{"doi":"10.1126/sciadv.abq0199","title":"Using digital traces to build prospective and real-time county-level early warning systems to anticipate COVID-19 outbreaks in the United States","abstract":"Coronavirus disease 2019 (COVID-19) continues to affect the world, and the design of strategies to curb disease outbreaks requires close monitoring of their trajectories. We present machine learning methods that leverage internet-based digital traces to anticipate sharp increases in COVID-19 activity in U.S. counties. In a complementary direction to the efforts led by the Centers for Disease Control and Prevention (CDC), our models are designed to detect the time when an uptrend in COVID-19 activity will occur. Motivated by the need for finer spatial resolution epidemiological insights, we build upon previous efforts conceived at the state level. Our methods—tested in an out-of-sample manner, as events were unfolding, in 97 counties representative of multiple population sizes across the United States—frequently anticipated increases in COVID-19 activity 1 to 6 weeks before local outbreaks, defined when the effective reproduction number R t becomes larger than 1 for a period of 2 weeks.","journal":"Science Advances","year":2023,"id":320176,"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":50,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9534,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":300525,"name":"Leonardo Clemente","orcid":null,"position":1,"is_corresponding":false},{"id":248964,"name":"Canelle Poirier","orcid":"0000-0002-6972-2621","position":2,"is_corresponding":false},{"id":614903,"name":"Kris V. Parag","orcid":"0000-0002-7806-3605","position":3,"is_corresponding":false},{"id":1030651,"name":"Atreyee Majumder","orcid":"0000-0002-8185-5785","position":4,"is_corresponding":false},{"id":1030652,"name":"Serge Masyn","orcid":"0000-0002-4296-1809","position":5,"is_corresponding":false},{"id":1030653,"name":"Bernd Resch","orcid":"0000-0002-2233-6926","position":6,"is_corresponding":false},{"id":105567,"name":"Mauricio Santillana","orcid":"0000-0002-4206-418X","position":7,"is_corresponding":false},{"id":232967,"name":"Lucas M. Stolerman","orcid":"0000-0001-6313-0126","position":0,"is_corresponding":true}],"reference_count":77,"raw_metadata":null,"created_at":"2026-07-19T01:07:22.377587Z","pmid":"36652520","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":[]}