NOTE: This dataset has been retired and marked as historical-only.

Only Chicago residents are included based on the home ZIP Code as provided by the medical provider. If a ZIP was missing or was not valid, it is displayed as "Unknown".

Cases with a positive molecular (PCR) or antigen test are included in this dataset. Cases are counted based on the week the test specimen was collected. For privacy reasons, until a ZIP Code reaches five cumulative cases, both the weekly and cumulative case counts will be blank. Therefore, summing the “Cases - Weekly” column is not a reliable way to determine case totals. Deaths are those that have occurred among cases based on the week of death.

For tests, each test is counted once, based on the week the test specimen was collected. Tests performed prior to 3/1/2020 are not included. Test counts include multiple tests for the same person (a change made on 10/29/2020). PCR and antigen tests reported to Chicago Department of Public Health (CDPH) through electronic lab reporting are included. Electronic lab reporting has taken time to onboard and testing availability has shifted over time, so these counts are likely an underestimate of community infection.

The “Percent Tested Positive” columns are calculated by dividing the number of positive tests by the number of total tests . Because of the data limitations for the Tests columns, such as persons being tested multiple times as a requirement for employment, these percentages may vary in either direction from the actual disease prevalence in the ZIP Code.

All data are provisional and subject to change. Information is updated as additional details are received.

To compare ZIP Codes to Chicago Community Areas, please see http://data.cmap.illinois.gov/opendata/uploads/CKAN/NONCENSUS/ADMINISTRATIVE_POLITICAL_BOUNDARIES/CCAzip.pdf. Both ZIP Codes and Community Areas are also geographic datasets on this data portal.

Data Source: Illinois National Electronic Disease Surveillance System, Cook County Medical Examiner’s Office, Illinois Vital Records, American Community Survey (2018)

NOTE: This dataset has been retired and marked as historical-only. This dataset is a companion to the COVID-19 Daily Cases and Deaths dataset (https://data.cityofchicago.org/d/naz8-j4nc). The major difference in this dataset is that the case, death, and hospitalization corresponding rates per 100,000 population are not those for the single date indicated. They are rolling averages for the seven-day period ending on that date. This rolling average is used to account for fluctuations that may occur in the data, such as fewer cases being reported on weekends, and small numbers. The intent is to give a more representative view of the ongoing COVID-19 experience, less affected by what is essentially noise in the data. All rates are per 100,000 population in the indicated group, or Chicago, as a whole, for “Total” columns. Only Chicago residents are included based on the home address as provided by the medical provider. Cases with a positive molecular (PCR) or antigen test are included in this dataset. Cases are counted based on the date the test specimen was collected. Deaths among cases are aggregated by day of death. Hospitalizations are reported by date of first hospital admission. Demographic data are based on what is reported by medical providers or collected by CDPH during follow-up investigation. Denominators are from the U.S. Census Bureau American Community Survey 1-year estimate for 2018 and can be seen in the Citywide, 2018 row of the Chicago Population Counts dataset (https://data.cityofchicago.org/d/85cm-7uqa). All data are provisional and subject to change. Information is updated as additional details are received and it is, in fact, very common for recent dates to be incomplete and to be updated as time goes on. At any given time, this dataset reflects cases and deaths currently known to CDPH. Numbers in this dataset may differ from other public sources due to definitions of COVID-19-related cases and deaths, sources used, how cases and deaths are associated to a specific date, and similar factors. Data Source: Illinois National Electronic Disease Surveillance System, Cook County Medical Examiner’s Office, U.S. Census Bureau American Community Survey

United States COVID-19: No. of Deaths: To Date: Illinois data was reported at 42,005.000 Person in 10 May 2023. This stayed constant from the previous number of 42,005.000 Person for 09 May 2023. United States COVID-19: No. of Deaths: To Date: Illinois data is updated daily, averaging 27,061.000 Person from Jan 2020 (Median) to 10 May 2023, with 1205 observations. The data reached an all-time high of 42,005.000 Person in 10 May 2023 and a record low of 0.000 Person in 16 Mar 2020. United States COVID-19: No. of Deaths: To Date: Illinois data remains active status in CEIC and is reported by Illinois Department of Public Health. The data is categorized under High Frequency Database’s Disease Outbreaks – Table US.D001: Center for Disease Control and Prevention: Coronavirus Disease 2019 (COVID-2019).

Il dataset contiene: i numeri totali dell’ultimo aggiornamento giornaliero disponibile e la variazione percentuale rispetto alla giornata precedente.

