Variation in COVID-19 outbreaks at the US state and county levels.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:32889227", "kind": "science-record", "revision": 8, "content_hash": "sha256:66730e069f4a4f415a4250524b6279d1fc10ace8f5d6a6ee514376f903107304", "created_at": "2026-11-01T15:41:09Z", "updated_at": "2026-11-01T22:45:17Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Variation in COVID-19 outbreaks at the US state and county levels.", "doi": "10.1016/j.puhe.2020.07.035", "pmid": "32889227", "authors": ["W Messner", "S E Payson"], "journal": "Public health", "year": 2020, "abstract": "OBJECTIVE: The COVID-19 pandemic poses an unprecedented threat to the health and economic prosperity of the world's population. Yet, because not all regions are affected equally, this research aims to understand whether the relative growth rate of the initial outbreak in early 2020 varied significantly between the US states and counties.\nSTUDY DESIGN: Based on publicly available case data from across the USA, the initial outbreak is statistically modeled as an exponential curve.\nMETHODS: Regional differences are visually compared using geo maps and spaghetti lines. In addition, they are statistically analyzed as an unconditional model (one-way random effects analysis of variance estimated with HLM 7.03); the bias between state- and county-level models is evidenced with distribution tests and Bland-Altman plots (using SPSS 26).\nRESULTS: At the state level, the outbreak rate follows a normal distribution with an average relative growth rate of 0.197 (doubling time 3.518 days). But there is a low degree of reliability between state-wide and county-specific data reported (Intraclass correlation coefficient ICC = 0.169, P < 0.001), with a bias of 0.070 (standard deviation 0.062) as shown with a Bland-Altman plot. Hence, there is a significant variation in the outbreak between the US states and counties.\nCONCLUSIONS: The results emphasize the need for policy makers to look at the pandemic from the smallest population subdivision possible, so that countermeasures can be implemented, and critical resources provided effectively. 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