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

Sachak-Patwa, Rahil; Lafferty, Erin I.; Schmit, Claude J; Thompson, Robin N; Byrne, Helen

doi:10.1016/j.jtbi.2023.111491

Cited by 5 publications

(4 citation statements)

References 70 publications

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“…Note that in these models, the initial target cell population and the virus production rate, π, are correlated and only identifiable as a product. Thus, an increase or decrease in target cells decreases or increases the virus production proportionally [25,39,86,87,109,110].…”

Section: Plos Computational Biologymentioning

confidence: 99%

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How robust are estimates of key parameters in standard viral dynamic models?

Zitzmann,

Ke,

Ribeiro

et al. 2024

PLoS Comput Biol

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Mathematical models of viral infection have been developed, fitted to data, and provide insight into disease pathogenesis for multiple agents that cause chronic infection, including HIV, hepatitis C, and B virus. However, for agents that cause acute infections or during the acute stage of agents that cause chronic infections, viral load data are often collected after symptoms develop, usually around or after the peak viral load. Consequently, we frequently lack data in the initial phase of viral growth, i.e., when pre-symptomatic transmission events occur. Missing data may make estimating the time of infection, the infectious period, and parameters in viral dynamic models, such as the cell infection rate, difficult. However, having extra information, such as the average time to peak viral load, may improve the robustness of the estimation. Here, we evaluated the robustness of estimates of key model parameters when viral load data prior to the viral load peak is missing, when we know the values of some parameters and/or the time from infection to peak viral load. Although estimates of the time of infection are sensitive to the quality and amount of available data, particularly pre-peak, other parameters important in understanding disease pathogenesis, such as the loss rate of infected cells, are less sensitive. Viral infectivity and the viral production rate are key parameters affecting the robustness of data fits. Fixing their values to literature values can help estimate the remaining model parameters when pre-peak data is missing or limited. We find a lack of data in the pre-peak growth phase underestimates the time to peak viral load by several days, leading to a shorter predicted growth phase. On the other hand, knowing the time of infection (e.g., from epidemiological data) and fixing it results in good estimates of dynamical parameters even in the absence of early data. While we provide ways to approximate model parameters in the absence of early viral load data, our results also suggest that these data, when available, are needed to estimate model parameters more precisely.

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Section: Plos Computational Biologymentioning

confidence: 99%

“…Nevertheless, estimates of π showed more variability, possibly due to lower sensitivity. It has been previously shown that the initial target cell population T(t inf ) also correlates with the virus production rate π and only their product [T(t inf )�π] is identifiable [87,90,109,127].…”

Section: Estimating β and π: What If β And π Are Unknown?mentioning

confidence: 99%

How robust are estimates of key parameters in standard viral dynamic models?

Zitzmann,

Ke,

Ribeiro

et al. 2024

PLoS Comput Biol

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show abstract

“…Despite many models published on respiratory virus-only [70][71][72][73][74][75][76][77][78] and bacteria-only [79][80][81][82][83] infections, few ODE models currently exist that can replicate influenza-bacteria coinfection dynamics [42,44,[84][85][86][87][88][89]. The majority of ODE modeling work in coinfection has been done in IAV and pneumococcus coinfection, one of the most common coinfections diagnosed in a typical influenza season [14].…”

Section: Mathematical Modeling Of Dynamics Of Respiratory Virus-bacte...mentioning

confidence: 99%

Mathematical Modeling of the Lethal Synergism of Coinfecting Pathogens in Respiratory Viral Infections: A Review

Mochan,

Sego

2023

Microorganisms

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Influenza A virus (IAV) infections represent a substantial global health challenge and are often accompanied by coinfections involving secondary viruses or bacteria, resulting in increased morbidity and mortality. The clinical impact of coinfections remains poorly understood, with conflicting findings regarding fatality. Isolating the impact of each pathogen and mechanisms of pathogen synergy during coinfections is challenging and further complicated by host and pathogen variability and experimental conditions. Factors such as cytokine dysregulation, immune cell function alterations, mucociliary dysfunction, and changes to the respiratory tract epithelium have been identified as contributors to increased lethality. The relative significance of these factors depends on variables such as pathogen types, infection timing, sequence, and inoculum size. Mathematical biological modeling can play a pivotal role in shedding light on the mechanisms of coinfections. Mathematical modeling enables the quantification of aspects of the intra-host immune response that are difficult to assess experimentally. In this narrative review, we highlight important mechanisms of IAV coinfection with bacterial and viral pathogens and survey mathematical models of coinfection and the insights gained from them. We discuss current challenges and limitations facing coinfection modeling, as well as current trends and future directions toward a complete understanding of coinfection using mathematical modeling and computer simulation.

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“…As reported by the World Health Organization, seasonal influenza is caused by influenza A and B viruses. It occurs annually, mainly in winter in the northern and southern hemispheres, while it can occur at any time throughout the year near the equator [2]. Every year, there are around a billion cases of seasonal influenza, including 3-5 million cases of severe illness.…”

Section: Introductionmentioning

confidence: 99%

A study of a seasonal influenza model in deterministic and stochastic environments

Zhang,

Jiang

2024

Preprint

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Seasonal influenza occurs annually and is one of the most common infectious diseases in the world, posing a threat to public health security. Therefore, it is essential to study the dynamics of seasonal influenza to raise public awareness and implement scientific prevention measures. Huo et al. studied a deterministic seasonal influenza model in Gansu, China, in this paper, we extend their study starting from proving the local asymptotic stability of the endemic equilibrium. In addition, considering the non-negligible effects of environmental disturbance on the transmission of influenza viruses, we assume that the transmission rate in the deterministic model follows a log-normal Ornstein-Uhlenbeck process, therefore, we formulate the corresponding stochastic model. To analyze the dynamics of the stochastic model, we first verify the existence and uniqueness of the global positive solution. Next, by constructing suitable Lyapunov functions, we obtain sufficient conditions for the stationary distribution and the extinction of the disease. More precisely, we deduce that the seasonal influenza persist when R0s > 1 while it dies out when R0 E < 1. Furthermore, we derive the exact expression of probability density function around the quasi-endemic equilibrium. Finally, we conduct numerical simulations to validate theoretical results and further explore the effects of environmental noise.

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

Cited by 5 publications

References 70 publications

How robust are estimates of key parameters in standard viral dynamic models?

How robust are estimates of key parameters in standard viral dynamic models?

Mathematical Modeling of the Lethal Synergism of Coinfecting Pathogens in Respiratory Viral Infections: A Review

A study of a seasonal influenza model in deterministic and stochastic environments

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