Mathematical Modeling of Viral Infection

A topical collection in Viruses (ISSN 1999-4915). This collection belongs to the section "General Virology".

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Editors

Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
Interests: respiratory infectious diseases; host–pathogen interaction; multi-pathogen infections; model prediction validation; parameter estimation
Special Issues, Collections and Topics in MDPI journals
Los Alamos National Laboratory, Santa Fe, NM 87545, USA
Interests: multiscale modeling of viral and immune dynamics; treatment strategies for viral infections; HIV; HCV; influenza
Special Issues, Collections and Topics in MDPI journals

Topical Collection Information

Dear Colleagues,

The dynamics of virus infections are complex and multifaceted and include the mechanisms by which a virus replicates, spreads within a host, interacts with host immune responses and with other pathogens, and the extent to which the virus can be targeted by or employed as a therapeutic agent. Mathematical models are essential tools that provide a quantitative understanding of viral infections and their strategies, which are often difficult to dissect through only clinical or experimental approaches. The tight and improved integration of data within modeling frameworks has enhanced the predictive capability of these models and led to new experimental and clinical designs that have led to further elucidations of complex biological interplay. In addition, recent innovations in experimental biology and a new appreciation for multiparameter quantitative datasets have enhanced model quality and provided new questions ripe for mathematical approaches.

This Topical Collection is the continuation of our previous Special Issue, “Mathematical Modeling of Viral Infection”, we will go on showcase recent advancements in mathematical modeling of virus infections. We welcome submissions reporting any aspect of modeling virus infections.

Dr. Amber M. Smith
Dr. Ruian Ke
Collection Editors

Manuscript Submission Information

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Keywords

  • viral dynamics
  • host-pathogen interaction
  • immune response
  • coinfection
  • oncolytic virotherapy
  • antiviral therapy
  • multiscale modeling
  • spatial modeling

Related Special Issue

Published Papers (29 papers)

