{"id":"https://openalex.org/W3187294826","doi":"https://doi.org/10.1145/3447548.3467197","title":"All Models Are Useful: Bayesian Ensembling for Robust High Resolution COVID-19 Forecasting","display_name":"All Models Are Useful: Bayesian Ensembling for Robust High Resolution COVID-19 Forecasting","publication_year":2021,"publication_date":"2021-08-12","ids":{"openalex":"https://openalex.org/W3187294826","doi":"https://doi.org/10.1145/3447548.3467197","mag":"3187294826"},"language":"en","primary_location":{"id":"doi:10.1145/3447548.3467197","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3447548.3467197","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3447548.3467197","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3447548.3467197","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031381738","display_name":"Aniruddha Adiga","orcid":"https://orcid.org/0000-0002-5396-1978"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Aniruddha Adiga","raw_affiliation_strings":["University of Virginia, Charlottesville, VA, USA"],"affiliations":[{"raw_affiliation_string":"University of Virginia, Charlottesville, VA, USA","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100330304","display_name":"Lijing Wang","orcid":"https://orcid.org/0000-0002-0836-9190"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lijing Wang","raw_affiliation_strings":["University of Virginia, Charlottesville, VA, USA"],"affiliations":[{"raw_affiliation_string":"University of Virginia, Charlottesville, VA, USA","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008261688","display_name":"Benjamin Hurt","orcid":"https://orcid.org/0000-0002-3803-2900"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Benjamin Hurt","raw_affiliation_strings":["University of Virginia, Charlottesville, VA, USA"],"affiliations":[{"raw_affiliation_string":"University of Virginia, Charlottesville, VA, USA","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082631549","display_name":"Akhil Sai Peddireddy","orcid":"https://orcid.org/0000-0001-8983-5941"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Akhil Peddireddy","raw_affiliation_strings":["University of Virginia, Charlottesville, VA, USA"],"affiliations":[{"raw_affiliation_string":"University of Virginia, Charlottesville, VA, USA","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017416729","display_name":"Przemyslaw Porebski","orcid":"https://orcid.org/0000-0001-8012-5791"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Przemyslaw Porebski","raw_affiliation_strings":["University of Virginia, Charlottesville, VA, USA"],"affiliations":[{"raw_affiliation_string":"University of Virginia, Charlottesville, VA, USA","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057315009","display_name":"Srinivasan Venkatramanan","orcid":"https://orcid.org/0000-0002-0874-8692"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Srinivasan Venkatramanan","raw_affiliation_strings":["University of Virginia, Charlottesville, VA, USA"],"affiliations":[{"raw_affiliation_string":"University of Virginia, Charlottesville, VA, USA","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072570670","display_name":"Bryan Lewis","orcid":"https://orcid.org/0000-0003-0793-6082"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bryan Leroy Lewis","raw_affiliation_strings":["University of Virginia, Charlottesville, VA, USA"],"affiliations":[{"raw_affiliation_string":"University of Virginia, Charlottesville, VA, USA","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020293284","display_name":"Madhav Marathe","orcid":"https://orcid.org/0000-0003-1653-0658"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Madhav Marathe","raw_affiliation_strings":["University of Virginia, Charlottesville, VA, USA"],"affiliations":[{"raw_affiliation_string":"University of Virginia, Charlottesville, VA, USA","institution_ids":["https://openalex.org/I51556381"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5031381738"],"corresponding_institution_ids":["https://openalex.org/I51556381"],"apc_list":null,"apc_paid":null,"fwci":1.9056,"has_fulltext":true,"cited_by_count":26,"citation_normalized_percentile":{"value":0.858341,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2505","last_page":"2513"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10410","display_name":"COVID-19 epidemiological studies","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/2611","display_name":"Modeling and Simulation"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10410","display_name":"COVID-19 epidemiological studies","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/2611","display_name":"Modeling and Simulation"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11819","display_name":"Data-Driven Disease Surveillance","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.9635000228881836,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7428544163703918},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.6332498788833618},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.6200852394104004},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5438629984855652},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5089881420135498},{"id":"https://openalex.org/keywords/probabilistic-forecasting","display_name":"Probabilistic forecasting","score":0.5067201256752014},{"id":"https://openalex.org/keywords/coronavirus-disease-2019","display_name":"Coronavirus disease 2019 (COVID-19)","score":0.4361761510372162},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.420773446559906},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.415706604719162},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4139016270637512},{"id":"https://openalex.org/keywords/infectious-disease","display_name":"Infectious disease (medical specialty)","score":0.14167767763137817},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1152447760105133}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7428544163703918},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6332498788833618},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6200852394104004},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5438629984855652},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5089881420135498},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.5067201256752014},{"id":"https://openalex.org/C3008058167","wikidata":"https://www.wikidata.org/wiki/Q84263196","display_name":"Coronavirus disease 2019 (COVID-19)","level":4,"score":0.4361761510372162},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.420773446559906},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.415706604719162},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4139016270637512},{"id":"https://openalex.org/C524204448","wikidata":"https://www.wikidata.org/wiki/Q788926","display_name":"Infectious disease (medical specialty)","level":3,"score":0.14167767763137817},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1152447760105133},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3447548.3467197","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3447548.3467197","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3447548.3467197","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3447548.3467197","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3447548.3467197","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3447548.3467197","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.6000000238418579,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[{"id":"https://openalex.org/G1505502640","display_name":null,"funder_award_id":"HDTRA1-19-D-0007","funder_id":"https://openalex.org/F4320332186","funder_display_name":"Defense Threat Reduction Agency"},{"id":"https://openalex.org/G2253905536","display_name":"BIGDATA: Collaborative Research: F: Efficient Distributed Computation of Large-Scale Graph Problems in Epidemiology and Contagion Dynamics","funder_award_id":"1633028","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2372087932","display_name":null,"funder_award_id":"CCF-1918656","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3416031122","display_name":null,"funder_award_id":"R01GM109718","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G3497591915","display_name":null,"funder_award_id":"(NIH)","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G3505574271","display_name":null,"funder_award_id":"CNS-2028004","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G3549164809","display_name":null,"funder_award_id":"IIS-1633028","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3611247453","display_name":null,"funder_award_id":"R01GM","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G3706291859","display_name":null,"funder_award_id":"1R01GM109718","funder_id":"https://openalex.org/F4320332186","funder_display_name":"Defense Threat Reduction Agency"},{"id":"https://openalex.org/G3828073557","display_name":null,"funder_award_id":"OAC-1916805","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G3858435652","display_name":null,"funder_award_id":"OAC-2027541","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G3933114365","display_name":null,"funder_award_id":"RAPID","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4656488385","display_name":null,"funder_award_id":"RAPID OAC-2027541","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4966575984","display_name":null,"funder_award_id":"OAC-1916805","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5024895080","display_name":null,"funder_award_id":"75D30119C05935","funder_id":"https://openalex.org/F4320332162","funder_display_name":"Centers for Disease Control and Prevention"},{"id":"https://openalex.org/G5145214560","display_name":null,"funder_award_id":"VDH-21-501-0141","funder_id":"https://openalex.org/F4320332186","funder_display_name":"Defense Threat Reduction Agency"},{"id":"https://openalex.org/G5260964573","display_name":null,"funder_award_id":"CCF-1918656","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G5514976981","display_name":null,"funder_award_id":"COVID-19","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G5845535585","display_name":null,"funder_award_id":"1R01GM109718","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G5921281487","display_name":null,"funder_award_id":"number","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6361321077","display_name":"RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks","funder_award_id":"2027541","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6541015203","display_name":null,"funder_award_id":"HDTRA1","funder_id":"https://openalex.org/F4320332186","funder_display_name":"Defense Threat Reduction Agency"},{"id":"https://openalex.org/G6542493234","display_name":null,"funder_award_id":"CCF-1917819","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G6654112443","display_name":null,"funder_award_id":"IIS-1633028","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G7180878835","display_name":null,"funder_award_id":"1917819","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7243635746","display_name":null,"funder_award_id":"C&P - VDH-21-501-0141","funder_id":"https://openalex.org/F4320308201","funder_display_name":"Virginia