In silico identification of viral loads in cough-generated droplets - Seamless integrated analysis of CFPD-HCD-EWF

BACKGROUND AND OBJECTIVE

Respiratory diseases caused by respiratory viruses have significantly threatened public health worldwide. This study presents a comprehensive approach to predict viral dynamics and the generation of stripped droplets within the mucus layer of the respiratory tract during coughing using a larynx-trachea-bifurcation (LTB) model.

METHODS

This study integrates computational fluid-particle dynamics (CFPD), host-cell dynamics (HCD), and the Eulerian wall film (EWF) model to propose a potential means for seamless integrated analysis. The verified CFPD-HCD coupling model based on a 3D-shell model was used to characterize the severe acute respiratory syndrome, coronavirus 2 (SARS-CoV-2) dynamics in the LTB mucus layer, whereas the EWF model was employed to account for the interfacial fluid to explore the generation mechanism and trace the origin site of droplets exhaled during a coughing event of an infected host.

RESULTS

The results obtained using CFPD delineated the preferential deposition sites for droplets in the laryngeal and tracheal regions. Thus, the analysis of the HCD model showed that the viral load increased rapidly in the laryngeal region during the peak of infection, whereas there was a growth delay in the tracheal region (up to day 8 after infection). After two weeks of infection, the high viral load gradually migrated towards the glottic region. Interestingly, the EWF model demonstrated a high concentration of exhaled droplets originating from the larynx. The coupling technique indicated a concurrent high viral load in the mucus layer and site of origin of the exhaled droplets.

CONCLUSIONS

This interdisciplinary research underscores the seamless analysis from initial exposure to virus-laden droplets, the dynamics of viral infection in the LTB mucus layer, and the re-emission from the coughing activities of an infected host. Our efforts aimed to address the complex challenges at the intersection of viral dynamics and respiratory health, which can contribute to a more detailed understanding and targeted prevention of respiratory diseases.

Revisiting the observability and identifiability properties of a popular HIV model

This paper revisits the observability and identifiability properties of a popular ODE model commonly adopted to characterize the HIV dynamics in HIV-infected patients with antiretroviral treatment. These properties are determined by using the general analytical solution of the unknown input observability problem, introduced very recently in Martinelli (2022). This solution provides the systematic procedures able to determine the state observability and the parameter identifiability of any ODE model, in particular, even in the presence of time varying parameters. Four variants of the HIV model are investigated. They differ because some of their parameters are considered constant or time varying. Fundamental new properties, which also highlight an error in the scientific literature, are automatically determined and discussed. Additionally, for each variant, the paper provides a quantitative answer to the following practical question: What is the minimal external information (external to the available measurements of the system outputs) required to make observable the state and identifiable all the model parameters? The answer to this fundamental question is obtained by exploiting the concept of continuous symmetry, recently introduced in Martinelli (2019). This concept allows us to determine a first preliminary general result which is then applied to the HIV model. Finally, for each variant, the paper concludes by providing a redefinition of the state and of the parameters in order to obtain a full description of the system only in terms of a state which is observable and a set of parameters which are identifiable (both constant and time varying).

Mathematical Modeling of Virus-Mediated Syncytia Formation: Past Successes and Future Directions

Many viruses have the ability to cause cells to fuse into large multi-nucleated cells, known as syncytia. While the existence of syncytia has long been known and its importance in helping spread viral infection within a host has been understood, few mathematical models have incorporated syncytia formation or examined its role in viral dynamics. This review examines mathematical models that have incorporated virus-mediated cell fusion and the insights they have provided on how syncytia can change the time course of an infection. While the modeling efforts are limited, they show promise in helping us understand the consequences of syncytia formation if future modeling efforts can be coupled with appropriate experimental efforts to help validate the models.

Epidemic dynamics with time-varying transmission risk reveal the role of disease stage-dependent infectiousness

A key characteristic of acute communicable diseases is the infectiousness that varies over time as the infection dynamics evolve within a host, which influences the risk of transmission in different stages of the disease. Despite the evidence of time-varying transmission risk, most dynamic models of epidemics assume a constant transmission rate during the infectious period. Recent work has shown the difference in epidemic dynamics when this assumption is relaxed and different transmission rates are used by discretizing the infectious period into multiple sub-periods. Here, we develop an age-structured model to integrate a continuous time-varying transmission risk, based on an established correlation between the viral dynamics and infectiousness profile. Taking into account the natural history and parameter estimates of COVID-19 caused by the original strain of SARS-CoV-2, we demonstrate the difference in temporal epidemic dynamics when a continuous time-varying transmission probability is used as compared to multiple constant transmission probabilities. Our results show a significant difference between the incidence curves in terms of the magnitude and peak time, even when the reproduction number and total number of infections are the same for continuous and discrete transmission probabilities. Finally, we demonstrate the spurious outcome of preventing an epidemic through the isolation of infectious individuals when constant transmission probabilities are used, highlighting the importance of integrating a continuous time-dependent transmission parameter in dynamic models. These findings suggest a more cautious interpretation of model outcomes, especially those that are intended to evaluate the effectiveness of interventions and inform policy decisions for disease mitigation strategies.

Choice of spatial discretisation influences the progression of viral infection within multicellular tissues

There has been an increasing recognition of the utility of models of the spatial dynamics of viral spread within tissues. Multicellular models, where cells are represented as discrete regions of space coupled to a virus density surface, are a popular approach to capture these dynamics. Conventionally, such models are simulated by discretising the viral surface and depending on the rate of viral diffusion and other considerations, a finer or coarser discretisation may be used. The impact that this choice may have on the behaviour of the system has not been studied. Here we demonstrate that under realistic parameter regimes - where viral diffusion is small enough to support the formation of familiar ring-shaped infection plaques - the choice of spatial discretisation of the viral surface can qualitatively change key model outcomes including the time scale of infection. Importantly, we show that the choice between implementing viral spread as a cell-scale process, or as a high-resolution converged PDE can generate distinct model outcomes, which raises important conceptual questions about the strength of assumptions underpinning the spatial structure of the model. We investigate the mechanisms driving these discretisation artefacts, the impacts they may have on model predictions, and provide guidance on the design and implementation of spatial and especially multicellular models of viral dynamics. We obtain our results using the simplest TIV construct for the viral dynamics, and therefore anticipate that the important effects we describe will also influence model predictions in more complex models of virus-cell-immune system interactions. This analysis will aid in the construction of models for robust and biologically realistic modelling and inference.

