{"id":"https://openalex.org/W7204146869","doi":"https://doi.org/10.48550/arxiv.2608.20406","title":"Machine Learning and ARIMA Model Averaging for Adaptive Public Health Forecasting: Comparative Evaluation and an Ontario COVID-19 Case Study","display_name":"Machine Learning and ARIMA Model Averaging for Adaptive Public Health Forecasting: Comparative Evaluation and an Ontario COVID-19 Case Study","publication_year":2026,"publication_date":"2026-08-07","ids":{"openalex":"https://openalex.org/W7204146869","doi":"https://doi.org/10.48550/arxiv.2608.20406"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2608.20406","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.20406","pdf_url":"https://arxiv.org/pdf/2608.20406","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2608.20406","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5108980541","display_name":"Yushu Zou","orcid":"https://orcid.org/0009-0004-1133-4724","observed_orcids":["https://orcid.org/0009-0004-1133-4724"]},"institutions":[{"id":"https://openalex.org/I185261750","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087","country_code":"CA","type":"education","lineage":["https://openalex.org/I185261750"]},{"id":"https://openalex.org/I4210125326","display_name":"Public Health Ontario","ror":"https://ror.org/025z8ah66","country_code":"CA","type":"healthcare","lineage":["https://openalex.org/I4210125326"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Zou, Yushu","raw_affiliation_strings":["Division of Biostatistics , Dalla Lana School of Public Health , University of Toronto , Canada","Public Health Ontario , Toronto , Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Division of Biostatistics , Dalla Lana School of Public Health , University of Toronto , Canada","institution_ids":["https://openalex.org/I185261750"]},{"raw_affiliation_string":"Public Health Ontario , Toronto , Canada","institution_ids":["https://openalex.org/I4210125326"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5147934211","display_name":"Ye Li","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I185261750","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087","country_code":"CA","type":"education","lineage":["https://openalex.org/I185261750"]},{"id":"https://openalex.org/I4210125326","display_name":"Public Health Ontario","ror":"https://ror.org/025z8ah66","country_code":"CA","type":"healthcare","lineage":["https://openalex.org/I4210125326"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Li, Ye","raw_affiliation_strings":["Division of Biostatistics , Dalla Lana School of Public Health , University of Toronto , Canada","Public Health Ontario , Toronto , Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Division of Biostatistics , Dalla Lana School of Public Health , University of Toronto , Canada","institution_ids":["https://openalex.org/I185261750"]},{"raw_affiliation_string":"Public Health Ontario , Toronto , Canada","institution_ids":["https://openalex.org/I4210125326"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5147960408","display_name":"Johra Moosa","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210125326","display_name":"Public Health Ontario","ror":"https://ror.org/025z8ah66","country_code":"CA","type":"healthcare","lineage":["https://openalex.org/I4210125326"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Moosa, Johra","raw_affiliation_strings":["Public Health Ontario , Toronto , Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Public Health Ontario , Toronto , Canada","institution_ids":["https://openalex.org/I4210125326"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053513823","display_name":"Martin David Grunnill","orcid":"https://orcid.org/0000-0002-4835-8007","observed_orcids":["https://orcid.org/0000-0002-4835-8007"]},"institutions":[{"id":"https://openalex.org/I4210125326","display_name":"Public Health Ontario","ror":"https://ror.org/025z8ah66","country_code":"CA","type":"healthcare","lineage":["https://openalex.org/I4210125326"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Grunnill, Martin","raw_affiliation_strings":["Public Health Ontario , Toronto , Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Public Health Ontario , Toronto , Canada","institution_ids":["https://openalex.org/I4210125326"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101763935","display_name":"Samir N. Patel","orcid":"https://orcid.org/0000-0001-9824-7623","observed_orcids":["https://orcid.org/0000-0001-9824-7623"]},"institutions":[{"id":"https://openalex.org/I185261750","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087","country_code":"CA","type":"education","lineage":["https://openalex.org/I185261750"]},{"id":"https://openalex.org/I4210125326","display_name":"Public