{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T04:55:36Z","timestamp":1783572936454,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":47,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,8,14]],"date-time":"2021-08-14T00:00:00Z","timestamp":1628899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"University of Virginia Strategic Investment Fund","award":["SIF160"],"award-info":[{"award-number":["SIF160"]}]},{"name":"NSF BIG DATA","award":["IIS-1633028"],"award-info":[{"award-number":["IIS-1633028"]}]},{"DOI":"10.13039\/100000774","name":"Defense Threat Reduction Agency","doi-asserted-by":"publisher","award":["HDTRA1-19-D-0007"],"award-info":[{"award-number":["HDTRA1-19-D-0007"]}],"id":[{"id":"10.13039\/100000774","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004989","name":"Virginia Department of Health","doi-asserted-by":"publisher","award":["C&P - VDH-21-501-0141"],"award-info":[{"award-number":["C&P - VDH-21-501-0141"]}],"id":[{"id":"10.13039\/100004989","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["OAC-1916805"],"award-info":[{"award-number":["OAC-1916805"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NSF RAPID","award":["CNS-2028004"],"award-info":[{"award-number":["CNS-2028004"]}]},{"name":"US Centers for Disease Control and Prevention","award":["75D30119C05935"],"award-info":[{"award-number":["75D30119C05935"]}]},{"name":"NSF Expeditions in Computing","award":["CCF-1918656"],"award-info":[{"award-number":["CCF-1918656"]}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["1R01GM109718"],"award-info":[{"award-number":["1R01GM109718"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,8,14]]},"DOI":"10.1145\/3447548.3467197","type":"proceedings-article","created":{"date-parts":[[2021,8,12]],"date-time":"2021-08-12T06:12:05Z","timestamp":1628748725000},"page":"2505-2513","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":20,"title":["All Models Are Useful: Bayesian Ensembling for Robust High Resolution COVID-19 Forecasting"],"prefix":"10.1145","author":[{"given":"Aniruddha","family":"Adiga","sequence":"first","affiliation":[{"name":"University of Virginia, Charlottesville, VA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijing","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Virginia, Charlottesville, VA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin","family":"Hurt","sequence":"additional","affiliation":[{"name":"University of Virginia, Charlottesville, VA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akhil","family":"Peddireddy","sequence":"additional","affiliation":[{"name":"University of Virginia, Charlottesville, VA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Przemyslaw","family":"Porebski","sequence":"additional","affiliation":[{"name":"University of Virginia, Charlottesville, VA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Srinivasan","family":"Venkatramanan","sequence":"additional","affiliation":[{"name":"University of Virginia, Charlottesville, VA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bryan Leroy","family":"Lewis","sequence":"additional","affiliation":[{"name":"University of Virginia, Charlottesville, VA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Madhav","family":"Marathe","sequence":"additional","affiliation":[{"name":"University of Virginia, Charlottesville, VA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,8,14]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0230405"},{"key":"e_1_3_2_1_2_1","first-page":"126","article-title":"A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models","volume":"4","author":"Bilmes Jeff A","year":"1998","unstructured":"Jeff A Bilmes 1998 . A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models . International Computer Science Institute , Vol. 4 , 510 (1998), 126 . Jeff A Bilmes et al. 1998. A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models. International Computer Science Institute, Vol. 4, 510 (1998), 126.","journal-title":"International Computer Science Institute"},{"key":"e_1_3_2_1_3_1","volume-title":"Evaluating epidemic forecasts in an interval format. arXiv preprint arXiv:2005.12881","author":"Bracher Johannes","year":"2020","unstructured":"Johannes Bracher , Evan L Ray , Tilmann Gneiting , and Nicholas G Reich . 