Temporal Pattern of the Emergence of a Mutant Virus Escaping Cross-Immunity and Stochastic Extinction Within a Host

A high mutation rate of the RNA virus results in the emergence of novel mutants that may escape the immunity activated by the original (wild-type) strain. However, many of them go extinct because of the stochasticity due to the small initial number of infected cells. In a previous paper, we studied the probability of escaping stochastic extinction when the novel mutant has a faster rate of infection and when it is resistant to a drug that suppresses the wild-type virus. In this study, we examine the effect of escaping the immune reaction of the host. Based on a continuous-time branching process with time-dependent rates, we conclude the chance for a mutant strain to be established [Formula: see text] decreases with time [Formula: see text] since the wild-type infection when the mutant is produced. The number of novel mutants that can escape extinction risk has a peak soon after the wild-type infection. The number of novel escape mutations produced per patient in the early phase of host infection is small both for very strong and very weak immune responses, and it attains its maximum value when immune activity is of an intermediate strength.

Visual prediction and parameter optimization of viral dynamics in the mucus milieu of the upper airway based on CFPD-HCD analysis

BACKGROUND AND OBJECTIVE

Respiratory diseases caused by viruses are a major human health problem. To better control the infection and understand the pathogenesis of these diseases, this paper studied SARS-CoV-2, a novel coronavirus outbreak, as an example.

METHODS

Based on coupled computational fluid and particle dynamics (CFPD) and host-cell dynamics (HCD) analyses, we studied the viral dynamics in the mucus layer of the human nasal cavity-nasopharynx. To reproduce the effect of mucociliary movement on the diffusive and convective transport of viruses in the mucus layer, a 3D-shell model was constructed using CT data of the upper respiratory tract (URT) of volunteers. Considering the mucus environment, the HCD model was established by coupling the target cell-limited model with the convection-diffusion term. Parameter optimization of the HCD model is the key problem in the simulation. Therefore, this study focused on the parameter optimization of the viral dynamics model, divided the geometric model into multiple compartments, and used Monolix to perform the nonlinear mixed effects (NLME) of pharmacometrics to discuss the influence of factors such as the number of mucus layers, number of compartments, diffusion rate, and mucus flow velocity on the prediction results.

RESULTS

The findings showed that sufficient experimental data can be used to estimate the corresponding parameters of the HCD model. The optimized convection-diffusion case with a two-layer multi-compartment low-velocity model could accurately predict the viral dynamics.

CONCLUSIONS

Its visualization process could explain the symptoms of the disease in the nose and contribute to the prevention and targeted treatment of respiratory diseases.

A target-cell limited model can reproduce influenza infection dynamics in hosts with differing immune responses

We consider a hierarchy of ordinary differential equation models that describe the within-host viral kinetics of influenza infections: the IR model explicitly accounts for an immune response to the virus, while the simpler, target-cell limited TEIV and TV models do not. We show that when the IR model is fitted to pooled experimental murine data of the viral load, fraction of dead cells, and immune response levels, its parameters values can be determined. However, if, as is common, only viral load data are available, we can estimate parameters of the TEIV and TV models but not the IR model. These results are substantiated by a structural and practical identifiability analysis. We then use the IR model to generate synthetic data representing infections in hosts whose immune responses differ. We fit the TV model to these synthetic datasets and show that it can reproduce the characteristic exponential increase and decay of viral load generated by the IR model. Furthermore, the values of the fitted parameters of the TV model can be mapped from the immune response parameters in the IR model. We conclude that, if only viral load data are available, a simple target-cell limited model can reproduce influenza infection dynamics and distinguish between hosts with differing immune responses.

Coupling the Within-Host Process and Between-Host Transmission of COVID-19 Suggests Vaccination and School Closures are Critical

Most models of COVID-19 are implemented at a single micro or macro scale, ignoring the interplay between immune response, viral dynamics, individual infectiousness and epidemiological contact networks. Here we develop a data-driven model linking the within-host viral dynamics to the between-host transmission dynamics on a multilayer contact network to investigate the potential factors driving transmission dynamics and to inform how school closures and antiviral treatment can influence the epidemic. Using multi-source data, we initially determine the viral dynamics and estimate the relationship between viral load and infectiousness. Then, we embed the viral dynamics model into a four-layer contact network and formulate an agent-based model to simulate between-host transmission. The results illustrate that the heterogeneity of immune response between children and adults and between vaccinated and unvaccinated infections can produce different transmission patterns. We find that school closures play a significant effect on mitigating the pandemic as more adults get vaccinated and the virus mutates. If enough infected individuals are diagnosed by testing before symptom onset and then treated quickly, the transmission can be effectively curbed. Our multiscale model reveals the critical role played by younger individuals and antiviral treatment with testing in controlling the epidemic.

