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

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Infectious Disease Modelling 2017年第3期2卷 379-398页

作者： gerardo Chowell Division of Epidemiology&Biostatistics School of Public HealthGeorgia State UniversityAtlantaGAUSA Division of International Epidemiology and Population Studies Fogarty International CenterNational Institutes of HealthBethesdaMDUSA

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 *** 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 *** 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 *** 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.

关键词： Parameter estimation Uncertainty quantification Bootstrap Parameter identifiability Model performance Forecasts Uncertainty propagation

Is it growing exponentially fast? -Impact of assuming exponential growth for characterizing and forecasting epidemics with initial near-exponential growth dynamics

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Infectious Disease Modelling 2016年第1期1卷 71-78页

作者： gerardo Chowell Cecile Viboud School of Public Health Georgia State UniversityAtlantaGAUSA Division of International Epidemiology and Population Studies Fogarty International CenterNational Institutes of HealthBethesdaMDUSA

The increasing use of mathematical models for epidemic forecasting has highlighted the importance of designing models that capture the baseline transmission characteristics in order to generate reliable epidemic *** models for epidemic forecasting could be achieved by identifying signature features of epidemic growth,which could inform the design of models of disease spread and reveal important characteristics of the transmission *** particular,it is often taken for granted that the early growth phase of different growth processes in nature follow early exponential growth *** the context of infectious disease spread,this assumption is often convenient to describe a transmission process with mass action kinetics using differential equations and generate analytic expressions and estimates of the reproduction *** this article,we carry out a simulation study to illustrate the impact of incorrectly assuming an exponential-growth model to characterize the early phase(e.g.,3e5 disease generation intervals)of an infectious disease outbreak that follows near-exponential growth ***,we assess the impact on:1)goodness of fit,2)bias on the growth parameter,and 3)the impact on short-term epidemic *** findings indicate that devising transmission models and statistical approaches that more flexibly capture the profile of epidemic growth could lead to enhanced model fit,improved estimates of key transmission parameters,and more realistic epidemic forecasts.

关键词： dynamics. process. exponential

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

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Infectious Disease Modelling 2017年第2期2卷 268-275页

作者： Alexandra Smirnova gerardo Chowell Department of Mathematics and Statistics Georgia State UniversityAtlantaUSA School of Public Health Georgia State UniversityAtlantaUSA Division of International Epidemiology and Population Studies Fogarty International CenterNational Institutes of HealthBethesdaMDUSA

Public health officials are increasingly recognizing the need to develop disease-forecasting systems to respond to epidemic and pandemic *** instance,simple epidemic models relying on a small number of parameters can play an important role in characterizing epidemic growth and generating short-term epidemic *** the absence of reliable information about transmission mechanisms of emerging infectious diseases,phenomenological models are useful to characterize epidemic growth patterns without the need to explicitly model transmission mechanisms and the natural history of the *** this article,our goal is to discuss and illustrate the role of regularization methods for estimating parameters and generating disease forecasts using the generalized Richards model in the context of the 2014e15 Ebola epidemic in West Africa.

关键词： Generalized Richards model Parameter estimation Regularization methods Epidemic forecasting Ebola

Systematic comparison of epidemic growth patterns using two different estimation approaches

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Infectious Disease Modelling 2021年第1期6卷 5-14页

作者： Yiseul Lee Kimberlyn Roosa gerardo Chowell Department of Population Health Sciences School of Public HealthGeorgia State UniversityAtlantaGA30303USA Division of International Epidemiology and Population Studies Fogarty International CenterNational Institute of HealthBethesdaMD20892USA UT One Health Initiative University of TennesseeKnoxvilleTN37996USA

Background:Different estimation approaches are frequently used to calibrate mathematical models to epidemiological data,particularly for analyzing infectious disease ***,we use two common methods to estimate parameters that characterize growth patterns using the generalized growth model(GGM)calibrated to real outbreak *** and methods:Data from 31 outbreaks are used to fit the GGM to the ascending phase of each outbreak and estimate the parameters using both least squares(LSQ)and maximum likelihood estimation(MLE)*** utilize parametric bootstrapping to construct confidence intervals for parameter *** compare the results including RMSE,Anscombe residual,and 95%prediction interval *** also evaluate the correlation between the estimates from both ***:Comparing LSQ and MLE estimates,most outbreaks have similar parameter estimates,RMSE,Anscombe,and 95%prediction interval *** estimates do not differ across methods when the model yields a good fit to the early growth ***,for two outbreaks,there are systematic deviations in model fit to the data that explain differences in parameter estimates(e.g.,residuals represent random error rather than systematic deviation).Conclusion:Our findings indicate that utilizing LSQ and MLE methods produce similar results in the context of characterizing epidemic growth patterns with the GGM,provided that the model yields a good fit to the data.

