Spatial and temporal dynamics of SARS‐CoV‐2: Modeling, analysis and simulation

Wu, Peng; Wang, Xiunan; Feng, Zhaosheng

doi:10.1016/j.apm.2022.09.006

Cited by 4 publications

(1 citation statement)

References 35 publications

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“…The so-called "standard model", derived from these early efforts and based on predator-prey dynamics, saw success in the mid-1990s when its validation against HIV viral load data from patients undergoing antiretroviral therapy allowed for the quantification of in-host dynamical quantities, such as the infected cell death rate [5]. Since that time, the standard model and its extensions have been used to further describe HIV [6][7][8][9][10][11][12][13][14][15][16][17] and a series of other viral infections, with pathogens such as hepatitis C (HCV) [18][19][20][21], hepatitis B (HBV) [22][23][24][25][26][27][28], influenza [29,30], ebola [31], and SARS-CoV-2 [32][33][34][35][36][37][38].…”

Section: Introductionmentioning

confidence: 99%

Incorporating Intracellular Processes in Virus Dynamics Models

Ciupe,

Conway

2024

Microorganisms

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In-host models have been essential for understanding the dynamics of virus infection inside an infected individual. When used together with biological data, they provide insight into viral life cycle, intracellular and cellular virus–host interactions, and the role, efficacy, and mode of action of therapeutics. In this review, we present the standard model of virus dynamics and highlight situations where added model complexity accounting for intracellular processes is needed. We present several examples from acute and chronic viral infections where such inclusion in explicit and implicit manner has led to improvement in parameter estimates, unification of conclusions, guidance for targeted therapeutics, and crossover among model systems. We also discuss trade-offs between model realism and predictive power and highlight the need of increased data collection at finer scale of resolution to better validate complex models.

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