32 datasets found
  1. d

    Seattle Coronavirus Assessment Network (SCAN) Dashboard

    • catalog.data.gov
    • data.kingcounty.gov
    Updated Feb 2, 2024
    + more versions
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    data.kingcounty.gov (2024). Seattle Coronavirus Assessment Network (SCAN) Dashboard [Dataset]. https://catalog.data.gov/dataset/seattle-coronavirus-assessment-network-scan-dashboard
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    Dataset updated
    Feb 2, 2024
    Dataset provided by
    data.kingcounty.gov
    Area covered
    Seattle
    Description

    The greater Seattle Coronavirus Assessment Network (SCAN) study is a response to the novel coronavirus outbreak (COVID-19). Since March 23rd, 2020, SCAN has worked in collaboration with Public Health Seattle & King County to deliver and collect at-home COVID-19 tests. The SCAN study is focused on testing people who are experiencing symptoms of COVID-19, and is working to increase testing in underrepresented communities and populations. The SCAN dashboard provides geographic and demographic information from King County about who is ordering a test kit (individuals, contacts and groups) and may differ from the testing data which includes all final results (positive, negative and inconclusive). Reported positives and positivity rate are a combination of general SCAN enrollment and contact testing results, and are not representative of overall population frequency. There was a pause in testing from May 13th through June 9th, during which time SCAN worked with the FDA to update procedures and certifications. Data is updated daily, subject to change and may vary across other technical reports due to the specific analyses being performed.

  2. c

    Systems Serology Measurements of Seattle COVID-19 Patients

    • data.cvisb.org
    Updated Oct 19, 2020
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    Galit Alter laboratory (2020). Systems Serology Measurements of Seattle COVID-19 Patients [Dataset]. https://data.cvisb.org/dataset/systems-serology-32783920
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    Dataset updated
    Oct 19, 2020
    Dataset provided by
    Center for Viral Systems Biology
    Authors
    Galit Alter laboratory
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    United States, Seattle
    Variables measured
    IgM, RCA, SNA, ADCD, ADCP, ADNP, IgA1, IgA2, IgG1, IgG2, and 8 more
    Measurement technique
    serology
    Description

    Systems Serology aims to define the features of the humoral immune response against a given pathogen. Systems Serology analysis includes measurement of the levels antigen-specific antibodies within individual patients, measurement of antibody-mediated induction of innate immune cell effector functions, measurement of binding of antigen-specific antibodies to Fc-receptors, and measurement of neutralizing activity. Samples are taken from COVID-19 patients in Seattle, Washington.

  3. c

    COVID-19 Impact on Food Insecurity

    • s.cnmilf.com
    • data.kingcounty.gov
    • +1more
    Updated Feb 2, 2024
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    data.kingcounty.gov (2024). COVID-19 Impact on Food Insecurity [Dataset]. https://s.cnmilf.com/user74170196/https/catalog.data.gov/dataset/covid-19-impact-on-food-insecurity
    Explore at:
    Dataset updated
    Feb 2, 2024
    Dataset provided by
    data.kingcounty.gov
    Description

    Public Health — Seattle & King County is monitoring changes in key economic, social, and other health indicators resulting from strategies to slow the spread of COVID-19.

  4. Social and Economic Inequities and COVID-19 Outcomes

    • data.kingcounty.gov
    • catalog.data.gov
    csv, xlsx, xml
    Updated Sep 22, 2021
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    Public Health – Seattle & King County (PHSKC) (2021). Social and Economic Inequities and COVID-19 Outcomes [Dataset]. https://data.kingcounty.gov/w/h5ux-n3kr/shwn-npxw?cur=0_Vv_oBYKbJ&from=XClixbmtdON
    Explore at:
    csv, xml, xlsxAvailable download formats
    Dataset updated
    Sep 22, 2021
    Dataset provided by
    Public Health – Seattle & King County
    Authors
    Public Health – Seattle & King County (PHSKC)
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Description

    Locally and across the United States, social and economic inequities have placed certain communities at higher risk of COVID-19. Public Health - Seattle & King County developed a social and economic risk index (SERI) to examine social and economic disparities in COVID-19 outcomes. This dashboard shows the index at census tract-level for King County.

    Higher scores on SERI indicate communities with higher levels of social and economic risk, and lower scores indicate lower levels of risk.

  5. f

    Datasheet_Streptococcus pneumoniae nasal carriage patterns with and without...

    • datasetcatalog.nlm.nih.gov
    • frontiersin.figshare.com
    Updated Jul 7, 2023
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    Emanuels, Anne; Bennett, Julia C.; Chow, Eric J.; Fay, Kairsten; Heimonen, Jessica; Lockwood, Christine M.; O'Hanlon, Jessica; Rolfes, Melissa A.; Hoag, Samara; Ogokeh, Constance E.; Sibley, Thomas R.; Shendure, Jay; Lee, Jover; Uyeki, Timothy M.; Starita, Lea M.; Han, Peter D.; Pfau, Brian; Englund, Janet A.; Chu, Helen Y.; Hughes, James P.; Rogers, Julia H. (2023). Datasheet_Streptococcus pneumoniae nasal carriage patterns with and without common respiratory virus detections in households in Seattle, WA, USA before and during the COVID-19 pandemic.pdf [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0001000723
    Explore at:
    Dataset updated
    Jul 7, 2023
    Authors
    Emanuels, Anne; Bennett, Julia C.; Chow, Eric J.; Fay, Kairsten; Heimonen, Jessica; Lockwood, Christine M.; O'Hanlon, Jessica; Rolfes, Melissa A.; Hoag, Samara; Ogokeh, Constance E.; Sibley, Thomas R.; Shendure, Jay; Lee, Jover; Uyeki, Timothy M.; Starita, Lea M.; Han, Peter D.; Pfau, Brian; Englund, Janet A.; Chu, Helen Y.; Hughes, James P.; Rogers, Julia H.
    Area covered
    United States, Seattle, Washington
    Description

    BackgroundRespiratory viruses might influence Streptococcus pneumoniae nasal carriage and subsequent disease risk. We estimated the association between common respiratory viruses and semiquantitative S. pneumoniae nasal carriage density in a household setting before and during the COVID-19 pandemic.MethodsFrom November 2019–June 2021, we enrolled participants in a remote household surveillance study of respiratory pathogens. Participants submitted weekly reports of acute respiratory illness (ARI) symptoms. Mid-turbinate or anterior nasal swabs were self-collected at enrollment, when ARI occurred, and, in the second year of the study only, from household contacts after SARS-CoV-2 was detected in a household member. Specimens were tested using multiplex reverse-transcription PCR for respiratory pathogens, including S. pneumoniae, rhinovirus, adenovirus, common human coronavirus, influenza A/B virus, respiratory syncytial virus (RSV) A/B, human metapneumovirus, enterovirus, and human parainfluenza virus. We estimated differences in semiquantitative S. pneumoniae nasal carriage density, estimated by the inverse of S. pneumoniae relative cycle threshold (Crt) values, with and without viral detection for any virus and for specific respiratory viruses using linear generalized estimating equations of S. pneumoniae Crt values on virus detection adjusted for age and swab type and accounting for clustering of swabs within households.ResultsWe collected 346 swabs from 239 individuals in 151 households that tested positive for S. pneumoniae (n = 157 with and 189 without ≥1 viruses co-detected). Difficulty breathing, cough, and runny nose were more commonly reported among individuals with specimens with viral co-detection compared to without (15%, 80% and 93% vs. 8%, 57%, and 51%, respectively) and ear pain and headache were less commonly reported (3% and 26% vs. 16% and 41%, respectively). For specific viruses among all ages, semiquantitative S. pneumoniae nasal carriage density was greater with viral co-detection for enterovirus, RSV A/B, adenovirus, rhinovirus, and common human coronavirus (P < 0.01 for each). When stratified by age, semiquantitative S. pneumoniae nasal carriage density was significantly greater with viral co-detection among children aged <5 (P = 0.002) and 5–17 years (P = 0.005), but not among adults aged 18–64 years (P = 0.29).ConclusionDetection of common respiratory viruses was associated with greater concurrent S. pneumoniae semiquantitative nasal carriage density in a household setting among children, but not adults.