Le info messe a disposizione: geografia, giorno, data, totale casi positivi, andamento dei casi positivi x1000 abitanti, deceduti, incremento deceduti rispetto alla giornata precedente x100, letalità, totale dei ricoveri, incremento ricoveri rispetto alla giornata precedente x100, attualmente positivi, dimessi, tamponi effettuati, incremento tamponi rispetto alla giornata precedente x100 suddivisi per provincia, AUSL (quando possibile). I dati saranno aggiornati entro le ore 20 di ogni giorno.

****** ATTENZIONE!! Dal 24 giugno 2022, il Ministero della Salute ha modificato il sistema di rilevazione dei dati sulla diffusione del Covid-19. I casi positivi non sono più indicati secondo la provincia di notifica bensì in base alla provincia di residenza o domicilio.

****** ATTENZIONE!! DA giugno 2023 avremo un solo aggiornamento settimanale.

United States SB: Illinois (IL): COVID-19 Impact: Large Negative Effect data was reported at 25.000 % in 11 Apr 2022. This records a decrease from the previous number of 25.500 % for 04 Apr 2022. United States SB: Illinois (IL): COVID-19 Impact: Large Negative Effect data is updated weekly, averaging 24.900 % from Nov 2021 (Median) to 11 Apr 2022, with 18 observations. The data reached an all-time high of 27.400 % in 21 Feb 2022 and a record low of 21.200 % in 27 Dec 2021. United States SB: Illinois (IL): COVID-19 Impact: Large Negative Effect data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.S047: Small Business Pulse Survey: by State: Midwest Region: Weekly, Beg Monday (Discontinued).

United States Excess Deaths excl COVID: Predicted: Above Expected: Illinois data was reported at 0.000 Number in 30 Oct 2021. This stayed constant from the previous number of 0.000 Number for 23 Oct 2021. United States Excess Deaths excl COVID: Predicted: Above Expected: Illinois data is updated weekly, averaging 0.000 Number from Jan 2017 (Median) to 30 Oct 2021, with 251 observations. The data reached an all-time high of 282.000 Number in 13 Jan 2018 and a record low of 0.000 Number in 30 Oct 2021. United States Excess Deaths excl COVID: Predicted: Above Expected: Illinois data remains active status in CEIC and is reported by Centers for Disease Control and Prevention. The data is categorized under Global Database’s United States – Table US.G012: Number of Excess Deaths: by States: All Causes excluding COVID-19: Predicted (Discontinued).

Context

Knowledge graph extracted from 14,229 papers and 6217 abstracts from the CORD-19 Dataset. Data taken from the University of Illinois Urbana-Champaign with permission from the authors: Qingyun Wang (UIUC), Heng Ji (UIUC), Jiawei Han (UIUC), Shih-Fu Chang (Columbia), Kyunghyun Cho (NYU)

Content

The Knowledge Graph follows the ontology introduced in the Comparative Toxicogenomics Database. It's comprised of 50,752 Gene nodes, 10,781 Disease nodes, 5,738 Chemical nodes, and 535 Organism nodes. These nodes are connected by 133 relation types including Gene–Chemical–Interaction Relationships, Chemical–Disease Associations, Gene–Disease Associations, Chemical–GO Enrichment Associations and Chemical–Pathway Enrichment Associations. Entities also play some certain roles in 13 Event types, including Gene expression, Transcription, Localization, Protein catabolism , Binding, Protein modification, Phosphorylation , Ubiquitination, Acetylation, Deacetylation, Regulation, Positive regulation, Negative regulation.

Acknowledgements

This work was created by Qingyun Wang (UIUC), Heng Ji (UIUC), Jiawei Han (UIUC), Shih-Fu Chang (Columbia), Kyunghyun Cho (NYU)

Their research is based upon work supported in part by U.S. DARPA KAIROS Program No. FA8750-19-2-1004, U.S. DARPA AIDA Program # FA8750-18-2-0014 and U.S. NSF No. 1741634. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of DARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein.

Effective April 1, 2022, the Cook County Medical Examiner’s Office no longer takes jurisdiction over hospital, nursing home or hospice COVID-19 deaths unless there is another factor that falls within the Office’s jurisdiction. Data continues to be collected for COVID-19 deaths in Cook County on the Illinois Dept. of Public Health COVID-19 dashboard (https://dph.illinois.gov/covid19/data.html).

This contains information about deaths that occurred in Cook County that were under the Medical Examiner’s jurisdiction. Not all deaths that occur in Cook County are reported to the Medical Examiner or fall under the jurisdiction of the Medical Examiner. The Medical Examiner’s Office determines cause and manner of death for those cases that fall under its jurisdiction. Cause of death describes the reason the person died. This dataset includes information from deaths starting in August 2014 to the present, with information updated daily.

