2023
Spatial Modeling of Mycobacterium Tuberculosis Transmission with Dyadic Genetic Relatedness Data
Warren J, Chitwood M, Sobkowiak B, Colijn C, Cohen T. Spatial Modeling of Mycobacterium Tuberculosis Transmission with Dyadic Genetic Relatedness Data. Biometrics 2023, 79: 3650-3663. PMID: 36745619, PMCID: PMC10404301, DOI: 10.1111/biom.13836.Peer-Reviewed Original Research
2022
vCOMBAT: a novel tool to create and visualize a computational model of bacterial antibiotic target-binding
Tran VN, Shams A, Ascioglu S, Martinecz A, Liang J, Clarelli F, Mostowy R, Cohen T, Abel zur Wiesch P. vCOMBAT: a novel tool to create and visualize a computational model of bacterial antibiotic target-binding. BMC Bioinformatics 2022, 23: 22. PMID: 34991453, PMCID: PMC8734216, DOI: 10.1186/s12859-021-04536-3.Peer-Reviewed Original ResearchMeSH KeywordsAnti-Bacterial AgentsBacteriaComputer SimulationDrug Resistance, BacterialModels, BiologicalConceptsOwn computational model
2021
The Impact of Changes in Diagnostic Testing Practices on Estimates of COVID-19 Transmission in the United States
Pitzer VE, Chitwood M, Havumaki J, Menzies NA, Perniciaro S, Warren JL, Weinberger DM, Cohen T. The Impact of Changes in Diagnostic Testing Practices on Estimates of COVID-19 Transmission in the United States. American Journal Of Epidemiology 2021, 190: 1908-1917. PMID: 33831148, PMCID: PMC8083380, DOI: 10.1093/aje/kwab089.Peer-Reviewed Original ResearchConceptsSevere acute respiratory syndrome coronavirus 2Acute respiratory syndrome coronavirus 2Diagnostic testingRespiratory syndrome coronavirus 2Reproductive numberPercentage of patientsSyndrome coronavirus 2Coronavirus disease 2019Diagnostic testing practicesFraction of casesCOVID-19 transmissionEffective reproductive numberCoronavirus 2Disease 2019Testing practicesCoronavirus diseaseBasic reproductive numberLevel of interventionEstimates of R0Daily numberProtective impacts of household-based tuberculosis contact tracing are robust across endemic incidence levels and community contact patterns
Havumaki J, Cohen T, Zhai C, Miller JC, Guikema SD, Eisenberg MC, Zelner J. Protective impacts of household-based tuberculosis contact tracing are robust across endemic incidence levels and community contact patterns. PLOS Computational Biology 2021, 17: e1008713. PMID: 33556077, PMCID: PMC7895355, DOI: 10.1371/journal.pcbi.1008713.Peer-Reviewed Original ResearchConceptsTuberculosis burdenCommunity transmissionGlobal tuberculosis control targetsSustained community transmissionHigh-incidence settingsHigh disease burdenTuberculosis contact tracingLow tuberculosis burdenHousehold contactsHousehold transmissionDisease burdenVaried incidenceContact patternsIncidence levelsProtective impactEpidemiological settingsInfected individualsNew casesProtective benefitsContact tracingCommunity contactsSuch interventionsInterventionBurdenProactive case
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 ResearchConceptsFilter 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 performanceUse of daily Internet search query data improves real-time projections of influenza epidemics
Zimmer C, Leuba SI, Yaesoubi R, Cohen T. Use of daily Internet search query data improves real-time projections of influenza epidemics. Journal Of The Royal Society Interface 2018, 15: 20180220. PMID: 30305417, PMCID: PMC6228485, DOI: 10.1098/rsif.2018.0220.Peer-Reviewed Original Research
2017
A Multistrain Mathematical Model To Investigate the Role of Pyrazinamide in the Emergence of Extensively Drug-Resistant Tuberculosis
Fofana MO, Shrestha S, Knight GM, Cohen T, White RG, Cobelens F, Dowdy DW. A Multistrain Mathematical Model To Investigate the Role of Pyrazinamide in the Emergence of Extensively Drug-Resistant Tuberculosis. Antimicrobial Agents And Chemotherapy 2017, 61: 10.1128/aac.00498-16. PMID: 27956422, PMCID: PMC5328532, DOI: 10.1128/aac.00498-16.Peer-Reviewed Original ResearchMeSH KeywordsAntitubercular AgentsBayes TheoremBiological AvailabilityComputer SimulationDrug Administration ScheduleDrug Resistance, Multiple, BacterialExtensively Drug-Resistant TuberculosisFluoroquinolonesHumansMicrobial Sensitivity TestsModels, StatisticalMycobacterium tuberculosisPyrazinamideRifampinTuberculosis, PulmonaryConceptsCompanion drugsExtensively Drug-Resistant TuberculosisSecond-line treatmentFirst-line treatmentSecond-line regimensDrug-resistant tuberculosisUse of pyrazinamideExtensive drug resistanceDrug resistance dataEmergence of strainsEmergence of mutationsXDR-TBSequential regimensHIV infectionAlternative drugsResistance amplificationPyrazinamide resistanceProlonged treatmentCombination antimicrobialsDrug resistanceInfectious diseasesPrevalenceProportion of simulationsAppropriate useRegimensA 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
2015
How could preventive therapy affect the prevalence of drug resistance? Causes and consequences