SARS-CoV-2 rate of spread in and across tissue, groundwater and soil: A meshless algorithm for the fractional diffusion equation

The epidemiological aspects of the viral dynamic of the SARS-CoV-2 have become increasingly crucial due to major questions and uncertainties around the unaddressed issues of how corpse burial or the disposal of contaminated waste impacts nearby soil and groundwater. Here, a theoretical framework base on a meshless algorithm using the moving least squares (MLS) shape functions is adopted for solving the time-fractional model of the viral diffusion in and across three different environments including water, tissue, and soil. Our computations predict that by considering the α (order of fractional derivative) best fit to experimental data, the virus has a traveling distance of 1 m m in water after 22, regardless of the source of contamination (e.g., from tissue or soil). The outcomes and extrapolations of our study are fundamental for providing valuable benchmarks for future experimentation on this topic and ultimately for the accurate description of viral spread across different environments. In addition to COVID-19 relief efforts, our methodology can be adapted for a wide range of applications such as studying virus ecology and genomic reservoirs in freshwater and marine environments.

Machine learning for mathematical models of HCV kinetics during antiviral therapy

Mathematical models for hepatitis C virus (HCV) dynamics have provided a means for evaluating the antiviral effectiveness of therapy and estimating treatment outcomes such as the time to cure. Recently, a mathematical modeling approach was used in the first proof-of-concept clinical trial assessing in real-time the utility of response-guided therapy with direct-acting antivirals (DAAs) in chronic HCV-infected patients. Several retrospective studies have shown that mathematical modeling of viral kinetics predicts time to cure of less than 12 weeks in the majority of individuals treated with sofosbuvir-based as well as other DAA regimens. A database of these studies was built, and machine learning methods were evaluated for their ability to estimate the time to cure for each patient to facilitate real-time modeling studies. Data from these studies exploring mathematical modeling of HCV kinetics under DAAs in 266 chronic HCV-infected patients were gathered. Different learning methods were applied and trained on part of the dataset ('train' set), to predict time to cure on the untrained part ('test' set). Our results show that this machine learning approach provides a means for establishing an accurate time to cure prediction that will support the implementation of individualized treatment.

Mathematical modelling of SARS-CoV-2 infection of human and animal host cells reveals differences in the infection rates and delays in viral particle production by infected cells

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV -2), a causative agent of COVID-19 disease, poses a significant threat to public health. Since its outbreak in December 2019, Wuhan, China, extensive collection of diverse data from cell culture and animal infections as well as population level data from an ongoing pandemic, has been vital in assessing strategies to battle its spread. Mathematical modelling plays a key role in quantifying determinants that drive virus infection dynamics, especially those relevant for epidemiological investigations and predictions as well as for proposing efficient mitigation strategies. We utilized a simple mathematical model to describe and explain experimental results on viral replication cycle kinetics during SARS-CoV-2 infection of animal and human derived cell lines, green monkey kidney cells, Vero-E6, and human lung epithelium cells, A549-ACE2, respectively. We conducted cell infections using two distinct initial viral concentrations and quantified viral loads over time. We then fitted the model to our experimental data and quantified the viral parameters. We showed that such cellular tropism generates significant differences in the infection rates and incubation times of SARS-CoV-2, that is, the times to the first release of newly synthesised viral progeny by SARS-CoV-2-infected cells. Specifically, the rate at which A549-ACE2 cells were infected by SARS-CoV-2 was 15 times lower than that in the case of Vero-E6 cell infection and the duration of latent phase of A549-ACE2 cells was 1.6 times longer than that of Vero-E6 cells. On the other hand, we found no statistically significant differences in other viral parameters, such as viral production rate or infected cell death rate. Since in vitro infection assays represent the first stage in the development of antiviral treatments against SARS-CoV-2, discrepancies in the viral parameter values across different cell hosts have to be identified and quantified to better target vaccine and antiviral research.

Virologic features of SARS-CoV-2 infection in children

BACKGROUND

Data on pediatric COVID-19 has lagged behind adults throughout the pandemic. An understanding of SARS-CoV-2 viral dynamics in children would enable data-driven public health guidance.

METHODS

Respiratory swabs were collected from children with COVID-19. Viral load was quantified by RT-PCR; viral culture was assessed by direct observation of cytopathic effects and semiquantitative viral titers. Correlations with age, symptom duration, and disease severity were analyzed. SARS-CoV-2 whole genome sequences were compared with contemporaneous sequences.

RESULTS

110 children with COVID-19 (median age 10 years, range 2 weeks-21 years) were included in this study. Age did not impact SARS-CoV-2 viral load. Children were most infectious within the first five days of illness, and severe disease did not correlate with increased viral loads. Pediatric SARS-CoV-2 sequences were representative of those in the community and novel variants were identified.

CONCLUSIONS

Symptomatic and asymptomatic children can carry high quantities of live, replicating SARS-CoV-2, creating a potential reservoir for transmission and evolution of genetic variants. As guidance around social distancing and masking evolves following vaccine uptake in older populations, a clear understanding of SARS-CoV-2 infection dynamics in children is critical for rational development of public health policies and vaccination strategies to mitigate the impact of COVID-19.

A quantitative systems pharmacology model for acute viral hepatitis B

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Mechanistic model characterizing acute immune response and HBV system interactions.

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Key role of the cellular and regulatory response triggering hepatitis B chronicity.

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Modelling framework to easily incorporate and explore additional biological mechanisms.

Hepatitis B liver infection is caused by hepatitis B virus (HBV) and represents a major global disease problem when it becomes chronic, as is the case for 80–90% of vertical or early life infections. However, in the vast majority (>95%) of adult exposures, the infected individuals are capable of mounting an effective immune response leading to infection resolution. A good understanding of HBV dynamics and the interaction between the virus and immune system during acute infection represents an essential step to characterize and understand the key biological processes involved in disease resolution, which may help to identify potential interventions to prevent chronic hepatitis B.

In this work, a quantitative systems pharmacology model for acute hepatitis B characterizing viral dynamics and the main components of the innate, adaptive, and tolerant immune response has been successfully developed. To do so, information from multiple sources and across different organization levels has been integrated in a common mechanistic framework. The final model adequately describes the chronology and plausibility of an HBV-triggered immune response, as well as clinical data from acute patients reported in the literature. Given the holistic nature of the framework, the model can be used to illustrate the relevance of the different immune pathways and biological processes to ultimate response, observing the negligible contribution of the innate response and the key contribution of the cellular response on viral clearance. More specifically, moderate reductions of the proliferation of activated cytotoxic CD8+ lymphocytes or increased immunoregulatory effects can drive the system towards chronicity.