  6. d

    Health Insurance and Access to Health Care COVID-19 Impacts

    • datasets.ai
    • data.kingcounty.gov
    • +2more
    21
    Updated Jul 23, 2021
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    King County, Washington (2021). Health Insurance and Access to Health Care COVID-19 Impacts [Dataset]. https://datasets.ai/datasets/health-insurance-and-access-to-health-care-covid-19-impacts
    Explore at:
    21Available download formats
    Dataset updated
    Jul 23, 2021
    Dataset authored and provided by
    King County, Washington
    Description

    Public Health — Seattle & King County is monitoring changes in key economic, social, and other health indicators resulting from strategies to slow the spread of COVID-19.

  7. Seattle Focuses on Economic Recovery, Starting Locally

    • coronavirus-resources.esri.com
    • coronavirus-disasterresponse.hub.arcgis.com
    Updated Apr 14, 2020
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    Esri’s Disaster Response Program (2020). Seattle Focuses on Economic Recovery, Starting Locally [Dataset]. https://coronavirus-resources.esri.com/documents/9a6943fc2d454af89390085ad5175095
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    Dataset updated
    Apr 14, 2020
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Esri’s Disaster Response Program
    Area covered
    Seattle
    Description

    Seattle Focuses on Economic Recovery, Starting LocallyAmid what is foremost a public health and safety crisis, many government leaders are looking for ways to support economic resilience and avoid longer-term impacts of the coronavirus disease 2019 (COVID-19) pandemic.In Seattle, Washington, city officials took early steps to provide for their local economy by launching the #SupportSeattleSmallBiz campaign. The goal of this campaign is to keep businesses open and keep the workforce supporting them employed. Helping Seattle businesses survive the pandemic required a location-based solution—connecting citizens with local businesses that are open. _Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

  8. Microsoft Bing Search For Corona Virus Intent

    • kaggle.com
    zip
    Updated Jan 24, 2021
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    Saurabh Shahane (2021). Microsoft Bing Search For Corona Virus Intent [Dataset]. https://www.kaggle.com/saurabhshahane/microsoft-bing-search-for-corona-virus-intent
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    zip(64939376 bytes)Available download formats
    Dataset updated
    Jan 24, 2021
    Authors
    Saurabh Shahane
    Description

    Context

    This dataset was curated from the Bing search logs (desktop users only) over the period of Jan 1st, 2020 – (Current Month - 1). Only searches that were issued many times by multiple users were included. Dataset includes queries from all over the world that had an intent related to the Coronavirus or Covid-19. In some cases this intent is explicit in the query itself, e.g. “Coronavirus updates Seattle” in other cases it is implicit , e.g. “Shelter in place”. Implicit intent of search queries (e.g. Toilet paper) were extracted by using Random walks on the click graph approach as outlined in the following paper by Nick Craswell et al at Microsoft Research: https://www.microsoft.com/en-us/research/wp-content/uploads/2007/07/craswellszummer-random-walks-sigir07.pdf All personal data was removed. Source - https://msropendata.com/datasets/c5031874-835c-48ed-8b6d-31de2dad0654

    Acknowledgements

    Data Source: Bing Coronavirus Query set (https://github.com/microsoft/BingCoronavirusQuerySet)

    License - Open Use of Data Agreement v1.0

    Content

    Inside the data folder there is a folder 2020 (for the year) which contains two kinds of files.

    QueriesByCountry_DateRange.tsv : A tab separated text file that contains queries with Coronavirus intent by Country. QueriesByState_DateRange.tsv : A tab separated text file that contains queries with Coronavirus intent by State.

    QueriesByCountry Date : string, Date on which the query was issued.

    Query : string, The actual search query issued by user(s).

    IsImplicitIntent : bool, True if query did not mention covid or coronavirus or sarsncov2 (e.g, “Shelter in place”). False otherwise.

    Country : string, Country from where the query was issued.

    PopularityScore : int, Value between 1 and 100 inclusive. 1 indicates least popular query on the day/Country with Coronavirus intent, and 100 indicates the most popular query for the same Country on the same day.

