{"id":"https://openalex.org/W4302763088","doi":"https://doi.org/10.1371/journal.pcbi.1010602","title":"An ensemble n-sub-epidemic modeling framework for short-term forecasting epidemic trajectories: Application to the COVID-19 pandemic in the USA","display_name":"An ensemble n-sub-epidemic modeling framework for short-term forecasting epidemic trajectories: Application to the COVID-19 pandemic in the USA","publication_year":2022,"publication_date":"2022-10-06","ids":{"openalex":"https://openalex.org/W4302763088","doi":"https://doi.org/10.1371/journal.pcbi.1010602","pmid":"https://pubmed.ncbi.nlm.nih.gov/36201534"},"language":"en","primary_location":{"id":"doi:10.1371/journal.pcbi.1010602","is_oa":true,"landing_page_url":"https://doi.org/10.1371/journal.pcbi.1010602","pdf_url":"https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1010602&type=printable","source":{"id":"https://openalex.org/S86033158","display_name":"PLoS Computational Biology","issn_l":"1553-734X","issn":["1553-734X","1553-7358"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310315706","host_organization_name":"Public Library of Science","host_organization_lineage":["https://openalex.org/P4310315706"],"host_organization_lineage_names":["Public Library of Science"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PLOS Computational Biology","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1010602&type=printable","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5058020677","display_name":"Gerardo Chowell","orcid":"https://orcid.org/0000-0003-2194-2251"},"institutions":[{"id":"https://openalex.org/I1299303238","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88","country_code":"US","type":"government","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238"]},{"id":"https://openalex.org/I181565077","display_name":"Georgia State University","ror":"https://ror.org/03qt6ba18","country_code":"US","type":"education","lineage":["https://openalex.org/I181565077"]},{"id":"https://openalex.org/I874236823","display_name":"Fogarty International Center","ror":"https://ror.org/02xey9a22","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238","https://openalex.org/I874236823"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Gerardo Chowell","raw_affiliation_strings":["Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, United States of America","Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America"],"raw_orcid":"https://orcid.org/0000-0003-2194-2251","affiliations":[{"raw_affiliation_string":"Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, United States of America","institution_ids":["https://openalex.org/I181565077"]},{"raw_affiliation_string":"Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America","institution_ids":["https://openalex.org/I874236823","https://openalex.org/I1299303238"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055464759","display_name":"Sushma Dahal","orcid":"https://orcid.org/0000-0003-4991-3110"},"institutions":[{"id":"https://openalex.org/I181565077","display_name":"Georgia State University","ror":"https://ror.org/03qt6ba18","country_code":"US","type":"education","lineage":["https://openalex.org/I181565077"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sushma Dahal","raw_affiliation_strings":["Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, United States of America"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, United States of America","institution_ids":["https://openalex.org/I181565077"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036683539","display_name":"Amna Tariq","orcid":"https://orcid.org/0000-0003-2344-5398"},"institutions":[{"id":"https://openalex.org/I181565077","display_name":"Georgia State University","ror":"https://ror.org/03qt6ba18","country_code":"US","type":"education","lineage":["https://openalex.org/I181565077"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Amna Tariq","raw_affiliation_strings":["Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, United States of America"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, United States of America","institution_ids":["https://openalex.org/I181565077"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084648330","display_name":"Kimberlyn Roosa","orcid":"https://orcid.org/0000-0003-1169-7125"},"institutions":[{"id":"https://openalex.org/I122345529","display_name":"National Institute for Mathematical and Biological Synthesis","ror":"https://ror.org/04vj69e88","country_code":"US","type":"facility","lineage":["https://openalex.org/I122345529","https://openalex.org/I1311060795"]},{"id":"https://openalex.org/I75027704","display_name":"University of Tennessee at Knoxville","ror":"https://ror.org/020f3ap87","country_code":"US","type":"education","lineage":["https://openalex.org/I75027704"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kimberlyn Roosa","raw_affiliation_strings":["National Institute for Mathematical and Biological Synthesis (NIMBioS), University of Tennessee, Knoxville, Tennessee, United States of America"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Institute for Mathematical and Biological Synthesis (NIMBioS), University of Tennessee, Knoxville, Tennessee, United