2018
Accurate quantification of uncertainty in epidemic parameter estimates and predictions using stochastic compartmental models
Zimmer C, Leuba SI, Cohen T, Yaesoubi R. Accurate quantification of uncertainty in epidemic parameter estimates and predictions using stochastic compartmental models. Statistical Methods In Medical Research 2018, 28: 3591-3608. PMID: 30428780, PMCID: PMC6517086, DOI: 10.1177/0962280218805780.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsComputer SimulationData AccuracyDisease OutbreaksEpidemicsForecastingHumansModels, StatisticalStochastic ProcessesUncertaintyUrban PopulationConceptsFilter degeneracyParameter estimatesPosterior distributionStochastic transmission-dynamic modelParameter posterior distributionsEpidemic compartmental modelKey epidemic parametersStochastic compartmental modelStochastic systemsPrediction intervalsCompartmental modelMultiple shootingArt calibration methodsEpidemic parametersDegeneracyDynamic modelInfluenza modelMSS approachLong-term predictionTransmission dynamic modelSimulation experimentsCalibration methodUncertaintyEstimatesCompetitive performance
2017
A Likelihood Approach for Real-Time Calibration of Stochastic Compartmental Epidemic Models
Zimmer C, Yaesoubi R, Cohen T. A Likelihood Approach for Real-Time Calibration of Stochastic Compartmental Epidemic Models. PLOS Computational Biology 2017, 13: e1005257. PMID: 28095403, PMCID: PMC5240920, DOI: 10.1371/journal.pcbi.1005257.Peer-Reviewed Original ResearchConceptsParameter estimationStochastic modelLinear noise approximationStochastic transmission-dynamic modelEnsemble Kalman filter methodReal-time parameter estimationKey epidemic parametersParticle filtering methodInfectious individualsStochastic systemsCompartmental epidemic modelLikelihood approximationMultiple shootingNoise approximationBenchmark methodsEpidemic modelPoisson observationsKalman filter methodUnobserved numberAccurate estimatesEpidemic parametersLikelihood approachFiltering methodDynamic modelApproximation