{"id":"https://openalex.org/W4280601173","doi":"https://doi.org/10.1371/journal.pcbi.1010100","title":"Enhancing long-term forecasting: Learning from COVID-19 models","display_name":"Enhancing long-term forecasting: Learning from COVID-19 models","publication_year":2022,"publication_date":"2022-05-19","ids":{"openalex":"https://openalex.org/W4280601173","doi":"https://doi.org/10.1371/journal.pcbi.1010100","pmid":"https://pubmed.ncbi.nlm.nih.gov/35587466"},"language":"en","primary_location":{"id":"doi:10.1371/journal.pcbi.1010100","is_oa":true,"landing_page_url":"https://doi.org/10.1371/journal.pcbi.1010100","pdf_url":"https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1010100&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.1010100&type=printable","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5019451889","display_name":"Hazhir Rahmandad","orcid":"https://orcid.org/0000-0002-2784-9042"},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hazhir Rahmandad","raw_affiliation_strings":["Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America"],"raw_orcid":"https://orcid.org/0000-0002-2784-9042","affiliations":[{"raw_affiliation_string":"Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040257073","display_name":"Ran Xu","orcid":"https://orcid.org/0000-0002-5832-9226"},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ran Xu","raw_affiliation_strings":["Department of Allied Health Sciences, University of Connecticut, Storrs, Connecticut, United States of America"],"raw_orcid":"https://orcid.org/0000-0002-5832-9226","affiliations":[{"raw_affiliation_string":"Department of Allied Health Sciences, University of Connecticut, Storrs, Connecticut, United States of America","institution_ids":["https://openalex.org/I140172145"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078713927","display_name":"Navid Ghaffarzadegan","orcid":"https://orcid.org/0000-0003-3632-8588"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Navid Ghaffarzadegan","raw_affiliation_strings":["Department of Industrial and Systems Engineering, Virginia Tech, Falls Church, Virginia, United States of America"],"raw_orcid":"https://orcid.org/0000-0003-3632-8588","affiliations":[{"raw_affiliation_string":"Department of Industrial and Systems Engineering, Virginia Tech, Falls Church, Virginia, United States of America","institution_ids":["https://openalex.org/I859038795"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5040257073"],"corresponding_institution_ids":["https://openalex.org/I140172145"],"apc_list":{"value":2655,"currency":"USD","value_usd":2655},"apc_paid":{"value":2655,"currency":"USD","value_usd":2655},"fwci":5.0686,"has_fulltext":true,"cited_by_count":45,"citation_normalized_percentile":{"value":0.96649146,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"18","issue":"5","first_page":"e1010100","last_page":"e1010100"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10410","display_name":"COVID-19 epidemiological studies","score":0.9998999834060669,"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":0.9998999834060669,"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/T11711","display_name":"COVID-19 Pandemic Impacts","score":0.983299970626831,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11819","display_name":"Data-Driven Disease Surveillance","score":0.9362999796867371,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.768742024898529},{"id":"https://openalex.org/keywords/randomness","display_name":"Randomness","score":0.657423734664917},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.6456642150878906},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6017903089523315},{"id":"https://openalex.org/keywords/predictive-power","display_name":"Predictive power","score":0.6017551422119141},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.4853035807609558},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4850551187992096},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.4807417392730713},{"id":"https://openalex.org/keywords/coronavirus-disease-2019","display_name":"Coronavirus disease 2019 (COVID-19)","score":0.4712567627429962},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4474935531616211},{"id":"https://openalex.org/keywords/pandemic","display_name":"Pandemic","score":0.44542422890663147},{"id":"https://openalex.org/keywords/exponential-growth","display_name":"Exponential