States of America","institution_ids":["https://openalex.org/I122345529","https://openalex.org/I75027704"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014553334","display_name":"James M. Hyman","orcid":"https://orcid.org/0000-0001-5247-5794"},"institutions":[{"id":"https://openalex.org/I114832834","display_name":"Tulane University","ror":"https://ror.org/04vmvtb21","country_code":"US","type":"education","lineage":["https://openalex.org/I114832834"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"James M. Hyman","raw_affiliation_strings":["Department of Mathematics, Center for Computational Science, Tulane University, New Orleans, Louisiana, United States of America"],"raw_orcid":"https://orcid.org/0000-0001-5247-5794","affiliations":[{"raw_affiliation_string":"Department of Mathematics, Center for Computational Science, Tulane University, New Orleans, Louisiana, United States of America","institution_ids":["https://openalex.org/I114832834"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024239427","display_name":"Ruiyan Luo","orcid":"https://orcid.org/0000-0003-4365-1148"},"institutions":[{"id":"https://openalex.org/I181565077","display_name":"Georgia State University","ror":"https://ror.org/03qt6ba18","country_code":"US","type":"education","lineage":["https://openalex.org/I181565077"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ruiyan Luo","raw_affiliation_strings":["Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, United States of America"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, United States of America","institution_ids":["https://openalex.org/I181565077"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5058020677"],"corresponding_institution_ids":["https://openalex.org/I1299303238","https://openalex.org/I181565077","https://openalex.org/I874236823"],"apc_list":{"value":2655,"currency":"USD","value_usd":2655},"apc_paid":{"value":2655,"currency":"USD","value_usd":2655},"fwci":4.3836,"has_fulltext":true,"cited_by_count":34,"citation_normalized_percentile":{"value":0.95578187,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"18","issue":"10","first_page":"e1010602","last_page":"e1010602"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10410","display_name":"COVID-19 epidemiological studies","score":0.9987999796867371,"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":0.9987999796867371,"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":0.9850000143051147,"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/T13398","display_name":"Data Analysis with R","score":0.9448999762535095,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/autoregressive-integrated-moving-average","display_name":"Autoregressive integrated moving average","score":0.7640625834465027},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.647232711315155},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.5973057746887207},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.5884824991226196},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5136637091636658},{"id":"https://openalex.org/keywords/pandemic","display_name":"Pandemic","score":0.46942025423049927},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4567076861858368},{"id":"https://openalex.org/keywords/prediction-interval","display_name":"Prediction interval","score":0.44233936071395874},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.4396984875202179},{"id":"https://openalex.org/keywords/coronavirus-disease-2019","display_name":"Coronavirus disease 2019 (COVID-19)","score":0.4345095753669739},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.30146288871765137},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24142807722091675},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2015511393547058},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.194782555103302}],"concepts":[{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.7640625834465027},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.647232711315155},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.5973057746887207},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.5884824991226196},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5136637091636658},{"id":"https://openalex.org/C89623803","wikidata":"https://www.wikidata.org/wiki/Q12184","display_name":"Pandemic","level":5,"score":0.46942025423049927},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4567076861858368},{"id":"https://openalex.org/C103402496","wikidata":"https://www.wikidata.org/wiki/Q1106171","display_name":"Prediction interval","level":2,"score":0.44233936071395874},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4396984875202179},{"id":"https://openalex.org/C3008058167","wikidata":"https://www.wikidata.org/wiki/Q84263196","display_name":"Coronavirus disease 2019 (COVID-19)","level":4,"score":0.4345095753669739},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.30146288871765137},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24142807722091675},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2015511393547058},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.194782555103302},{"id":"https://openalex.org/C524204448","wikidata":"https://www.wikidata.org/wiki/Q788926","display_name":"Infectious