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
2020
Drug-target binding quantitatively predicts optimal antibiotic dose levels in quinolones
Clarelli F, Palmer A, Singh B, Storflor M, Lauksund S, Cohen T, Abel S, Wiesch P. Drug-target binding quantitatively predicts optimal antibiotic dose levels in quinolones. PLOS Computational Biology 2020, 16: e1008106. PMID: 32797079, PMCID: PMC7449454, DOI: 10.1371/journal.pcbi.1008106.Peer-Reviewed Original ResearchConceptsDose levelsAntibiotic efficacyDrug-target bindingDose-response relationshipBeta-lactams ampicillinTime-kill curvesDose-response curveNovel resistance mutationsMeasurable biochemical parametersClinical isolatesAntibiotic dose-response curvesAntibiotic actionResistance mutationsBiochemical parameters
2018
Use 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 ResearchRisk ratios for contagious outcomes
Morozova O, Cohen T, Crawford FW. Risk ratios for contagious outcomes. Journal Of The Royal Society Interface 2018, 15: 20170696. PMID: 29343627, PMCID: PMC5805970, DOI: 10.1098/rsif.2017.0696.Peer-Reviewed Original Research
2017
Using Chemical Reaction Kinetics to Predict Optimal Antibiotic Treatment Strategies
Wiesch P, Clarelli F, Cohen T. Using Chemical Reaction Kinetics to Predict Optimal Antibiotic Treatment Strategies. PLOS Computational Biology 2017, 13: e1005321. PMID: 28060813, PMCID: PMC5257006, DOI: 10.1371/journal.pcbi.1005321.Peer-Reviewed Original ResearchConceptsOptimal dosingTreatment strategiesOnset of actionAntibiotic treatment strategiesAntibiotic concentrationsOptimal therapyFrequent dosingHigh dosesBacterial infectionsDrug concentrationsDosingBacterial replicationAntibioticsPhysiological fluctuationsAntibiotic effectCellsHIVBacterial growthTherapyCancerInfectionMalariaDoses
2015
The potential impact of coinfection on antimicrobial chemotherapy and drug resistance
Birger RB, Kouyos RD, Cohen T, Griffiths EC, Huijben S, Mina MJ, Volkova V, Grenfell B, Metcalf CJE. The potential impact of coinfection on antimicrobial chemotherapy and drug resistance. Trends In Microbiology 2015, 23: 537-544. PMID: 26028590, PMCID: PMC4835347, DOI: 10.1016/j.tim.2015.05.002.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsCoinfectionDrug Resistance, MicrobialHost-Pathogen InteractionsHumansImmunologic FactorsMicrobial InteractionsModels, Biological
2014
Magnitude and sources of bias in the detection of mixed strain M. tuberculosis infection
Plazzotta G, Cohen T, Colijn C. Magnitude and sources of bias in the detection of mixed strain M. tuberculosis infection. Journal Of Theoretical Biology 2014, 368: 67-73. PMID: 25553967, PMCID: PMC7011203, DOI: 10.1016/j.jtbi.2014.12.009.Peer-Reviewed Original ResearchConceptsMixed infectionsM. tuberculosis infectionIncidence of TBOutcome of treatmentPopulation-level interventionsFraction of casesTuberculosis infectionMinority strainsActual prevalenceInfected individualsInfectionStudy designMycobacterium tuberculosisPrevalenceSputumTuberculosisDistinct strainsDifferent strainsSources of biasPrevious studiesPatientsSpecific reasonsIncidenceIndividuals
2013
Bayesian Estimation of Mixture Models with Prespecified Elements to Compare Drug Resistance in Treatment-Naïve and Experienced Tuberculosis Cases
Izu A, Cohen T, DeGruttola V. Bayesian Estimation of Mixture Models with Prespecified Elements to Compare Drug Resistance in Treatment-Naïve and Experienced Tuberculosis Cases. PLOS Computational Biology 2013, 9: e1002973. PMID: 23555210, PMCID: PMC3605089, DOI: 10.1371/journal.pcbi.1002973.Peer-Reviewed Original ResearchConceptsTreatment-experienced patientsDrug-resistant strainsMultiple drug-resistant strainsTreatment-naïve patientsDrug-resistant tuberculosisMycobacterium tuberculosis isolatesWorld Health OrganizationDrug resistance pathwaysTreatment-naïveTuberculosis casesTuberculosis isolatesWorldwide surveillanceDrug resistancePatientsHealth OrganizationResistant strainsResistant pathogensResistance pathwaysLow transmissibilityPathwayTuberculosis
2012
The impact of new tuberculosis diagnostics on transmission: why context matters
