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Different models that operate under different parameters and assumptions produce radically different predictions, creating confusion among policy-makers and the general population and limiting the usefulness of the models. This newsletter article proposes a novel ensemble modeling approach that uses representative clustering to identify where existing model predictions of COVID-19 spread agree and unify these predictions into a smaller set of predictions. The proposed ensemble prediction approach is composed of the following stages: (1) the selection of the ensemble components, (2) the imputation of missing predictions for each component, and (3) representative clustering in application to time-series data to determine the degree of agreement between simulation predictions. The results of the proposed approach will produce a set of ensemble model predictions that identify where simulation results converge so that policy-makers and the general public are informed with more comprehensive predictions and the uncertainty among them.<\/jats:p>","DOI":"10.1145\/3431843.3431848","type":"journal-article","created":{"date-parts":[[2020,10,26]],"date-time":"2020-10-26T16:28:56Z","timestamp":1603729736000},"page":"33-41","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["COVID-19 ensemble models using representative clustering"],"prefix":"10.1145","volume":"12","author":[{"given":"Joon-Seok","family":"Kim","sequence":"first","affiliation":[{"name":"George Mason University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hamdi","family":"Kavak","sequence":"additional","affiliation":[{"name":"George Mason University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas","family":"Z\u00fcfle","sequence":"additional","affiliation":[{"name":"George Mason University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taylor","family":"Anderson","sequence":"additional","affiliation":[{"name":"George Mason University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,10,26]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.12688\/wellcomeopenres.16006.1"},{"key":"e_1_2_1_2_1","unstructured":"P. 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The effect of travel restrictions on the spread of the 2019 novel coronavirus (covid-19) outbreak. Science, 2020."},{"key":"e_1_2_1_5_1","unstructured":"S. Del Valle. Los Alamos COVID-19 Confirmed and Forecasted Case Data. https:\/\/covid-19.bsvgateway.org\/. Accessed: 2020-04-11.  S. Del Valle. Los Alamos COVID-19 Confirmed and Forecasted Case Data. https:\/\/covid-19.bsvgateway.org\/. Accessed: 2020-04-11."},{"key":"e_1_2_1_6_1","unstructured":"G. Espana R. Oidtman S. Cavany A. Costello A. Wieler A. Lerch C. Barbera M. Poterek Q. Tran S. Moore and A. Perkins. NotreDame-FRED (https:\/\/github.com\/confunguido\/covid19_ND_forecasting).  G. Espana R. Oidtman S. Cavany A. Costello A. Wieler A. Lerch C. Barbera M. Poterek Q. Tran S. Moore and A. Perkins. 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Csalog\u00e1ny. Methods for large scale svd with missing values. In Proceedings of KDD cup and workshop, volume 12, pages 31--38. Citeseer, 2007."},{"key":"e_1_2_1_20_1","unstructured":"M. L. Li H. T. Bouardi O. S. Lami T. A. Trikalinos N. K. Trichakis and D. Bertsimas. CovidAnalytics at MIT (https:\/\/www.covidanalytics.io\/).  M. L. Li H. T. Bouardi O. S. Lami T. A. Trikalinos N. K. Trichakis and D. Bertsimas. CovidAnalytics at MIT (https:\/\/www.covidanalytics.io\/)."},{"key":"e_1_2_1_21_1","volume-title":"Mathematics of climate change: a new discipline for an uncertain century","author":"Mackenzie D.","year":"2007","unstructured":"D. Mackenzie . Mathematics of climate change: a new discipline for an uncertain century . Mathematical Sciences Research Institute , 2007 . D. Mackenzie. Mathematics of climate change: a new discipline for an uncertain century. 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Vespignani etal COVID-19 MODELING IN THE UNITED STATES. https:\/\/covid19.gleamproject.org\/. Accessed: 2020-04-11.  A. Vespignani et al. COVID-19 MODELING IN THE UNITED STATES. https:\/\/covid19.gleamproject.org\/. Accessed: 2020-04-11."},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.epidem.2017.08.002"},{"key":"e_1_2_1_38_1","volume-title":"Assessing the impact of land use change on hydrology by ensemble modelling (luchem) ii: Ensemble combinations and predictions. Advances in water resources, 32(2):147--158","author":"Viney N. R.","year":"2009","unstructured":"N. R. Viney , H. Bormann , L. Breuer , A. Bronstert , B. F. Croke , H. Frede , T. Gr\u00e4ff , L. Hubrechts , J. A. Huisman , A. J. Jakeman , Assessing the impact of land use change on hydrology by ensemble modelling (luchem) ii: Ensemble combinations and predictions. Advances in water resources, 32(2):147--158 , 2009 . N. R. Viney, H. Bormann, L. Breuer, A. Bronstert, B. F. Croke, H. Frede, T. 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