2024
How many Monte Carlo samples are needed for probabilistic cost-effectiveness analyses?
Yaesoubi R. How many Monte Carlo samples are needed for probabilistic cost-effectiveness analyses? Value In Health 2024, 27: 1553-1563. PMID: 38977192, DOI: 10.1016/j.jval.2024.06.016.Peer-Reviewed Original ResearchProbabilistic sensitivity analysesIncremental cost-effectiveness ratioProbabilistic cost-effectiveness analysisCost-effectiveness analysisEstimate incremental cost-effectiveness ratiosIncremental cost-effectiveness ratio estimatesCost-effectiveness ratioDecision optionsStochastic modelPresence of parameter uncertaintiesCostParameter uncertaintiesUncertaintyLevel of accuracyInequalitySensitivity analysisEstimationMonteModelMonte Carlo samplingParameter samplesChebyshev inequality
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