Lin HH, Dowdy D, Dye C, Murray M, Cohen T. The impact of new tuberculosis diagnostics on transmission: why context matters. Bulletin Of The World Health Organization 2012, 90: 739-747. PMID: 23109741, PMCID: PMC3471051, DOI: 10.2471/blt.11.101436.Peer-Reviewed Original ResearchConceptsNew tuberculosis diagnosticsNew diagnostic toolsPatient lossHuman immunodeficiency virus (HIV) infectionTuberculosis diagnosticsSmear-negative pulmonary tuberculosisDiagnostic toolImmunodeficiency virus infectionTreatment success rateSmear-negative casesIncidence of tuberculosisEpidemiology of tuberculosisPatient defaultPulmonary tuberculosisTuberculosis careDiagnostic pathwayTuberculosis transmissionSymptomatic individualsVirus infectionSmear microscopyTuberculosisAnnual declineDiagnosisAbsolute changeSuccess rate
2011
Modelling 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
2008
Latent Coinfection and the Maintenance of Strain Diversity
Colijn C, Cohen T, Murray M. Latent Coinfection and the Maintenance of Strain Diversity. Bulletin Of Mathematical Biology 2008, 71: 247. PMID: 19082663, PMCID: PMC2652765, DOI: 10.1007/s11538-008-9361-y.Peer-Reviewed Original ResearchNo coexistence for free: Neutral null models for multistrain pathogens
Lipsitch M, Colijn C, Cohen T, Hanage WP, Fraser C. No coexistence for free: Neutral null models for multistrain pathogens. Epidemics 2008, 1: 2-13. PMID: 21352747, PMCID: PMC3099423, DOI: 10.1016/j.epidem.2008.07.001.Peer-Reviewed Original ResearchModeling the effects of strain diversity and mechanisms of strain competition on the potential performance of new tuberculosis vaccines
Cohen T, Colijn C, Murray M. Modeling the effects of strain diversity and mechanisms of strain competition on the potential performance of new tuberculosis vaccines. Proceedings Of The National Academy Of Sciences Of The United States Of America 2008, 105: 16302-16307. PMID: 18849476, PMCID: PMC2570977, DOI: 10.1073/pnas.0808746105.Peer-Reviewed Original ResearchConceptsNew tuberculosis vaccinesTuberculosis vaccineBacillus Calmette-Guérin (BCG) vaccinationCurrent vaccine candidatesM. tuberculosis strainsPerformance of vaccinesTuberculosis infectionTuberculosis controlStrain diversityMass vaccinationVaccine candidatesNew vaccinesTuberculosis strainsStrain replacementVaccineM. tuberculosisMycobacterium tuberculosisTransmission of diseaseVaccinationTuberculosisInfectionDiseaseStrain specificityPotential effectsMorbidity
2007
Emergent heterogeneity in declining tuberculosis epidemics
Colijn C, Cohen T, Murray M. Emergent heterogeneity in declining tuberculosis epidemics. Journal Of Theoretical Biology 2007, 247: 765-774. PMID: 17540410, PMCID: PMC2652758, DOI: 10.1016/j.jtbi.2007.04.015.Peer-Reviewed Original Research
2006
Exogenous re-infection and the dynamics of tuberculosis epidemics: local effects in a network model of transmission
Cohen T, Colijn C, Finklea B, Murray M. Exogenous re-infection and the dynamics of tuberculosis epidemics: local effects in a network model of transmission. Journal Of The Royal Society Interface 2006, 4: 523-531. PMID: 17251134, PMCID: PMC2373405, DOI: 10.1098/rsif.2006.0193.Peer-Reviewed Original ResearchConceptsTB control strategiesPublic health policy makersType of TBMolecular epidemiologic toolsHealth policy makersTB diseasePrimary diseasePrimary progressionTB incidenceRecent infectionTB transmissionTuberculosis diseaseInfectious disease transmissionTuberculosis epidemicRecent transmissionEpidemiological dataForce of infectionInfectious casesLatent infectionEpidemiologic toolInfectionMycobacterium tuberculosisSpecific populationsDiseaseEpidemic trajectories
2004
Modeling epidemics of multidrug-resistant M. tuberculosis of heterogeneous fitness
Cohen T, Murray M. Modeling epidemics of multidrug-resistant M. tuberculosis of heterogeneous fitness. Nature Medicine 2004, 10: 1117-1121. PMID: 15378056, PMCID: PMC2652755, DOI: 10.1038/nm1110.Peer-Reviewed Original ResearchConceptsDrug-sensitive strainsMDR strainsMultidrug resistanceMultidrug-resistant M. tuberculosisM. tuberculosisMultidrug-resistant tuberculosisTuberculosis control effortsEmergence of resistanceTB controlFuture burdenMDR diseaseMDRTB strainsEpidemiological measuresTuberculosisMDRSmall subpopulationMDRTBControl programsBurdenSingle locus mutationsSequential acquisitionDisease