2020. Evaluating epidemic forecasts in an interval format. arXiv preprint arXiv:2005.12881 ( 2020 ). Johannes Bracher, Evan L Ray, Tilmann Gneiting, and Nicholas G Reich. 2020. Evaluating epidemic forecasts in an interval format. arXiv preprint arXiv:2005.12881 (2020)."},{"key":"e_1_3_2_1_4_1","volume-title":"Peter Jan van Leeuwen, and Geir Evensen","author":"Burgers Gerrit","year":"1998","unstructured":"Gerrit Burgers , Peter Jan van Leeuwen, and Geir Evensen . 1998 . Analysis scheme in the ensemble Kalman filter. Monthly weather review, Vol. 126 , 6 (1998), 1719--1724. Gerrit Burgers, Peter Jan van Leeuwen, and Geir Evensen. 1998. Analysis scheme in the ensemble Kalman filter. Monthly weather review, Vol. 126, 6 (1998), 1719--1724."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2020.138817"},{"key":"e_1_3_2_1_6_1","volume-title":"Kunpeng Mu, Luca Rossi, Kaiyuan Sun, et al.","author":"Chinazzi Matteo","year":"2020","unstructured":"Matteo Chinazzi , Jessica T Davis , Marco Ajelli , Corrado Gioannini , Maria Litvinova , Stefano Merler , Ana Pastore y Piontti , Kunpeng Mu, Luca Rossi, Kaiyuan Sun, et al. 2020 . The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science , Vol. 368 , 6489 (2020), 395--400. Matteo Chinazzi, Jessica T Davis, Marco Ajelli, Corrado Gioannini, Maria Litvinova, Stefano Merler, Ana Pastore y Piontti, Kunpeng Mu, Luca Rossi, Kaiyuan Sun, et al. 2020. The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science, Vol. 368, 6489 (2020), 395--400."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411975"},{"key":"e_1_3_2_1_8_1","volume-title":"An interactive web-based dashboard to track COVID-19 in real time. The Lancet infectious diseases","author":"Dong Ensheng","year":"2020","unstructured":"Ensheng Dong , Hongru Du , and Lauren Gardner . 2020. An interactive web-based dashboard to track COVID-19 in real time. The Lancet infectious diseases , Vol. 20 , 5 ( 2020 ), 533--534. Ensheng Dong, Hongru Du, and Lauren Gardner. 2020. An interactive web-based dashboard to track COVID-19 in real time. The Lancet infectious diseases, Vol. 20, 5 (2020), 533--534."},{"key":"e_1_3_2_1_9_1","volume-title":"Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany. arXiv preprint arXiv:2101.00661","author":"Fritz Cornelius","year":"2021","unstructured":"Cornelius Fritz , Emilio Dorigatti , and David R\u00fcgamer . 2021. Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany. arXiv preprint arXiv:2101.00661 ( 2021 ). Cornelius Fritz, Emilio Dorigatti, and David R\u00fcgamer. 2021. Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany. arXiv preprint arXiv:2101.00661 (2021)."},{"key":"e_1_3_2_1_10_1","volume-title":"international conference on machine learning. 1050--1059","author":"Gal Yarin","year":"2016","unstructured":"Yarin Gal and Zoubin Ghahramani . 2016 . Dropout as a bayesian approximation: Representing model uncertainty in deep learning . In international conference on machine learning. 1050--1059 . Yarin Gal and Zoubin Ghahramani. 2016. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In international conference on machine learning. 1050--1059."},{"key":"e_1_3_2_1_11_1","volume-title":"STAN: Spatio-Temporal Attention Network for Pandemic Prediction Using Real World Evidence. arXiv preprint arXiv:2008.04215","author":"Gao Junyi","year":"2020","unstructured":"Junyi Gao , Rakshith Sharma , Cheng Qian , Lucas M Glass , Jeffrey Spaeder , Justin Romberg , Jimeng Sun , and Cao Xiao . 2020 . STAN: Spatio-Temporal Attention Network for Pandemic Prediction Using Real World Evidence. arXiv preprint arXiv:2008.04215 (2020). Junyi Gao, Rakshith Sharma, Cheng Qian, Lucas M Glass, Jeffrey Spaeder, Justin Romberg, Jimeng Sun, and Cao Xiao. 2020. STAN: Spatio-Temporal Attention Network for Pandemic Prediction Using Real World Evidence. arXiv preprint arXiv:2008.04215 (2020)."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.2307\/2346830"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106610"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.2307\/2529336"},{"key":"e_1_3_2_1_16_1","unstructured":"Jennifer A Hoeting David Madigan Adrian E Raftery and Chris T Volinsky. 1999. Bayesian model averaging: a tutorial. Statist. Sci. (1999) 382--401.  Jennifer A Hoeting David Madigan Adrian E Raftery and Chris T Volinsky. 1999. Bayesian model averaging: a tutorial. Statist. Sci. (1999) 382--401."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2016.05.040"},{"key":"e_1_3_2_1_18_1","unstructured":"Rob Hyndman George Athanasopoulos Christoph Bergmeir Gabriel Caceres Leanne Chhay Mitchell O'Hara-Wild Fotios Petropoulos Slava Razbash Earo Wang and Farah Yasmeen. 2020. forecast: Forecasting functions for time series and linear models. https:\/\/pkg.robjhyndman.com\/forecast\/ R package version 8.13.  Rob Hyndman George Athanasopoulos Christoph Bergmeir Gabriel Caceres Leanne Chhay Mitchell O'Hara-Wild Fotios Petropoulos Slava Razbash Earo Wang and Farah Yasmeen. 2020. forecast: Forecasting functions for time series and linear models. https:\/\/pkg.robjhyndman.com\/forecast\/ R package version 8.13."},{"key":"e_1_3_2_1_19_1","volume-title":"Kalman and Bayesian Filters in Python. https:\/\/github.com\/rlabbe\/Kalman-and-Bayesian-Filters-in-Python Retrieved","author":"Labbe Roger R.","year":"2020","unstructured":"Roger R. Labbe Jr . [n.d.]. Kalman and Bayesian Filters in Python. https:\/\/github.com\/rlabbe\/Kalman-and-Bayesian-Filters-in-Python Retrieved October 20, 2020 from Roger R. Labbe Jr. [n.d.]. Kalman and Bayesian Filters in Python. https:\/\/github.com\/rlabbe\/Kalman-and-Bayesian-Filters-in-Python Retrieved October 20, 2020 from"},{"key":"e_1_3_2_1_20_1","volume-title":"Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks. arXiv preprint arXiv:2007.03113","author":"Kapoor Amol","year":"2020","unstructured":"Amol Kapoor , Xue Ben , Luyang Liu , Bryan Perozzi , Matt Barnes , Martin Blais , and Shawn O'Banion . 2020. Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks. arXiv preprint arXiv:2007.03113 ( 2020 ). Amol Kapoor, Xue Ben, Luyang Liu, Bryan Perozzi, Matt Barnes, Martin Blais, and Shawn O'Banion. 2020. Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks. arXiv preprint arXiv:2007.03113 (2020)."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1090\/S0002-9939-1953-0055639-3"},{"key":"e_1_3_2_1_22_1","first-page":"181","article-title":"ARIMA-based forecasting of the dynamics of confirmed Covid-19 cases for selected European countries. Equilibrium","volume":"15","author":"Tadeusz Kufel","year":"2020","unstructured":"Tadeusz Kufel et al. 2020 . ARIMA-based forecasting of the dynamics of confirmed Covid-19 cases for selected European countries. Equilibrium . Quarterly Journal of Economics and Economic Policy , Vol. 15 , 2 (2020), 181 -- 204 . Tadeusz Kufel et al. 2020. ARIMA-based forecasting of the dynamics of confirmed Covid-19 cases for selected European countries. Equilibrium. Quarterly Journal of Economics and Economic Policy, Vol. 15, 2 (2020), 181--204.","journal-title":"Quarterly Journal of Economics and Economic Policy"},{"key":"e_1_3_2_1_23_1","volume-title":"FilterPy - Kalman filters and other optimal and non-optimal estimation filters in Python. https:\/\/pypi.org\/project\/filterpy\/ Retrieved","author":"Labbe Roger","year":"2020","unstructured":"Roger Labbe . [n.d.]. FilterPy - Kalman filters and other optimal and non-optimal estimation filters in Python. https:\/\/pypi.org\/project\/filterpy\/ Retrieved October 20, 2020 from Roger Labbe. [n.d.]. FilterPy - Kalman filters and other optimal and non-optimal estimation filters in Python. https:\/\/pypi.org\/project\/filterpy\/ Retrieved October 20, 2020 from"},{"key":"e_1_3_2_1_24_1","unstructured":"Vinod Nair and Geoffrey E Hinton. 2010. Rectified linear units improve restricted boltzmann machines. In Icml .  Vinod Nair and Geoffrey E Hinton. 2010. Rectified linear units improve restricted boltzmann machines. In Icml ."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/2723372.2737793"},{"key":"e_1_3_2_1_26_1","volume-title":"Twitter improves influenza forecasting. PLoS currents","author":"Paul Michael J","year":"2014","unstructured":"Michael J Paul , Mark Dredze , and David Broniatowski . 2014. Twitter improves influenza forecasting. PLoS currents , Vol. 6 ( 2014 ). Michael J Paul, Mark Dredze, and David Broniatowski. 2014. Twitter improves influenza forecasting. PLoS currents, Vol. 6 (2014)."},{"key":"e_1_3_2_1_27_1","volume-title":"Using Bayesian model averaging to calibrate forecast ensembles. Monthly weather review","author":"Raftery Adrian E","year":"2005","unstructured":"Adrian E Raftery , Tilmann Gneiting , Fadoua Balabdaoui , and Michael Polakowski . 