Kenneth Gunasekera
DownloadHi-Res Photo
About
Biography
Kenneth is an MD-PhD student pursuing PhD training in the Department of Epidemiology of Microbial Disease advised by Ted Cohen. His work uses spatial and decision-analytic methods to improve the control and care of childhood tuberculosis. His scientific interests are in using modeling to extend clinical and epidemiological research to address critical problems in public health. His career aspiration is to be a physician-scientist who advocates for children, families, and communities, with a particular focus on improving access to evidence-based healthcare.
Education & Training
- BS
- Yale University, Molecular Biophysics and Biochemistry (2015)
Research
Research at a Glance
Yale Co-Authors
Frequent collaborators of Kenneth Gunasekera's published research.
Publications Timeline
A big-picture view of Kenneth Gunasekera's research output by year.
Ted Cohen, DPH, MD, MPH
Patrick Cudahy, MD, MSc
Reza Yaesoubi, PhD
6Publications
98Citations
Publications
2022
Reconstructing the course of the COVID-19 epidemic over 2020 for US states and counties: Results of a Bayesian evidence synthesis model
Chitwood MH, Russi M, Gunasekera K, Havumaki J, Klaassen F, Pitzer VE, Salomon JA, Swartwood NA, Warren JL, Weinberger DM, Cohen T, Menzies NA. Reconstructing the course of the COVID-19 epidemic over 2020 for US states and counties: Results of a Bayesian evidence synthesis model. PLOS Computational Biology 2022, 18: e1010465. PMID: 36040963, PMCID: PMC9467347, DOI: 10.1371/journal.pcbi.1010465.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsSARS-CoV-2 infectionCOVID-19 casesIncident SARS-CoV-2 infectionCOVID-19 disease burdenSymptomatic COVID-19 casesLocal epidemiological trendsSARS-CoV-2 transmissionBayesian evidence synthesis modelCOVID-19 outcomesEvidence synthesis modelMagnitude of infectionCOVID-19 deathsCumulative incidenceDisease burdenExcess mortalityCase ascertainmentEpidemiological trendsSeroprevalence estimatesUnderlying incidenceUS populationDisease trendsViral transmission dynamicsInfectionEpidemiological driversCOVID-19 epidemicPredicting resistance to fluoroquinolones among patients with rifampicin-resistant tuberculosis using machine learning methods
You S, Chitwood MH, Gunasekera KS, Crudu V, Codreanu A, Ciobanu N, Furin J, Cohen T, Warren JL, Yaesoubi R. Predicting resistance to fluoroquinolones among patients with rifampicin-resistant tuberculosis using machine learning methods. PLOS Digital Health 2022, 1: e0000059. PMID: 36177394, PMCID: PMC9518704, DOI: 10.1371/journal.pdig.0000059.Peer-Reviewed Original ResearchCitationsAltmetricConceptsDrug susceptibility testXpert MTB/RIFMachine learning-based modelsLearning-based modelsMachine learning methodsRifampicin-resistant tuberculosisTime of diagnosisRifampin-resistant tuberculosisMTB/RIFNeural network modelLearning methodsNetwork modelMulti-drug resistant tuberculosisNational TB surveillanceDrug-resistant tuberculosisOptimism-corrected areaSelection of antibioticsAnti-TB agentsDistrict-level prevalenceLow-resource settingsPatient characteristicsResistant tuberculosisTB surveillanceAppropriate treatmentDST resultsPhylogeography and transmission of M. tuberculosis in Moldova: A prospective genomic analysis
Yang C, Sobkowiak B, Naidu V, Codreanu A, Ciobanu N, Gunasekera KS, Chitwood MH, Alexandru S, Bivol S, Russi M, Havumaki J, Cudahy P, Fosburgh H, Allender CJ, Centner H, Engelthaler DM, Menzies NA, Warren JL, Crudu V, Colijn C, Cohen T. Phylogeography and transmission of M. tuberculosis in Moldova: A prospective genomic analysis. PLOS Medicine 2022, 19: e1003933. PMID: 35192619, PMCID: PMC8903246, DOI: 10.1371/journal.pmed.1003933.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsMultidrug-resistant tuberculosisDrug-resistant M. tuberculosisM. tuberculosis strainsPutative transmission clustersM. tuberculosisTransmission clustersMultidrug-resistant M. tuberculosis strainsTuberculosis strainsMultiple M. tuberculosis strainsCulture-positive TB casesLocal transmissionMDR-TB epidemicDrug-susceptible tuberculosisDrug resistance mutationsDrug resistance profilesUrgency of interventionTB casesDemographic dataNew casesTuberculosisInadequate treatmentNatural historyResistance mutationsBeijing lineageMycobacterium tuberculosis
