2020
Poisson Kalman filter for disease surveillance
Ebeigbe D, Berry T, Schiff S, Sauer T. Poisson Kalman filter for disease surveillance. Physical Review Research 2020, 2: 043028. PMID: 39211287, PMCID: PMC11360429, DOI: 10.1103/physrevresearch.2.043028.Peer-Reviewed Original Research
2012
Reconstructing Mammalian Sleep Dynamics with Data Assimilation
Sedigh-Sarvestani M, Schiff S, Gluckman B. Reconstructing Mammalian Sleep Dynamics with Data Assimilation. PLOS Computational Biology 2012, 8: e1002788. PMID: 23209396, PMCID: PMC3510073, DOI: 10.1371/journal.pcbi.1002788.Peer-Reviewed Original ResearchConceptsUnscented Kalman filterData assimilationData assimilation frameworkParameter estimation methodNonlinear computational modelSleep-wake regulatory networkAssimilation frameworkUnknown parametersHidden variablesCovariance inflationNoisy variablesSlow dynamicsSparse measurementsComputational modelModel parametersKalman filterModel statesEstimation methodSimulation studyComplex systemsOptimal variablesFilter modelUKF frameworkModel variablesPartial observability
2010
Towards model-based control of Parkinson's disease
Schiff S. Towards model-based control of Parkinson's disease. Philosophical Transactions Of The Royal Society A Mathematical Physical And Engineering Sciences 2010, 368: 2269-2308. PMID: 20368246, PMCID: PMC2944387, DOI: 10.1098/rsta.2010.0050.Peer-Reviewed Original Research