Health Ontario","ror":"https://ror.org/025z8ah66","country_code":"CA","type":"healthcare","lineage":["https://openalex.org/I4210125326"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Patel, Samir N.","raw_affiliation_strings":["Department of Laboratory Medicine and Pathobiology , University of Toronto , Canada","Public Health Ontario , Toronto , Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Laboratory Medicine and Pathobiology , University of Toronto , Canada","institution_ids":["https://openalex.org/I185261750"]},{"raw_affiliation_string":"Public Health Ontario , Toronto , Canada","institution_ids":["https://openalex.org/I4210125326"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026851154","display_name":"Venkata R. S. K. Duvvuri","orcid":"https://orcid.org/0000-0001-7356-338X","observed_orcids":["https://orcid.org/0000-0001-7356-338X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Duvvuri, Venkata R.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10410","display_name":"COVID-19 epidemiological studies","score":0.8461999893188477,"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":0.8461999893188477,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.1031000018119812,"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"}},{"id":"https://openalex.org/T11819","display_name":"Data-Driven Disease Surveillance","score":0.01600000075995922,"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/public-health-forecasting","display_name":"public health forecasting","score":0.9409999847412109},{"id":"https://openalex.org/keywords/covid-19-case-counts","display_name":"COVID-19 case counts","score":0.9729999899864197},{"id":"https://openalex.org/keywords/arima","display_name":"ARIMA","score":0.8820000290870667},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.9100000262260437},{"id":"https://openalex.org/keywords/xgboost","display_name":"XGBoost","score":0.9549999833106995},{"id":"https://openalex.org/keywords/model-averaging","display_name":"model averaging","score":0.7540000081062317},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"ensemble forecasting","score":0.9380000233650208},{"id":"https://openalex.org/keywords/turning-points","display_name":"turning points","score":0.7480000257492065},{"id":"https://openalex.org/keywords/forecast-horizon","display_name":"forecast horizon","score":0.8330000042915344},{"id":"https://openalex.org/keywords/ontario","display_name":"Ontario","score":0.3100000023841858},{"id":"https://openalex.org/keywords/time-series-cross-validation","display_name":"time series cross-validation","score":0.7020000219345093}],"concepts":[{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.8474000096321106},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6173999905586243},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5292999744415283},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5148000121116638},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5001999735832214},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48010000586509705},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.47369998693466187},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.46619999408721924},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4431000053882599},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.43709999322891235},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.43650001287460327},{"id":"https://openalex.org/C150217764","wikidata":"https://www.wikidata.org/wiki/Q6803607","display_name":"Mean absolute percentage error","level":3,"score":0.430400013923645},{"id":"https://openalex.org/C170061395","wikidata":"https://www.wikidata.org/wiki/Q5468164","display_name":"Forecast skill","level":2,"score":0.39500001072883606},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.3495999872684479},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.3352999985218048},{"id":"https://openalex.org/C175706884","wikidata":"https://www.wikidata.org/wiki/Q1130194","display_name":"Moving average","level":2,"score":0.335099995136261},{"id":"https://openalex.org/C188154048","wikidata":"https://www.wikidata.org/wiki/Q6803609","display_name":"Mean absolute error","level":3,"score":0.3239000141620636},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.32190001010894775},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30469998717308044},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.29820001125335693},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2980000078678131},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.28769999742507935},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.26499998569488525},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.2639999985694885},{"id":"https://openalex.org/C28761237","wikidata":"https://www.wikidata.org/wiki/Q7805321","display_name":"Time