrics. We found that, even if the new variant has the same death rate, its high transmissibility can increase the number of infected people, those hospitalized, and deaths. The simulation results show that health institutions need to focus on increasing non-pharmaceutical interventions and the pace of vaccine inoculation since a new variant with higher transmissibility, such as, for example, VOC-202012/01 of lineage B.1.1.7, may cause more devastating outcomes in the population.
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24
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Perelson AS, Ke R. Mechanistic Modeling of SARS-CoV-2 and Other Infectious Diseases and the Effects of Therapeutics. Clin Pharmacol Ther 2021; 109:829-840. [PMID: 33410134 DOI: 10.1002/cpt.2160] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 12/24/2020] [Indexed: 12/11/2022]
Abstract
Modern viral kinetic modeling and its application to therapeutics is a field that attracted the attention of the medical, pharmaceutical, and modeling communities during the early days of the AIDS epidemic. Its successes led to applications of modeling methods not only to HIV but a plethora of other viruses, such as hepatitis C virus (HCV), hepatitis B virus and cytomegalovirus, which along with HIV cause chronic diseases, and viruses such as influenza, respiratory syncytial virus, West Nile virus, Zika virus, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which generally cause acute infections. Here we first review the historical development of mathematical models to understand HIV and HCV infections and the effects of treatment by fitting the models to clinical data. We then focus on recent efforts and contributions of applying these models towards understanding SARS-CoV-2 infection and highlight outstanding questions where modeling can provide crucial insights and help to optimize nonpharmaceutical and pharmaceutical interventions of the coronavirus disease 2019 (COVID-19) pandemic. The review is written from our personal perspective emphasizing the power of simple target cell limited models that provided important insights and then their evolution into more complex models that captured more of the virology and immunology. To quote Albert Einstein, "Everything should be made as simple as possible, but not simpler," and this idea underlies the modeling we describe below.
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Affiliation(s)
- Alan S Perelson
- Los Alamos National Laboratory, Theoretical Biology and Biophysics Group, Los Alamos, New Mexico, USA.,New Mexico Consortium, Los Alamos, New Mexico, USA
| | - Ruian Ke
- Los Alamos National Laboratory, Theoretical Biology and Biophysics Group, Los Alamos, New Mexico, USA.,New Mexico Consortium, Los Alamos, New Mexico, USA
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25
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Kim KS, Ejima K, Iwanami S, Fujita Y, Ohashi H, Koizumi Y, Asai Y, Nakaoka S, Watashi K, Aihara K, Thompson RN, Ke R, Perelson AS, Iwami S. A quantitative model used to compare within-host SARS-CoV-2, MERS-CoV, and SARS-CoV dynamics provides insights into the pathogenesis and treatment of SARS-CoV-2. PLoS Biol 2021; 19:e3001128. [PMID: 33750978 PMCID: PMC7984623 DOI: 10.1371/journal.pbio.3001128] [Citation(s) in RCA: 69] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 02/01/2021] [Indexed: 12/11/2022] Open
Abstract
The scientific community is focused on developing antiviral therapies to mitigate the impacts of the ongoing novel coronavirus disease 2019 (COVID-19) outbreak. This will be facilitated by improved understanding of viral dynamics within infected hosts. Here, using a mathematical model in combination with published viral load data, we compare within-host viral dynamics of SARS-CoV-2 with analogous dynamics of MERS-CoV and SARS-CoV. Our quantitative analyses using a mathematical model revealed that the within-host reproduction number at symptom onset of SARS-CoV-2 was statistically significantly larger than that of MERS-CoV and similar to that of SARS-CoV. In addition, the time from symptom onset to the viral load peak for SARS-CoV-2 infection was shorter than those of MERS-CoV and SARS-CoV. These findings suggest the difficulty of controlling SARS-CoV-2 infection by antivirals. We further used the viral dynamics model to predict the efficacy of potential antiviral drugs that have different modes of action. The efficacy was measured by the reduction in the viral load area under the curve (AUC). Our results indicate that therapies that block de novo infection or virus production are likely to be effective if and only if initiated before the viral load peak (which appears 2-3 days after symptom onset), but therapies that promote cytotoxicity of infected cells are likely to have effects with less sensitivity to the timing of treatment initiation. Furthermore, combining a therapy that promotes cytotoxicity and one that blocks de novo infection or virus production synergistically reduces the AUC with early treatment. Our unique modeling approach provides insights into the pathogenesis of SARS-CoV-2 and may be useful for development of antiviral therapies.
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Affiliation(s)
- Kwang Su Kim
- Department of Biology, Faculty of Sciences, Kyushu University, Fukuoka, Japan
| | - Keisuke Ejima
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health–Bloomington, Bloomington, Indiana, United States of America
| | - Shoya Iwanami
- Department of Biology, Faculty of Sciences, Kyushu University, Fukuoka, Japan
| | - Yasuhisa Fujita
- Department of Biology, Faculty of Sciences, Kyushu University, Fukuoka, Japan
| | - Hirofumi Ohashi
- Department of Virology II, National Institute of Infectious Diseases, Tokyo, Japan
| | - Yoshiki Koizumi
- National Center for Global Health and Medicine, Tokyo, Japan
| | - Yusuke Asai
- National Center for Global Health and Medicine, Tokyo, Japan
| | - Shinji Nakaoka
- Faculty of Advanced Life Science, Hokkaido University, Sapporo, Japan
| | - Koichi Watashi
- Department of Virology II, National Institute of Infectious Diseases, Tokyo, Japan
- Department of Applied Biological Science, Tokyo University of Science, Noda, Japan
- Institute for Frontier Life and Medical Sciences, Kyoto University, Kyoto, Japan
- JST-Mirai, Japan Science and Technology Agency, Saitama, Japan
| | - Kazuyuki Aihara
- International Research Center for Neurointelligence, University of Tokyo Institutes for Advanced Study, University of Tokyo, Tokyo, Japan
| | - Robin N. Thompson
- Mathematics Institute, University of Warwick, Coventry, United Kingdom
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom
| | - Ruian Ke
- New Mexico Consortium, Los Alamos, New Mexico, United States of America
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Alan S. Perelson
- New Mexico Consortium, Los Alamos, New Mexico, United States of America
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Shingo Iwami
- Department of Biology, Faculty of Sciences, Kyushu University, Fukuoka, Japan
- JST-Mirai, Japan Science and Technology Agency, Saitama, Japan
- Institute for the Advanced Study of Human Biology, Kyoto University, Kyoto, Japan
- NEXT-Ganken Program, Japanese Foundation for Cancer Research, Tokyo, Japan
- Science Groove, Fukuoka, Japan
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Abuin P, Anderson A, Ferramosca A, Hernandez-Vargas EA, Gonzalez AH. Dynamical characterization of antiviral effects in COVID-19. ANNUAL REVIEWS IN CONTROL 2021; 52:587-601. [PMID: 34093069 PMCID: PMC8162791 DOI: 10.1016/j.arcontrol.2021.05.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 04/21/2021] [Accepted: 05/01/2021] [Indexed: 05/02/2023]
Abstract
Mathematical models describing SARS-CoV-2 dynamics and the corresponding immune responses in patients with COVID-19 can be critical to evaluate possible clinical outcomes of antiviral treatments. In this work, based on the concept of virus spreadability in the host, antiviral effectiveness thresholds are determined to establish whether or not a treatment will be able to clear the infection. In addition, the virus dynamic in the host - including the time-to-peak and the final monotonically decreasing behavior - is characterized as a function of the time to treatment initiation. Simulation results, based on nine patient data, show the potential clinical benefits of a treatment classification according to patient critical parameters. This study is aimed at paving the way for the different antivirals being developed to tackle SARS-CoV-2.
