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

View daily updates and historical trends for Illinois Coronavirus Cases Currently Hospitalized. Source: US Department of Health & Human Services. Track ec…

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 filtered view contains information about COVID-19 related deaths that occurred in Cook County that were under the Medical Examiner’s jurisdiction.This view was created by looking for "covid" in any of these fields: Primary Cause, Primary Cause Line A, Primary Cause Line B, Primary Cause Line C, or Secondary Cause.

For more information see: https://datacatalog.cookcountyil.gov/stories/s/ttk4-trbu

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.

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/latest-data.html.

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

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".

Confirmed 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 confirmed cases based on the week of death.

For tests, each individual is counted once, based on the week the test specimen was collected. Tests performed prior to 3/1/2020 are not included. Test counts do not include multiple tests for the same person or some negative tests not reported to CDPH.

The “Percent Tested Positive” columns are calculated by dividing the corresponding Cases and Tests columns. Because of the data limitations for the Tests columns, as well as strict criteria for performing COVID-19 tests, these percentages may vary in either direction from the actual disease prevalence in the ZIP Code. Of particular note, these rates do not represent population-level disease surveillance.

Population counts are from the 2010 Decennial Census.

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

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.

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

Weekly rates of COVID-19 cases, hospitalizations, and deaths among people living in Chicago by vaccination status and age.

Rates for fully vaccinated and unvaccinated begin the week ending April 3, 2021 when COVID-19 vaccines became widely available in Chicago. Rates for boosted begin the week ending October 23, 2021 after booster shots were recommended by the Centers for Disease Control and Prevention (CDC) for adults 65+ years old and adults in certain populations and high risk occupational and institutional settings who received Pfizer or Moderna for their primary series or anyone who received the Johnson & Johnson vaccine.

Chicago residency is based on home address, as reported in the Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE) and Illinois National Electronic Disease Surveillance System (I-NEDSS).

Outcomes: • Cases: People with a positive molecular (PCR) or antigen COVID-19 test result from an FDA-authorized COVID-19 test that was reported into I-NEDSS. A person can become re-infected with SARS-CoV-2 over time and so may be counted more than once in this dataset. Cases are counted by week the test specimen was collected. • Hospitalizations: COVID-19 cases who are hospitalized due to a documented COVID-19 related illness or who are admitted for any reason within 14 days of a positive SARS-CoV-2 test. Hospitalizations are counted by week of hospital admission. • Deaths: COVID-19 cases who died from COVID-19-related health complications as determined by vital records or a public health investigation. Deaths are counted by week of death.

Vaccination status: • Fully vaccinated: Completion of primary series of a U.S. Food and Drug Administration (FDA)-authorized or approved COVID-19 vaccine at least 14 days prior to a positive test (with no other positive tests in the previous 45 days). • Boosted: Fully vaccinated with an additional or booster dose of any FDA-authorized or approved COVID-19 vaccine received at least 14 days prior to a positive test (with no other positive tests in the previous 45 days). • Unvaccinated: No evidence of having received a dose of an FDA-authorized or approved vaccine prior to a positive test.

CLARIFYING NOTE: Those who started but did not complete all recommended doses of an FDA-authorized or approved vaccine prior to a positive test (i.e., partially vaccinated) are excluded from this dataset.

Incidence rates for fully vaccinated but not boosted people (Vaccinated columns) are calculated as total fully vaccinated but not boosted with outcome divided by cumulative fully vaccinated but not boosted at the end of each week. Incidence rates for boosted (Boosted columns) are calculated as total boosted with outcome divided by cumulative boosted at the end of each week. Incidence rates for unvaccinated (Unvaccinated columns) are calculated as total unvaccinated with outcome divided by total population minus cumulative boosted, fully, and partially vaccinated at the end of each week. All rates are multiplied by 100,000.

Incidence rate ratios (IRRs) are calculated by dividing the weekly incidence rates among unvaccinated people by those among fully vaccinated but not boosted and boosted people.

Overall age-adjusted incidence rates and IRRs are standardized using the 2000 U.S. Census standard population.

Population totals are from U.S. Census Bureau American Community Survey 1-year estimates for 2019.

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. This dataset reflects data known to CDPH at the time when the dataset is updated each week.

