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
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