Dataset Information

A syndromic surveillance tool to detect anomalous clusters of COVID-19 symptoms in the United States.

ABSTRACT: Coronavirus SARS-COV-2 infections continue to spread across the world, yet effective large-scale disease detection and prediction remain limited. COVID Control: A Johns Hopkins University Study, is a novel syndromic surveillance approach, which collects body temperature and COVID-like illness (CLI) symptoms across the US using a smartphone app and applies spatio-temporal clustering techniques and cross-correlation analysis to create maps of abnormal symptomatology incidence that are made publicly available. The results of the cross-correlation analysis identify optimal temporal lags between symptoms and a range of COVID-19 outcomes, with new taste/smell loss showing the highest correlations. We also identified temporal clusters of change in taste/smell entries and confirmed COVID-19 incidence in Baltimore City and County. Further, we utilized an extended simulated dataset to showcase our analytics in Maryland. The resulting clusters can serve as indicators of emerging COVID-19 outbreaks, and support syndromic surveillance as an early warning system for disease prevention and control.

SUBMITTER: Guemes A

PROVIDER: S-EPMC7907397 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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A syndromic surveillance tool to detect anomalous clusters of COVID-19 symptoms in the United States.

Güemes Amparo A Ray Soumyajit S Aboumerhi Khaled K Desjardins Michael R MR Kvit Anton A Corrigan Anne E AE Fries Brendan B Shields Timothy T Stevens Robert D RD Curriero Frank C FC Etienne-Cummings Ralph R

Scientific reports 20210225 1

Coronavirus SARS-COV-2 infections continue to spread across the world, yet effective large-scale disease detection and prediction remain limited. COVID Control: A Johns Hopkins University Study, is a novel syndromic surveillance approach, which collects body temperature and COVID-like illness (CLI) symptoms across the US using a smartphone app and applies spatio-temporal clustering techniques and cross-correlation analysis to create maps of abnormal symptomatology incidence that are made publicl ...[more]

PMID: 33633250

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Project description:In the United States, sepsis is a major public health problem accounting for over 200,000 annual deaths. The aims of this study were to identify U.S. counties with high sepsis mortality and to assess the community characteristics associated with increased sepsis mortality.We performed a descriptive analysis of 2003 through 2012 Compressed Mortality File data. We defined sepsis deaths as deaths associated with an infection, classified according to the International Classification of Diseases, 10th Version.Three thousand one hundred and eight counties in the contiguous U.S. counties, excluding Hawaii and Alaska.Using geospatial autocorrelation methods, we defined county-level sepsis mortality as strongly clustered, moderately clustered, and nonclustered. We approximated the mean crude, age-adjusted, and community-adjusted sepsis mortality rates nationally and for clustering groups. We contrasted demographic and community characteristics between clustering groups. We performed logistic regression for the association between strongly clustered counties and community characteristics. Among 3,108 U.S. counties, the age-adjusted sepsis mortality rate was 59.6 deaths per 100,000 persons (95% CI, 58.9-60.4). Sepsis mortality was higher in the Southern U.S. and clustered in three major regions: Mississippi Valley, Middle Georgia, and Central Appalachia. Among 161 (5.2%) strongly clustered counties, age-adjusted sepsis mortality was 93.1 deaths per 100,000 persons (95% CI, 90.5-95.7). Strongly clustered sepsis counties were more likely to be located in the south (92.6%; p < 0.001), exhibit lower education, higher impoverished population, without medical insurance, higher medically uninsured rates, and had higher unemployment rates (p < 0.001).Sepsis mortality is higher in the Southern United States, with three regional clusters: "Mississippi Valley," "Middle Georgia," and "Central Appalachia": Regions of high sepsis mortality are characterized by lower education, income, employment, and insurance coverage.

Dataset Information

A syndromic surveillance tool to detect anomalous clusters of COVID-19 symptoms in the United States.

Publications

A syndromic surveillance tool to detect anomalous clusters of COVID-19 symptoms in the United States.

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