    QueriesByState Date : string, Date on which the query was issued.

    Query : string, The actual search query issued by user(s).

    IsImplicitIntent : bool, True if query did not mention covid or coronavirus or sarsncov2 (e.g, “Shelter in place”). False otherwise.

    State : string, State from where the query was issued.

    Country :string, Country from where the query was issued.

    PopularityScore : int, Value between 1 and 100 inclusive. 1 indicates least popular query on the day/State/Country with Coronavirus intent, and 100 indicates the most popular query for the same geogrpahy on the same day.

  9. d

    COVID-19 Key Economic, Social, and Overall Health Impacts in King County

    • datasets.ai
    • data.kingcounty.gov
    • +1more
    21
    Updated Jul 23, 2021
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    King County, Washington (2021). COVID-19 Key Economic, Social, and Overall Health Impacts in King County [Dataset]. https://datasets.ai/datasets/covid-19-key-economic-social-and-overall-health-impacts-in-king-county
    Explore at:
    21Available download formats
    Dataset updated
    Jul 23, 2021
    Dataset authored and provided by
    King County, Washington
    Area covered
    King County
    Description

    Updated weekly Public Health — Seattle & King County is monitoring changes in key economic, social, and other health indicators resulting from strategies to slow the spread of COVID-19. The metrics below were selected based on studies from previous outbreaks, which have linked strategies such as social distancing, school closures, and business closures to specific outcomes. Individual indicators in the grid below are updated daily, weekly, or monthly, depending on the source of data. Additional data will be added over time.

  10. d

    Homelessness and COVID-19

    • catalog.data.gov
    • data.kingcounty.gov
    • +1more
    Updated Feb 2, 2024
    + more versions
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    data.kingcounty.gov (2024). Homelessness and COVID-19 [Dataset]. https://catalog.data.gov/dataset/homelessness-and-covid-19
    Explore at:
    Dataset updated
    Feb 2, 2024
    Dataset provided by
    data.kingcounty.gov
    Description

    Updated every Thursday People experiencing homelessness are at risk for infection through community spread of COVID-19. The data below describes impacts of COVID-19 on individuals who are experiencing homelessness, whether they are able to access a congregate shelter or unsheltered (sleeping outside or in places not meant for human habitation). For COVID-19 investigation purposes, people experiencing homelessness are defined as those who have lived on the streets or stayed in a shelter, vehicle, abandoned building, encampment, tiny house village/tent city, or supportive housing program (transitional or permanent supportive) at any time during the 12 months prior to COVID-19 testing, without evidence that they were otherwise permanently housed. Public Health, the Department of Community and Human Services, homeless service providers, healthcare providers, and the City of Seattle have partnered for increased testing in this community.

  11. Average number of COVID-19 deaths in last 7 days in select countries, Mar....

    • statista.com
    Updated Aug 29, 2020
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    Statista (2020). Average number of COVID-19 deaths in last 7 days in select countries, Mar. 1-Oct. 27 [Dataset]. https://www.statista.com/statistics/1111867/trailing-seven-day-average-number-of-covid-19-deaths-select-countries-worldwide/
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    Dataset updated
    Aug 29, 2020
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Mar 1, 2020 - Oct 27, 2020
    Area covered
    Worldwide
    Description

    The seven-day average number of COVID-19 deaths in the U.S. decreased significantly from April to July 2020, but it remained higher than in other countries. Seven-day rolling averages are used to adjust for administrative delays in the reporting of deaths by authorities, commonly over weekends.

    The challenges of tracking and reporting the disease The U.S. confirmed its first coronavirus case in mid-January 2020 – the virus was detected in a passenger who arrived in Seattle from China. Since that first case, around 945 people have died every day from COVID-19 in the United States as of August 23, 2020. In total, the U.S. has recorded more coronavirus deaths than any other country worldwide. Accurately tracking the number of COVID-19 deaths has proved complicated, with countries having different rules for what deaths to include in their official figures. Some nations have even changed which deaths they can attribute to the disease during the pandemic.

