Corpus ID: 214743310

Total Variation Regularization for Compartmental Epidemic Models with Time-varying Dynamics

@article{Zheng2020TotalVR,
  title={Total Variation Regularization for Compartmental Epidemic Models with Time-varying Dynamics},
  author={Wenjie Zheng},
  journal={ArXiv},
  year={2020},
  volume={abs/2004.00412}
}
  • W. Zheng
  • Published 1 April 2020
  • Mathematics, Computer Science, Biology
  • ArXiv
Compartmental epidemic models are among the most popular ones in epidemiology. For the parameters (e.g., the transmission rate) characterizing these models, the majority of researchers simplify them as constants, while some others manage to detect their continuous variations. In this paper, we aim at capturing, on the other hand, discontinuous variations, which better describe the impact of many noteworthy events, such as city lockdowns, the opening of field hospitals, and the mutation of the… Expand
Structural identifiability and observability of compartmental models of the COVID-19 pandemic☆
TLDR
This paper surveys the different models proposed in the literature, assembling a list of 36 model structures and assessing their ability to provide reliable information, and analyses 255 different model versions to address the problem of structural identifiability and observability. Expand
An integrated framework for building trustworthy data-driven epidemiological models: Application to the COVID-19 outbreak in New York City
TLDR
A general framework for building a trustworthy data-driven epidemiological model is developed, consisting of a workflow that integrates data acquisition and event timeline, model development, identifiability analysis, sensitivity analysis, model calibration, model robustness analysis, and forecasting with uncertainties in different scenarios. Expand
Structural identifiability and observability of compartmental models of the COVID-19 pandemic
This research has received funding from the Spanish Ministry of Science, Innovation and Universities and the European Union FEDER under project grant SYNBIOCONTROL (DPI2017-82896-C2-2-R) and theExpand
Epidemic Models for COVID-19 during the First Wave from February to May 2020: a Methodological Review
TLDR
A methodological review of epidemiological models for the propagation of the COVID-19 pandemic during the early months of the outbreak from February to May 2020 and an opportunity to witness how the scientific community reacted to this unique situation is provided. Expand

References

SHOWING 1-10 OF 23 REFERENCES
Accounting for non-stationarity in epidemiology by embedding time-varying parameters in stochastic models
TLDR
It is demonstrated that time-varying parameters can improve the accuracy of model performances, and it is suggested that the methodology can be used as a first step towards a better understanding of a complex epidemic, in situation where data is limited and/or uncertain. Expand
Capturing the time-varying drivers of an epidemic using stochastic dynamical systems.
TLDR
This paper considers stochastic extensions in order to capture unknown influences (changing behaviors, public interventions, seasonal effects, etc.) and introduces diffusion-driven susceptible exposed infected retired-type models with age structure. Expand
Forecasting Epidemics Through Nonparametric Estimation of Time-Dependent Transmission Rates Using the SEIR Model
TLDR
This work introduces a novel approach for the reconstruction of nonparametric time-dependent transmission rates by projecting onto a finite subspace spanned by Legendre polynomials and compares three regularization algorithms: variational (Tikhonov’s) regularization, truncated singular value decomposition (TSVD), and modified TSVD to determine the stabilizing strategy that is most effective in terms of reliability of forecasting from limited data. Expand
Model selection and parameter estimation for dynamic epidemic models via iterated filtering: application to rotavirus in Germany
TLDR
This work addresses the question of adequate inclusion of variability by demonstrating a systematic approach for model selection and parameter inference for dynamic epidemic models by performing inference for six partially observed Markov process models. Expand
Inference for Nonlinear Epidemiological Models Using Genealogies and Time Series
TLDR
The approach using a nonlinear Susceptible-Infected-Recovered (SIR) model for the transmission dynamics of an infectious disease is demonstrated and shown that it provides accurate estimates of past disease dynamics and key epidemiological parameters from genealogies with or without accompanying time series data. Expand
Mathematical modeling of infectious disease dynamics
TLDR
The main approaches that are used for the surveillance and modeling of infectious disease dynamics are discussed and the basic concepts underpinning their implementation and practice are presented. Expand
Bayesian non-parametric inference for stochastic epidemic models using Gaussian Processes
TLDR
A Bayesian non-parametric approach using Gaussian Processes is developed, specifically to estimate the infection process, and it is illustrated that the methods can recover the true infection process quite well in practice. Expand
Early dynamics of transmission and control of COVID-19: a mathematical modelling study
TLDR
The results show that COVID-19 transmission likely declined in Wuhan during late January 2020, coinciding with the introduction of control measures, and it is likely many chains of transmission will fail to establish initially, but may still cause new outbreaks eventually. Expand
Early dynamics of transmission and control of COVID-19: a mathematical modelling study
TLDR
A stochastic transmission model is combined with data on cases of coronavirus disease 2019 (COVID-19) in Wuhan and international cases that originated inWuhan to estimate how transmission had varied over time during January, 2020, and February, 2020. Expand
A contribution to the mathematical theory of epidemics
TLDR
The present communication discussion will be limited to the case in which all members of the community are initially equally susceptible to the disease, and it will be further assumed that complete immunity is conferred by a single infection. Expand
...
1
2
3
...