Dataset Information

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

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. STUDY DESIGN:Based on publicly available case data from across the USA, the initial outbreak is statistically modeled as an exponential curve. METHODS: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). RESULTS: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. CONCLUSIONS: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. Further research is needed to understand the reasons for these regional differences.

SUBMITTER: Messner W

PROVIDER: S-EPMC7396895 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Messner W W Payson S E SE

Public health 20200803

<h4>Objective</h4>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.<h4>Study design</h4>Based on publicly available case data from across the USA, the initial outbreak is statistically modeled as an exponential curve.<h4> ...[more]

PMID: 32889227

Similar Datasets

Project description:BackgroundForecasting methods rely on trends and averages of prior observations to forecast COVID-19 case counts. COVID-19 forecasts have received much media attention, and numerous platforms have been created to inform the public. However, forecasting effectiveness varies by geographic scope and is affected by changing assumptions in behaviors and preventative measures in response to the pandemic. Due to time requirements for developing a COVID-19 vaccine, evidence is needed to inform short-term forecasting method selection at county, health district, and state levels.ObjectiveCOVID-19 forecasts keep the public informed and contribute to public policy. As such, proper understanding of forecasting purposes and outcomes is needed to advance knowledge of health statistics for policy makers and the public. Using publicly available real-time data provided online, we aimed to evaluate the performance of seven forecasting methods utilized to forecast cumulative COVID-19 case counts. Forecasts were evaluated based on how well they forecast 1, 3, and 7 days forward when utilizing 1-, 3-, 7-, or all prior-day cumulative case counts during early virus onset. This study provides an objective evaluation of the forecasting methods to identify forecasting model assumptions that contribute to lower error in forecasting COVID-19 cumulative case growth. This information benefits professionals, decision makers, and the public relying on the data provided by short-term case count estimates at varied geographic levels.MethodsWe created 1-, 3-, and 7-day forecasts at the county, health district, and state levels using (1) a naïve approach, (2) Holt-Winters (HW) exponential smoothing, (3) a growth rate approach, (4) a moving average (MA) approach, (5) an autoregressive (AR) approach, (6) an autoregressive moving average (ARMA) approach, and (7) an autoregressive integrated moving average (ARIMA) approach. Forecasts relied on Virginia's 3464 historical county-level cumulative case counts from March 7 to April 22, 2020, as reported by The New York Times. Statistically significant results were identified using 95% CIs of median absolute error (MdAE) and median absolute percentage error (MdAPE) metrics of the resulting 216,698 forecasts.ResultsThe next-day MA forecast with 3-day look-back length obtained the lowest MdAE (median 0.67, 95% CI 0.49-0.84, P<.001) and statistically significantly differed from 39 out of 59 alternatives (66%) to 53 out of 59 alternatives (90%) at each geographic level at a significance level of .01. For short-range forecasting, methods assuming stationary means of prior days' counts outperformed methods with assumptions of weak stationarity or nonstationarity means. MdAPE results revealed statistically significant differences across geographic levels.ConclusionsFor short-range COVID-19 cumulative case count forecasting at the county, health district, and state levels during early onset, the following were found: (1) the MA method was effective for forecasting 1-, 3-, and 7-day cumulative case counts; (2) exponential growth was not the best representation of case growth during early virus onset when the public was aware of the virus; and (3) geographic resolution was a factor in the selection of forecasting methods.

Project description:The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has emerged as an unprecedented global crisis challenging health systems. This paper aims to assess and characterise SARS-CoV-2 outbreaks in the state of Baden-Wuerttemberg to identify groups at greatest risk, to establish early measures to curb transmission. We analysed all mandatory notified (i.e. laboratory-confirmed) coronavirus disease (COVID-19) outbreaks with more than two cases in Baden-Wuerttemberg from calendar weeks 18-49 (from 27 April to 6 December 2020). We used the following classification for settings: asylum and refugee accommodation, care homes, care facilities, day care child centres, hobby-related, hospitality, hospitals, households, other, residence halls, schools, supported housing, training schools, transportation, treatment facilities and workplace (occupational). We used R program version 3.6.3 for analysis. In our analysis, 3219 outbreaks with 22 238 individuals were included. About 48% were in household and hobby-related settings. Care homes accounted for 9.5% of outbreaks and 21.6% of cases. The median age across all settings was 43 (interquartile range (IQR) 24-63). The median age of cases in care homes was 81 (IQR 56-88). Of all reported cases in care homes, 72.1% were women. Over 30% (466/1511) of hospitalisations were among cases in care homes compared to 17.7% (268/1511) in households. Overall, 70% (500/715) of all deceased persons in outbreaks in the study period were in care homes compared to 4.2% in household settings (30/715). We observed an exponential increase in the number of notified outbreaks starting around the 41st week with N = 291 outbreaks reported in week 49. The median number of cases in outbreaks in care homes and care facilities after the 40th week was 14 (IQR 5-29) and 11 (IQR 5-20), respectively, compared to 3 (IQR 3-5) in households. We observed an increase in hospitalisations, and mortality associated with COVID-19 outbreaks in care homes after the 40th week. We found the care home demographic to be at greatest risk after the 40th week, based on the exponential increase in outbreaks, the number of cases, hospitalisations and mortality trends. Our analysis highlights the necessity of targeted, setting-specific approaches to control transmission in this vulnerable population. Regular screening of staff members and visitors' using rapid antigen point-of-care-tests could be a game-changer in curbing transmission in this setting.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets