Data-driven models for replication kinetics of Orthohantavirus infections

Adams, Alison; Murphy, Quiyana M.; Dougherty, Owen P; Sawyer, Aubrey M.; Bai, Fan; Edholm, Christina J.; Williams, Evan P.; Allen, Linda J. S.; Jonsson, Colleen B.

doi:10.1016/j.mbs.2022.108834

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…Our finding that a target-cell limited model with k > 1 has more support compared to that with k = 1 mirrors findings for model fitting of other in-host infections [35,[55][56][57]. For example, recent rigorous modelling of the within-host kinetics of Orthohantavirus also concluded that k > 1 yielded more preferred models, where k = 3 was also found to have the strongest support [58]. Further, two-parameter distributions, such as Weibul, are considered more appropriate when stochastically modelling eclipse waiting times, where eclipse waiting times have been shown to have a significant affect on viral co-infection [59].…”

Section: R 0 Analysissupporting

confidence: 76%

Multiple cohort study of hospitalized SARS-CoV-2 in-host infection dynamics: parameter estimates, sensitivity and the eclipse phase profile

Korosec

Betti

Dick

et al. 2022

Preprint

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Within-host SARS-CoV-2 modelling studies have been published throughout the COVID-19 pandemic. These studies contain highly variable numbers of individuals and capture varying timescales of pathogen dynamics; some studies capture the time of disease onset, the peak viral load and subsequent heterogeneity in clearance dynamics across individuals, while others capture late-time post-peak dynamics. In this study, we curate multiple previously published SARS-CoV-2 viral load data sets, fit these data with a consistent modelling approach, and estimate the variability of in-host parameters including the basic reproduction number, R0. We find that fitted dynamics can be highly variable across data sets, and highly variable within data sets, particularly when key components of the dynamic trajectories (e.g. peak viral load) are not represented in the data. Further, we investigated the role of the eclipse phase time distribution in fitting SARS-CoV-2 viral load data. By varying the shape parameter of an Erlang distribution, we demonstrate that models with either no eclipse phase, or with an exponentially-distributed eclipse phase, offer significantly worse fits to these data, whereas models with less dispersion around the mean eclipse time (shape parameter two or more) offered the best fits to the available data.

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Section: R 0 Analysissupporting

confidence: 76%

Multiple cohort study of hospitalized SARS-CoV-2 in-host infection dynamics: parameter estimates, sensitivity and the eclipse phase profile

Korosec

Betti

Dick

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

Multiple cohort study of hospitalized SARS-CoV-2 in-host infection dynamics: Parameter estimates, identifiability, sensitivity and the eclipse phase profile

Korosec

Betti

Dick

et al. 2023

Journal of Theoretical Biology

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Overcoming bias in estimating epidemiological parameters with realistic history-dependent disease spread dynamics

Hong,

Eom,

Lee

et al. 2024

Nat Commun

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Epidemiological parameters such as the reproduction number, latent period, and infectious period provide crucial information about the spread of infectious diseases and directly inform intervention strategies. These parameters have generally been estimated by mathematical models that involve an unrealistic assumption of history-independent dynamics for simplicity. This assumes that the chance of becoming infectious during the latent period or recovering during the infectious period remains constant, whereas in reality, these chances vary over time. Here, we find that conventional approaches with this assumption cause serious bias in epidemiological parameter estimation. To address this bias, we developed a Bayesian inference method by adopting more realistic history-dependent disease dynamics. Our method more accurately and precisely estimates the reproduction number than the conventional approaches solely from confirmed cases data, which are easy to obtain through testing. It also revealed how the infectious period distribution changed throughout the COVID-19 pandemic during 2020 in South Korea. We also provide a user-friendly package, IONISE, that automates this method.

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Data-driven models for replication kinetics of Orthohantavirus infections

Cited by 3 publications

References 32 publications

Multiple cohort study of hospitalized SARS-CoV-2 in-host infection dynamics: parameter estimates, sensitivity and the eclipse phase profile

Multiple cohort study of hospitalized SARS-CoV-2 in-host infection dynamics: parameter estimates, sensitivity and the eclipse phase profile

Multiple cohort study of hospitalized SARS-CoV-2 in-host infection dynamics: Parameter estimates, identifiability, sensitivity and the eclipse phase profile

Overcoming bias in estimating epidemiological parameters with realistic history-dependent disease spread dynamics

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