Virologic features of SARS-CoV-2 infection in children

BACKGROUND

Data on pediatric COVID-19 has lagged behind adults throughout the pandemic. An understanding of SARS-CoV-2 viral dynamics in children would enable data-driven public health guidance.

METHODS

Respiratory swabs were collected from children with COVID-19. Viral load was quantified by RT-PCR; viral culture was assessed by direct observation of cytopathic effects and semiquantitative viral titers. Correlations with age, symptom duration, and disease severity were analyzed. SARS-CoV-2 whole genome sequences were compared with contemporaneous sequences.

RESULTS

110 children with COVID-19 (median age 10 years, range 2 weeks-21 years) were included in this study. Age did not impact SARS-CoV-2 viral load. Children were most infectious within the first five days of illness, and severe disease did not correlate with increased viral loads. Pediatric SARS-CoV-2 sequences were representative of those in the community and novel variants were identified.

CONCLUSIONS

Symptomatic and asymptomatic children can carry high quantities of live, replicating SARS-CoV-2, creating a potential reservoir for transmission and evolution of genetic variants. As guidance around social distancing and masking evolves following vaccine uptake in older populations, a clear understanding of SARS-CoV-2 infection dynamics in children is critical for rational development of public health policies and vaccination strategies to mitigate the impact of COVID-19.

A diffusive virus model with a fixed intracellular delay and combined drug treatments

The method of administration of an effective drug treatment to eradicate viruses within a host is an important issue in studying viral dynamics. Overuse of a drug can lead to deleterious side effects, and overestimating the efficacy of a drug can result in failure to treat infection. In this study, we proposed a reaction-diffusion within-host virus model which incorporated information regarding spatial heterogeneity, drug treatment, and the intracellular delay to produce productively infected cells and viruses. We also calculated the basic reproduction number [Formula: see text] under the assumptions of spatial heterogeneity. We have shown that the infection-free periodic solution is globally asymptotically stable when [Formula: see text], whereas when [Formula: see text] there is an infected periodic solution and the infection is uniformly persistent. By conducting numerical simulations, we also revealed the effects of various parameters on the value of [Formula: see text]. First, we showed that the dependence of [Formula: see text] on the intracellular delay could be monotone or non-monotone, depending on the death rate of infected cells in the immature stage. Second, we found that the mobility of infected cells or virions could facilitate the virus clearance. Third, we found that the spatial fragmentation of the virus environment enhanced viral infection. Finally, we showed that the combination of spatial heterogeneity and different sets of diffusion rates resulted in complicated viral dynamic outcomes.

Effects of Waning Maternal Immunity on Infection Dynamics in Seasonally Breeding Wildlife

Increasing outbreaks of emerging infectious diseases originating from wildlife have intensified interests in understanding their dynamics in reservoir hosts. The effect of waning maternally derived antibodies on epidemics in a seasonally breeding wild mammal population is unclear. We examined how the population structure, influenced by seasonal breeding and maternally derived immunity, affects viral invasion and persistence using a hypothetical system based on Hendra virus infection in black flying foxes (Pteropus alecto). A deterministic Hendra virus epidemic model with uncertainty in parameter values was used to simulate transient epidemics following viral introduction into an infection-free population, including various timings within a year and differences in pre-existing seroprevalence. Additionally, we applied different modelling methods of waning maternal immunity to examine whether different models notably affected modelling outputs. The waning of maternally derived immunity temporally dispersed the supply of susceptible individuals in seasonally breeding populations, diminishing the effect of birth pulses generating the temporally synchronised supply of susceptible newborns. Thus, even in a population with seasonal births, a considerable level of probabilities of viral invasion and persistence could occur no matter when infectious individuals were introduced into the population. Viral invasion and persistence were substantially influenced by the modelling method of maternally derived immunity, emphasising the need to select an appropriate method and further investigate the waning pattern of maternally derived antibodies.

Modeling the Effects of Latency Reversing Drugs During HIV-1 and SIV Brain Infection with Implications for the "Shock and Kill" Strategy

Combination antiretroviral therapy (cART) has greatly increased life expectancy for human immunodeficiency virus-1 (HIV-1)-infected patients. Even given the remarkable success of cART, the virus persists in many different cells and tissues. The presence of viral reservoirs represents a major obstacle to HIV-1 eradication. These viral reservoirs contain latently infected long-lived cells. The "Shock and Kill" therapeutic strategy aims to reactivate latently infected cells by latency reversing agents (LRAs) and kill these reactivated cells by strategies involving the host immune system. The brain is a natural anatomical reservoir for HIV-1 infection. Brain macrophages, including microglia and perivascular macrophages, display productive HIV-1 infection. A mathematical model was used to analyze the dynamics of latently and productively infected brain macrophages during viral infection and this mathematical model enabled prediction of the effects of LRAs applied to the "Shock and Kill" strategy in the brain. The model was calibrated using reported data from simian immunodeficiency virus (SIV) studies. Our model produces the overarching observation that effective cART can suppress productively infected brain macrophages but leaves a residual latent viral reservoir in brain macrophages. In addition, our model demonstrates that there exists a parameter regime wherein the "Shock and Kill" strategy can be safe and effective for SIV infection in the brain. The results indicate that the "Shock and Kill" strategy can restrict brain viral RNA burden associated with severe neuroinflammation and can lead to the eradication of the latent reservoir of brain macrophages.