2023

Jump to: 2022, 2021, 2020

24 pages, 1414 KiB  
Article
Computational Modeling Insights into Extreme Heterogeneity in COVID-19 Nasal Swab Data
by Leyi Zhang, Han Cao, Karen Medlin, Jason Pearson, Andreas C. Aristotelous, Alexander Chen, Timothy Wessler and M. Gregory Forest
Viruses 2024, 16(1), 69; https://doi.org/10.3390/v16010069 - 30 Dec 2023
Viewed by 788
Abstract
Throughout the COVID-19 pandemic, an unprecedented level of clinical nasal swab data from around the globe has been collected and shared. Positive tests have consistently revealed viral titers spanning six orders of magnitude! An open question is whether such extreme population heterogeneity is [...] Read more.
Throughout the COVID-19 pandemic, an unprecedented level of clinical nasal swab data from around the globe has been collected and shared. Positive tests have consistently revealed viral titers spanning six orders of magnitude! An open question is whether such extreme population heterogeneity is unique to SARS-CoV-2 or possibly generic to viral respiratory infections. To probe this question, we turn to the computational modeling of nasal tract infections. Employing a physiologically faithful, spatially resolved, stochastic model of respiratory tract infection, we explore the statistical distribution of human nasal infections in the immediate 48 h of infection. The spread, or heterogeneity, of the distribution derives from variations in factors within the model that are unique to the infected host, infectious variant, and timing of the test. Hypothetical factors include: (1) reported physiological differences between infected individuals (nasal mucus thickness and clearance velocity); (2) differences in the kinetics of infection, replication, and shedding of viral RNA copies arising from the unique interactions between the host and viral variant; and (3) differences in the time between initial cell infection and the clinical test. Since positive clinical tests are often pre-symptomatic and independent of prior infection or vaccination status, in the model we assume immune evasion throughout the immediate 48 h of infection. Model simulations generate the mean statistical outcomes of total shed viral load and infected cells throughout 48 h for each “virtual individual”, which we define as each fixed set of model parameters (1) and (2) above. The “virtual population” and the statistical distribution of outcomes over the population are defined by collecting clinically and experimentally guided ranges for the full set of model parameters (1) and (2). This establishes a model-generated “virtual population database” of nasal viral titers throughout the initial 48 h of infection of every individual, which we then compare with clinical swab test data. Support for model efficacy comes from the sampling of infection dynamics over the virtual population database, which reproduces the six-order-of-magnitude clinical population heterogeneity. However, the goal of this study is to answer a deeper biological and clinical question. What is the impact on the dynamics of early nasal infection due to each individual physiological feature or virus–cell kinetic mechanism? To answer this question, global data analysis methods are applied to the virtual population database that sample across the entire database and de-correlate (i.e., isolate) the dynamic infection outcome sensitivities of each model parameter. These methods predict the dominant, indeed exponential, driver of population heterogeneity in dynamic infection outcomes is the latency time of infected cells (from the moment of infection until onset of viral RNA shedding). The shedding rate of the viral RNA of infected cells in the shedding phase is a strong, but not exponential, driver of infection. Furthermore, the unknown timing of the nasal swab test relative to the onset of infection is an equally dominant contributor to extreme population heterogeneity in clinical test data since infectious viral loads grow from undetectable levels to more than six orders of magnitude within 48 h. Full article
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15 pages, 977 KiB  
Article
Mathematical Modeling of Oncolytic Virus Therapy Reveals Role of the Immune Response
by Ela Guo and Hana M. Dobrovolny
Viruses 2023, 15(9), 1812; https://doi.org/10.3390/v15091812 - 25 Aug 2023
Cited by 1 | Viewed by 1296
Abstract
Oncolytic adenoviruses (OAds) present a promising path for cancer treatment due to their selectivity in infecting and lysing tumor cells and their ability to stimulate the immune response. In this study, we use an ordinary differential equation (ODE) model of tumor growth inhibited [...] Read more.
Oncolytic adenoviruses (OAds) present a promising path for cancer treatment due to their selectivity in infecting and lysing tumor cells and their ability to stimulate the immune response. In this study, we use an ordinary differential equation (ODE) model of tumor growth inhibited by oncolytic virus activity to parameterize previous research on the effect of genetically re-engineered OAds in A549 lung cancer tumors in murine models. We find that the data are best fit by a model that accounts for an immune response, and that the immune response provides a mechanism for elimination of the tumor. We also find that parameter estimates for the most effective OAds share characteristics, most notably a high infection rate and low viral clearance rate, that might be potential reasons for these viruses’ efficacy in delaying tumor growth. Further studies observing E1A and P19 recombined viruses in different tumor environments may further illuminate the extent of the effects of these genetic modifications. Full article
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24 pages, 10086 KiB  
Article
How Interactions during Viral–Viral Coinfection Can Shape Infection Kinetics
by Lubna Pinky, Joseph R. DeAguero, Christopher H. Remien and Amber M. Smith
Viruses 2023, 15(6), 1303; https://doi.org/10.3390/v15061303 - 31 May 2023
Cited by 4 | Viewed by 1545
Abstract
Respiratory viral infections are a leading global cause of disease with multiple viruses detected in 20–30% of cases, and several viruses simultaneously circulating. Some infections with unique viral copathogens result in reduced pathogenicity, while other viral pairings can worsen disease. The mechanisms driving [...] Read more.
Respiratory viral infections are a leading global cause of disease with multiple viruses detected in 20–30% of cases, and several viruses simultaneously circulating. Some infections with unique viral copathogens result in reduced pathogenicity, while other viral pairings can worsen disease. The mechanisms driving these dichotomous outcomes are likely variable and have only begun to be examined in the laboratory and clinic. To better understand viral–viral coinfections and predict potential mechanisms that result in distinct disease outcomes, we first systematically fit mathematical models to viral load data from ferrets infected with respiratory syncytial virus (RSV), followed by influenza A virus (IAV) after 3 days. The results suggest that IAV reduced the rate of RSV production, while RSV reduced the rate of IAV infected cell clearance. We then explored the realm of possible dynamics for scenarios that had not been examined experimentally, including a different infection order, coinfection timing, interaction mechanisms, and viral pairings. IAV coinfection with rhinovirus (RV) or SARS-CoV-2 (CoV2) was examined by using human viral load data from single infections together with murine weight-loss data from IAV-RV, RV-IAV, and IAV-CoV2 coinfections to guide the interpretation of the model results. Similar to the results with RSV-IAV coinfection, this analysis shows that the increased disease severity observed during murine IAV-RV or IAV-CoV2 coinfection was likely due to the slower clearance of IAV-infected cells by the other viruses. The improved outcome when IAV followed RV, on the other hand, could be replicated when the rate of RV infected cell clearance was reduced by IAV. Simulating viral–viral coinfections in this way provides new insights about how viral–viral interactions can regulate disease severity during coinfection and yields testable hypotheses ripe for experimental evaluation. Full article
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15 pages, 3258 KiB  
Article
Epidemiological Surveillance Reveals the Rise and Establishment of the Omicron SARS-CoV-2 Variant in Brazil
by Joice do Prado Silva, Aline Brito de Lima, Luige Biciati Alvim, Frederico Scott Varella Malta, Cristiane Pinheiro Toscano Brito Mendonça, André Henrique Barbosa de Carvalho, Jéssica Silqueira Hickson Rios, Paula Luize Camargos Fonseca, Daniel Costa Queiroz, Luíza Campos Guerra de Araújo e Santos, Alessandro Clayton de Souza Ferreira, Renan Pedra de Souza, Renato Santana de Aguiar and Danielle Alves Gomes Zauli
Viruses 2023, 15(4), 1017; https://doi.org/10.3390/v15041017 - 20 Apr 2023
Cited by 2 | Viewed by 1617
Abstract
The introduction of SARS-CoV-2 variants of concern (VOCs) in Brazil has been associated with major impacts on the epidemiological and public health scenario. In this study, 291,571 samples were investigated for SARS-CoV-2 variants from August 2021 to March 2022 (the highest peak of [...] Read more.
The introduction of SARS-CoV-2 variants of concern (VOCs) in Brazil has been associated with major impacts on the epidemiological and public health scenario. In this study, 291,571 samples were investigated for SARS-CoV-2 variants from August 2021 to March 2022 (the highest peak of positive cases) in four geographical regions of Brazil. To identify the frequency, introduction, and dispersion of SARS-CoV-2 variants in 12 Brazilian capitals, VOCs defining spike mutations were identified in 35,735 samples through genotyping and viral genome sequencing. Omicron VOC was detected in late November 2021 and replaced the Delta VOC in approximately 3.5 weeks. We estimated viral load differences between SARS-CoV-2 Delta and Omicron through the evaluation of the RT-qPCR cycle threshold (Ct) score in 77,262 samples. The analysis demonstrated that the Omicron VOC has a lower viral load in infected patients than the Delta VOC. Analyses of clinical outcomes in 17,586 patients across the country indicated that individuals infected with Omicron were less likely to need ventilatory support. The results of our study reinforce the importance of surveillance programs at the national level and showed the introduction and faster dispersion of Omicron over Delta VOC in Brazil without increasing the numbers of severe cases of COVID-19. Full article
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16 pages, 3265 KiB  
Article
Key Factors and Parameter Ranges for Immune Control of Equine Infectious Anemia Virus Infection
by Dylan Hull-Nye, Tyler Meadows, Stacey R. Smith? and Elissa J. Schwartz
Viruses 2023, 15(3), 691; https://doi.org/10.3390/v15030691 - 06 Mar 2023
Viewed by 1739
Abstract
Equine Infectious Anemia Virus (EIAV) is an important infection in equids, and its similarity to HIV creates hope for a potential vaccine. We analyze a within-host model of EIAV infection with antibody and cytotoxic T lymphocyte (CTL) responses. In this model, the stability [...] Read more.
Equine Infectious Anemia Virus (EIAV) is an important infection in equids, and its similarity to HIV creates hope for a potential vaccine. We analyze a within-host model of EIAV infection with antibody and cytotoxic T lymphocyte (CTL) responses. In this model, the stability of the biologically relevant endemic equilibrium, characterized by the coexistence of long-term antibody and CTL levels, relies upon a balance between CTL and antibody growth rates, which is needed to ensure persistent CTL levels. We determine the model parameter ranges at which CTL and antibody proliferation rates are simultaneously most influential in leading the system towards coexistence and can be used to derive a mathematical relationship between CTL and antibody production rates to explore the bifurcation curve that leads to coexistence. We employ Latin hypercube sampling and least squares to find the parameter ranges that equally divide the endemic and boundary equilibria. We then examine this relationship numerically via a local sensitivity analysis of the parameters. Our analysis is consistent with previous results showing that an intervention (such as a vaccine) intended to control a persistent viral infection with both immune responses should moderate the antibody response to allow for stimulation of the CTL response. Finally, we show that the CTL production rate can entirely determine the long-term outcome, regardless of the effect of other parameters, and we provide the conditions for this result in terms of the identified ranges for all model parameters. Full article
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19 pages, 1450 KiB  
Article
Identifying High-Risk Events for COVID-19 Transmission: Estimating the Risk of Clustering Using Nationwide Data
by Minami Ueda, Katsuma Hayashi and Hiroshi Nishiura
Viruses 2023, 15(2), 456; https://doi.org/10.3390/v15020456 - 06 Feb 2023
Viewed by 2428
Abstract
The transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is known to be overdispersed, meaning that only a fraction of infected cases contributes to super-spreading. While cluster interventions are an effective measure for controlling pandemics due to the viruses’ overdispersed nature, a [...] Read more.
The transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is known to be overdispersed, meaning that only a fraction of infected cases contributes to super-spreading. While cluster interventions are an effective measure for controlling pandemics due to the viruses’ overdispersed nature, a quantitative assessment of the risk of clustering has yet to be sufficiently presented. Using systematically collected cluster surveillance data for coronavirus disease 2019 (COVID-19) from June 2020 to June 2021 in Japan, we estimated the activity-dependent risk of clustering in 23 establishment types. The analysis indicated that elderly care facilities, welfare facilities for people with disabilities, and hospitals had the highest risk of clustering, with 4.65 (95% confidence interval [CI]: 4.43–4.87), 2.99 (2.59–3.46), and 2.00 (1.88–2.12) cluster reports per million event users, respectively. Risks in educational settings were higher overall among older age groups, potentially being affected by activities with close and uncontrollable contact during extracurricular hours. In dining settings, drinking and singing increased the risk by 10- to 70-fold compared with regular eating settings. The comprehensive analysis of the COVID-19 cluster records provides an additional scientific basis for the design of customized interventions. Full article
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23 pages, 5594 KiB  
Article
Stochastic Modelling of HIV-1 Replication in a CD4 T Cell with an IFN Response
by Igor Sazonov, Dmitry Grebennikov, Rostislav Savinkov, Arina Soboleva, Kirill Pavlishin, Andreas Meyerhans and Gennady Bocharov
Viruses 2023, 15(2), 296; https://doi.org/10.3390/v15020296 - 20 Jan 2023
Cited by 1 | Viewed by 1490
Abstract
A mathematical model of the human immunodeficiency virus Type 1 (HIV-1) life cycle in CD4 T cells was constructed and calibrated. It describes the activation of the intracellular Type I interferon (IFN-I) response and the IFN-induced suppression of viral replication. The model includes [...] Read more.
A mathematical model of the human immunodeficiency virus Type 1 (HIV-1) life cycle in CD4 T cells was constructed and calibrated. It describes the activation of the intracellular Type I interferon (IFN-I) response and the IFN-induced suppression of viral replication. The model includes viral replication inhibition by interferon-induced antiviral factors and their inactivation by the viral proteins Vpu and Vif. Both deterministic and stochastic model formulations are presented. The stochastic model was used to predict efficiency of IFN-I-induced suppression of viral replication in different initial conditions for autocrine and paracrine effects. The probability of virion excretion for various MOIs and various amounts of IFN-I was evaluated and the statistical properties of the heterogeneity of HIV-1 and IFN-I production characterised. Full article
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2022

Jump to: 2023, 2021, 2020

17 pages, 2465 KiB  
Article
In Silico Evaluation of Paxlovid’s Pharmacometrics for SARS-CoV-2: A Multiscale Approach
by Ferenc A. Bartha, Nóra Juhász, Sadegh Marzban, Renji Han and Gergely Röst
Viruses 2022, 14(5), 1103; https://doi.org/10.3390/v14051103 - 20 May 2022
Cited by 2 | Viewed by 2605
Abstract
Paxlovid is a promising, orally bioavailable novel drug for SARS-CoV-2 with excellent safety profiles. Our main goal here is to explore the pharmacometric features of this new antiviral. To provide a detailed assessment of Paxlovid, we propose a hybrid multiscale mathematical approach. We [...] Read more.
Paxlovid is a promising, orally bioavailable novel drug for SARS-CoV-2 with excellent safety profiles. Our main goal here is to explore the pharmacometric features of this new antiviral. To provide a detailed assessment of Paxlovid, we propose a hybrid multiscale mathematical approach. We demonstrate that the results of the present in silico evaluation match the clinical expectations remarkably well: on the one hand, our computations successfully replicate the outcome of an actual in vitro experiment; on the other hand, we verify both the sufficiency and the necessity of Paxlovid’s two main components (nirmatrelvir and ritonavir) for a simplified in vivo case. Moreover, in the simulated context of our computational framework, we visualize the importance of early interventions and identify the time window where a unit-length delay causes the highest level of tissue damage. Finally, the results’ sensitivity to the diffusion coefficient of the virus is explored in detail. Full article
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17 pages, 8999 KiB  
Article
Mathematical Modeling Finds Disparate Interferon Production Rates Drive Strain-Specific Immunodynamics during Deadly Influenza Infection
by Emily E. Ackerman, Jordan J. A. Weaver and Jason E. Shoemaker
Viruses 2022, 14(5), 906; https://doi.org/10.3390/v14050906 - 27 Apr 2022
Cited by 2 | Viewed by 7346
Abstract
The timing and magnitude of the immune response (i.e., the immunodynamics) associated with the early innate immune response to viral infection display distinct trends across influenza A virus subtypes in vivo. Evidence shows that the timing of the type-I interferon response and the [...] Read more.
The timing and magnitude of the immune response (i.e., the immunodynamics) associated with the early innate immune response to viral infection display distinct trends across influenza A virus subtypes in vivo. Evidence shows that the timing of the type-I interferon response and the overall magnitude of immune cell infiltration are both correlated with more severe outcomes. However, the mechanisms driving the distinct immunodynamics between infections of different virus strains (strain-specific immunodynamics) remain unclear. Here, computational modeling and strain-specific immunologic data are used to identify the immune interactions that differ in mice infected with low-pathogenic H1N1 or high-pathogenic H5N1 influenza viruses. Computational exploration of free parameters between strains suggests that the production rate of interferon is the major driver of strain-specific immune responses observed in vivo, and points towards the relationship between the viral load and lung epithelial interferon production as the main source of variance between infection outcomes. A greater understanding of the contributors to strain-specific immunodynamics can be utilized in future efforts aimed at treatment development to improve clinical outcomes of high-pathogenic viral strains. Full article
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81 pages, 116538 KiB  
Article
Multiscale Model of Antiviral Timing, Potency, and Heterogeneity Effects on an Epithelial Tissue Patch Infected by SARS-CoV-2
by Juliano Ferrari Gianlupi, Tarunendu Mapder, T. J. Sego, James P. Sluka, Sara K. Quinney, Morgan Craig, Robert E. Stratford, Jr. and James A. Glazier
Viruses 2022, 14(3), 605; https://doi.org/10.3390/v14030605 - 14 Mar 2022
Cited by 6 | Viewed by 3737
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 [...] Read more.
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. Full article
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22 pages, 6451 KiB  
Article
Modeling the Transmission of the SARS-CoV-2 Delta Variant in a Partially Vaccinated Population
by Ugo Avila-Ponce de León, Eric Avila-Vales and Kuan-lin Huang
Viruses 2022, 14(1), 158; https://doi.org/10.3390/v14010158 - 16 Jan 2022
Cited by 7 | Viewed by 2885
Abstract
In a population with ongoing vaccination, the trajectory of a pandemic is determined by how the virus spreads in unvaccinated and vaccinated individuals that exhibit distinct transmission dynamics based on different levels of natural and vaccine-induced immunity. We developed a mathematical model that [...] Read more.
In a population with ongoing vaccination, the trajectory of a pandemic is determined by how the virus spreads in unvaccinated and vaccinated individuals that exhibit distinct transmission dynamics based on different levels of natural and vaccine-induced immunity. We developed a mathematical model that considers both subpopulations and immunity parameters, including vaccination rates, vaccine effectiveness, and a gradual loss of protection. The model forecasted the spread of the SARS-CoV-2 delta variant in the US under varied transmission and vaccination rates. We further obtained the control reproduction number and conducted sensitivity analyses to determine how each parameter may affect virus transmission. Although our model has several limitations, the number of infected individuals was shown to be a magnitude greater (~10×) in the unvaccinated subpopulation compared to the vaccinated subpopulation. Our results show that a combination of strengthening vaccine-induced immunity and preventative behavioral measures like face mask-wearing and contact tracing will likely be required to deaccelerate the spread of infectious SARS-CoV-2 variants. Full article
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2021