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

For all datasets related to COVID-19, see https://data.cityofchic

NOTE: This dataset is historical-only as of 5/10/2023. All data currently in the dataset will remain, but new data will not be added. The recommended alternative dataset for similar data beyond that date is  https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/anag-cw7u. (This is not a City of Chicago site. Please direct any questions or comments through the contact information on the site.)

During the COVID-19 pandemic, the Chicago Department of Public Health (CDPH) required EMS Region XI (Chicago area) hospitals to report hospital capacity and patient impact metrics related to COVID-19 to CDPH through the statewide EMResource system. This requirement has been lifted as of May 9, 2023, in alignment with the expiration of the national and statewide COVID-19 public health emergency declarations on May 11, 2023. However, all hospitals will still be required by the U.S. Department of Health and Human Services (HHS) to report COVID-19 hospital capacity and utilization metrics into the HHS Protect system through the CDC’s National Healthcare Safety Network until April 30, 2024. Facility-level data from the HHS Protect system can be found at healthdata.gov.

Until May 9, 2023, all Chicago (EMS Region XI) hospitals (n=28) were required to report bed and ventilator capacity, availability, and occupancy to the Chicago Department of Public Health (CDPH) daily. A list of reporting hospitals is included below. All data represent hospital status as of 11:59 pm for that calendar day. Counts include Chicago residents and non-residents.

ICU bed counts include both adult and pediatric ICU beds. Neonatal ICU beds are not included. Capacity refers to all staffed adult and pediatric ICU beds. Availability refers to all available/vacant adult and pediatric ICU beds. Hospitals began reporting COVID-19 confirmed and suspected (PUI) cases in ICU on 03/19/2020. Hospitals began reporting ICU surge capacity as part of total capacity on 5/18/2020.

Acute non-ICU bed counts include burn unit, emergency department, medical/surgery (ward), other, pediatrics (pediatric ward) and psychiatry beds. Burn beds include those approved by the American Burn Association or self-designated. Capacity refers to all staffed acute non-ICU beds. An additional 500 acute/non-ICU beds were added at the McCormick Place Treatment Facility on 4/15/2020. These beds are not included in the total capacity count. The McCormick Place Treatment Facility closed on 05/08/2020. Availability refers to all available/vacant acute non-ICU beds. Hospitals began reporting COVID-19 confirmed and suspected (PUI) cases in acute non-ICU beds on 04/03/2020.

Ventilator counts prior to 04/24/2020 include all full-functioning mechanical ventilators, with ventilators with bilevel positive airway pressure (BiPAP), anesthesia machines, and portable/transport ventilators counted as surge. Beginning 04/24/2020, ventilator counts include all full-functioning mechanical ventilators, BiPAP, anesthesia machines and portable/transport ventilators. Ventilators are counted regardless of ability to staff. Hospitals began reporting COVID-19 confirmed and suspected (PUI) cases on ventilators on 03/19/2020. CDPH has access to additional ventilators from the EAMC (Emergency Asset Management Center) cache. These ventilators are included in the total capacity count.

Chicago (EMS Region 11) hospitals: Advocate Illinois Masonic Medical Center, Advocate Trinity Hospital, AMITA Resurrection Medical Center Chicago, AMITA Saint Joseph Hospital Chicago, AMITA Saints Mary & Elizabeth Medical Center, Ann & Robert H Lurie Children's Hospital, Comer Children's Hospital, Community First Medical Center, Holy Cross Hospital, Jackson Park Hospital & Medical Center, John H. Stroger Jr. Hospital of Cook County, Loretto Hospital, Mercy Hospital and Medical Center, , Mount Sinai Hospital, Northwestern Memorial Hospital, Norwegian American Hospital, Roseland Community Hospital, Rush University Medical Center, Saint Anthony Hospital, Saint Bernard Hospital, South Shore Hospital, Swedish Hospital, Thorek Memorial Hospital, Thorek Hospital Andersonville. University of Chicago Medical Center, University of Illinois Hospital & Health Sciences System, Weiss Memorial Hospital.