Quantifying mechanistic traits of influenza viral dynamics using in vitro data

When analysing in vitro data, growth kinetics of influenza virus strains are often compared by computing their growth rates, which are sometimes used as proxies for fitness. However, analogous to mathematical models for epidemics, the growth rate can be defined as a function of mechanistic traits: the basic reproduction number (the average number of cells each infected cell infects) and the mean generation time (the average length of a replication cycle). Fitting a model to previously published and newly generated data from experiments in human lung cells, we compared estimates of growth rate, reproduction number and generation time for six influenza A strains. Of four strains in previously published data, A/Canada/RV733/2003 (seasonal H1N1) had the lowest basic reproduction number, followed by A/Mexico/INDRE4487/2009 (pandemic H1N1), then A/Indonesia/05/2005 (spill-over H5N1) and A/Anhui/1/2013 (spill-over H7N9). This ordering of strains was preserved for both generation time and growth rate, suggesting a positive biological correlation between these quantities which have not been previously observed. We further investigated these potential correlations using data from reassortant viruses with different internal proteins (from A/England/195/2009 (pandemic H1N1) and A/Turkey/05/2005 (H5N1)), and the same surface proteins (from A/Puerto Rico/8/34 (lab-adapted H1N1)). Similar correlations between traits were observed for these viruses, confirming our initial findings and suggesting that these patterns were related to the degree of human adaptation of internal genes. Also, the model predicted that strains with a smaller basic reproduction number, shorter generation time and slower growth rate underwent more replication cycles by the time of peak viral load, potentially accumulating mutations more quickly. These results illustrate the utility of mathematical models in inferring traits driving observed differences in in vitro growth of influenza strains.

Bovine leukemia virus detection and dynamics following experimental inoculation

Bovine leukemia virus (BLV) infects more than 40% of the United States cattle population and impacts animal health and production. Control programs aiming to reduce disease prevalence and incidence depend on the ability to detect the BLV provirus, anti-BLV antibodies, and differences in blood lymphocyte counts following infection. These disease parameters also can be indicative of long-term disease progression. The objectives of this study were to determine the timing and to describe early fluctuations of BLV-detection by qPCR, ELISA, and lymphocyte counts. Fifteen Holstein steers were experimentally inoculated with 100 μL of a blood saline inoculum. Three steers served as in-pen negative controls and were housed with the experimentally infected steers to observe the potential for contract transmission. Five additional negative controls were housed separately. Steers were followed for 147 days post-inoculation (DPI). Infections were detected in experimentally infected steers by qPCR and ELISA an average of 24- and 36 DPI, respectively. Significant differences in lymphocyte counts between experimentally infected and control steers were observed from 30 to 45 DPI. Furthermore, a wide variation in peak proviral load and establishment was observed between experimentally infected steers. The results of this study can be used to inform control programs focused on the detection and removal of infectious cattle.

Modelling within-host macrophage dynamics in influenza virus infection

Human respiratory disease associated with influenza virus infection is of significant public health concern. Macrophages, as part of the front line of host innate cellular defence, have been shown to play an important role in controlling viral replication. However, fatal outcomes of infection, as evidenced in patients infected with highly pathogenic viral strains, are often associated with prompt activation and excessive accumulation of macrophages. Activated macrophages can produce a large amount of pro-inflammatory cytokines, which leads to severe symptoms and at times death. However, the mechanism for rapid activation and excessive accumulation of macrophages during infection remains unclear. It has been suggested that the phenomena may arise from complex interactions between macrophages and influenza virus. In this work, we develop a novel mathematical model to study the relationship between the level of macrophage activation and the level of viral load in influenza infection. Our model combines a dynamic model of viral infection, a dynamic model of macrophages and the essential interactions between the virus and macrophages. Our model predicts that the level of macrophage activation can be negatively correlated with the level of viral load when viral infectivity is sufficiently high. We further identify that temporary depletion of resting macrophages in response to viral infection is a major driver in our model for the negative relationship between the level of macrophage activation and viral load, providing new insight into the mechanisms that regulate macrophage activation. Our model serves as a framework to study the complex dynamics of virus-macrophage interactions and provides a mechanistic explanation for existing experimental observations, contributing to an enhanced understanding of the role of macrophages in influenza viral infection.

Spatial and temporal dynamics of SARS-CoV-2 in COVID-19 patients: A systematic review and meta-analysis

BACKGROUND

The spatial and temporal dynamics of SARS-CoV-2 have been described in case series and retrospective studies. In this study, we provide a coherent overview of the duration of viral detection and viral RNA load in COVID-19 patients, stratified by specimen type, clinical severity, and age.

METHOD

We systematically searched PubMed/MEDLINE and Cochrane review database for studies published between 1.11.2019 and 23.04.2020. We pooled the data of selected studies (22/7226 (650 patients) for meta-analysis) to estimate duration of viral detection and visualized viral load over time.

FINDINGS

Our analysis showed consistent viral detection from specimen from the upper respiratory tract (URT), the lower respiratory tract (LRT), and faeces, irrespective of the clinical severity of COVID-19. Our analysis suggests that SARS-CoV-2 persists for a longer duration in the LRT compared to the URT in adult patients (5•7 days in mild; 5•9 days in moderate-severe patients). The differences in the duration of viral detection between mild and moderate-severe patients is limited in the LRT, but an indication of longer duration of viral detection for moderate-severe patients was observed in feces (15 days in mild vs. 21 days in moderate-severe patients) and the URT (12 days in mild vs. 16 days in moderate-severe patients). Further, viral load was demonstrated to peak in earlier stages of infection in the URT compared to LRT.

INTERPRETATION

This review may aid mathematical modelling and help in defining appropriate endpoints for clinical trails with antivirals in COVID-19.

FUNDING

The project has received funding support from Innovation Fund Denmark.

