2023
Predictive power of wastewater for nowcasting infectious disease transmission: A retrospective case study of five sewershed areas in Louisville, Kentucky
Klaassen F, Holm R, Smith T, Cohen T, Bhatnagar A, Menzies N. Predictive power of wastewater for nowcasting infectious disease transmission: A retrospective case study of five sewershed areas in Louisville, Kentucky. Environmental Research 2023, 240: 117395. PMID: 37838198, PMCID: PMC10863376, DOI: 10.1016/j.envres.2023.117395.Peer-Reviewed Original ResearchConceptsDeath dataSurveillance dataSARS-CoV-2 casesClinical surveillance dataLow-resource settingsRetrospective case studyInfectious disease transmissionTrue infectionEpidemiologic dataSerosurvey dataDeath reportsTraditional surveillance dataDisease trendsInfectious diseasesWastewater dataDisease transmissionPredictive performance
2022
Predicting 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 ResearchDrug 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 results