Jump to: 2023, 2022, 2020

17 pages, 752 KiB  
Article
Modeling the Influence of Vaccine Administration on COVID-19 Testing Strategies
by Jonathan E. Forde and Stanca M. Ciupe
Viruses 2021, 13(12), 2546; https://doi.org/10.3390/v13122546 - 19 Dec 2021
Cited by 5 | Viewed by 2929
Abstract
Vaccination is considered the best strategy for limiting and eliminating the COVID-19 pandemic. The success of this strategy relies on the rate of vaccine deployment and acceptance across the globe. As these efforts are being conducted, the severe acute respiratory syndrome coronavirus 2 [...] Read more.
Vaccination is considered the best strategy for limiting and eliminating the COVID-19 pandemic. The success of this strategy relies on the rate of vaccine deployment and acceptance across the globe. As these efforts are being conducted, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is continuously mutating, which leads to the emergence of variants with increased transmissibility, virulence, and resistance to vaccines. One important question is whether surveillance testing is still needed in order to limit SARS-CoV-2 transmission in a vaccinated population. In this study, we developed a multi-scale mathematical model of SARS-CoV-2 transmission in a vaccinated population and used it to predict the role of testing in an outbreak with variants of increased transmissibility. We found that, for low transmissibility variants, testing was most effective when vaccination levels were low to moderate and its impact was diminished when vaccination levels were high. For high transmissibility variants, widespread vaccination was necessary in order for testing to have a significant impact on preventing outbreaks, with the impact of testing having maximum effects when focused on the non-vaccinated population. Full article
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15 pages, 693 KiB  
Article
Modelling Mutation in Equine Infectious Anemia Virus Infection Suggests a Path to Viral Clearance with Repeated Vaccination
by Elissa J. Schwartz, Christian Costris-Vas and Stacey R. Smith?
Viruses 2021, 13(12), 2450; https://doi.org/10.3390/v13122450 - 06 Dec 2021
Cited by 1 | Viewed by 2671
Abstract
Equine infectious anemia virus (EIAV) is a lentivirus similar to HIV that infects horses. Clinical and experimental studies demonstrating immune control of EIAV infection hold promise for efforts to produce an HIV vaccine. Antibody infusions have been shown to block both wild-type and [...] Read more.
Equine infectious anemia virus (EIAV) is a lentivirus similar to HIV that infects horses. Clinical and experimental studies demonstrating immune control of EIAV infection hold promise for efforts to produce an HIV vaccine. Antibody infusions have been shown to block both wild-type and mutant virus infection, but the mutant sometimes escapes. Using these data, we develop a mathematical model that describes the interactions between antibodies and both wild-type and mutant virus populations, in the context of continual virus mutation. The aim of this work is to determine whether repeated vaccinations through antibody infusions can reduce both the wild-type and mutant strains of the virus below one viral particle, and if so, to examine the vaccination period and number of infusions that ensure eradication. The antibody infusions are modelled using impulsive differential equations, a technique that offers insight into repeated vaccination by approximating the time-to-peak by an instantaneous change. We use impulsive theory to determine the maximal vaccination intervals that would be required to reduce the wild-type and mutant virus levels below one particle per horse. We show that seven boosts of the antibody vaccine are sufficient to eradicate both the wild-type and the mutant strains. In the case of a mutant virus infection that is given infusions of antibodies targeting wild-type virus (i.e., simulation of a heterologous infection), seven infusions were likewise sufficient to eradicate infection, based upon the data set. However, if the period between infusions was sufficiently increased, both the wild-type and mutant virus would eventually persist in the form of a periodic orbit. These results suggest a route forward to design antibody-based vaccine strategies to control viruses subject to mutant escape. Full article
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23 pages, 1856 KiB  
Article
Quantification of Type I Interferon Inhibition by Viral Proteins: Ebola Virus as a Case Study
by Macauley Locke, Grant Lythe, Martín López-García, César Muñoz-Fontela, Miles Carroll and Carmen Molina-París
Viruses 2021, 13(12), 2441; https://doi.org/10.3390/v13122441 - 04 Dec 2021
Cited by 1 | Viewed by 2541
Abstract
Type I interferons (IFNs) are cytokines with both antiviral properties and protective roles in innate immune responses to viral infection. They induce an antiviral cellular state and link innate and adaptive immune responses. Yet, viruses have evolved different strategies to inhibit such host [...] Read more.
Type I interferons (IFNs) are cytokines with both antiviral properties and protective roles in innate immune responses to viral infection. They induce an antiviral cellular state and link innate and adaptive immune responses. Yet, viruses have evolved different strategies to inhibit such host responses. One of them is the existence of viral proteins which subvert type I IFN responses to allow quick and successful viral replication, thus, sustaining the infection within a host. We propose mathematical models to characterise the intra-cellular mechanisms involved in viral protein antagonism of type I IFN responses, and compare three different molecular inhibition strategies. We study the Ebola viral protein, VP35, with this mathematical approach. Approximate Bayesian computation sequential Monte Carlo, together with experimental data and the mathematical models proposed, are used to perform model calibration, as well as model selection of the different hypotheses considered. Finally, we assess if model parameters are identifiable and discuss how such identifiability can be improved with new experimental data. Full article
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30 pages, 9123 KiB  
Article
Mathematical Modeling of Within-Host, Untreated, Cytomegalovirus Infection Dynamics after Allogeneic Transplantation
by Elizabeth R. Duke, Florencia A. T. Boshier, Michael Boeckh, Joshua T. Schiffer and E. Fabian Cardozo-Ojeda
Viruses 2021, 13(11), 2292; https://doi.org/10.3390/v13112292 - 16 Nov 2021
Cited by 4 | Viewed by 2745
Abstract
Cytomegalovirus (CMV) causes significant morbidity and mortality in recipients of allogeneic hematopoietic cell transplantation (HCT). Whereas insights gained from mathematical modeling of other chronic viral infections such as HIV, hepatitis C, and herpes simplex virus-2 have aided in optimizing therapy, previous CMV modeling [...] Read more.
Cytomegalovirus (CMV) causes significant morbidity and mortality in recipients of allogeneic hematopoietic cell transplantation (HCT). Whereas insights gained from mathematical modeling of other chronic viral infections such as HIV, hepatitis C, and herpes simplex virus-2 have aided in optimizing therapy, previous CMV modeling has been hindered by a lack of comprehensive quantitative PCR viral load data from untreated episodes of viremia in HCT recipients. We performed quantitative CMV DNA PCR on stored, frozen serum samples from the placebo group of participants in a historic randomized controlled trial of ganciclovir for the early treatment of CMV infection in bone marrow transplant recipients. We developed four main ordinary differential Equation mathematical models and used model selection theory to choose between 38 competing versions of these models. Models were fit using a population, nonlinear, mixed-effects approach. We found that CMV kinetics from untreated HCT recipients are highly variable. The models that recapitulated the observed patterns most parsimoniously included explicit, dynamic immune cell compartments and did not include dynamic target cell compartments, consistent with the large number of tissue and cell types that CMV infects. In addition, in our best-fitting models, viral clearance was extremely slow, suggesting severe impairment of the immune response after HCT. Parameters from our best model correlated well with participants’ clinical risk factors and outcomes from the trial, further validating our model. Our models suggest that CMV dynamics in HCT recipients are determined by host immune response rather than target cell limitation in the absence of antiviral treatment. Full article
