Parameter identifiability and optimal control of an SARS-CoV-2 model early in the pandemic

Tuncer, Necibe; Timsina, Archana Neupane; Nuño, Miriam A; Chowell, Gerardo; Martcheva, Maia

doi:10.1080/17513758.2022.2078899

Cited by 21 publications

(13 citation statements)

References 33 publications

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“…Depend on the types of model identifiability, there are various examples and techniques to address the issues of model identifiability [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ]. Here, we offer our perspective on this issue.…”

Section: Materials and Methodsmentioning

confidence: 99%

Practical Understanding of Cancer Model Identifiability in Clinical Applications

Phan

Bennett

Patten

2023

Life

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Mathematical models are a core component in the foundation of cancer theory and have been developed as clinical tools in precision medicine. Modeling studies for clinical applications often assume an individual’s characteristics can be represented as parameters in a model and are used to explain, predict, and optimize treatment outcomes. However, this approach relies on the identifiability of the underlying mathematical models. In this study, we build on the framework of an observing-system simulation experiment to study the identifiability of several models of cancer growth, focusing on the prognostic parameters of each model. Our results demonstrate that the frequency of data collection, the types of data, such as cancer proxy, and the accuracy of measurements all play crucial roles in determining the identifiability of the model. We also found that highly accurate data can allow for reasonably accurate estimates of some parameters, which may be the key to achieving model identifiability in practice. As more complex models required more data for identification, our results support the idea of using models with a clear mechanism that tracks disease progression in clinical settings. For such a model, the subset of model parameters associated with disease progression naturally minimizes the required data for model identifiability.

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Section: Materials and Methodsmentioning

confidence: 99%

Practical Understanding of Cancer Model Identifiability in Clinical Applications

Phan

Bennett

Patten

2023

Life

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“…With this concept, we estimated the population-level average viral shedding rate during the infectious phase using clinical reported SARS-CoV-2 concentrations in hospitalized patients’ stool samples (Figure 1B). This estimated viral shedding rate is an average of infected individuals in the population and does not consider the heterogenous viral shedding dynamics among infected individuals (Wölfel et al, 2020; Killingley et al, 2022; Stanca and Tuncer, 2022). Thus, our model can be improved by feeding viral shedding data during the early phase of the infection and large-scale individual-level shedding dynamics data.…”

Section: Discussionmentioning

confidence: 99%

“…For example, the product of ߙ ൌ 1, ߚ ൌ 2, ߛ ൌ 0.5 is the same as when ߙ ൌ 10, ߚ ൌ 1, ߛ ൌ 0.9. This reflects the pertinent issue of model identifiability in mathematical models in biology and epidemiology (Tuncer et al, 2022;Eisenberg et al, 2013;Wu et al, 2019;Ciupe and Tuncer, 2022). Thus, an important step in our approach is the direct estimations of ߚ and ߛ, which would allow us to identify ߙ uniquely.…”

Section: Simple Wastewater Epidemiological Modelmentioning

confidence: 97%

A simple SEIR-V model to estimate COVID-19 prevalence and predict SARS-CoV-2 transmission using wastewater-based surveillance data

Phan

Brozak

Pell

et al. 2022

Preprint

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Wastewater-based surveillance (WBS) has been widely used as a public health tool to monitor SARS-CoV-2 transmission. However, epidemiological inference from WBS data remains understudied and limits its application. In this study, we have established a quantitative framework to estimate COVID-19 prevalence and predict SARS-CoV-2 transmission through integrating WBS data into an SEIR-V model. We conceptually divide the individual-level viral shedding course into exposed, infectious, and recovery phases as an analogy to the compartments in population-level SEIR model. We demonstrated that the temperature effect on viral losses in the sewer can be straightforwardly incorporated in our framework. Using WBS data from the second wave of the pandemic (Oct 02, 2020 – Jan 25, 2021) in the Great Boston area, we showed that the SEIR-V model successfully recapitulates the temporal dynamics of viral load in wastewater and predicts the true number of cases peaked earlier and higher than the number of reported cases by 16 days and 8.6 folds (R = 0.93), respectively. This work showcases a simple, yet effective method to bridge WBS and quantitative epidemiological modeling to estimate the prevalence and transmission of SARS-CoV-2 in the sewershed, which could facilitate the application of wastewater surveillance of infectious diseases for epidemiological inference and inform public health actions.

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“…The COVID-19 pandemic which has constrained the world forces us to review virus populations and the technologies of their observation [1][2][3], as well as to pay particular attention to analyses of viruses of zoo-genetic origin [4][5][6][7][8] and mathematical modelling of COVID-19-related phenomena [9][10][11][12][13]. In this paper, the authors would like to draw attention to the application of covariance functions for an analysis of the parameters of digital images of coronaviruses and present some results based on electroencephalography measurements.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Results of Digital Electroencephalography and Digital Vectors of Coronavirus Images upon Applying the Theory of Covariance Functions

Skeivalas

Paršeliūnas

2023

Symmetry

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This paper analyses the structures of covariance functions of digital electroencephalography measurement vectors and digital vectors of two coronavirus images. For this research, we used the measurement results of 30-channel electroencephalography (E1–E30) and digital vectors of images of two SARS-CoV-2 variants (cor2 and cor4), where the magnitudes of intensity of the electroencephalography parameters and the parameters of the digital images of coronaviruses were encoded. The estimators of cross-covariance functions of the digital electroencephalography measurements’ vectors and the digital vectors of the coronavirus images and the estimators of auto-covariance functions of separate vectors were derived by applying random functions constructed according to the vectors’ parameter measurement data files. The estimators of covariance functions were derived by changing the values of the quantised interval k on the time and image pixel scales. The symmetric matrices of correlation coefficients were calculated to estimate the level of dependencies between the electroencephalography measurement results’ vectors and the digital vectors of the coronavirus images. The graphical images of the normalised cross-covariance functions for the electroencephalography measurement results’ vectors and the digital vectors of the coronavirus images within the period of all measurements are asymmetric. For all calculations, a computer program was developed by applying a package of Matlab procedures. A probabilistic interdependence between the results of the electroencephalography measurements and the parameters of the coronavirus vectors, as well as their variation on the time and image pixel scales, was established.

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Parameter identifiability and optimal control of an SARS-CoV-2 model early in the pandemic

Cited by 21 publications

References 33 publications

Practical Understanding of Cancer Model Identifiability in Clinical Applications

Practical Understanding of Cancer Model Identifiability in Clinical Applications

A simple SEIR-V model to estimate COVID-19 prevalence and predict SARS-CoV-2 transmission using wastewater-based surveillance data

Analysis of Results of Digital Electroencephalography and Digital Vectors of Coronavirus Images upon Applying the Theory of Covariance Functions

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