{"doi":"10.1101/2022.06.06.22275840","title":"Forecasting hospital-level COVID-19 admissions using real-time mobility data","abstract":"Abstract For each of the COVID-19 pandemic waves, hospitals have had to plan for deploying surge capacity and resources to manage large but transient increases in COVID-19 admissions. While a lot of effort has gone into predicting regional trends in COVID-19 cases and hospitalizations, there are far fewer successful tools for creating accurate hospital-level forecasts. At the same time, anonymized phone-collected mobility data proved to correlate well with the number of cases for the first two waves of the pandemic (spring 2020, and fall-winter 2021). In this work, we show how mobility data could bolster hospital-specific COVID-19 admission forecasts for five hospitals in Massachusetts during the initial COVID-19 surge. The high predictive capability of the model was achieved by combining anonymized, aggregated mobile device data about users’ contact patterns, commuting volume, and mobility range with COVID hospitalizations and test-positivity data. We conclude that mobility-informed forecasting models can increase the lead-time of accurate predictions for individual hospitals, giving managers valuable time to strategize how best to allocate resources to manage forthcoming surges.","journal":"medRxiv","year":2022,"id":303463,"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":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8852,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":995972,"name":"Ana Cecilia Zenteno","orcid":null,"position":1,"is_corresponding":false},{"id":995973,"name":"Daisha Joseph","orcid":null,"position":2,"is_corresponding":false},{"id":995974,"name":"Mohammadmehdi Zahedi","orcid":null,"position":3,"is_corresponding":false},{"id":984470,"name":"M. Hu","orcid":"0000-0003-2858-6931","position":4,"is_corresponding":false},{"id":995653,"name":"Martin S. Copenhaver","orcid":"0000-0002-9988-260X","position":5,"is_corresponding":false},{"id":48494,"name":"Moritz U. G. Kraemer","orcid":"0000-0001-8838-7147","position":6,"is_corresponding":false},{"id":107959,"name":"Matteo Chinazzi","orcid":"0000-0002-5955-1929","position":7,"is_corresponding":false},{"id":237090,"name":"Michael Klompas","orcid":"0000-0001-8641-4498","position":8,"is_corresponding":false},{"id":44593,"name":"Alessandro Vespignani","orcid":"0000-0003-3419-4205","position":9,"is_corresponding":false},{"id":235271,"name":"Samuel V. Scarpino","orcid":"0000-0001-5716-2770","position":10,"is_corresponding":false},{"id":995654,"name":"Hojjat Salmasian","orcid":"0000-0002-9004-7149","position":11,"is_corresponding":false},{"id":555549,"name":"Brennan Klein","orcid":"0000-0001-8326-5044","position":0,"is_corresponding":true}],"reference_count":40,"raw_metadata":null,"created_at":"2026-07-19T00:32:24.307938Z","pmid":null,"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":[]}