disease (medical specialty)","level":3,"score":0.0},{"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[{"descriptor_ui":"D000086382","descriptor_name":"COVID-19","qualifier_ui":"Q000453","qualifier_name":"epidemiology","is_major_topic":true},{"descriptor_ui":"D000086382","descriptor_name":"COVID-19","qualifier_ui":"Q000453","qualifier_name":"epidemiology","is_major_topic":true},{"descriptor_ui":"D000086382","descriptor_name":"COVID-19","qualifier_ui":"Q000453","qualifier_name":"epidemiology","is_major_topic":true},{"descriptor_ui":"D005544","descriptor_name":"Forecasting","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005544","descriptor_name":"Forecasting","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005544","descriptor_name":"Forecasting","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D013995","descriptor_name":"Time","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D013995","descriptor_name":"Time","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D013995","descriptor_name":"Time","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D014481","descriptor_name":"United States","qualifier_ui":"Q000453","qualifier_name":"epidemiology","is_major_topic":false},{"descriptor_ui":"D014481","descriptor_name":"United States","qualifier_ui":"Q000453","qualifier_name":"epidemiology","is_major_topic":false},{"descriptor_ui":"D014481","descriptor_name":"United States","qualifier_ui":"Q000453","qualifier_name":"epidemiology","is_major_topic":false},{"descriptor_ui":"D015233","descriptor_name":"Models, Statistical","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D015233","descriptor_name":"Models, Statistical","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D015233","descriptor_name":"Models, Statistical","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D058873","descriptor_name":"Pandemics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D058873","descriptor_name":"Pandemics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D058873","descriptor_name":"Pandemics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":5,"locations":[{"id":"doi:10.1371/journal.pcbi.1010602","is_oa":true,"landing_page_url":"https://doi.org/10.1371/journal.pcbi.1010602","pdf_url":"https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1010602&type=printable","source":{"id":"https://openalex.org/S86033158","display_name":"PLoS Computational Biology","issn_l":"1553-734X","issn":["1553-734X","1553-7358"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310315706","host_organization_name":"Public Library of Science","host_organization_lineage":["https://openalex.org/P4310315706"],"host_organization_lineage_names":["Public Library of Science"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PLOS Computational Biology","raw_type":"journal-article"},{"id":"pmid:36201534","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/36201534","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PLoS computational biology","raw_type":null},{"id":"pmh:oai:RePEc:plo:pcbi00:1010602","is_oa":false,"landing_page_url":"https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010602","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},{"id":"pmh:oai:doaj.org/article:c4c71ea7fb4b4e9793b448c045201ba2","is_oa":true,"landing_page_url":"https://doaj.org/article/c4c71ea7fb4b4e9793b448c045201ba2","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"PLoS Computational Biology, Vol 18, Iss 10, p e1010602 (2022)","raw_type":"article"},{"id":"pmh:oai:pubmedcentral.nih.gov:9578588","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9578588","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"PLoS Comput Biol","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.1371/journal.pcbi.1010602","is_oa":true,"landing_page_url":"https://doi.org/10.1371/journal.pcbi.1010602","pdf_url":"https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1010602&type=printable","source":{"id":"https://openalex.org/S86033158","display_name":"PLoS Computational Biology","issn_l":"1553-734X","issn":["1553-734X","1553-7358"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310315706","host_organization_name":"Public Library of Science","host_organization_lineage":["https://openalex.org/P4310315706"],"host_organization_lineage_names":["Public Library of Science"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PLOS Computational Biology","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.8799999952316284,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[{"id":"https://openalex.org/G412807661","display_name":null,"funder_award_id":"R01 GM 130900","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5364349311","display_name":null,"funder_award_id":"1633381","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8030107427","display_name":null,"funder_award_id":"R01 GM 130900","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320310607","display_name":"Georgia State