Comparison of different cytomegalovirus diseases following haploidentical hematopoietic stem cell transplantation

Cytomegalovirus (CMV) can cause end-organ diseases including pneumonia, gastroenteritis, retinitis, and encephalitis in hematopoietic stem cell transplantation recipients. Potential differences among different CMV diseases remain uncertain. This study aimed to compare the clinical characteristics, risk factors, and mortality among different CMV diseases. A retrospective nested case-control study was performed based on a cohort of 3862 patients who underwent haploidentical hematopoietic stem cell transplantation at a single-center. CMV diseases occurred in 113 (2.92%) of 3862 haplo-HSCT recipients, including probable CMV pneumonia (CMVP, n = 34), proven CMV gastroenteritis (CMVG, n = 34), CMV retinitis (CMVR, n = 31), probable CMV encephalitis (CMVE, n = 7), and disseminated CMV disease (Di-CMVD, n = 7). Most (91.2%) cases of CMVG developed within 100 days, while most (90.3%) cases of CMVR were late onset. Refractory CMV infection and CMV viral load at different levels were associated with an increased risk of CMVP, CMVG, and CMVR. Compared with patients without CMV diseases, significantly higher non-relapse mortality at 1 year after transplantation was observed in patients with CMVP and CMVR, rather than CMVG. Patients with CMVP, Di-CMVD, and CMVE had higher overall mortality after diagnosis than that of patients with CMVG and CMVR (61.7%, 57.1%, 40.0% vs 27.7%, 18.6%, P = 0.001). In conclusion, the onset time, viral dynamics, and mortality differ among different CMV diseases. The mortality of CMV diseases remains high, especially for CMVP, Di-CMVD, and CMVE.

COVID-19: From bench to bed side

BACKGROUND AND AIMS

The last two decades have experienced the outbreaks of three different coronaviruses in the different parts of the world namely; Severe acute respiratory syndrome cornonavirus-1 (SARS-CoV-1), Middle East respiratory syndrome (MERS-CoV) and Severe acute respiratory syndrome cornonavirus-2 (SARS-CoV-2). We aimed to delineate the differences in viral dynamics and clinical features between them and tried to focus on every basic details of SARS-COV-2 (COVID-19) that every health care provider must know.

METHODS

We systematically searched the PubMed database up till April 2, 2020 and retrieved all the articles published on SARS-CoV-2, SARS-CoV-1, MERS-CoV that dealt with viral dynamics.

RESULTS

Ample data is available to suggest the differences in etiology, transmission cycle, diagnosis, genetics, hosts, reproductive rates, clinical features, laboratory diagnosis and radiological features between SARS-CoV-1, MERS-CoV and SARS-CoV-2.

CONCLUSION

Although SARS-CoV-2 (COVID-19) is more infectious than SARS-CoV-1 and MERS-CoV, most infections are generally mild and self-limiting. However, case-fatality rates are very high in patients with COVID-19 with comorbidities, compared to SARS-CoV-1 and MERS-CoV.

Varying Inoculum Dose to Assess the Roles of the Immune Response and Target Cell Depletion by the Pathogen in Control of Acute Viral Infections

It is difficult to determine whether an immune response or target cell depletion by the infectious agent is most responsible for the control of acute primary infection. Both mechanisms can explain the basic dynamics of an acute infection-exponential growth of the pathogen followed by control and clearance-and can also be represented by many different differential equation models. Consequently, traditional model comparison techniques using time series data can be ambiguous or inconclusive. We propose that varying the inoculum dose and measuring the subsequent infectious load can rule out target cell depletion by the pathogen as the main control mechanism. Infectious load can be any measure that is proportional to the number of infected cells, such as viraemia. We show that a twofold or greater change in infectious load is unlikely when target cell depletion controls infection, regardless of the model details. Analyzing previously published data from mice infected with influenza, we find the proportion of lung epithelial cells infected was 21-fold greater (95% confidence interval 14-32) in the highest dose group than in the lowest. This provides evidence in favor of an alternative to target cell depletion, such as innate immunity, in controlling influenza infections in this experimental system. Data from other experimental animal models of acute primary infection have a similar pattern.

Modelling the Host Immune Response to Mature and Immature Dengue Viruses

Immature dengue virions contained in patient blood samples are essentially not infectious because the uncleaved surface protein prM renders them incompetent for membrane fusion. However, the immature virions regain full infectivity when they interact with anti-prM antibodies, and once opsonised virion fusion into Fc receptor-expressing cells is facilitated. We propose a within-host mathematical model for the immune response which takes into account the dichotomy between mature infectious and immature noninfectious dengue virions. The model accounts for experimental observations on the different interactions of plasmacytoid dendritic cells with infected cells producing virions with different infectivity. We compute the basic reproduction number as a function of the proportion of infected cells producing noninfectious virions and use numerical simulations to compare the host's immune response in a primary and a secondary dengue infections. The results can be placed in the immunoregulatory framework with plasmacytoid dendritic cells serving as a bridge between the innate and adaptive immune response, and pose questions for potential experimental work to validate hypothesis about the evolutionary context whereby the virus strives to maximise its chance for transmission from the human host to the mosquito vector.

Mathematical Analysis of a Transformed ODE from a PDE Multiscale Model of Hepatitis C Virus Infection

Mathematical modeling has revealed the quantitative dynamics during the process of viral infection and evolved into an important tool in modern virology. Coupled with analyses of clinical and experimental data, the widely used basic model of viral dynamics described by ordinary differential equations (ODEs) has been well parameterized. In recent years, age-structured models, called "multiscale model," formulated by partial differential equations (PDEs) have also been developed and become useful tools for more detailed data analysis. However, in general, PDE models are considerably more difficult to subject to mathematical and numerical analyses. In our recently reported study, we successfully derived a mathematically identical ODE model from a PDE model, which helps to overcome the limitations of the PDE model with regard to clinical data analysis. Here, we derive the basic reproduction number from the identical ODE model and provide insight into the global stability of all possible steady states of the ODE model.