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19 pages, 2299 KiB  
Article
Mathematical Modeling of Vaccines That Prevent SARS-CoV-2 Transmission
by David A. Swan, Ashish Goyal, Chloe Bracis, Mia Moore, Elizabeth Krantz, Elizabeth Brown, Fabian Cardozo-Ojeda, Daniel B. Reeves, Fei Gao, Peter B. Gilbert, Lawrence Corey, Myron S. Cohen, Holly Janes, Dobromir Dimitrov and Joshua T. Schiffer
Viruses 2021, 13(10), 1921; https://doi.org/10.3390/v13101921 - 24 Sep 2021
Cited by 7 | Viewed by 3372
Abstract
SARS-CoV-2 vaccine clinical trials assess efficacy against disease (VEDIS), the ability to block symptomatic COVID-19. They only partially discriminate whether VEDIS is mediated by preventing infection completely, which is defined as detection of virus in the airways (VESUSC), [...] Read more.
SARS-CoV-2 vaccine clinical trials assess efficacy against disease (VEDIS), the ability to block symptomatic COVID-19. They only partially discriminate whether VEDIS is mediated by preventing infection completely, which is defined as detection of virus in the airways (VESUSC), or by preventing symptoms despite infection (VESYMP). Vaccine efficacy against transmissibility given infection (VEINF), the decrease in secondary transmissions from infected vaccine recipients, is also not measured. Using mathematical modeling of data from King County Washington, we demonstrate that if the Moderna (mRNA-1273QS) and Pfizer-BioNTech (BNT162b2) vaccines, which demonstrated VEDIS > 90% in clinical trials, mediate VEDIS by VESUSC, then a limited fourth epidemic wave of infections with the highly infectious B.1.1.7 variant would have been predicted in spring 2021 assuming rapid vaccine roll out. If high VEDIS is explained by VESYMP, then high VEINF would have also been necessary to limit the extent of this fourth wave. Vaccines which completely protect against infection or secondary transmission also substantially lower the number of people who must be vaccinated before the herd immunity threshold is reached. The limited extent of the fourth wave suggests that the vaccines have either high VESUSC or both high VESYMP and high VEINF against B.1.1.7. Finally, using a separate intra-host mathematical model of viral kinetics, we demonstrate that a 0.6 log vaccine-mediated reduction in average peak viral load might be sufficient to achieve 50% VEINF, which suggests that human challenge studies with a relatively low number of infected participants could be employed to estimate all three vaccine efficacy metrics. Full article
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19 pages, 2994 KiB  
Article
Personalized Virus Load Curves for Acute Viral Infections
by Carlos Contreras, Jay M. Newby and Thomas Hillen
Viruses 2021, 13(9), 1815; https://doi.org/10.3390/v13091815 - 13 Sep 2021
Cited by 6 | Viewed by 2433
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 [...] Read more.
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. Full article
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18 pages, 633 KiB  
Article
Intracellular Life Cycle Kinetics of SARS-CoV-2 Predicted Using Mathematical Modelling
by Dmitry Grebennikov, Ekaterina Kholodareva, Igor Sazonov, Antonina Karsonova, Andreas Meyerhans and Gennady Bocharov
Viruses 2021, 13(9), 1735; https://doi.org/10.3390/v13091735 - 31 Aug 2021
Cited by 12 | Viewed by 5835
Abstract
SARS-CoV-2 infection represents a global threat to human health. Various approaches were employed to reveal the pathogenetic mechanisms of COVID-19. Mathematical and computational modelling is a powerful tool to describe and analyze the infection dynamics in relation to a plethora of processes contributing [...] Read more.
SARS-CoV-2 infection represents a global threat to human health. Various approaches were employed to reveal the pathogenetic mechanisms of COVID-19. Mathematical and computational modelling is a powerful tool to describe and analyze the infection dynamics in relation to a plethora of processes contributing to the observed disease phenotypes. In our study here, we formulate and calibrate a deterministic model of the SARS-CoV-2 life cycle. It provides a kinetic description of the major replication stages of SARS-CoV-2. Sensitivity analysis of the net viral progeny with respect to model parameters enables the identification of the life cycle stages that have the strongest impact on viral replication. These three most influential parameters are (i) degradation rate of positive sense vRNAs in cytoplasm (negative effect), (ii) threshold number of non-structural proteins enhancing vRNA transcription (negative effect), and (iii) translation rate of non-structural proteins (positive effect). The results of our analysis could be used for guiding the search for antiviral drug targets to combat SARS-CoV-2 infection. Full article
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18 pages, 4629 KiB  
Article
Modeling Within-Host Dynamics of SARS-CoV-2 Infection: A Case Study in Ferrets
by Naveen K. Vaidya, Angelica Bloomquist and Alan S. Perelson
Viruses 2021, 13(8), 1635; https://doi.org/10.3390/v13081635 - 18 Aug 2021
Cited by 18 | Viewed by 3543
Abstract
The pre-clinical development of antiviral agents involves experimental trials in animals and ferrets as an animal model for the study of SARS-CoV-2. Here, we used mathematical models and experimental data to characterize the within-host infection dynamics of SARS-CoV-2 in ferrets. We also performed [...] Read more.
The pre-clinical development of antiviral agents involves experimental trials in animals and ferrets as an animal model for the study of SARS-CoV-2. Here, we used mathematical models and experimental data to characterize the within-host infection dynamics of SARS-CoV-2 in ferrets. We also performed a global sensitivity analysis of model parameters impacting the characteristics of the viral infection. We provide estimates of the viral dynamic parameters in ferrets, such as the infection rate, the virus production rate, the infectious virus proportion, the infected cell death rate, the virus clearance rate, as well as other related characteristics, including the basic reproduction number, pre-peak infectious viral growth rate, post-peak infectious viral decay rate, pre-peak infectious viral doubling time, post-peak infectious virus half-life, and the target cell loss in the respiratory tract. These parameters and indices are not significantly different between animals infected with viral strains isolated from the environment and isolated from human hosts, indicating a potential for transmission from fomites. While the infection period in ferrets is relatively short, the similarity observed between our results and previous results in humans supports that ferrets can be an appropriate animal model for SARS-CoV-2 dynamics-related studies, and our estimates provide helpful information for such studies. Full article
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25 pages, 4944 KiB  
Article
Comparative Computational Modeling of the Bat and Human Immune Response to Viral Infection with the Comparative Biology Immune Agent Based Model
by Chase Cockrell and Gary An
Viruses 2021, 13(8), 1620; https://doi.org/10.3390/v13081620 - 16 Aug 2021
Cited by 8 | Viewed by 2802
Abstract
Given the impact of pandemics due to viruses of bat origin, there is increasing interest in comparative investigation into the differences between bat and human immune responses. The practice of comparative biology can be enhanced by computational methods used for dynamic knowledge representation [...] Read more.