    Young people urged to act responsibly Between January and May 2020, case fatality rates among COVID-19 patients in the United States increased with age, highlighting the particular risks faced by the elderly. However, COVID-19 is not only a disease that affects older adults. Surges in the number of new cases throughout July 2020 were blamed on young people. The World Health Organization has urged young people not to become complacent, reminding them to maintain social distancing guidelines and take precautions to protect themselves and others.

  12. d

    King County jail COVID-19 statistics

    • datasets.ai
    • data.kingcounty.gov
    • +2more
    23, 40, 55, 8
    Updated Nov 10, 2020
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    King County, Washington (2020). King County jail COVID-19 statistics [Dataset]. https://datasets.ai/datasets/king-county-jail-covid-19-statistics
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    23, 55, 40, 8Available download formats
    Dataset updated
    Nov 10, 2020
    Dataset authored and provided by
    King County, Washington
    Area covered
    King County
    Description

    The Department of Adult and Juvenile Detention is taking emergency actions to ensure the safety of everyone at King County correctional facilities, based on recommendations from the Centers for Disease Control and Prevention as well as Public Health – Seattle & King County. https://kingcounty.gov/depts/jails/covid-updates.aspx

  13. Medical supplies required in US for COVID-19

    • kaggle.com
    zip
    Updated May 20, 2020
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    Aman Kumar (2020). Medical supplies required in US for COVID-19 [Dataset]. https://www.kaggle.com/aestheteaman01/medical-supplies-required-in-us-for-covid19
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    zip(667560 bytes)Available download formats
    Dataset updated
    May 20, 2020
    Authors
    Aman Kumar
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Area covered
    United States
    Description

    Context

    IHME has produced forecasts which show hospital bed use, need for intensive care beds, and ventilator use due to COVID-19 based on projected deaths for all 50 U.S. states. These projections are produced by models based on observed death rates from COVID-19 and include uncertainty intervals.

    They incorporate information about social distancing and other protective measures and are being updated daily with new data. These forecasts were developed in order to provide hospitals, policymakers, and the public with crucial information about how expected need aligns with existing resources so that cities and states can best prepare.

    All the column descriptors and details are attached in the PDF.

    Acknowledgements

    Institute for Health Metrics and Evaluation (IHME). United States COVID-19 Hospital Needs and Death Projections. Seattle, United States of America: Institute for Health Metrics and Evaluation (IHME), University of Washington, 2020

  14. Data from: Interplay of demographics, geography and COVID-19 pandemic...

    • data-staging.niaid.nih.gov
    • data.niaid.nih.gov
    • +2more
    zip
    Updated May 31, 2023
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    James Bristow; Jamie Hamilton; Vashon Medical Reserve Corps COVID-19 Steering Committee; John Weinshel; Robert Rovig; Rick Wallace; Clayton Olney; Karla Lindquist (2023). Interplay of demographics, geography and COVID-19 pandemic responses in the Puget Sound region: The Vashon, Washington Medical Reserve Corps experience [Dataset]. http://doi.org/10.7272/Q6BK19M6
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    zipAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    Medical Reserve Corpshttps://aspr.hhs.gov/MRC/Pages/index.aspx
    VashonBePrepared
    University of California, San Francisco
    Atlas Genomics
    Island County Public Health Department
    Authors
    James Bristow; Jamie Hamilton; Vashon Medical Reserve Corps COVID-19 Steering Committee; John Weinshel; Robert Rovig; Rick Wallace; Clayton Olney; Karla Lindquist
    License

    https://spdx.org/licenses/CC0-1.0.htmlhttps://spdx.org/licenses/CC0-1.0.html

    Area covered
    Puget Sound region, Puget Sound, Vashon, Washington
    Description