Changes: December 16, 2022: The Cook County Commissioner District field now reflects the boundaries that went into effect December 5, 2022.

September 8, 2023: The Primary Cause field is now a combination of the Primary Cause Line A, Line B, and Line C fields.

United States SB: IL: COVID-19 Impact: Moderate Positive Effect data was reported at 9.100 % in 11 Apr 2022. This records an increase from the previous number of 6.500 % for 04 Apr 2022. United States SB: IL: COVID-19 Impact: Moderate Positive Effect data is updated weekly, averaging 6.950 % from Nov 2021 (Median) to 11 Apr 2022, with 18 observations. The data reached an all-time high of 9.100 % in 11 Apr 2022 and a record low of 5.200 % in 03 Jan 2022. United States SB: IL: COVID-19 Impact: Moderate Positive Effect data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.S037: Small Business Pulse Survey: by State: Midwest Region: Weekly, Beg Monday (Discontinued).

As of March 10, 2023, the death rate from COVID-19 in the state of New York was 397 per 100,000 people. New York is one of the states with the highest number of COVID-19 cases.

United States Excess Death excl COVID: Predicted: Total Estimate: Illinois data was reported at 11,995.000 Number in 16 Sep 2023. This stayed constant from the previous number of 11,995.000 Number for 09 Sep 2023. United States Excess Death excl COVID: Predicted: Total Estimate: Illinois data is updated weekly, averaging 11,995.000 Number from Jan 2017 (Median) to 16 Sep 2023, with 350 observations. The data reached an all-time high of 11,995.000 Number in 16 Sep 2023 and a record low of 11,995.000 Number in 16 Sep 2023. United States Excess Death excl COVID: Predicted: Total Estimate: Illinois data remains active status in CEIC and is reported by Centers for Disease Control and Prevention. The data is categorized under Global Database’s United States – Table US.G012: Number of Excess Deaths: by States: All Causes excluding COVID-19: Predicted (Discontinued).

NOTE: This dataset replaces two previous ones. Please see below.

Chicago residents who are up to date with COVID-19 vaccines, based on the reported address, race-ethnicity, sex, and age group of the person vaccinated, as provided by the medical provider in the Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE).

“Up to date” refers to individuals who meet the CDC’s updated COVID-19 vaccination criteria based on their age and prior vaccination history. For surveillance purposes, up to date is defined based on the following criteria:

People ages 5 years and older:

· Are up to date when they receive a single dose of an updates (2023-2024 formula) vaccine (Pfizer-BioNTech, Moderna, or Novavax) regardless of their prior vaccination history

Children aged 6 months to 4 years:

· Children who have received at least two prior COVID-19 vaccine doses are up to date when they receive one additional dose of updated COVID-19 vaccine (2023-2024 formula), regardless of vaccine product.

· Children who have received only one prior COVID-19 vaccine dose are up to date when they receive one additional dose of updated Moderna COVID-19 vaccine (2023-2024 formula) or two additional doses of updated Pfizer-BioNTech COVID-19 vaccine (2023-2024 formula).

· Children who have never received a COVID-19 vaccination are up to date when they receive either two doses of the updated Moderna vaccine (2023-2024 formula) or three doses of the updated Pfizer-BioNTech vaccine (2023-2024 formula)

This dataset takes the place of two previous datasets, which cover doses administered from December 15, 2020 through September 13, 2023 and will be marked as historical shortly after the launch of this dataset:

https://data.cityofchicago.org/Health-Human-Services/COVID-19-Daily-Vaccinations-Chicago-Residents/2vhs-cf6b

https://data.cityofchicago.org/Health-Human-Services/COVID-19-Vaccinations-by-Age-and-Race-Ethnicity/37ac-bbe3.

Data Notes:

Weekly cumulative totals of people up to date are shown for each combination of race-ethnicity, sex, and age group. Note that race-ethnicity, age, and sex all have an option for “All” so care should be taken when summing rows.

Coverage percentages are calculated based on the cumulative number of people in each race-ethnicity/age/sex population subgroup who are considered up to date as of the week ending date divided by the estimated number of people in that subgroup. Population counts are obtained from the 2020 U.S. Decennial Census. Actual counts may exceed population estimates and lead to coverage estimates that are greater than 100%, especially in smaller demographic groupings with smaller populations. Additionally, the medical provider may report incorrect demographic information for the person receiving the vaccination, which may lead to over- or underestimation of vaccination coverage. All coverage percentages are capped at 99%.

Weekly cumulative counts and coverage percentages are reported from the week ending Saturday, September 16, 2023 onward through the Saturday prior to the dataset being updated.

