{"doi":"10.1093/ofid/ofaf307","title":"Machine Learning–Driven COVID-19 Hospitalization Forecasting: From Theory to Practice in a Major Northeastern Academic Medical Center","abstract":"Background: Predicting seasonal and emerging waves of respiratory viruses is crucial for effective public health responses. Despite significant efforts in developing coronavirus disease 2019 (COVID-19) forecast models, there remains a need for improvement in model performances. Methods: We developed and evaluated a machine learning model to forecast COVID-19 hospitalizations by extending the Neural Basis Expansion Analysis for Time Series Forecasting (N-BEATS) architecture. Specifically, we integrated a temporal convolutional network to incorporate exogenous variables and added additional residual blocks to create a variance-forecasting network component for probabilistic predictions. We compared the performance of our model to the ensemble models from the COVID-19 Forecast Hub. Additionally, we implemented the model in a large academic medical center, applying transfer learning to adapt the model to local hospitalization data. Results: Our model demonstrated a 34.0% improvement in mean absolute error over the performance-weighted ensemble and 37.0% over the unweighted ensemble in predicting total US hospitalizations. Similar trends were obtained using mean absolute percent error and symmetric mean absolute percent error. In a real-world implementation, the model provided actionable forecasts for hospital leadership to optimize resource allocation and surge preparation. Conclusions: The enhanced architecture significantly improves the forecasting of COVID-19 hospitalizations, particularly in anticipating peaks and resurgences. Its successful implementation in a hospital system highlights its potential for aiding decision-making and resource planning during pandemics and other respiratory disease outbreaks.","journal":"Open Forum Infectious Diseases","year":2025,"id":533888,"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":0.9517,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1416076,"name":"Xihan Zhao","orcid":"0000-0002-6834-7989","position":1,"is_corresponding":false},{"id":1416077,"name":"Nodar Kipshidze","orcid":"0000-0003-1748-6679","position":2,"is_corresponding":false},{"id":311689,"name":"Jeremiah S. Hinson","orcid":"0000-0002-2024-2360","position":3,"is_corresponding":false},{"id":1012596,"name":"Fardad Haghpanah","orcid":"0000-0002-2158-8486","position":4,"is_corresponding":false},{"id":310734,"name":"Eili Klein","orcid":"0000-0002-1304-5289","position":5,"is_corresponding":false},{"id":1416504,"name":"Alexander Tulchinsky","orcid":null,"position":0,"is_corresponding":true}],"reference_count":17,"raw_metadata":null,"created_at":"2026-07-19T02:51:43.451278Z","pmid":"40510428","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":[]}