Given the impact of pandemics due to viruses of bat origin, there is increasing interest in comparative investigation into the differences between bat and human immune responses. The practice of comparative biology can be enhanced by computational methods used for dynamic knowledge representation to visualize and interrogate the putative differences between the two systems. We present an agent based model that encompasses and bridges differences between bat and human responses to viral infection: the comparative biology immune agent based model, or CBIABM. The CBIABM examines differences in innate immune mechanisms between bats and humans, specifically regarding inflammasome activity and type 1 interferon dynamics, in terms of tolerance to viral infection. Simulation experiments with the CBIABM demonstrate the efficacy of bat-related features in conferring viral tolerance and also suggest a crucial role for endothelial inflammasome activity as a mechanism for bat systemic viral tolerance and affecting the severity of disease in human viral infections. We hope that this initial study will inspire additional comparative modeling projects to link, compare, and contrast immunological functions shared across different species, and in so doing, provide insight and aid in preparation for future viral pandemics of zoonotic origin. Full article
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16 pages, 1462 KiB  
Article
Mathematical Modelling of the Molecular Mechanisms of Interaction of Tenofovir with Emtricitabine against HIV
by Sara Iannuzzi and Max von Kleist
Viruses 2021, 13(7), 1354; https://doi.org/10.3390/v13071354 - 13 Jul 2021
Cited by 3 | Viewed by 2535
Abstract
The combination of the two nucleoside reverse transcriptase inhibitors (NRTI) tenofovir disoproxil fumarate (TDF) and emtricitabine (FTC) is used in most highly active antiretroviral therapies for treatment of HIV-1 infection, as well as in pre-exposure prophylaxis against HIV acquisition. Administered as prodrugs, these [...] Read more.
The combination of the two nucleoside reverse transcriptase inhibitors (NRTI) tenofovir disoproxil fumarate (TDF) and emtricitabine (FTC) is used in most highly active antiretroviral therapies for treatment of HIV-1 infection, as well as in pre-exposure prophylaxis against HIV acquisition. Administered as prodrugs, these drugs are taken up by HIV-infected target cells, undergo intracellular phosphorylation and compete with natural deoxynucleoside triphosphates (dNTP) for incorporation into nascent viral DNA during reverse transcription. Once incorporated, they halt reverse transcription. In vitro studies have proposed that TDF and FTC act synergistically within an HIV-infected cell. However, it is unclear whether, and which, direct drug–drug interactions mediate the apparent synergy. The goal of this work was to refine a mechanistic model for the molecular mechanism of action (MMOA) of nucleoside analogues in order to analyse whether putative direct interactions may account for the in vitro observed synergistic effects. Our analysis suggests that depletion of dNTP pools can explain apparent synergy between TDF and FTC in HIV-infected cells at clinically relevant concentrations. Dead-end complex (DEC) formation does not seem to significantly contribute to the synergistic effect. However, in the presence of non-nucleoside reverse transcriptase inhibitors (NNRTIs), its role might be more relevant, as previously reported in experimental in vitro studies. Full article
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16 pages, 1565 KiB  
Article
Fitness Estimation for Viral Variants in the Context of Cellular Coinfection
by Huisheng Zhu, Brent E. Allman and Katia Koelle
Viruses 2021, 13(7), 1216; https://doi.org/10.3390/v13071216 - 23 Jun 2021
Viewed by 2214
Abstract
Animal models are frequently used to characterize the within-host dynamics of emerging zoonotic viruses. More recent studies have also deep-sequenced longitudinal viral samples originating from experimental challenges to gain a better understanding of how these viruses may evolve in vivo and between transmission [...] Read more.
Animal models are frequently used to characterize the within-host dynamics of emerging zoonotic viruses. More recent studies have also deep-sequenced longitudinal viral samples originating from experimental challenges to gain a better understanding of how these viruses may evolve in vivo and between transmission events. These studies have often identified nucleotide variants that can replicate more efficiently within hosts and also transmit more effectively between hosts. Quantifying the degree to which a mutation impacts viral fitness within a host can improve identification of variants that are of particular epidemiological concern and our ability to anticipate viral adaptation at the population level. While methods have been developed to quantify the fitness effects of mutations using observed changes in allele frequencies over the course of a host’s infection, none of the existing methods account for the possibility of cellular coinfection. Here, we develop mathematical models to project variant allele frequency changes in the context of cellular coinfection and, further, integrate these models with statistical inference approaches to demonstrate how variant fitness can be estimated alongside cellular multiplicity of infection. We apply our approaches to empirical longitudinally sampled H5N1 sequence data from ferrets. Our results indicate that previous studies may have significantly underestimated the within-host fitness advantage of viral variants. These findings underscore the importance of considering the process of cellular coinfection when studying within-host viral evolutionary dynamics. Full article
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14 pages, 5411 KiB  
Article
Structural Analysis of the Novel Variants of SARS-CoV-2 and Forecasting in North America
by Elena Quinonez, Majid Vahed, Abdolrazagh Hashemi Shahraki and Mehdi Mirsaeidi
Viruses 2021, 13(5), 930; https://doi.org/10.3390/v13050930 - 17 May 2021
Cited by 13 | Viewed by 4390
Abstract
Background: little is known about the forecasting of new variants of SARS-COV-2 in North America and the interaction of variants with vaccine-derived neutralizing antibodies. Methods: the affinity scores of the spike receptor-binding domain (S-RBD) of B.1.1.7, B. 1.351, B.1.617, and P.1 variants in [...] Read more.
Background: little is known about the forecasting of new variants of SARS-COV-2 in North America and the interaction of variants with vaccine-derived neutralizing antibodies. Methods: the affinity scores of the spike receptor-binding domain (S-RBD) of B.1.1.7, B. 1.351, B.1.617, and P.1 variants in interaction with the neutralizing antibody (CV30 isolated from a patient), and human angiotensin-converting enzyme 2 (hACE2) receptor were predicted using the template-based computational modeling. From the Nextstrain global database, we identified prevalent mutations of S-RBD of SARS-CoV-2 from December 2019 to April 2021. Pre- and post-vaccination time series forecasting models were developed based on the prediction of neutralizing antibody affinity scores for S-RBD of the variants. Results: the proportion of the B.1.1.7 variant in North America is growing rapidly, but the rate will reduce due to high affinity (~90%) to the neutralizing antibody once herd immunity is reached. Currently, the rates of isolation of B. 1.351, B.1.617, and P.1 variants are slowly increasing in North America. Herd immunity is able to relatively control these variants due to their low affinity (~70%) to the neutralizing antibody. The S-RBD of B.1.617 has a 110% increased affinity score to the human angiotensin-converting enzyme 2 (hACE2) in comparison to the wild-type structure, making it highly infectious. Conclusion: The newly emerged B.1.351, B.1.617, and P.1 variants escape from vaccine-induced neutralizing immunity and continue circulating in North America in post- herd immunity era. Our study strongly suggests that a third dose of vaccine is urgently needed to cover novel variants with affinity scores (equal or less than 70%) to eliminate developing viral mutations and reduce transmission rates. Full article
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23 pages, 2308 KiB  
Article
Will SARS-CoV-2 Become Just Another Seasonal Coronavirus?
by Alexander B. Beams, Rebecca Bateman and Frederick R. Adler