Kunkel A, Colijn C, Lipsitch M, Cohen T. How could preventive therapy affect the prevalence of drug resistance? Causes and consequences. Philosophical Transactions Of The Royal Society B Biological Sciences 2015, 370: 20140306. PMID: 25918446, PMCID: PMC4424438, DOI: 10.1098/rstb.2014.0306.Peer-Reviewed Original ResearchMeSH KeywordsAntibiotic ProphylaxisCommunicable Disease ControlComputer SimulationDrug Resistance, MicrobialHumansInfectionsModels, TheoreticalPrevalenceConceptsPreventative therapyDrug resistanceDrug-sensitive pathogensProphylactic antimicrobial therapyLong-term prevalenceSmall pilot studyActive diseaseOverall prevalenceAntimicrobial therapyPrevalencePilot studyTherapyPopulation-level changesPotential population-level effectsDirect effectLevel of coveragePopulation-level effectsHIVTuberculosisMalariaDiseaseCareEvaluating the potential impact of enhancing HIV treatment and tuberculosis control programmes on the burden of tuberculosis
Chindelevitch L, Menzies NA, Pretorius C, Stover J, Salomon JA, Cohen T. Evaluating the potential impact of enhancing HIV treatment and tuberculosis control programmes on the burden of tuberculosis. Journal Of The Royal Society Interface 2015, 12: 20150146. PMID: 25878131, PMCID: PMC4424692, DOI: 10.1098/rsif.2015.0146.Peer-Reviewed Original ResearchConceptsAntiretroviral therapyTB incidenceTB burdenBurden of tuberculosisTuberculosis Control ProgrammePotential epidemiological impactART eligibilityMortality benefitTB programsHIV treatmentTB prevalenceTuberculosis incidenceEpidemiological impactART useProgram improvementHIVTreatment effectivenessIncidenceMortalityGreater reductionBurdenTBSaharan AfricaControl programsEligibility
2013
Mycobacterium tuberculosis mutation rate estimates from different lineages predict substantial differences in the emergence of drug-resistant tuberculosis
Ford CB, Shah RR, Maeda MK, Gagneux S, Murray MB, Cohen T, Johnston JC, Gardy J, Lipsitch M, Fortune SM. Mycobacterium tuberculosis mutation rate estimates from different lineages predict substantial differences in the emergence of drug-resistant tuberculosis. Nature Genetics 2013, 45: 784-790. PMID: 23749189, PMCID: PMC3777616, DOI: 10.1038/ng.2656.Peer-Reviewed Original Research
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 introductionBayesian methods for fitting mixture models that characterize branching tree processes: An application to development of resistant TB strains
Izu A, Cohen T, Mitnick C, Murray M, De Gruttola V. Bayesian methods for fitting mixture models that characterize branching tree processes: An application to development of resistant TB strains. Statistics In Medicine 2011, 30: 2708-2720. PMID: 21717491, PMCID: PMC3219798, DOI: 10.1002/sim.4287.Peer-Reviewed Original ResearchConceptsCharacterization of uncertaintyBayesian approachBayesian methodsBranching tree modelStatistical methodsMixture modelBranching treeNatural wayPrior informationDrug resistance-conferring mutationsSuch cross-sectional dataDrug-resistant TBResistant TB strainsCombination of antibioticsDrug resistance mutationsMeasurement errorResistance-conferring mutationsTB strainsSingle patientTreatment policyPatientsMultiple drugsDiagnostic specimensCross-sectional dataGenetic mutationsModelling the performance of isoniazid preventive therapy for reducing tuberculosis in HIV endemic settings: the effects of network structure
Mills HL, Cohen T, Colijn C. Modelling the performance of isoniazid preventive therapy for reducing tuberculosis in HIV endemic settings: the effects of network structure. Journal Of The Royal Society Interface 2011, 8: 1510-1520. PMID: 21508012, PMCID: PMC3163428, DOI: 10.1098/rsif.2011.0160.Peer-Reviewed Original ResearchConceptsTB diseaseLatent M. tuberculosis infectionEffects of IptHIV-endemic settingsActive tuberculosis diseaseIsoniazid preventive therapyIntact immune systemM. tuberculosis infectionWorld Health OrganizationPreventive therapyTB casesTuberculosis infectionTuberculosis diseaseClinical trialsEndemic settingsIPT programHigh riskLatent infectionSingle drugCommunity-wide levelRespiratory contactHIVImmune systemPopulation-level impactUse of IPT
2007
Antiviral Resistance and the Control of Pandemic Influenza
Lipsitch M, Cohen T, Murray M, Levin BR. Antiviral Resistance and the Control of Pandemic Influenza. PLOS Medicine 2007, 4: e15. PMID: 17253900, PMCID: PMC1779817, DOI: 10.1371/journal.pmed.0040015.Peer-Reviewed Original ResearchMeSH KeywordsAntiviral AgentsComputer SimulationDisease OutbreaksDrug Resistance, ViralHumansInfluenza, HumanOrthomyxoviridaeOseltamivirPlanning TechniquesConceptsResistant strainsPandemic influenzaInfluenza pandemicAntiviral resistanceTransmission-reducing measuresNext influenza pandemicAntiviral drug useUse of antiviralsStrains of influenzaSuboptimal vaccinesAntiviral treatmentInfluenza infectionRisk of resistanceDeterministic compartmental modelAntiviral agentsAntiviral drugsPandemic planningDrug useVaccine developmentDrug resistanceControl measuresAntiviralsInfluenzaPandemic spreadPandemic