growth","score":0.4257700741291046},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.41751453280448914},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.24412184953689575},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18617022037506104},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.0852416455745697}],"concepts":[{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.768742024898529},{"id":"https://openalex.org/C125112378","wikidata":"https://www.wikidata.org/wiki/Q176640","display_name":"Randomness","level":2,"score":0.657423734664917},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.6456642150878906},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6017903089523315},{"id":"https://openalex.org/C2778136018","wikidata":"https://www.wikidata.org/wiki/Q10350689","display_name":"Predictive power","level":2,"score":0.6017551422119141},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4853035807609558},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4850551187992096},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.4807417392730713},{"id":"https://openalex.org/C3008058167","wikidata":"https://www.wikidata.org/wiki/Q84263196","display_name":"Coronavirus disease 2019 (COVID-19)","level":4,"score":0.4712567627429962},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4474935531616211},{"id":"https://openalex.org/C89623803","wikidata":"https://www.wikidata.org/wiki/Q12184","display_name":"Pandemic","level":5,"score":0.44542422890663147},{"id":"https://openalex.org/C75235859","wikidata":"https://www.wikidata.org/wiki/Q582659","display_name":"Exponential growth","level":2,"score":0.4257700741291046},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.41751453280448914},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.24412184953689575},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18617022037506104},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0852416455745697},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C524204448","wikidata":"https://www.wikidata.org/wiki/Q788926","display_name":"Infectious disease (medical specialty)","level":3,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"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":"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":6,"locations":[{"id":"doi:10.1371/journal.pcbi.1010100","is_oa":true,"landing_page_url":"https://doi.org/10.1371/journal.pcbi.1010100","pdf_url":"https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1010100&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:35587466","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35587466","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:vtechworks.lib.vt.edu:10919/113560","is_oa":true,"landing_page_url":"http://hdl.handle.net/10919/113560","pdf_url":"https://vtechworks.lib.vt.edu/bitstreams/4b0cf503-69fc-4935-a6ff-6cf6e1f7e93f/download","source":{"id":"https://openalex.org/S4306400248","display_name":"VTechWorks (Virginia Tech)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I859038795","host_organization_name":"Virginia Tech","host_organization_lineage":["https://openalex.org/I859038795"],"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":null,"raw_type":"Text"},{"id":"pmh:oai:RePEc:plo:pcbi00:1010100","is_oa":false,"landing_page_url":"https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010100","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:416897fd67ee41b9a1077de52000b6ee","is_oa":true,"landing_page_url":"https://doaj.org/article/416897fd67ee41b9a1077de52000b6ee","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 5, p e1010100 (2022)","raw_type":"article"},{"id":"pmh:oai:pubmedcentral.nih.gov:9119494","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9119494","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.1010100","is_oa":true,"landing_page_url":"https://doi.org/10.1371/journal.pcbi.1010100","pdf_url":"https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1010100&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":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.8500000238418579}],"awards":[],"funders":[{"id":"https://openalex.org/F4320309292","display_name":"Princeton University","ror":"https://ror.org/00hx57361"},{"id":"https://openalex.org/F4320309622","display_name":"Harvard