Viruses 2021, 13(5), 854; https://doi.org/10.3390/v13050854 - 07 May 2021
Cited by 9 | Viewed by 9553
Abstract
The future prevalence and virulence of SARS-CoV-2 is uncertain. Some emerging pathogens become avirulent as populations approach herd immunity. Although not all viruses follow this path, the fact that the seasonal coronaviruses are benign gives some hope. We develop a general mathematical model [...] Read more.
The future prevalence and virulence of SARS-CoV-2 is uncertain. Some emerging pathogens become avirulent as populations approach herd immunity. Although not all viruses follow this path, the fact that the seasonal coronaviruses are benign gives some hope. We develop a general mathematical model to predict when the interplay among three factors, correlation of severity in consecutive infections, population heterogeneity in susceptibility due to age, and reduced severity due to partial immunity, will promote avirulence as SARS-CoV-2 becomes endemic. Each of these components has the potential to limit severe, high-shedding cases over time under the right circumstances, but in combination they can rapidly reduce the frequency of more severe and infectious manifestation of disease over a wide range of conditions. As more reinfections are captured in data over the next several years, these models will help to test if COVID-19 severity is beginning to attenuate in the ways our model predicts, and to predict the disease. Full article
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14 pages, 475 KiB  
Article
Modelling the Effect of MUC1 on Influenza Virus Infection Kinetics and Macrophage Dynamics
by Ke Li, Pengxing Cao and James M. McCaw
Viruses 2021, 13(5), 850; https://doi.org/10.3390/v13050850 - 07 May 2021
Cited by 4 | Viewed by 2423
Abstract
MUC1 belongs to the family of cell surface (cs-) mucins. Experimental evidence indicates that its presence reduces in vivo influenza viral infection severity. However, the mechanisms by which MUC1 influences viral dynamics and the host immune response are not yet well understood, limiting [...] Read more.
MUC1 belongs to the family of cell surface (cs-) mucins. Experimental evidence indicates that its presence reduces in vivo influenza viral infection severity. However, the mechanisms by which MUC1 influences viral dynamics and the host immune response are not yet well understood, limiting our ability to predict the efficacy of potential treatments that target MUC1. To address this limitation, we use available in vivo kinetic data for both virus and macrophage populations in wildtype and MUC1 knockout mice. We apply two mathematical models of within-host influenza dynamics to this data. The models differ in how they categorise the mechanisms of viral control. Both models provide evidence that MUC1 reduces the susceptibility of epithelial cells to influenza virus and regulates macrophage recruitment. Furthermore, we predict and compare some key infection-related quantities between the two mice groups. We find that MUC1 significantly reduces the basic reproduction number of viral replication as well as the number of cumulative macrophages but has little impact on the cumulative viral load. Our analyses suggest that the viral replication rate in the early stages of infection influences the kinetics of the host immune response, with consequences for infection outcomes, such as severity. We also show that MUC1 plays a strong anti-inflammatory role in the regulation of the host immune response. This study improves our understanding of the dynamic role of MUC1 against influenza infection and may support the development of novel antiviral treatments and immunomodulators that target MUC1. Full article
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15 pages, 1702 KiB  
Article
Endogenously Produced SARS-CoV-2 Specific IgG Antibodies May Have a Limited Impact on Clearing Nasal Shedding of Virus during Primary Infection in Humans
by Shuyi Yang, Keith R. Jerome, Alexander L. Greninger, Joshua T. Schiffer and Ashish Goyal
Viruses 2021, 13(3), 516; https://doi.org/10.3390/v13030516 - 20 Mar 2021
Cited by 4 | Viewed by 3156
Abstract
While SARS-CoV-2 specific neutralizing antibodies have been developed for therapeutic purposes, the specific viral triggers that drive the generation of SARS-CoV-2 specific IgG and IgM antibodies remain only partially characterized. Moreover, it is unknown whether endogenously derived antibodies drive viral clearance that might [...] Read more.
While SARS-CoV-2 specific neutralizing antibodies have been developed for therapeutic purposes, the specific viral triggers that drive the generation of SARS-CoV-2 specific IgG and IgM antibodies remain only partially characterized. Moreover, it is unknown whether endogenously derived antibodies drive viral clearance that might result in mitigation of clinical severity during natural infection. We developed a series of non-linear mathematical models to investigate whether SARS-CoV-2 viral and antibody kinetics are coupled or governed by separate processes. Patients with severe disease had a higher production rate of IgG but not IgM antibodies. Maximal levels of both isotypes were governed by their production rate rather than different saturation levels between people. Our results suggest that an exponential surge in IgG levels occurs approximately 5–10 days after symptom onset with no requirement for continual antigenic stimulation. SARS-CoV-2 specific IgG antibodies appear to have limited to no effect on viral dynamics but may enhance viral clearance late during primary infection resulting from the binding effect of antibody to virus, rather than neutralization. In conclusion, SARS-CoV-2 specific IgG antibodies may play only a limited role in clearing infection from the nasal passages despite providing long-term immunity against infection following vaccination or prior infection. Full article
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18 pages, 1179 KiB  
Article
Quantification of the Tradeoff between Test Sensitivity and Test Frequency in a COVID-19 Epidemic—A Multi-Scale Modeling Approach
by Jonathan E. Forde and Stanca M. Ciupe
Viruses 2021, 13(3), 457; https://doi.org/10.3390/v13030457 - 11 Mar 2021
Cited by 15 | Viewed by 3194
Abstract
Control strategies that employ real time polymerase chain reaction (RT-PCR) tests for the diagnosis and surveillance of COVID-19 epidemic are inefficient in fighting the epidemic due to high cost, delays in obtaining results, and the need of specialized personnel and equipment for laboratory [...] Read more.
Control strategies that employ real time polymerase chain reaction (RT-PCR) tests for the diagnosis and surveillance of COVID-19 epidemic are inefficient in fighting the epidemic due to high cost, delays in obtaining results, and the need of specialized personnel and equipment for laboratory processing. Cheaper and faster alternatives, such as antigen and paper-strip tests, have been proposed. They return results rapidly, but have lower sensitivity thresholds for detecting virus. To quantify the effects of the tradeoffs between sensitivity, cost, testing frequency, and delay in test return on the overall course of an outbreak, we built a multi-scale immuno-epidemiological model that connects the virus profile of infected individuals with transmission and testing at the population level. We investigated various randomized testing strategies and found that, for fixed testing capacity, lower sensitivity tests with shorter return delays slightly flatten the daily incidence curve and delay the time to the peak daily incidence. However, compared with RT-PCR testing, they do not always reduce the cumulative case count at half a year into the outbreak. When testing frequency is increased to account for the lower cost of less sensitive tests, we observe a large reduction in cumulative case counts, from 55.4% to as low as 1.22% half a year into the outbreak. The improvement is preserved even when the testing budget is reduced by one half or one third. Our results predict that surveillance testing that employs low-sensitivity tests at high frequency is an effective tool for epidemic control. Full article
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2020