University","ror":"https://ror.org/03qt6ba18"},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4302763088.pdf","grobid_xml":"https://content.openalex.org/works/W4302763088.grobid-xml"},"referenced_works_count":57,"referenced_works":["https://openalex.org/W429766147","https://openalex.org/W1496857014","https://openalex.org/W1588163064","https://openalex.org/W1598813349","https://openalex.org/W1771535374","https://openalex.org/W1968371014","https://openalex.org/W1992126082","https://openalex.org/W2009435671","https://openalex.org/W2025720061","https://openalex.org/W2032238738","https://openalex.org/W2054137409","https://openalex.org/W2100956194","https://openalex.org/W2116512828","https://openalex.org/W2140095656","https://openalex.org/W2171033594","https://openalex.org/W2331820705","https://openalex.org/W2410602045","https://openalex.org/W2531458834","https://openalex.org/W2551299342","https://openalex.org/W2604976044","https://openalex.org/W2747968860","https://openalex.org/W2772258443","https://openalex.org/W2899037356","https://openalex.org/W2909758842","https://openalex.org/W2910427770","https://openalex.org/W2912023657","https://openalex.org/W2944910877","https://openalex.org/W2952959099","https://openalex.org/W2969976360","https://openalex.org/W2988205563","https://openalex.org/W2994819083","https://openalex.org/W3006028741","https://openalex.org/W3031381838","https://openalex.org/W3037870417","https://openalex.org/W3040134252","https://openalex.org/W3043618167","https://openalex.org/W3081269630","https://openalex.org/W3088534488","https://openalex.org/W3094397675","https://openalex.org/W3095636618","https://openalex.org/W3109158109","https://openalex.org/W3110800957","https://openalex.org/W3111524185","https://openalex.org/W3125060705","https://openalex.org/W3137381400","https://openalex.org/W3139301149","https://openalex.org/W3147757130","https://openalex.org/W3197610615","https://openalex.org/W3202880803","https://openalex.org/W3207646715","https://openalex.org/W4205265935","https://openalex.org/W4206576368","https://openalex.org/W4206997113","https://openalex.org/W4214909547","https://openalex.org/W4220757792","https://openalex.org/W4223487063","https://openalex.org/W6663834780"],"related_works":["https://openalex.org/W3175321409","https://openalex.org/W4312561791","https://openalex.org/W2389894046","https://openalex.org/W2215717369","https://openalex.org/W2974356760","https://openalex.org/W4312309719","https://openalex.org/W4313123484","https://openalex.org/W4386362517","https://openalex.org/W2146461990","https://openalex.org/W4200142652"],"abstract_inverted_index":{"We":[0,56,83],"analyze":[1],"an":[2],"ensemble":[3,16,101,170],"of":[4,11,53,116,125,186,194,203,214,222,235],"n-sub-epidemic":[5],"modeling":[6,17,35],"for":[7,68,150,161,219],"forecasting":[8,32,63],"the":[9,69,73,105,111,117,123,126,131,142,151,162,169,173,181,187,190,195,204,209,215,220,223,233],"trajectory":[10],"epidemics":[12,236],"and":[13,19,46,61,99,122,134,189,237,251],"pandemics.":[14],"These":[15],"approaches,":[18],"models":[20,102,108,113,178],"that":[21,217,253],"integrate":[22],"sub-epidemics":[23],"to":[24,79,148,159,231],"capture":[25],"complex":[26,39],"temporal":[27],"dynamics,":[28],"have":[29],"demonstrated":[30],"powerful":[31],"capability.":[33],"This":[34,225],"framework":[36,226],"can":[37,227],"characterize":[38],"epidemic":[40,44,47],"patterns,":[41],"including":[42],"plateaus,":[43],"resurgences,":[45],"waves":[48],"characterized":[49],"by":[50],"multiple":[51],"peaks":[52],"different":[54],"sizes.":[55],"systematically":[57],"assess":[58],"their":[59,85],"calibration":[60],"short-term":[62,66,136,167,257],"performance":[64,86,211],"in":[65,72,114,197,201,212,249],"forecasts":[67,200],"COVID-19":[70],"pandemic":[71],"USA":[74],"from":[75,146,157,256],"late":[76,80],"April":[77],"2020":[78],"February":[81],"2022.":[82],"compare":[84],"with":[87],"two":[88],"commonly":[89],"used":[90],"statistical":[91],"ARIMA":[92,112,163,183,191],"models.":[93,164],"The":[94],"best":[95,210],"fit":[96],"sub-epidemic":[97,107,152,177],"model":[98,171,184],"three":[100],"constructed":[103],"using":[104],"top-ranking":[106],"consistently":[109,207],"outperformed":[110,180],"terms":[115,202,213],"weighted":[118],"interval":[119,129],"score":[120],"(WIS)":[121],"coverage":[124],"95%":[127],"prediction":[128],"across":[130],"10-,":[132],"20-,":[133],"30-day":[135,140,198],"forecasts.":[137],"In":[138],"our":[139],"forecasts,":[141,168],"average":[143],"WIS":[144],"ranged":[145,156],"377.6":[147],"421.3":[149],"models,":[153],"whereas":[154],"it":[155],"439.29":[158],"767.05":[160],"Across":[165],"98":[166],"incorporating":[172],"top":[174],"four":[175],"ranking":[176],"(Ensemble(4))":[179],"(log)":[182],"66.3%":[185],"time,":[188],"model,":[192],"69.4%":[193],"time":[196],"ahead":[199],"WIS.":[205],"Ensemble(4)":[206],"yielded":[208],"metrics":[216],"account":[218],"uncertainty":[221],"predictions.":[224,258],"be":[228],"readily":[229],"applied":[230],"investigate":[232],"spread":[234],"pandemics":[238],"beyond":[239],"COVID-19,":[240],"as":[241,243],"well":[242],"other":[244],"dynamic":[245],"growth":[246],"processes":[247],"found":[248],"nature":[250],"society":[252],"would":[254],"benefit":[255]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":1}],"updated_date":"2026-04-25T08:17:42.794288","created_date":"2025-10-10T00:00:00"}