Immune response and within-host viral evolution: Immune response can accelerate evolution

The objectives of this paper are to explore the impact of immune response on within-host viral evolution towards higher Darwinian fitness and, in particular, to verify a hypothesis that immune response, which is insufficient to annihilate a viral infection, can accelerate this evolution. To address this issue, a model of within-host viral evolution with immune response is formulated. This model is an extension of a continuous phenotype space model of viral evolution that was earlier suggested by A. Korobeinikov and C. Dempsey, which incorporates strain-specific immune response with cross-immunity. The model is based upon Nowak-May and Wodarz models of within-host HIV dynamics and is mechanistic (based upon first principles); this allows straightforward interpretation of the model's parameters and simulation results, as well as its further developments. In order to make the simulation results and conclusions robust and reliable and to ensure that they do not depend on the particularities of an immune response model, four different mathematical models of cell-mediated immune response are considered with the proposed model. Simulations confirmed that immune response, when it is unable to eliminate viruses, accelerates viral evolution.

Clinical course and pathogenicity of variant rabbit haemorrhagic disease virus in experimentally infected adult and kit rabbits: Significance towards control and spread

RHDVb has become the dominant RHDV on the Iberian Peninsula. A better understanding of its pathogenicity is required to aid control measures. Thus, the clinical course, humoral immune response, viraemia and kinetics of RHDV-N11 (a Spanish RHDVb isolate) infection in different tissues at both viral RNA and protein levels were studied in experimentally infected young and adult rabbits. The case fatality rate differed between the two age groups, with 21% of kits succumbing while no deaths were observed in adults. Fever and viremia were strongly associated with death, which occurred 48 h post infection (PI) too fast for an effective humoral immune response to be mounted. A significant effect on the number of viral RNA copies with regard to the variables age, tissue and time PI (p < 0.0001 in all cases) was detected. Histological lesions in infected rabbits were consistently more frequent and severe in liver and spleen and additionally intestine in kits, these tissues containing the highest levels of viral RNA and protein. Although no adults showed lesions or virus antigen in intestine, both kits and adults maintained steady viral RNA levels from days 1 to 7 PI in this organ. Analysis revealed the fecal route as the main dissemination route of RHDV-N11. Subclinically infected rabbits had detectable viral RNA in their faeces for up to seven days and thus may play an important role spreading the virus. This study allows a better understanding of the transmission of this virus and improvement of the control strategies for this disease.

Modeling brain lentiviral infections during antiretroviral therapy in AIDS

Understanding HIV-1 replication and latency in different reservoirs is an ongoing challenge in the care of patients with HIV/AIDS. A mathematical model was created to describe and predict the viral dynamics of HIV-1 and SIV infection within the brain during effective combination antiretroviral therapy (cART). The mathematical model was formulated based on the biology of lentiviral infection of brain macrophages and used to describe the dynamics of transmission and progression of lentiviral infection in brain. Based on previous reports quantifying total viral DNA levels in brain from HIV-1 and SIV infections, estimates of integrated proviral DNA burden were made, which were used to calibrate the mathematical model predicting viral accrual in brain macrophages from primary infection. The annual rate at which susceptible brain macrophages become HIV-1 infected was estimated to be 2.90×10^-7-4.87×10^-6 per year for cART-treated HIV/AIDS patients without comorbid neurological disorders. The transmission rate for SIV infection among untreated macaques was estimated to be 5.30×10^-6-1.37×10^-5 per year. An improvement in cART effectiveness (1.6-48%) would suppress HIV-1 infection in patients without neurological disorders. Among patients with advanced disease, a substantial improvement in cART effectiveness (70%) would eradicate HIV-1 provirus from the brain within 3-32 (interquartile range 3-9) years in patients without neurological disorders, whereas 4-51 (interquartile range 4-16) years of efficacious cART would be required for HIV/AIDS patients with comorbid neurological disorders. HIV-1 and SIV provirus burdens in the brain increase over time. A moderately efficacious antiretroviral therapy regimen could eradicate HIV-1 infection in the brain that was dependent on brain macrophage lifespan and the presence of neurological comorbidity.

Modelling cross-reactivity and memory in the cellular adaptive immune response to influenza infection in the host

The cellular adaptive immune response plays a key role in resolving influenza infection. Experiments where individuals are successively infected with different strains within a short timeframe provide insight into the underlying viral dynamics and the role of a cross-reactive immune response in resolving an acute infection. We construct a mathematical model of within-host influenza viral dynamics including three possible factors which determine the strength of the cross-reactive cellular adaptive immune response: the initial naive T cell number, the avidity of the interaction between T cells and the epitopes presented by infected cells, and the epitope abundance per infected cell. Our model explains the experimentally observed shortening of a second infection when cross-reactivity is present, and shows that memory in the cellular adaptive immune response is necessary to protect against a second infection.

On the extinction probability in models of within-host infection: the role of latency and immunity

Not every exposure to virus establishes infection in the host; instead, the small amount of initial virus could become extinct due to stochastic events. Different diseases and routes of transmission have a different average number of exposures required to establish an infection. Furthermore, the host immune response and antiviral treatment affect not only the time course of the viral load provided infection occurs, but can prevent infection altogether by increasing the extinction probability. We show that the extinction probability when there is a time-dependent immune response depends on the chosen form of the model-specifically, on the presence or absence of a delay between infection of a cell and production of virus, and the distribution of latent and infectious periods of an infected cell. We hypothesise that experimentally measuring the extinction probability when the virus is introduced at different stages of the immune response, alongside the viral load which is usually measured, will improve parameter estimates and determine the most suitable mathematical form of the model.