2021
Development of a Treatment-decision Algorithm for Human Immunodeficiency Virus–uninfected Children Evaluated for Pulmonary Tuberculosis
Gunasekera KS, Walters E, van der Zalm MM, Palmer M, Warren JL, Hesseling AC, Cohen T, Seddon JA. Development of a Treatment-decision Algorithm for Human Immunodeficiency Virus–uninfected Children Evaluated for Pulmonary Tuberculosis. Clinical Infectious Diseases 2021, 73: e904-e912. PMID: 33449999, PMCID: PMC8366829, DOI: 10.1093/cid/ciab018.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsPulmonary tuberculosisClinical evidenceXpert MTB/RIFBaseline clinical evaluationChest radiographic resultsRapid treatment initiationNational Tuberculosis ProgrammeTreatment decision algorithmsEvidence-based algorithmMTB/RIFDiagnosis of tuberculosisChest radiographicAntituberculosis treatmentProspective cohortRadiographic resultsTreatment initiationTuberculosis casesTuberculosis ProgrammeClinical evaluationCase definitionTreatment decisionsGlobal burdenChildhood mortalityChildren EvaluatedRapid clinical diagnosis
2020
Children as sentinels of tuberculosis transmission: disease mapping of programmatic data
Gunasekera KS, Zelner J, Becerra MC, Contreras C, Franke MF, Lecca L, Murray MB, Warren JL, Cohen T. Children as sentinels of tuberculosis transmission: disease mapping of programmatic data. BMC Medicine 2020, 18: 234. PMID: 32873309, PMCID: PMC7466499, DOI: 10.1186/s12916-020-01702-x.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsNational Tuberculosis ProgrammeActive case-finding interventionsCase-finding interventionsTuberculosis transmissionNotification dataTuberculosis ProgrammeTuberculosis incidenceChild casesChildhood tuberculosis casesRecent transmission eventsProportion of casesCase notification dataMolecular epidemiological methodsMolecular epidemiologic methodsEndemic infectious diseasesTuberculosis casesProspective studyAdult casesDisease progressionNotification registerProgrammatic dataDistricts of LimaEpidemiological methodsInfectious diseasesTransmission hotspotsSmoking and HIV associated with subclinical tuberculosis: analysis of a population-based prevalence survey
Gunasekera K, Cohen T, Gao W, Ayles H, Godfrey-Faussett P, Claassens M. Smoking and HIV associated with subclinical tuberculosis: analysis of a population-based prevalence survey. The International Journal Of Tuberculosis And Lung Disease 2020, 24: 340-346. PMID: 32228765, DOI: 10.5588/ijtld.19.0387.Peer-Reviewed Original ResearchCitationsMeSH Keywords and ConceptsConceptsActive TBPrevalence surveyPopulation-based prevalence surveyCurrent tobacco smokingTypical symptomsHIV-positive statusTuberculosis prevalence surveyPrevalence survey dataSubclinical tuberculosisPositive TBCrude prevalenceTobacco smokingEpidemiological burdenPrevalent casesReduction TrialSubclinical TBMedical variablesSecondary analysisEstimate associationsSouth African communitySymptomsHIVSmokingTuberculosisDisease
Links & Media
News
- July 24, 2023
Anne Havlik and Kenneth Gunasekera Win Global Health Awards
- June 20, 2023
Office of Global Health Hosts 12th Annual Global Health Day
- March 13, 2023
New Algorithms Could Improve Pediatric Tuberculosis Diagnosis
- March 10, 2022
YSPH-led Study Identifies Drug-Resistant Tuberculosis Strains Spreading in Moldova