2019
Modeling Approaches Toward Understanding Infectious Disease Transmission
Skrip L, Townsend J. Modeling Approaches Toward Understanding Infectious Disease Transmission. 2019, 227-243. PMCID: PMC7121152, DOI: 10.1007/978-3-030-25553-4_14.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsModern statistical inferenceInfectious disease dynamicsStatistical inferenceMathematical modelData-driven modelsPublic health strategiesDisease dynamicsGlobal health infrastructureInfluenza vaccinationInfectious disease transmissionHealth strategiesDisease trendsBehavioral interventionsHealth infrastructureEbola epidemicPowerful toolDisease transmissionModelDiseaseCommunity-based initiativesIncorporation of dataInferenceFundamental aspectsDynamicsPredictive powerModelling microbial infection to address global health challenges
Fitzpatrick MC, Bauch CT, Townsend JP, Galvani AP. Modelling microbial infection to address global health challenges. Nature Microbiology 2019, 4: 1612-1619. PMID: 31541212, PMCID: PMC6800015, DOI: 10.1038/s41564-019-0565-8.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsOptimization of modelsGlobal health challengeDisease transmission dynamicsHealth challengesEpidemiological modellingAccuracy of predictionDifferent intervention strategiesPandemic preparednessInfectious diseasesTransmission dynamicsMicrobial infectionsIntervention strategiesModel developmentPublic healthDiseaseHIV crisisModelOptimizationRiskHealthDynamicsModellingClose collaborationMethodological advancesModelers
2009
Maximum-Likelihood Model Averaging To Profile Clustering of Site Types across Discrete Linear Sequences
Zhang Z, Townsend JP. Maximum-Likelihood Model Averaging To Profile Clustering of Site Types across Discrete Linear Sequences. PLOS Computational Biology 2009, 5: e1000421. PMID: 19557160, PMCID: PMC2695770, DOI: 10.1371/journal.pcbi.1000421.Peer-Reviewed Original ResearchConceptsInformation criterionModel averagingBayesian information criterionMaximum likelihood methodModel likelihoodModel uncertaintyModel selectionDescription of clustersLevel of clusteringPrecision of estimationAkaike information criterionParameter rangeCluster countsLikelihood methodComputational biologyCluster sizeGood accuracyConquer strategyAveragingClusteringModelHierarchical clusteringClustersStatisticsEstimation