{"doi":"10.1093/cid/ciaa934","title":"Spatiotemporal Characteristics of the COVID-19 Epidemic in the United States","abstract":"BACKGROUND: A range of near-real-time online/mobile mapping dashboards and applications have been used to track the coronavirus disease 2019 (COVID-19) pandemic worldwide; however, small area-based spatiotemporal patterns of COVID-19 in the United States remain unknown. METHODS: We obtained county-based counts of COVID-19 cases confirmed in the United States from 22 January to 13 May 2020 (N = 1 386 050). We characterized the dynamics of the COVID-19 epidemic through detecting weekly hotspots of newly confirmed cases using Spatial and Space-Time Scan Statistics and quantifying the trends of incidence of COVID-19 by county characteristics using the Joinpoint analysis. RESULTS: Along with the national plateau reached in early April, COVID-19 incidence significantly decreased in the Northeast (estimated weekly percentage change [EWPC]: -16.6%) but continued increasing in the Midwest, South, and West (EWPCs: 13.2%, 5.6%, and 5.7%, respectively). Higher risks of clustering and incidence of COVID-19 were consistently observed in metropolitan versus rural counties, counties closest to core airports, the most populous counties, and counties with the highest proportion of racial/ethnic minorities. However, geographic differences in incidence have shrunk since early April, driven by a significant decrease in the incidence in these counties (EWPC range: -2.0%, -4.2%) and a consistent increase in other areas (EWPC range: 1.5-20.3%). CONCLUSIONS: To substantially decrease the nationwide incidence of COVID-19, strict social-distancing measures should be continuously implemented, especially in geographic areas with increasing risks, including rural areas. Spatiotemporal characteristics and trends of COVID-19 should be considered in decision making on the timeline of re-opening for states and localities.","journal":"Clinical Infectious Diseases","year":2020,"id":58675,"datarank":3.2790794928349136,"base_score":4.276666119016055,"endowment":4.276666119016055,"self_citation_contribution":0.6414999178524083,"citation_network_contribution":2.6375795749825053,"self_endowment_contribution":0.6414999178524083,"citer_contribution":2.6375795749825053,"corpus_percentile":93.23895722131972,"corpus_rank":875,"citation_count":71,"citer_count":71,"citers_with_citation_signal":56,"citers_with_endowment":56,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.639,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":307834,"name":"Ying Liu","orcid":"0000-0001-9636-990X","position":1,"is_corresponding":false},{"id":309803,"name":"James Struthers","orcid":null,"position":2,"is_corresponding":false},{"id":307835,"name":"Min Lian","orcid":"0000-0002-8485-5721","position":3,"is_corresponding":false},{"id":307833,"name":"Yun Wang","orcid":"0000-0001-9438-5586","position":0,"is_corresponding":true}],"reference_count":26,"raw_metadata":null,"created_at":"2026-07-18T21:07:33.416320Z","pmid":"32640020","pmcid":"PMC7454424","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":[]}