Chicago (EMS Region 11) specialty hospitals: Provident Hospital/Cook County, RML Specialty Hospital, Chicago, Montrose Behavioral Health (previously Lakeshore Hospital.) Shirley Ryan AbilityLab (previously RIC), Jesse Brown VA Medical Center, Kindred Chicago – North, Hartgrove Hospital, Kindred Chicago – Lakeshore, Kindred Chicago – Central, Shriners Hospital for Children – Chicago, LaRabida Hospital.

Data Source: Hospitals reporting to CDPH via EMResource (Juvare)

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)

The information presented here is compiled from the Cook County Medical Examiner’s Office.The data sets include information from deaths starting in August 2014 to the present, with information updated daily.It 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.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).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.Manner of death falls under one of five categories:· Homicide· Suicide· Natural· Accident· UndeterminedThe information posted here may be graphic in nature and may not be appropriate for all users.Published 11/21/17 and updated daily.

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.

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.

A country specific data of COVID19 cases in Israel. This data is published by the MOH and can be found on the official site. https://govextra.gov.il/ministry-of-health/corona/corona-virus/

We have the regular suspects inside, with the much needed (In my opinion) test count.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), is a global health threat with the potential to cause severe disease manifestations in the lungs. Although COVID-19 has been extensively characterized clinically, the factors distinguishing SARS-CoV-2 from other respiratory viruses are unknown. Here, we compared the clinical, histopathological, and immunological characteristics of patients with COVID-19 and pandemic influenza A(H1N1). We observed a higher frequency of respiratory symptoms, increased tissue injury markers, and a histological pattern of alveolar pneumonia in pandemic influenza A(H1N1) patients. Conversely, dry cough, gastrointestinal symptoms and interstitial lung pathology were observed in COVID-19 cases. Pandemic influenza A(H1N1) was characterized by higher levels of IL-1RA, TNF-α, CCL3, G-CSF, APRIL, sTNF-R1, sTNF-R2, sCD30, and sCD163. Meanwhile, COVID-19 displayed an immune profile distinguished by increased Th1 (IL-12, IFN-γ) and Th2 (IL-4, IL-5, IL-10, IL-13) cytokine levels, along with IL-1β, IL-6, CCL11, VEGF, TWEAK, TSLP, MMP-1, and MMP-3. Our data suggest that SARS-CoV-2 induces a dysbalanced polyfunctional inflammatory response that is different from the immune response against pandemic influenza A(H1N1). Furthermore, we demonstrated the diagnostic potential of some clinical and immune factors to differentiate both diseases. These findings might be relevant for the ongoing and future influenza seasons in the Northern Hemisphere, which are historically unique due to their convergence with the COVID-19 pandemic.

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.

BackgroundThe SARS-CoV-2 Omicron variant is associated with milder COVID-19 symptoms than previous strains. This study analyzed alterations in natural killer (NK) cell-associated immunity dynamics in mild and moderate COVID-19 cases during the Omicron phase of the COVID-19 pandemic.MethodsWe conducted a retrospective observational cohort study of patients aged ≥16 with confirmed SARS-CoV-2 infection who were hospitalized at Tottori University Hospital between January 2022 and May 2022. A total of 27 patients were included in the analysis. Of these, 11 and 16 were diagnosed with mild and moderate COVID-19, respectively, based on the Japanese COVID-19 clinical practice guideline. Peripheral blood NK cell subsets and surface markers, including the activating receptor NKG2D and the inhibitory receptor TIGIT, as well as serum levels of 24 immunoregulatory markers, such as cytokines and cytotoxic mediators, were measured at admission and recovery. In addition, to explore immune patterns associated with disease severity, differences in 24 serum markers and soluble UL16-binding protein 2 (sULBP2) at the clinically most symptomatic time point during hospitalization were visualized using a volcano plot and analyzed with Spearman’s rank correlation analysis and principal component analysis (PCA).ResultsPatients with mild COVID-19 exhibited expanded subsets of unconventional CD56dimCD16- NK cells with elevated NKG2D expression and lower levels of cytotoxic mediators (granzyme A, granzyme B, and granulysin). In contrast, patients with moderate disease exhibited NK cell exhaustion, characterized by upregulation of TIGIT, along with increased levels of NK cell-associated cytokines and cytotoxic mediators. The volcano plot identified that the patients with moderate COVID-19 exhibited significantly elevated IL-6 and sULBP2 levels. Spearman’s rank correlation analysis revealed that IL-6, IFN-γ, soluble Fas, and CXCL8 were correlated with increased sULBP2. The PCA identified distinct clusters based on disease severity.ConclusionsThe results of study highlight the differences in NK cell-associated immune alterations between mild and moderate COVID-19 cases. Elevated IL-6 and sULBP2 levels, along with their correlations with inflammatory mediators, reflects differences in immune response based on disease severity. These findings provide insight into the immune response to infection caused by the Omicron variant of SARS-CoV-2 and improve our understanding of its immunological features.