All data are provisional and subject to change. Information is updated as additional details are received and it is, in fact, very common for recent dates to be incomplete and to be updated as time goes on. At any given time, this dataset reflects data currently known to CDPH.

Numbers in this dataset may differ from other public sources due to when data are reported and how City of Chicago boundaries are defined.

CDPH uses the most complete data available to estimate COVID-19 vaccination coverage among Chicagoans, but there are several limitations that impact our estimates. Data reported in I-CARE only included doses administered in Illinois and some doses administered outside of Illinois reported historically by Illinois providers. Due to people receiving vaccinations that are not recorded in I-CARE that can be linked to their record, such as someone receiving a vaccine dose in anoth

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Introduction: The spread of Coronavirus Disease 2019 (COVID-19) across the United States has highlighted the long-standing nationwide health inequalities with socioeconomically challenged communities experiencing a higher burden of the disease. We assessed the impact of neighborhood socioeconomic characteristics on the COVID-19 prevalence across seven selected states (i.e., Arizona, Florida, Illinois, Maryland, North Carolina, South Carolina, and Virginia).Methods: We obtained cumulative COVID-19 cases reported at the neighborhood aggregation level by Departments of Health in selected states on two dates (May 3rd, 2020, and May 30th, 2020) and assessed the correlation between the COVID-19 prevalence and neighborhood characteristics. We developed Area Deprivation Index (ADI), a composite measure to rank neighborhoods by their socioeconomic characteristics, using the 2018 US Census American Community Survey. The higher ADI rank represented more disadvantaged neighborhoods.Results: After controlling for age, gender, and the square mileage of each community we identified Zip-codes with higher ADI (more disadvantaged neighborhoods) in Illinois and Maryland had higher COVID-19 prevalence comparing to zip-codes across the country and in the same state with lower ADI (less disadvantaged neighborhoods) using data on May 3rd. We detected the same pattern across all states except for Florida and Virginia using data on May 30th, 2020.Conclusion: Our study provides evidence that not all Americans are at equal risk for COVID-19. Socioeconomic characteristics of communities appear to be associated with their COVID-19 susceptibility, at least among those study states with high rates of disease.

Discover the latest resources, maps and information about the coronavirus (COVID-19) outbreak in your community

What's inside?

This dataset contains data retrieved from Israeli Ministry of Health open database. The source data, as well as descriptions and disclaimers for each dataset can be found at https://data.gov.il/dataset/covid-19, although it's in Hebrew... So why not to use the source data itself? While this is not a problem if you want to conduct a "one off" analysis because all data is available for download in CSV format, or even EXCEL when possible, it is cumbersome when an update is needed. On the other hand getting the data via API calls is limited and takes long time.

Acknowledgements

Kudos to the Israeli Ministry of Health for providing this data. Disclaimers aside, it's not without risk for governmental authority to publish updated data which may have to be adjudicated at a later point in time.
ISR MoH Covid19 Database ISR DataGov

Discover the latest resources, maps and information about the coronavirus (COVID-19) outbreak in your community

NOTE: This dataset is no longer being updated but is being kept for historical reference. For current data on respiratory illness visits and respiratory laboratory testing data please see Influenza, COVID-19, RSV, and Other Respiratory Virus Laboratory Surveillance and Inpatient, Emergency Department, and Outpatient Visits for Respiratory Illnesses.

This is the place to look for important information about how to use this dataset, so please expand this box and read on!

This is the source data for some of the metrics available at https://www.chicago.gov/city/en/sites/covid-19/home/reopening-chicago.html#reopeningmetrics.

For all datasets related to COVID-19, see https://data.cityofchicago.org/browse?limitTo=datasets&sortBy=alpha&tags=covid-19.

The National Syndromic Surveillance Program (NSSP), a collaboration among CDC, federal partners, local and state health departments, and academic and private sector partners, is used to capture information during an Emergency Department (ED) visit. ED data can include information that are collected before cases are diagnosed or laboratory results are confirmed, providing an early warning system for infections, like COVID-19.

This dataset includes reports of COVID-19-Like illness (CLI) and COVID-19 diagnosed during an ED visit. CLI is defined as fever and cough or shortness of breath or difficulty breathing with or without the presence of a coronavirus diagnosis code. Visits meeting the CLI definition that also have mention of flu or influenza are excluded.

This dataset also includes ED visits among persons who have been diagnosed or laboratory confirmed to have COVID-19. During the initial months of the COVID-19 pandemic COVID-19 diagnoses counts are artificially low, due to varying eligibility requirements and availability of testing.