    Background Rural U.S. communities are at risk from COVID-19 due to advanced age and limited access to acute care. Recognizing this, the Vashon Medical Reserve Corps (VMRC) in King County, Washington, implemented an all-volunteer, community-based COVID-19 response program. This program integrated public engagement, SARS-CoV-2 testing, contact tracing, vaccination, and material community support, and was associated with the lowest cumulative COVID-19 case rate in King County. This study aimed to investigate the contributions of demographics, geography and public health interventions to Vashon’s low COVID-19 rates. Methods This observational cross-sectional study compares cumulative COVID-19 rates and success of public health interventions from February 2020 through November 2021 for Vashon Island with King County (including metropolitan Seattle) and Whidbey Island, located ~50 km north of Vashon. To evaluate the role of demography, we developed multiple linear regression models of COVID-19 rates using metrics of age, race/ethnicity, wealth and educational attainment across 77 King County zip codes. To investigate the role of remote geography we expanded the regression models to include North, Central and South Whidbey, similarly remote island communities with varying demographic features. To evaluate the effectiveness of VMRC’s community-based public health measures, we directly compared Vashon’s success of vaccination and contact tracing with that of King County and South Whidbey, the Whidbey community most similar to Vashon. Results Vashon’s cumulative COVID-19 case rate was 29% that of King County overall (22.2 vs 76.8 cases/K). A multiple linear regression model based on King County demographics found educational attainment to be a major correlate of COVID-19 rates, and Vashon’s cumulative case rate was just 38% of predicted (p<.05), so demographics alone do not explain Vashon’s low COVID-19 case rate. Inclusion of Whidbey communities in the model identified a major effect of remote geography (-49 cases/K, p<.001), such that observed COVID-19 rates for all remote communities fell within the model’s 95% prediction interval. VMRC’s vaccination effort was highly effective, reaching a vaccination rate of 1500 doses/K four months before South Whidbey and King County and maintaining a cumulative vaccination rate 200 doses/K higher throughout the latter half of 2021 (p<.001). Including vaccination rates in the model reduced the effect of remote geography to -41 cases/K (p<.001). VMRC case investigation was also highly effective, interviewing 96% of referred cases in an average of 1.7 days compared with 69% in 3.7 days for Washington Department of Health investigating South Whidbey cases and 80% in 3.4 days for Public Health–Seattle & King County (both p<0.001). VMRC’s public health interventions were associated with a 30% lower case rate (p<0.001) and 55% lower hospitalization rate (p=0.056) than South Whidbey. Conclusion While the overall magnitude of the pre-Omicron COVID-19 pandemic in rural and urban U.S. communities was similar, we show that island communities in the Puget Sound region were substantially protected from COVID-19 by their geography. We further show that a volunteer community-based COVID-19 response program was highly effective in the Vashon community, augmenting the protective effect of geography. We suggest that Medical Reserve Corps should be an important element of future pandemic planning. Methods The study period extended from the pandemic onset in February 2020 through November 2021. Daily COVID-19 cases, hospitalizations, deaths and test numbers for King County as a whole and by zip code were downloaded from the King County COVID-19 dashboard (Feb 22, 2022 update). Population data for King County and Vashon are from the April 2020 US Census. Zip code level population data are the average of two zip code tabulation area estimates from the WA Office of Financial Management and Cubit (a commercial data vendor providing access to US Census information). The Asset Limited, Income Constrained, and Employed (ALICE) metric, a measure of the working poor, was obtained from United Way.

  15. n

    Temporal variability of microparticles under the Seattle Aquarium, WA:...

    • data-staging.niaid.nih.gov
    • data.niaid.nih.gov
    • +1more
    zip
    Updated Sep 6, 2021
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    Lyda S. T. Harris; Laura La Beur; Amy Y. Olsen; Angela Smith; Lindsey Eggers; Emily Pedersen; Jennifer Van Brocklin; Susanne M. Brander; Shawn Larson (2021). Temporal variability of microparticles under the Seattle Aquarium, WA: Documenting the global Covid‐19 pandemic [Dataset]. http://doi.org/10.5061/dryad.zpc866t90
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    zipAvailable download formats
    Dataset updated
    Sep 6, 2021
    Dataset provided by
    Oregon State University
    Seattle Aquarium
    Authors
    Lyda S. T. Harris; Laura La Beur; Amy Y. Olsen; Angela Smith; Lindsey Eggers; Emily Pedersen; Jennifer Van Brocklin; Susanne M. Brander; Shawn Larson
    License