horizon","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2608.20406","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.20406","pdf_url":"https://arxiv.org/pdf/2608.20406","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},{"id":"doi:10.48550/arxiv.2608.20406","doi":"https://doi.org/10.48550/arxiv.2608.20406","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2608.20406","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2608.20406","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.20406","pdf_url":"https://arxiv.org/pdf/2608.20406","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},"sustainable_development_goals":[{"score":0.8048999905586243,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7204146869.pdf","grobid_xml":"https://content.openalex.org/works/W7204146869.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4409622877","https://openalex.org/W4302027119","https://openalex.org/W4283775449","https://openalex.org/W3092639027","https://openalex.org/W4214688107","https://openalex.org/W4280493743","https://openalex.org/W4389559615","https://openalex.org/W3120753229","https://openalex.org/W3037083213","https://openalex.org/W4281638788"],"abstract_inverted_index":{"Public":[0],"health":[1],"forecasts":[2,149],"must":[3],"respond":[4],"to":[5,48,78,200],"abrupt":[6],"changes":[7],"in":[8,231],"surveillance":[9],"data":[10,162],"without":[11],"over-extrapolating":[12],"noise,":[13],"reporting":[14],"artifacts,":[15],"or":[16],"temporary":[17],"trends.":[18],"We":[19,88],"evaluated":[20],"autoregressive":[21],"integrated":[22],"moving":[23],"average":[24],"(ARIMA),":[25],"random":[26],"forest,":[27],"and":[28,60,81,93,109,132,184,221,238],"extreme":[29],"gradient":[30],"boosting":[31],"(XGBoost)":[32],"models":[33,198],"using":[34,164],"190":[35],"weekly":[36],"observations":[37],"of":[38,76,84,153],"publicly":[39],"available":[40],"Ontario":[41],"COVID-19":[42],"case":[43],"counts":[44],"from":[45],"January":[46],"2020":[47],"October":[49],"2023.":[50],"Rolling-origin":[51],"time-series":[52],"cross-validation":[53],"preserved":[54],"temporal":[55],"order":[56],"during":[57],"model":[58],"tuning":[59],"evaluation.":[61],"Performance":[62],"was":[63],"assessed":[64],"across":[65,180,190],"three":[66],"operating":[67,201],"dimensions:":[68],"responsiveness":[69,110,191],"following":[70],"selected":[71],"turning":[72,120],"points,":[73],"forecast":[74,107,182],"horizons":[75,183],"one":[77],"six":[79],"weeks,":[80],"the":[82,151,154,159,173,187],"amount":[83],"historical":[85,166],"training":[86,157],"data.":[87],"also":[89],"developed":[90],"Machine":[91],"Learning":[92],"ARIMA":[94,116],"Model":[95],"Averaging":[96],"(MLAMA),":[97],"a":[98,207,214,232],"non-negative":[99],"performance-weighted":[100],"ensemble":[101],"with":[102],"weights":[103],"that":[104,115],"vary":[105],"by":[106],"horizon":[108],"setting.":[111],"Retrospective":[112],"comparisons":[113],"showed":[114],"adapted":[117],"rapidly":[118],"after":[119],"points":[121],"but":[122,138],"its":[123],"normalized":[124,142,175],"error":[125,143,179],"increased":[126],"at":[127,150],"longer":[128,145,165],"horizons.":[129,146],"Random":[130],"forest":[131],"XGBoost":[133],"were":[134],"less":[135],"responsive":[136],"initially":[137],"maintained":[139,230],"more":[140],"stable":[141],"over":[144],"For":[147],"two-week":[148],"end":[152],"study":[155],"period,":[156],"on":[158,206],"most":[160,181],"recent":[161],"outperformed":[163],"periods,":[167],"particularly":[168],"for":[169,217],"XGBoost.":[170],"MLAMA":[171,212],"achieved":[172],"lowest":[174],"mean":[176],"absolute":[177],"percentage":[178],"ranked":[185],"among":[186],"best-performing":[188],"methods":[189],"settings.":[192],"These":[193],"findings":[194],"support":[195],"selecting":[196],"forecasting":[197],"according":[199],"conditions":[202],"rather":[203],"than":[204],"relying":[205],"single":[208],"universally":[209],"preferred":[210],"approach.":[211],"provides":[213],"practical":[215],"framework":[216],"combining":[218],"complementary":[219],"statistical":[220],"machine-learning":[222],"forecasts.":[223],"The":[224],"accompanying":[225],"Python":[226],"package":[227],"is":[228],"currently":[229],"private":[233],"repository":[234],"while":[235],"software":[236],"validation":[237],"reproducibility":[239],"testing":[240],"are":[241],"completed.":[242]},"counts_by_year":[],"updated_date":"2026-11-05T12:25:35.769452","created_date":"2026-08-25T00:00:00"}