Variability in long-term hepatitis B virus dynamics under antiviral therapy

Hepatitis B virus (HBV) dynamics in treated patients can be complex and differ considerably from other viral infections. We analyse dynamics of liver and serum levels of HBV DNA in 24 chronically HBV-infected individuals undergoing 1 year of combination therapy with pegylated interferon alpha and adefovir dipivoxil (ADV), followed by 2 years of ADV monotherapy. Serum viral dynamics differentiated the patients into four response groups dependent on how quickly viremia became undetectable: quickly suppressed (HBV DNA <100 copies/ml within 8 weeks and staying suppressed, GRP1); quickly suppressed but some rebound (<10,000 copies/ml, GRP2); slow decay (GRP3); virological failures (>10,000 copies/ml, GRP4). These groups did not differ before start of therapy by serum HBV DNA (p=0.2), HBsAg (p=0.1), ALT (p=0.4), total HBV DNA within the liver (p=0.08), or cccDNA (p=0.3). Despite very different serum HBV DNA levels after 3 years, there was no statistical difference in total HBV DNA within the liver (p=0.08), nor in cccDNA levels (p=0.1), but HBsAg levels in serum were significantly lower for GRP1 compared to GRP4 (p=0.02). Efficacy in terms of reduction over the 3 years of serum HBV DNA, liver HBV DNA, cccDNA, and ratios of liver HBV DNA to cccDNA were 99.98%, 99.5%, 98.4%, and 83.2% respectively, exhibiting larger antiviral effects in serum than in liver. Over the course of therapy, HBV DNA viremia exhibited large oscillations for some individuals. Mathematical modelling reproduced the dynamics of these diverse groups by assuming a number of viral clones arose that experienced delayed recognition by the antibody response. Large viremia oscillations under therapy suggest sequential outgrowth of viral clones with delayed recognition by the humoral response.

Isolation and experimental inoculation of an S INDEL strain of porcine epidemic diarrhea virus in Japan

In 2013, porcine epidemic diarrhea (PED) was reported in Japan for the first time in 7 years and caused significant economic losses. In the present study, we isolated PED virus (PEDV) circulating in Japan using Vero cell cultures and analyzed sequences of S1 genes of these PEDV isolates. Sequence analysis revealed that one of these strains contained distinct insertion and deletions in the S gene (i.e., S INDEL). Furthermore, inoculation of PEDV into 1-week-old pigs demonstrated that the S INDEL strain had a lower pathogenicity than the North American (NA) prototype strain. This is the first report comparing pathogenicity of an S INDEL strain with the NA prototype strain following experimental inoculation. Excretion of PEDV in the feces of S INDEL strain-inoculated pigs occurred later than in NA prototype strain-inoculated pigs. Thus, our findings suggested that the S INDEL strain had different viral dynamics than the NA prototype strain.

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We isolated and sequenced PEDV strains circulating in Japan.

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One strain contained distinct insertion and deletions in the S gene.

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This S INDEL strain had lower pathogenicity than the NA prototype strain in piglets.

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Excretion of the S INDEL strain in feces from inoculated piglets was delayed.

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Viral dynamics of the S INDEL strain differed from that of the NA prototype strain.

Effects of the binding of calcium ions on the structure and dynamics of the ΦX174 virus investigated using molecular dynamics

It is known that the presence of calcium ions (Ca(2 + )) is necessary for the enterobacterial virus ΦX174 to inject its DNA into the host cell, and that some mutations in the major capsid proteins lead to better survivability at higher temperatures. Our goal in the current study is to determine the physical changes in both the wild-type and mutant virus due to the binding of Ca(2 + ). Thus, we performed molecular dynamics simulations of the ΦX174 major capsid protein complex with and without Ca(2 + ) bound. Our results show that binding of Ca(2 + ) leads to energetic and dynamical changes in the virus proteins. In particular, the results suggest that binding of Ca(2 + ) is energetically favorable and that the mutation leads to increased fluctuations of the protein complex (especially with the calcium ions bound to the complex), which may increase the rate of genome packaging and ejection for ΦX174.

Bio-mathematical models of viral dynamics to tailor antiviral therapy in chronic viral hepatitis

The simulation of the dynamics of viral infections by mathematical equations has been applied successfully to the study of viral infections during antiviral therapy. Standard models applied to viral hepatitis describe the viral load decline in the first 2-4 wk of antiviral therapy, but do not adequately simulate the dynamics of viral infection for the following period. The hypothesis of a constant clearance rate of the infected cells provides an unrealistic estimation of the time necessary to reach the control or the clearance of hepatitis B virus (HBV)/hepatitis C virus (HCV) infection. To overcome the problem, we have developed a new multiphasic model in which the immune system activity is modulated by a negative feedback caused by the infected cells reduction, and alanine aminotransferase kinetics serve as a surrogate marker of infected-cell clearance. By this approach, we can compute the dynamics of infected cells during the whole treatment course, and find a good correlation between the number of infected cells at the end of therapy and the long-term virological response in patients with chronic hepatitis C. The new model successfully describes the HBV infection dynamics far beyond the third month of antiviral therapy under the assumption that the sum of infected and non-infected cells remains roughly constant during therapy, and both target and infected cells concur in the hepatocyte turnover. In clinical practice, these new models will allow the development of simulators of treatment response that will be used as an “automatic pilot” for tailoring antiviral therapy in chronic hepatitis B as well as chronic hepatitis C patients.

HIV - 1 SUBTYPES, ITS IMPLICATIONS AND VIRAL DYNAMICS

Subtyping of HIV has important implications for developing candidate vaccine and understanding the biological behaviour and dynamics of HIV transmission in various populations. The third variable region (V3) in the envelope gene of HIV-1 has been shown to be a major determinant influencing a number of biological characteristics of the virus. HIV-1 evolves by rapid mutation and by recombination, both processes actively contributing to its genetic diversity. Most of the multiple genetic subtypes and intersubtype recombination of HIV-1 that comprise the global pandemic have not been characterized by full genome sequencing. The development of an effective human immunodeficiency virus type-1 (HIV-1) vaccine is likely to depend on knowledge of circulating variants of genes other than the commonly sequenced gag and env genes.