University","ror":"https://ror.org/03vek6s52"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4280601173.pdf","grobid_xml":"https://content.openalex.org/works/W4280601173.grobid-xml"},"referenced_works_count":40,"referenced_works":["https://openalex.org/W177483260","https://openalex.org/W2042303597","https://openalex.org/W2145736592","https://openalex.org/W2161501234","https://openalex.org/W2489782956","https://openalex.org/W2584419678","https://openalex.org/W2760427150","https://openalex.org/W2885353869","https://openalex.org/W2909758842","https://openalex.org/W2991460415","https://openalex.org/W3003573988","https://openalex.org/W3008443627","https://openalex.org/W3013182397","https://openalex.org/W3015056889","https://openalex.org/W3015988827","https://openalex.org/W3016528319","https://openalex.org/W3016545960","https://openalex.org/W3022787740","https://openalex.org/W3023190093","https://openalex.org/W3023441241","https://openalex.org/W3023447730","https://openalex.org/W3025512159","https://openalex.org/W3031381838","https://openalex.org/W3037163353","https://openalex.org/W3038807872","https://openalex.org/W3048645635","https://openalex.org/W3080376427","https://openalex.org/W3080387602","https://openalex.org/W3089225093","https://openalex.org/W3093610264","https://openalex.org/W3095197851","https://openalex.org/W3106040718","https://openalex.org/W3106407603","https://openalex.org/W3138716508","https://openalex.org/W3201751138","https://openalex.org/W3206422332","https://openalex.org/W4200194600","https://openalex.org/W4223487063","https://openalex.org/W6782096947","https://openalex.org/W6782180662"],"related_works":["https://openalex.org/W2884834684","https://openalex.org/W3133294580","https://openalex.org/W3160244858","https://openalex.org/W3088815947","https://openalex.org/W3150895494","https://openalex.org/W4287660321","https://openalex.org/W2903185896","https://openalex.org/W1859434220","https://openalex.org/W3213457837","https://openalex.org/W3096234001"],"abstract_inverted_index":{"While":[0],"much":[1],"effort":[2],"has":[3],"gone":[4],"into":[5],"building":[6],"predictive":[7,160],"models":[8,53,151],"of":[9,25,31,59,87,90,162,177,188],"the":[10,22,28,51,56,85,115,149,153,158,174,184],"COVID-19":[11,60,164],"pandemic,":[12],"some":[13],"have":[14],"argued":[15],"that":[16,77,128],"early":[17],"exponential":[18],"growth":[19],"combined":[20],"with":[21,68,145,148],"stochastic":[23],"nature":[24],"epidemics":[26],"make":[27],"long-term":[29,46,79,159],"prediction":[30,69],"contagion":[32],"trajectories":[33,165],"impossible.":[34],"We":[35,75],"conduct":[36],"two":[37],"complementary":[38],"studies":[39],"to":[40,55,64,99,108,157],"assess":[41],"model":[42,116],"features":[43],"supporting":[44],"better":[45,78],"predictions.":[47],"First,":[48],"we":[49,121],"leverage":[50],"diverse":[52],"contributing":[54],"CDC":[57,154],"repository":[58],"USA":[61],"death":[62,178],"projections":[63],"identify":[65],"factors":[66],"associated":[67],"accuracy":[70,146],"across":[71],"different":[72],"projection":[73],"horizons.":[74],"find":[76],"predictions":[80,138],"correlate":[81],"with:":[82],"(1)":[83],"capturing":[84,167],"physics":[86],"transmission":[88,181],"(instead":[89],"using":[91],"black-box":[92],"models);":[93],"(2)":[94],"projecting":[95],"human":[96],"behavioral":[97,168],"reactions":[98],"an":[100],"evolving":[101],"pandemic;":[102],"and":[103,132,186],"(3)":[104],"resetting":[105],"state":[106],"variables":[107],"account":[109],"for":[110,139],"randomness":[111],"not":[112],"captured":[113],"in":[114,152],"before":[117],"starting":[118],"projection.":[119],"Second,":[120],"introduce":[122],"a":[123],"very":[124],"simple":[125],"model,":[126],"SEIRb,":[127],"incorporates":[129],"these":[130],"features,":[131],"few":[133],"other":[134],"nuances,":[135],"offers":[136],"informative":[137],"as":[140,142],"far":[141],"20-weeks":[143],"ahead,":[144],"comparable":[147],"best":[150],"set.":[155],"Key":[156],"power":[161],"multi-wave":[163],"is":[166],"responses":[169],"endogenously:":[170],"balancing":[171],"feedbacks":[172],"where":[173],"perceived":[175],"risk":[176],"continuously":[179],"changes":[180],"rates":[182],"through":[183],"adoption":[185],"relaxation":[187],"various":[189],"Non-Pharmaceutical":[190],"Interventions":[191],"(NPIs).":[192]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":15},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2026-01-13T01:12:25.745995","created_date":"2025-10-10T00:00:00"}