{"doi":"10.1101/2024.06.24.24309416","title":"Multi-model ensembles in infectious disease and public health: Methods, interpretation, and implementation in R","abstract":"Combining predictions from multiple models into an ensemble is a widely used practice across many fields with demonstrated performance benefits. Popularized through domains such as weather forecasting and climate modeling, multi-model ensembles are becoming increasingly common in public health and biological applications. For example, multi-model outbreak forecasting provides more accurate and reliable information about the timing and burden of infectious disease outbreaks to public health officials and medical practitioners. Yet, understanding and interpreting multi-model ensemble results can be difficult, as there are a diversity of methods proposed in the literature with no clear consensus on which is best. Moreover, a lack of standard, easy-to-use software implementations impedes the generation of multi-model ensembles in practice. To address these challenges, we provide an introduction to the statistical foundations of applied probabilistic forecasting, including the role of multi-model ensembles. We introduce the hubEnsembles package, a flexible framework for ensembling various types of predictions using a range of methods. Finally, we present a tutorial and case-study of ensemble methods using the hubEnsembles package on a subset of real, publicly available data from the FluSight Forecast Hub.","journal":"medRxiv","year":2024,"id":488654,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.939,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":552070,"name":"Emily Howerton","orcid":"0000-0002-0639-3728","position":1,"is_corresponding":false},{"id":882228,"name":"Lucie Contamin","orcid":"0000-0001-5797-1279","position":2,"is_corresponding":false},{"id":72907,"name":"Harry Hochheiser","orcid":"0000-0001-8793-9982","position":3,"is_corresponding":false},{"id":1333254,"name":"Anna Krystalli","orcid":"0000-0002-2378-4915","position":4,"is_corresponding":false},{"id":78333,"name":"Nicholas G. Reich","orcid":"0000-0003-3503-9899","position":5,"is_corresponding":false},{"id":550105,"name":"Evan L Ray","orcid":"0000-0003-4035-0243","position":6,"is_corresponding":false},{"id":1164367,"name":"Li Shandross","orcid":"0009-0008-1348-1954","position":0,"is_corresponding":true}],"reference_count":46,"raw_metadata":null,"created_at":"2026-07-19T02:08:19.655720Z","pmid":"38978658","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}