    https://spdx.org/licenses/CC0-1.0.htmlhttps://spdx.org/licenses/CC0-1.0.html

    Area covered
    Seattle
    Description

    Anthropogenic debris including microparticles (MP; <5mm) are ubiquitous in marine environments. The Salish Sea experiences seasonal fluctuations in precipitation, river discharge, sewage overflow events, and tourism– all variables previously thought to have an impact on MP transport and concentrations. Our goals are two-fold: 1) Describe long-term MP contamination data including concentration, type, and size and 2) Determine if seasonal MP concentrations are dependent on environmental or tourism variables in Elliott Bay, Salish Sea. We sampled 100 L of seawater at depth (~9 m) at the Seattle Aquarium approximately every two weeks 2019 – 2020 and used an oil extraction protocol to separate MP. We found MP concentrations ranged from 0 – 0.64 particles L⁻¹ and fibers were the most common type observed. Microparticle concentration exhibited a breakpoint on April 10, 2020, where estimated slope and associated MP concentration significantly declined. Further, when considering both environmental as well as tourism variables, temporal MP concentration was best described by a mixed-effects model with tourism as the fixed effect and the person counting MP as the random effect. While monitoring efforts presented here set out to identify effects of seasonality and interannual differences in MP concentrations, it instead captured an effect of decreased tourism due to the global Covid-19 pandemic. Long-term monitoring is critical to establish temporal MP concentrations and to help researchers understand if there are certain events, both seasonal and sporadic (e.g. rain events, tourism, or global pandemics), when the marine environment is more at risk from anthropogenic pollution.

  16. g

    Seattle Rescue Plan | gimi9.com

    • gimi9.com
    + more versions
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    Seattle Rescue Plan | gimi9.com [Dataset]. https://gimi9.com/dataset/data-gov_seattle-rescue-plan/
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    Area covered
    Seattle
    Description

    This dataset contains information on all Seattle Rescue Plan programs, their objectives, and budgeted and spending amounts. The Seattle Rescue Plan encompasses nearly $300 million of federal relief the City of Seattle received to help our community respond to and recover from the COVID-19 pandemic’s health and economic impacts. This data will be updated on a quarterly basis to reflect changes in expenditures.

  17. Leading tech companies' donations towards COVID-19 2021

    • statista.com
    Updated Oct 18, 2021
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    Statista (2021). Leading tech companies' donations towards COVID-19 2021 [Dataset]. https://www.statista.com/statistics/1106386/leading-tech-companies-donations-towards-covid-19/
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    Dataset updated
    Oct 18, 2021
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Oct 2021
    Area covered
    Worldwide
    Description

    The internet giant Google has made total donation of more than around 1.3 billion U.S. dollars to support businesses, organizations and healthcare workers to combat the novel coronavirus (COVID-19) - most of the donation will come in form of ad grants and ad credits. The COVID-19 pandemic has had a global impact on many different industries. Over the past weeks, many of the leading technology companies have announced financial contributions in support of resolving the COVID-19 worldwide crisis. Cisco The networking equipment giant Cisco planned to dedicate 226 million U.S. dollars in cash, in-kind, and planned-giving to support different causes combating the outbreak. Facebook Facebook would donate around 20 million U.S. dollars to support relief efforts for the virus. In addition, the leading social network company launched a 100 million U.S. dollars fund, divided between small businesses in 30 different countries to help them stay afloat. Netflix Netflix established a 100 million U.S. dollars fund for cast and crew on productions halted by the COVID-19 pandemic. An additional 15 million U.S. dollars is set to be distributed among third parties in the countries where the company has a large production base. Amazon Amazon's one-million donaton would be split among four foundations in Washington D.C. supporting the vulnerable during the crisis. Being one of the few businesses growing during the coronavirus pandemic, Amazon announced a 25 million U.S. dollars relief fund for its network of independant Amazon Flex drivers, as well as 50 thousand dollars worth of supplies to quarantine housing. Another one million was donated to a new Seattle Foundations fund for members affected by the pandemic. Apple, Microsoft & others Alongside Apple sourcing supplies needed by healthcare workers, as well as donating millions of masks, the company will donate 15 million U.S. dollars as a COVID-19 response. Microsoft's donation to the COVID-19 Response Fund amounted to 61.9 million U.S. dollars. A number of other tech giants contribute to the pandemic handling, including Tesla's CEO Elon Musk's donation of 1,200 ventilators.

