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000005910 02470 $$ahttps://doi.org/10.1371/journal.pcbi.1009363$$2doi
000005910 037__ $$aTEXTUAL$$bArticle
000005910 041__ $$aeng
000005910 245__ $$aUniversal risk phenotype of US counties for flu-like transmission to improve county-specific COVID-19 incidence forecasts
000005910 269__ $$a2021-10-14
000005910 336__ $$aArticle
000005910 520__ $$a<p>The spread of a communicable disease is a complex spatio-temporal process shaped by the specific transmission mechanism, and diverse factors including the behavior, socio-economic and demographic properties of the host population. While the key factors shaping transmission of influenza and COVID-19 are beginning to be broadly understood, making precise forecasts on case count and mortality is still difficult. In this study we introduce the concept of a universal geospatial risk phenotype of individual US counties facilitating flu-like transmission mechanisms. We call this the Universal Influenza-like Transmission (UnIT) score, which is computed as an information-theoretic divergence of the local incidence time series from an high-risk process of epidemic initiation, inferred from almost a decade of flu season incidence data gleaned from the diagnostic history of nearly a third of the US population. Despite being computed from the past seasonal flu incidence records, the UnIT score emerges as the dominant factor explaining incidence trends for the COVID-19 pandemic over putative demographic and socio-economic factors. The predictive ability of the UnIT score is further demonstrated via county-specific weekly case count forecasts which consistently outperform the state of the art models throughout the time-line of the COVID-19 pandemic. This study demonstrates that knowledge of past epidemics may be used to chart the course of future ones, if transmission mechanisms are broadly similar, despite distinct disease processes and causative pathogens.</p>
000005910 536__ $$oUnited States Defense Advanced Research Projects Agency$$cHR00111890043/P00004
000005910 540__ $$a<p>© 2021 Huang, Chattopadhyay.</p> <p>This is an open access article distributed under the terms of the <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">Creative Commons Attribution License</a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</p>
000005910 542__ $$fCC BY
000005910 594__ $$a<p><span>With the exception of Truven MarketScan, the sources are in the public domain. Data on confirmed cases of COVID-19 were compiled and released at the COVID-19 Data Repository by the Center for Systems Science and Engineering at Johns Hopkins University (</span><a href="https://github.com/CSSEGISandData/COVID-19">https://github.com/CSSEGISandData/COVID-19</a><span>). The John Hopkins COVID-19 data represent data collated by the US Centers for Disease Control & Prevention (CDC) from individual states and local health agencies. Using the John Hopkins COVID-19 data resource, we obtained county-level confirmed new weekly case counts for all weeks up to the current point in time (2021-05-30) for 3094 US counties. We calculated COVID-19 case per capita using the 2019 population estimate provided by the US Census Bureau generated from 2010 US decennial census (</span><a href="https://www.census.gov/data/datasets/time-series/demo/popest/2010s-counties-detail.html">https://www.census.gov/data/datasets/time-series/demo/popest/2010s-counties-detail.html</a><span>). We include five demographic independent variables: 1) total population, 2) percent of the total population aged 65+, 3) percent of Hispanics in the total population, 4) percent of black/African-American in the total population, 5) percent of minority groups in the total population. For socioeconomic factors, we consider: 1) percent of the total population in poverty and 2) median household income, which are also obtained from the US Census Bureau, based on the 2010 US decennial census. This data is publicly available. Generated models are publicly available at </span><a href="https://github.com/zeroknowledgediscovery/unitcov">https://github.com/zeroknowledgediscovery/unitcov</a><span>, which includes the complete forecast software. (DOI: </span><a href="https://doi.org/10.5281/zenodo.5361628">10.5281/zenodo.5361628</a><span>) The Truven dataset is a third party dataset, which the authors are not authorized to distribute publicly. The dataset can be procured by interested researchers, under license, from </span><a href="https://www.ibm.com/watson-health/about/truven-health-analytics">https://www.ibm.com/watson-health/about/truven-health-analytics</a><span>. The Truven MarketScan database is a US national database collating data contributed by over 150 insurance carriers and large, self-insuring companies, contains over 4.6 billion inpatient and outpatient service claims, with over six billion diagnostic codes. We processed the Truven database to obtain the reported weekly number of influenza cases over a period of 471 weeks spanning from January 2003 to December 2011, at the spatial resolution of US counties. Standard ICD9 diagnostic codes corresponding to Influenza infection is used to determine the county-specific incidence time series, which are: 1) 487 Influenza, 2) 487.0 Influenza with pneumonia, and 3) 487.1 Influenza with other respiratory manifestations and 4) 487.8 Influenza with other manifestations.</span></p>
000005910 690__ $$aBiological Sciences Division
000005910 691__ $$aMedicine
000005910 692__ $$aCenter for Health Statistics
000005910 7001_ $$1https://orcid.org/0000-0002-7024-7469$$2ORCID$$aHuang, Yi$$uUniversity of Chicago
000005910 7001_ $$1https://orcid.org/0000-0001-8339-8162$$2ORCID$$aChattopadhyay, Ishanu$$uUniversity of Chicago
000005910 773__ $$tPLOS Computational Biology
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