2005. Using Bayesian model averaging to calibrate forecast ensembles. Monthly weather review , Vol. 133 , 5 ( 2005 ), 1155--1174. Adrian E Raftery, Tilmann Gneiting, Fadoua Balabdaoui, and Michael Polakowski. 2005. Using Bayesian model averaging to calibrate forecast ensembles. Monthly weather review, Vol. 133, 5 (2005), 1155--1174."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214503000000891"},{"key":"e_1_3_2_1_29_1","volume-title":"DeepCOVIDNet: An Interpretable Deep Learning Model for Predictive Surveillance of COVID-19 Using Heterogeneous Features and Their Interactions. arXiv preprint arXiv:2008.00115","author":"Ramchandani Ankit","year":"2020","unstructured":"Ankit Ramchandani , Chao Fan , and Ali Mostafavi . 2020. DeepCOVIDNet: An Interpretable Deep Learning Model for Predictive Surveillance of COVID-19 Using Heterogeneous Features and Their Interactions. arXiv preprint arXiv:2008.00115 ( 2020 ). Ankit Ramchandani, Chao Fan, and Ali Mostafavi. 2020. DeepCOVIDNet: An Interpretable Deep Learning Model for Predictive Surveillance of COVID-19 Using Heterogeneous Features and Their Interactions. arXiv preprint arXiv:2008.00115 (2020)."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1007518"},{"key":"e_1_3_2_1_31_1","volume-title":"Abdul Hannan Kanji","author":"Ray Evan L","unstructured":"Evan L Ray , Nutcha Wattanachit , Jarad Niemi , Abdul Hannan Kanji , Katie House , Estee Y Cramer, Johannes Bracher, Andrew Zheng, Teresa K Yamana, Xinyue Xiong, et al. 2020. Ensemble Forecasts of Coronavirus Disease 2019 (COVID-19) in the us. MedRXiv (2020). Evan L Ray, Nutcha Wattanachit, Jarad Niemi, Abdul Hannan Kanji, Katie House, Estee Y Cramer, Johannes Bracher, Andrew Zheng, Teresa K Yamana, Xinyue Xiong, et al. 2020. Ensemble Forecasts of Coronavirus Disease 2019 (COVID-19) in the us. MedRXiv (2020)."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1812594116"},{"key":"e_1_3_2_1_33_1","volume-title":"DeepCOVID: An Operational Deep Learning-driven Framework for Explainable Real-time COVID-19 Forecasting. medRxiv","author":"Rodriguez Alexander","year":"2020","unstructured":"Alexander Rodriguez , Anika Tabassum , Jiaming Cui , Jiajia Xie , Javen Ho , Pulak Agarwal , Bijaya Adhikari , and B Aditya Prakash . 2020. DeepCOVID: An Operational Deep Learning-driven Framework for Explainable Real-time COVID-19 Forecasting. medRxiv ( 2020 ). Alexander Rodriguez, Anika Tabassum, Jiaming Cui, Jiajia Xie, Javen Ho, Pulak Agarwal, Bijaya Adhikari, and B Aditya Prakash. 2020. DeepCOVID: An Operational Deep Learning-driven Framework for Explainable Real-time COVID-19 Forecasting. medRxiv (2020)."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0009450"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1007111"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2888585"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.epidem.2017.08.002"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0188941"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData50022.2020.9377904"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33019607"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783291"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210077"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1098\/rsif.2016.0410"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1005801"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.idm.2020.08.001"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1515373112"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1003583"}],"event":{"name":"KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Virtual Event Singapore","acronym":"KDD '21","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467197","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3447548.3467197","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3447548.3467197","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:27Z","timestamp":1750191507000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467197"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,14]]},"references-count":47,"alternative-id":["10.1145\/3447548.3467197","10.1145\/3447548"],"URL":"https:\/\/doi.org\/10.1145\/3447548.3467197","relation":{},"subject":[],"published":{"date-parts":[[2021,8,14]]},"assertion":[{"value":"2021-08-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}