Over the course of the COVID-19 pandemic, public health best practices migrated from focusing on CLI to focusing on diagnosed cases. This dataset originally contained only CLI columns. In June 2021, the diagnosis columns were added, back filled to the start of the pandemic but with the caveat noted above. Roughly simultaneously, updating of the CLI columns was discontinued, although previously existing data were kept. Reflecting the new columns, the name of the dataset was changed from “COVID-Like Illness (CLI) Emergency Department Visits” to “COVID-Like Illness (CLI) and COVID-19 Diagnosis Emergency Department Visits” at the same time.

Data Source: Illinois Hospital Emergency Departments reporting to CDPH through the National Syndromic Surveillance Project (NSSP)

BackgroundMany COVID-19 patients reveal a marked decrease in their lymphocyte counts, a condition that translates clinically into immunodepression and is common among these patients. Outcomes for infected patients vary depending on their lymphocytopenia status, especially their T-cell counts. Patients are more likely to recover when lymphocytopenia is resolved. When lymphocytopenia persists, severe complications can develop and often lead to death. Similarly, IL-10 concentration is elevated in severe COVID-19 cases and may be associated with the depression observed in T-cell counts. Accordingly, this systematic review and meta-analysis aims to analyze T-cell subsets and IL-10 levels among COVID-19 patients. Understanding the underlying mechanisms of the immunodepression observed in COVID-19, and its consequences, may enable early identification of disease severity and reduction of overall morbidity and mortality.MethodsA systematic search was conducted covering PubMed MEDLINE, Scopus, Web of Science, and EBSCO databases for journal articles published from December 1, 2019 to March 14, 2021. In addition, we reviewed bibliographies of relevant reviews and the medRxiv preprint server for eligible studies. Our search covered published studies reporting laboratory parameters for T-cell subsets (CD4/CD8) and IL-10 among confirmed COVID-19 patients. Six authors carried out the process of data screening, extraction, and quality assessment independently. The DerSimonian-Laird random-effect model was performed for this meta-analysis, and the standardized mean difference (SMD) and 95% confidence interval (CI) were calculated for each parameter.ResultsA total of 52 studies from 11 countries across 3 continents were included in this study. Compared with mild and survivor COVID-19 cases, severe and non-survivor cases had lower counts of CD4/CD8 T-cells and higher levels of IL-10.ConclusionOur findings reveal that the level of CD4/CD8 T-cells and IL-10 are reliable predictors of severity and mortality in COVID-19 patients. The study protocol is registered with the International Prospective Register of Systematic Reviews (PROSPERO); registration number CRD42020218918.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020218918, identifier: CRD42020218918.

This dataset is used in the analyses reported in the review entitled "Interleukin (IL)-1 blocking agents for the treatment of COVID-19 A living systematic review"

IL-1 blockers are beneficial in inflammation-associated pathologies, such as rheumatoid arthritis (Mertens 2009) and possibly also in the subgroup of patients with severe sepsis where the inflammasome pathway is involved (Shakoory 2016). Similar benefits were reported in children with secondary macrophage activation syndrome, including cases triggered by viral infections (Mehta 2020b).

In this review we aimed to assess the effectiveness of IL-1 blocking agents compared to placebo, standard of care or no treatment on outcomes in patients with COVID-19.

This review is part of a larger project: the COVID-NMA project. We set-up a platform (https://covid-nma.com) where all our results are made available and updated bi-weekly.

Context

The dataset analyses the impact of the COVID-19 pandemic in Romania.

Content

The dataset contains 4 columns: * date - the date of each record, starting from 26 February 2020 * cases - the cumulative number of cases reported each day, in the first days of the pandemic there were multiple press releases about the number of cases, but the sum per day is already aggregated * recovered - the cumulative number of recovered cases * deaths - the cumulative number of deaths * tests - number of tests performed by the date, for the dates with no information, the difference split equally in that interval

Acknowledgements

This data was collected from: * https://en.wikipedia.org/wiki/2020_coronavirus_pandemic_in_Romania * https://www.digi24.ro/stiri/actualitate/informatii-oficiale-despre-coronavirus-in-romania-1266261 * https://stirioficiale.ro/informatii

Other great data souces: * http://www.ms.ro/comunicate/ * http://www.cnscbt.ro/ * https://instnsp.maps.arcgis.com/apps/opsdashboard/index.html#/5eced796595b4ee585bcdba03e30c127

Thank you for the photo: * https://playtech.ro/stiri/o-minciuna-despre-coronavirus-il-va-costa-ani-grei-de-inchisoare-ce-a-facut-un-barbat-din-campia-turzii-95782

Inspiration

Thanks, https://www.kaggle.com/bjoernjostein/corona-virus-in-norway!