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Affiliation(s)
- Pablo Abuin
- Institute of Technological Development for the Chemical Industry (INTEC), CONICET-UNL, Santa Fe, Argentina
| | - Alejandro Anderson
- Institute of Technological Development for the Chemical Industry (INTEC), CONICET-UNL, Santa Fe, Argentina
| | - Antonio Ferramosca
- Department of Management, Information and Production Engineering, University of Bergamo, Via Marconi 5, 24044, Dalmine (BG), Italy
| | | | - Alejandro H Gonzalez
- Institute of Technological Development for the Chemical Industry (INTEC), CONICET-UNL, Santa Fe, Argentina
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González-Parra G, Arenas AJ. Qualitative analysis of a mathematical model with presymptomatic individuals and two SARS-CoV-2 variants. COMPUTATIONAL AND APPLIED MATHEMATICS 2021; 40:199. [PMCID: PMC8325548 DOI: 10.1007/s40314-021-01592-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 07/01/2021] [Accepted: 07/22/2021] [Indexed: 05/31/2023]
Abstract
The SARS-CoV-2 continues to spread across the world. During this COVID-19 pandemic, several variants of the SARS-CoV-2 have been found. Some of these new variants like the VOC-202012/01 of lineage B.1.1.7 or the most recently B.1.617 emerging in India have a higher infectiousness than those previously prevalent. We propose a mathematical model based on ordinary differential equations to investigate potential consequences of the appearance of a new more transmissible SARS-CoV-2 strain in a given region. The proposed mathematical model incorporates the presymptomatic and asymptomatic subpopulations in addition to the more usual susceptible, exposed, infected, and recovered subpopulations. This is important from a realistic point of view since it has been found recently that presymptomatic and asymptomatic individuals are relevant spreaders of the SARS-CoV-2. Using the next-generation matrix method, we find the basic reproduction number, \documentclass[12pt]{minimal}
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\begin{document}$${\mathcal {R}}_{0}$$\end{document}R0, an important threshold parameter that provides insight regarding the evolution and outcome of a certain instance of the COVID-19 pandemic. The local and global stability of system equilibria are also presented. In particular, for the global stability we construct a Lyapunov functional and use the LaSalle invariant principle to prove that if the basic reproduction ratio is less than unity, the infection-free equilibrium is globally asymptotically stable. On the other hand, if \documentclass[12pt]{minimal}
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\begin{document}$${\mathcal {R}}_{0}>1$$\end{document}R0>1 the endemic equilibrium is globally asymptotically stable. Finally, we present numerical simulations to numerically support the analytic results and to show the impact of the introduction of a new more contagious SARS-CoV-2 variant in a population.
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Affiliation(s)
| | - Abraham J. Arenas
- Departamento de Matemáticas y Estadística, Universidad de Córdoba, Montería, Colombia
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Pinky L, Dobrovolny HM. SARS-CoV-2 coinfections: Could influenza and the common cold be beneficial? J Med Virol 2020; 92:2623-2630. [PMID: 32557776 PMCID: PMC7300957 DOI: 10.1002/jmv.26098] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 05/26/2020] [Accepted: 05/27/2020] [Indexed: 12/15/2022]
Abstract
The novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread around the world, causing serious illness and death and creating a heavy burden on the healthcare systems of many countries. Since the virus first emerged in late November 2019, its spread has coincided with peak circulation of several seasonal respiratory viruses, yet some studies have noted limited coinfections between SARS-CoV-2 and other viruses. We use a mathematical model of viral coinfection to study SARS-CoV-2 coinfections, finding that SARS-CoV-2 replication is easily suppressed by many common respiratory viruses. According to our model, this suppression is because SARS-CoV-2 has a lower growth rate (1.8/d) than the other viruses examined in this study. The suppression of SARS-CoV-2 by other pathogens could have implications for the timing and severity of a second wave.
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Affiliation(s)
- Lubna Pinky
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, Tennessee
| | - Hana M Dobrovolny
- Department of Physics & Astronomy, Texas Christian University, Fort Worth, Texas
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