Department of Health"},{"id":"https://openalex.org/G7315863584","display_name":"Collaborative Research: Framework: Software: CINES: A Scalable Cyberinfrastructure for Sustained Innovation in Network Engineering and Science","funder_award_id":"1916805","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G775681259","display_name":null,"funder_award_id":"75D30119C0593","funder_id":"https://openalex.org/F4320332162","funder_display_name":"Centers for Disease Control and Prevention"},{"id":"https://openalex.org/G8034655586","display_name":null,"funder_award_id":"CNS-2028004","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8146162564","display_name":null,"funder_award_id":"CCF-1917819","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G887741455","display_name":null,"funder_award_id":"1918656","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320308201","display_name":"Virginia Department of Health","ror":"https://ror.org/00j2wht38"},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320332162","display_name":"Centers for Disease Control and Prevention","ror":"https://ror.org/042twtr12"},{"id":"https://openalex.org/F4320332186","display_name":"Defense Threat Reduction Agency","ror":"https://ror.org/04tz64554"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3187294826.pdf","grobid_xml":"https://content.openalex.org/works/W3187294826.grobid-xml"},"referenced_works_count":36,"referenced_works":["https://openalex.org/W1486632395","https://openalex.org/W1603903339","https://openalex.org/W1665214252","https://openalex.org/W1977556410","https://openalex.org/W2006085716","https://openalex.org/W2035035907","https://openalex.org/W2037537012","https://openalex.org/W2051066849","https://openalex.org/W2059676477","https://openalex.org/W2064675550","https://openalex.org/W2079884780","https://openalex.org/W2130094219","https://openalex.org/W2158840489","https://openalex.org/W2317866228","https://openalex.org/W2408821405","https://openalex.org/W2531458834","https://openalex.org/W2747968860","https://openalex.org/W2767745493","https://openalex.org/W2772780441","https://openalex.org/W2798329844","https://openalex.org/W2909758842","https://openalex.org/W2953101261","https://openalex.org/W2964059111","https://openalex.org/W2965118797","https://openalex.org/W2974209544","https://openalex.org/W2990499930","https://openalex.org/W3008443627","https://openalex.org/W3013649595","https://openalex.org/W3019351867","https://openalex.org/W3038075184","https://openalex.org/W3046978353","https://openalex.org/W3048453320","https://openalex.org/W3091413779","https://openalex.org/W3093695087","https://openalex.org/W3097661700","https://openalex.org/W3137647102"],"related_works":["https://openalex.org/W2909436466","https://openalex.org/W2769304616","https://openalex.org/W2561944894","https://openalex.org/W3188413760","https://openalex.org/W2963188571","https://openalex.org/W2008291043","https://openalex.org/W2373467473","https://openalex.org/W2791458617","https://openalex.org/W3121565704","https://openalex.org/W4319323736"],"abstract_inverted_index":{"Timely,":[0],"high-resolution":[1],"forecasts":[2,78,171],"of":[3,28,67],"infectious":[4],"disease":[5],"incidence":[6],"are":[7,172],"useful":[8],"for":[9,37,47,96,134,178],"policy":[10],"makers":[11],"in":[12,106],"deciding":[13],"intervention":[14],"measures":[15],"and":[16,56,62,84,92,103,138,157],"estimating":[17],"healthcare":[18],"resource":[19],"burden.":[20],"In":[21],"this":[22,48],"paper,":[23],"we":[24,125,166],"consider":[25],"the":[26,34,38,63,68,107,113,122],"task":[27],"forecasting":[29,73,192],"COVID-19":[30,154],"confirmed":[31],"cases":[32],"at":[33,117,161],"county":[35],"level":[36],"United":[39,108],"States.":[40,109],"Although":[41],"multiple":[42,80,199],"methods":[43,86,200],"have":[44],"been":[45,94],"explored":[46],"task,":[49],"their":[50],"performance":[51,145,160],"has":[52,93],"varied":[53],"across":[54],"space":[55],"time":[57,139,177],"due":[58],"to":[59,174,205],"noisy":[60],"data":[61],"inherent":[64],"dynamic":[65],"nature":[66],"pandemic.":[69],"We":[70,141],"present":[71],"a":[72,88,188,202],"pipeline":[74,193],"which":[75],"incorporates":[76],"probabilistic":[77],"from":[79],"statistical,":[81],"machine":[82],"learning":[83],"mechanistic":[85,180],"through":[87],"Bayesian":[89,114],"ensembling":[90],"scheme,":[91],"operational":[95],"nearly":[97],"6":[98],"months":[99],"serving":[100],"local,":[101],"state":[102],"federal":[104],"policymakers":[105],"While":[110],"showing":[111],"that":[112,128,186],"ensemble":[115,204],"is":[116],"least":[118],"as":[119,121],"good":[120],"individual":[123,130],"methods,":[124],"also":[126,167],"show":[127,158],"each":[129],"method":[131],"contributes":[132],"significantly":[133],"different":[135],"spatial":[136],"regions":[137],"points.":[140],"compare":[142],"our":[143],"model's":[144],"with":[146],"other":[147],"similar":[148],"models":[149],"being":[150],"integrated":[151],"into":[152],"CDC-initiated":[153],"Forecast":[155],"Hub,":[156],"better":[159],"longer":[162],"forecast":[163],"horizons.":[164],"Finally,":[165],"describe":[168],"how":[169],"such":[170,187],"used":[173],"increase":[175],"lead":[176],"training":[179],"scenario":[181],"projections.":[182],"Our":[183],"work":[184],"demonstrates":[185],"real-time":[189],"high":[190],"resolution":[191],"can":[194],"be":[195],"developed":[196],"by":[197],"integrating":[198],"within":[201],"performance-based":[203],"support":[206],"pandemic":[207],"response.":[208]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
