A primer on stable parameter estimation and forecasting in epidemiology by a problem-oriented regularized least squares algorithm

Smirnova, Alexandra; Chowell, Gerardo

doi:10.1016/j.idm.2017.05.004

Cited by 16 publications

(12 citation statements)

References 10 publications

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“…In practice, this time variation of the contact and diagnose rates leads to sub-exponential rather than exponential growth dynamics, and hence provides better estimates of epidemic size compared to fully exponential growth models. We refer to (Pell, Kuang, Viboud, & Chowell, 2018;Smirnova & Chowell, 2017) for earlier studies on sub-exponential growth of modern epidemics.…”

Section: Resultsmentioning

confidence: 99%

An updated estimation of the risk of transmission of the novel coronavirus (2019-nCov)

Tang

Bragazzi

et al. 2020

Infectious Disease Modelling

703

686

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a b s t r a c tThe basic reproduction number of an infectious agent is the average number of infections one case can generate over the course of the infectious period, in a naïve, uninfected population. It is well-known that the estimation of this number may vary due to several methodological issues, including different assumptions and choice of parameters, utilized models, used datasets and estimation period. With the spreading of the novel coronavirus (2019-nCoV) infection, the reproduction number has been found to vary, reflecting the dynamics of transmission of the coronavirus outbreak as well as the case reporting rate. Due to significant variations in the control strategies, which have been changing over time, and thanks to the introduction of detection technologies that have been rapidly improved, enabling to shorten the time from infection/symptoms onset to diagnosis, leading to faster confirmation of the new coronavirus cases, our previous estimations on the transmission risk of the 2019-nCoV need to be revised. By using time-dependent contact and diagnose rates, we refit our previously proposed dynamics transmission model to the data available until January 29th , 2020 and re-estimated the effective daily reproduction ratio that better quantifies the evolution of the interventions. We estimated when the effective daily reproduction ratio has fallen below 1 and when the epidemics will peak. Our updated findings suggest that the best measure is persistent and strict self-isolation. The epidemics will continue to grow, and can peak soon with the peak time depending highly on the public health interventions practically implemented.

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Section: Resultsmentioning

confidence: 99%

An updated estimation of the risk of transmission of the novel coronavirus (2019-nCov)

Tang

Bragazzi

et al. 2020

Infectious Disease Modelling

703

686

View full text Add to dashboard Cite

show abstract

“…On the other hand, practical parameter non-identifiability issues could be fixed by 1) employing an alternative model of lower complexity when possible, 2) collecting more data about other states in the system to better characterize the system dynamical features, 3) increasing the spatial-temporal resolution of the data to better constrain the model parameters and/or 4) reducing the number of parameters that are jointly estimated, perhaps by constraining a subset of the unknown parameters based on estimates previously reported in similar studies and conducting extensive sensitivity analyses on those parameters ( Arriola et al., 2009 ). Finally, specific approaches have been adapted to address parameter identifiability including regularization techniques that aim for stable parameter reconstruction ( Smirnova & Chowell, 2017 ).…”

Section: Parameter Identifiabilitymentioning

confidence: 99%

Fitting dynamic models to epidemic outbreaks with quantified uncertainty: A primer for parameter uncertainty, identifiability, and forecasts

Chowell

2017

Infectious Disease Modelling

Self Cite

313

458

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Mathematical models provide a quantitative framework with which scientists can assess hypotheses on the potential underlying mechanisms that explain patterns in observed data at different spatial and temporal scales, generate estimates of key kinetic parameters, assess the impact of interventions, optimize the impact of control strategies, and generate forecasts. We review and illustrate a simple data assimilation framework for calibrating mathematical models based on ordinary differential equation models using time series data describing the temporal progression of case counts relating, for instance, to population growth or infectious disease transmission dynamics. In contrast to Bayesian estimation approaches that always raise the question of how to set priors for the parameters, this frequentist approach relies on modeling the error structure in the data. We discuss issues related to parameter identifiability, uncertainty quantification and propagation as well as model performance and forecasts along examples based on phenomenological and mechanistic models parameterized using simulated and real datasets.

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“…If, for example, the Gompertz function is replaced by a model that fits data of pathogens' spreading in humans or in plants, this approach can be use to forecast the kinetics of pathogens' spreading. Thus, the proposed method can be used not only for cancer research, but also for parameter estimation and forecasting in epidemiology [35][36][37].…”

Section: Discussionmentioning

confidence: 99%

A comparison between Nonlinear Least Squares and Maximum Likelihood estimation for the prediction of tumor growth on experimental data of human and rat origin

Patmanidis

Chignola

Charalampidis

et al. 2019

Biomedical Signal Processing and Control

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Several mathematical models have been developed to explain the growth of tumors and used to fit experimental or clinical data. Their predictive poweri.e. their ability to forecast the future growth on the basis of present knowledge-however, has been rarely explored. Here, we investigate whether a Hidden Markov Model (HMM) based on the well-established Gompertz tumor growth function with additive Gaussian noise could effectively be used to predict the future growth of experimental tumors. The idea behind this work is that one might achieve more accurate predictions if estimates of the unknown parameters of the HMM are used instead of those obtained by fits of the deterministic Gompertz model to the data. We use the principle of Maximum Likelihood

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A primer on stable parameter estimation and forecasting in epidemiology by a problem-oriented regularized least squares algorithm

Cited by 16 publications

References 10 publications

An updated estimation of the risk of transmission of the novel coronavirus (2019-nCov)

An updated estimation of the risk of transmission of the novel coronavirus (2019-nCov)

Fitting dynamic models to epidemic outbreaks with quantified uncertainty: A primer for parameter uncertainty, identifiability, and forecasts

A comparison between Nonlinear Least Squares and Maximum Likelihood estimation for the prediction of tumor growth on experimental data of human and rat origin

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