    For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Fact and Figures page.

  18. Case investigation and contact tracing on Vashon, South Whidbey and in King...

    • plos.figshare.com
    bin
    Updated Aug 16, 2023
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    James Bristow; Jamie Hamilton; John Weinshel; Robert Rovig; Rick Wallace; Clayton Olney; Karla J. Lindquist (2023). Case investigation and contact tracing on Vashon, South Whidbey and in King County. [Dataset]. http://doi.org/10.1371/journal.pone.0274345.t002
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    binAvailable download formats
    Dataset updated
    Aug 16, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    James Bristow; Jamie Hamilton; John Weinshel; Robert Rovig; Rick Wallace; Clayton Olney; Karla J. Lindquist
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Whidbey Island, Vashon, King County
    Description

    Case investigation and contact tracing on Vashon, South Whidbey and in King County.

  19. U.S. local newscasts: coronavirus viewership impact 2020

    • statista.com
    Updated Jul 11, 2025
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    Statista (2025). U.S. local newscasts: coronavirus viewership impact 2020 [Dataset]. https://www.statista.com/statistics/1105701/local-newscast-viewership-coronavirus-usa/
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    Dataset updated
    Jul 11, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Feb 10, 2020 - Mar 9, 2020
    Area covered
    United States
    Description

    Between the weeks of February 10 and March 9, 2020, the top 25 DMAs (Designated Market Areas) in the United States saw an average increase of ** percent in daily household viewership of local news. Several markets saw growth of more than ** percent, though the highest was Seattle-Tacoma with ** percent more households viewing local news on a daily basis in the week ending March 9 than in the corresponding week of February 2020.

  20. D

    Mobile Recreation Programming

    • data.seattle.gov
    • catalog.data.gov
    • +1more
    csv, xlsx, xml
    Updated Feb 3, 2025
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    (2025). Mobile Recreation Programming [Dataset]. https://data.seattle.gov/dataset/Mobile-Recreation-Programming/xnhe-62h3
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    xml, xlsx, csvAvailable download formats
    Dataset updated
    Feb 3, 2025
    Area covered
    Mobile
    Description

    SPR Recreation Division programming locations including Rec'N the Streets Mobile Recreation, Park Ambassadors, Summer Lunch & Playground, and others. In response to the COVID-19 pandemic, Recreation Division programming continues to offer outdoor opportunities to recreate under existing safety precautions. The mission of Rec'N the Streets in particular is: everyone should have access to recreation in their communities; for those in areas with health disparities and no access to these activities, we bring the recreation to them.


    Refresh Cycle: Daily

    Feature Class: DPR.MobileRec_PT

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data.kingcounty.gov (2024). Seattle Coronavirus Assessment Network (SCAN) Dashboard [Dataset]. https://catalog.data.gov/dataset/seattle-coronavirus-assessment-network-scan-dashboard

Seattle Coronavirus Assessment Network (SCAN) Dashboard

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Dataset updated
Feb 2, 2024
Dataset provided by
data.kingcounty.gov
Area covered
Seattle
Description

The greater Seattle Coronavirus Assessment Network (SCAN) study is a response to the novel coronavirus outbreak (COVID-19). Since March 23rd, 2020, SCAN has worked in collaboration with Public Health Seattle & King County to deliver and collect at-home COVID-19 tests. The SCAN study is focused on testing people who are experiencing symptoms of COVID-19, and is working to increase testing in underrepresented communities and populations. The SCAN dashboard provides geographic and demographic information from King County about who is ordering a test kit (individuals, contacts and groups) and may differ from the testing data which includes all final results (positive, negative and inconclusive). Reported positives and positivity rate are a combination of general SCAN enrollment and contact testing results, and are not representative of overall population frequency. There was a pause in testing from May 13th through June 9th, during which time SCAN worked with the FDA to update procedures and certifications. Data is updated daily, subject to change and may vary across other technical reports due to the specific analyses being performed.

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