关键词： Parameter estimation Generalized growth model Least squares estimation Maximum likelihood estimation Epidemiological models

SubEpiPredict:A tutorial-based primer and toolbox for fitting and forecasting growth trajectories using the ensemble n-sub-epidemic modeling framework

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Infectious Disease Modelling 2024年第2期9卷 411-436页

作者： gerardo Chowell Sushma Dahal Amanda Bleichrodt Amna Tariq James M.Hyman Ruiyan Luo Department of Population Health Sciences School of Public HealthGeorgia State UniversityAtlantaGAUSA Department of International Epidemiology and Population Studies Fogarty International CenterNational Institutes of HealthBethesdaMDUSA Department of Pediatrics School of MedicineStanford UniversityPalo AltoCAUSA Department of Mathematics Center for Computational ScienceTulane UniversityNew OrleansLAUSA

An ensemble n-sub-epidemic modeling framework that integrates sub-epidemics to capture complex temporal dynamics has demonstrated powerful forecasting capability in previous *** modeling framework can characterize complex epidemic patterns,including plateaus,epidemic resurgences,and epidemic waves characterized by multiple peaks of different *** this tutorial paper,we introduce and illustrate SubEpiPredict,a user-friendly MATLAB toolbox for fitting and forecasting time series data using an ensemble n-sub-epidemic modeling *** toolbox can be used for model fitting,forecasting,and evaluation of model performance of the calibration and forecasting periods using metrics such as the weighted interval score(WIS).We also provide a detailed description of these methods including the concept of the n-sub-epidemic model,constructing ensemble forecasts from the top-ranking models,*** the illustration of the toolbox,we utilize publicly available daily COVID-19 death data at the national level for the United *** MATLAB toolbox introduced in this paper can be very useful for a wider group of audiences,including policymakers,and can be easily utilized by those without extensive coding and modeling backgrounds.

关键词： n-Sub-epidemic model Forecasting MATLAB Sub-epidemics Phenomenological models Performance metrics Model evaluation

应用他克莫司后的阿片类药物相关痛觉超敏与快速脱毒

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麻醉与镇痛 2009年第2期5卷 90-92页

作者： Antonio Siniscalchi, md Emanuele Piraccini, md Zuzana Miklosova, md Stefania Taddei, md Stefano Faenza, md, Mph gerardo Martinelli, md, Mph 石金山(译) Department of Anesthesiology University of Bologna Bologna Italy Department of Anesthesiology S. Orsola-Malpighi Hospital Bologna Italy 不详

阿片类药物能促使中枢敏化及诱导痛觉超敏，称之为阿片类药物诱导的痛觉超敏（Opioid-Induced Hyperalgesia）。本文报道1例女性患者在接受肠移植手术后发生了与免疫抑制剂相关的神经病理性疼痛。在对其疼痛治疗的3年时间里，我们选择... 详细信息

阿片类药物能促使中枢敏化及诱导痛觉超敏，称之为阿片类药物诱导的痛觉超敏（Opioid-Induced Hyperalgesia）。本文报道1例女性患者在接受肠移植手术后发生了与免疫抑制剂相关的神经病理性疼痛。在对其疼痛治疗的3年时间里，我们选择了包括静脉注射吗啡在内的不同镇痛方法，可是收效甚微。该患者产生了阿片类药物诱导的痛觉超敏现象，然而此现象在全身麻醉下通过快速脱毒得以成功治疗。脱毒疗法改善了她的生活质量，包括能够重新接受物理治疗。治疗后6个月，仍无需使用阿片类药物。我们的经验表明，全身麻醉下快速脱毒，可能是治疗阿片类药物诱导的痛觉超敏的一种有效方法，值得和传统的脱毒方法进行比较。

关键词：阿片类药物痛觉超敏快速脱毒他克莫司神经病理性疼痛免疫抑制剂相关物相药物诱导