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20 pages, 1998 KiB  
Article
Modeling the Molecular Impact of SARS-CoV-2 Infection on the Renin-Angiotensin System
by Fabrizio Pucci, Philippe Bogaerts and Marianne Rooman
Viruses 2020, 12(12), 1367; https://doi.org/10.3390/v12121367 - 30 Nov 2020
Cited by 12 | Viewed by 2820
Abstract
SARS-CoV-2 infection is mediated by the binding of its spike protein to the angiotensin-converting enzyme 2 (ACE2), which plays a pivotal role in the renin-angiotensin system (RAS). The study of RAS dysregulation due to SARS-CoV-2 infection is fundamentally important for a better understanding [...] Read more.
SARS-CoV-2 infection is mediated by the binding of its spike protein to the angiotensin-converting enzyme 2 (ACE2), which plays a pivotal role in the renin-angiotensin system (RAS). The study of RAS dysregulation due to SARS-CoV-2 infection is fundamentally important for a better understanding of the pathogenic mechanisms and risk factors associated with COVID-19 coronavirus disease and to design effective therapeutic strategies. In this context, we developed a mathematical model of RAS based on data regarding protein and peptide concentrations; the model was tested on clinical data from healthy normotensive and hypertensive individuals. We used our model to analyze the impact of SARS-CoV-2 infection on RAS, which we modeled through a downregulation of ACE2 as a function of viral load. We also used it to predict the effect of RAS-targeting drugs, such as RAS-blockers, human recombinant ACE2, and angiotensin 1–7 peptide, on COVID-19 patients; the model predicted an improvement of the clinical outcome for some drugs and a worsening for others. Our model and its predictions constitute a valuable framework for in silico testing of hypotheses about the COVID-19 pathogenic mechanisms and the effect of drugs aiming to restore RAS functionality. Full article
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20 pages, 4903 KiB  
Article
Convolutional Neural Network Based Approach to In Silico Non-Anticipating Prediction of Antigenic Distance for Influenza Virus
by Majid Forghani and Michael Khachay
Viruses 2020, 12(9), 1019; https://doi.org/10.3390/v12091019 - 12 Sep 2020
Cited by 10 | Viewed by 3574
Abstract
Evaluation of the antigenic similarity degree between the strains of the influenza virus is highly important for vaccine production. The conventional method used to measure such a degree is related to performing the immunological assays of hemagglutinin inhibition. Namely, the antigenic distance between [...] Read more.
Evaluation of the antigenic similarity degree between the strains of the influenza virus is highly important for vaccine production. The conventional method used to measure such a degree is related to performing the immunological assays of hemagglutinin inhibition. Namely, the antigenic distance between two strains is calculated on the basis of HI assays. Usually, such distances are visualized by using some kind of antigenic cartography method. The known drawback of the HI assay is that it is rather time-consuming and expensive. In this paper, we propose a novel approach for antigenic distance approximation based on deep learning in the feature spaces induced by hemagglutinin protein sequences and Convolutional Neural Networks (CNNs). To apply a CNN to compare the protein sequences, we utilize the encoding based on the physical and chemical characteristics of amino acids. By varying (hyper)parameters of the CNN architecture design, we find the most robust network. Further, we provide insight into the relationship between approximated antigenic distance and antigenicity by evaluating the network on the HI assay database for the H1N1 subtype. The results indicate that the best-trained network gives a high-precision approximation for the ground-truth antigenic distances, and can be used as a good exploratory tool in practical tasks. Full article
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