2021
Adaptive Policies to Balance Health Benefits and Economic Costs of Physical Distancing Interventions during the COVID-19 Pandemic
Yaesoubi R, Havumaki J, Chitwood MH, Menzies NA, Gonsalves G, Salomon JA, Paltiel AD, Cohen T. Adaptive Policies to Balance Health Benefits and Economic Costs of Physical Distancing Interventions during the COVID-19 Pandemic. Medical Decision Making 2021, 41: 386-392. PMID: 33504258, PMCID: PMC8084913, DOI: 10.1177/0272989x21990371.Peer-Reviewed Original ResearchMeSH KeywordsCost-Benefit AnalysisCosts and Cost AnalysisCOVID-19Decision MakingDecision Support TechniquesHumansModels, TheoreticalPandemicsPhysical DistancingPolicyPolicy MakingSARS-CoV-2ConceptsAdaptive policiesClear decision rulesSocial costsEconomic costsPolicy makersPhysical distancing policiesDecision toolPolicyPhysical distancing interventionsDistancing policiesModel-based experimentsDecision rulesReal-time surveillance dataCostCOVID-19 pandemicMakersShorter overall durationMore complex modelsHealth benefitsComplex modelsPandemicCurrent pandemicCOVID-19BenefitsRules
2016
Tradeoffs in Introduction Policies for the Anti-Tuberculosis Drug Bedaquiline: A Model-Based Analysis
Kunkel A, Cobelens FG, Cohen T. Tradeoffs in Introduction Policies for the Anti-Tuberculosis Drug Bedaquiline: A Model-Based Analysis. PLOS Medicine 2016, 13: e1002142. PMID: 27727274, PMCID: PMC5058480, DOI: 10.1371/journal.pmed.1002142.Peer-Reviewed Original ResearchConceptsTreatment of tuberculosisMDR-TB treatmentTB treatmentSecondary casesDrug-resistant (XDR) TBResistance patternsLife expectancyMultidrug-resistant TB patientsDrug bedaquilineMDR-TB drugsAnti-tuberculosis drug bedaquilineCulture conversionMDR patientsTB patientsMortality benefitBedaquiline resistanceMedian timeRisk of resistanceTB drugsDecrease transmissionHypothetical cohortSources of heterogeneityDrug interactionsInitial cohortTime of initiationIdentifying cost‐effective dynamic policies to control epidemics
Yaesoubi R, Cohen T. Identifying cost‐effective dynamic policies to control epidemics. Statistics In Medicine 2016, 35: 5189-5209. PMID: 27449759, PMCID: PMC5096998, DOI: 10.1002/sim.7047.Peer-Reviewed Original ResearchMeSH KeywordsCost-Benefit AnalysisDecision Support TechniquesEpidemicsHealth PolicyHumansInfluenza, HumanModels, TheoreticalVaccinationConceptsNet health benefitHighest net health benefitHealth benefitsTransmission-reducing interventionsDynamic policiesNovel viral pathogensCurrent interventionsHealth policyMathematical decision modelViral pathogensMonetary outcomesPolicy makersInterventionPolicyDecision modelStatic policyEpidemicEpidemic dataVaccinationVaccinePerformance measures
2014
How can mathematical models advance tuberculosis control in high HIV prevalence settings?
Houben RM, Dowdy DW, Vassall A, Cohen T, Nicol MP, Granich RM, Shea JE, Eckhoff P, Dye C, Kimerling ME, White RG, . How can mathematical models advance tuberculosis control in high HIV prevalence settings? The International Journal Of Tuberculosis And Lung Disease 2014, 18: 509-514. PMID: 24903784, PMCID: PMC4436821, DOI: 10.5588/ijtld.13.0773.Peer-Reviewed Original ResearchConceptsHigh HIV prevalence settingsHIV prevalence settingsTB-HIVTuberculosis controlPrevalence settingsHigh human immunodeficiency virus (HIV) prevalenceHuman immunodeficiency virus (HIV) prevalenceTB ModellingHealth policy makersDifficult diagnosisDisease progressionHigh riskHigh mortalityHealth systemNatural progressionVirus prevalencePublic healthProgressionMortalityPrevalenceSettingAnalysis ConsortiumDiagnosisExpert discussion
2013
Identifying dynamic tuberculosis case-finding policies for HIV/TB coepidemics
Yaesoubi R, Cohen T. Identifying dynamic tuberculosis case-finding policies for HIV/TB coepidemics. Proceedings Of The National Academy Of Sciences Of The United States Of America 2013, 110: 9457-9462. PMID: 23690585, PMCID: PMC3677479, DOI: 10.1073/pnas.1218770110.Peer-Reviewed Original ResearchMeSH KeywordsDecision Support TechniquesEpidemicsEpidemiological MonitoringHIV InfectionsHumansModels, TheoreticalPrevalencePublic HealthPublic PolicyTuberculosisConceptsUndiagnosed casesHigh-burden settingsSmear-negative casesTB control programsDuration of roundsBurden settingsTB suspectsTB transmissionAggressive approachOnward transmissionIncremental yieldPopulation healthIncremental benefitInfectious individualsDiagnostic toolControl programsICFPassive caseSymptomsCasesSettingDiagnosis
2011
A modelling framework to support the selection and implementation of new tuberculosis diagnostic tools [State of the art series. Operational research. Number 8 in the series]
Lin HH, Langley I, Mwenda R, Doulla B, Egwaga S, Millington KA, Mann GH, Murray M, Squire SB, Cohen T. A modelling framework to support the selection and implementation of new tuberculosis diagnostic tools [State of the art series. Operational research. Number 8 in the series]. The International Journal Of Tuberculosis And Lung Disease 2011, 15: 996-1004. PMID: 21740663, DOI: 10.5588/ijtld.11.0062.Peer-Reviewed Original ResearchConceptsDiagnostic strategiesHealth systemTB transmission dynamicsDiagnosis of tuberculosisHealth system requirementsDiagnostic toolHealth system componentsPolicy makersNew diagnostic strategiesHealth care infrastructureNovel diagnostic toolPatient outcomesDifferent epidemiologyJoint modelling frameworkModelling frameworkTest characteristicsCare infrastructureTransmission dynamicsRational choiceTuberculosisTechnological innovationStrategy decisionsDifficult decisionsMakersRecent introduction