Optimizing the number of models included in outbreak forecasting ensembles

Based on historical influenza and COVID-19 forecasts, we quantify the relationship between the number of models in an ensemble and its accuracy and introduce an ensemble approach that can outperform the current standard. Our results can assist collaborative forecasting efforts by identifying target participation rates and improving ensemble forecast performance. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement SJF and LAM were supported by the Council for State and Territorial Epidemiologists (NU38OT000297) and the CDC (75D30122C14776). MK, ELR, and NGR were supported by the National Institutes of General Medical Sciences (R35GM119582) and the US CDC (1U01IP001122). The content is solely the responsibility of the authors and does not necessarily represent the official views of CSTE, CDC, NIGMS, or the National Institutes of Health. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All forecasts and ground truth data used in the analysis are publicly available in their specific forecast repositories. Code that gathers the data from the individual competitions and replicates the analysis presented in this manuscript can be found at https://github.com/sjfox/ensemble-size.