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.

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.

In Illinois, influenza associated Intensive Care Unit (ICU) hospitalizations are reportable as soon as possible, but within 24 hours. Influenza associated ICU hospitalizations are defined as individuals hospitalized in an ICU with a positive laboratory test for influenza A or B, including specimens identified as influenza A/H3N2, A/H1N1pdm09, and specimens not subtyped (e.g., influenza positive cases by PCR or any rapid test such as EIA).

This dataset represents weekly aggregated information for influenza-associated ICU hospitalizations among Chicago residents, which is a reportable condition in Illinois.

Information includes demographics, influenza laboratory results, vaccination status, and death status.

Column names containing "REPORTED" indicate the number of cases for which the indicated data element was reported. This, rather than the total number of cases, is used to calculate the corresponding percentage.

All data are provisional and subject to change. Information is updated as additional details are received. At any given time, this dataset reflects data currently known to CDPH. Numbers in this dataset may differ from other public sources.

ABSTRACT Background: Cases of coronavirus disease 2019 (COVID-19) requiring hospitalization continue to appear in vulnerable populations, highlighting the importance of novel treatments. The hyperinflammatory response underlies the severity of the disease, and targeting this pathway may be useful. Herein, we tested whether immunomodulation focusing on interleukin (IL)-6, IL-17, and IL-2, could improve the clinical outcomes of patients admitted with COVID-19. Methods: This multicenter, open-label, prospective, randomized controlled trial was conducted in Brazil. Sixty hospitalized patients with moderate-to-critical COVID-19 received in addition to standard of care (SOC): IL-17 inhibitor (ixekizumab 80 mg SC/week) 1 dose every 4 weeks; low-dose IL-2 (1.5 million IU per day) for 7 days or until discharge; or indirect IL-6 inhibitor (colchicine) orally (0.5 mg) every 8 hours for 3 days, followed by 4 weeks at 0.5 mg 2x/day; or SOC alone. The primary outcome was accessed in the “per protocol” population as the proportion of patients with clinical improvement, defined as a decrease greater or equal to two points on the World Health Organization’s (WHO) seven-category ordinal scale by day 28. Results: All treatments were safe, and the efficacy outcomes did not differ significantly from those of SOC. Interestingly, in the colchicine group, all participants had an improvement of greater or equal to two points on the WHO seven-category ordinal scale and no deaths or patient deterioration were observed. Conclusions: Ixekizumab, colchicine, and IL-2 were demonstrated to be safe but ineffective for COVID-19 treatment. These results must be interpreted cautiously because of the limited sample size.

Thromboembolic complications and excessive inflammation are frequent in severe COVID-19, potentially leading to long COVID. In non-COVID studies, we and others demonstrated that circulating Reelin promotes leukocyte infiltration and thrombosis. Thus, we hypothesized that Reelin participates in endothelial dysfunction and hyperinflammation during COVID-19. We showed that Reelin was increased in COVID-19 patients and correlated with the disease activity. In the severe COVID-19 group, we observed a hyperinflammatory state, as judged by increased concentration of cytokines (IL-1α, IL-4, IL-6, IL-10 and IL-17A), chemokines (IP-10 and MIP-1β), and adhesion markers (E-selectin and ICAM-1). Reelin level was correlated with IL-1α, IL-4, IP-10, MIP-1β, and ICAM-1, suggesting a specific role for Reelin in COVID-19 progression. Furthermore, Reelin and all of the inflammatory markers aforementioned returned to normal in a long COVID cohort, showing that the hyperinflammatory state was resolved. Finally, we tested Reelin inhibition with the anti-Reelin antibody CR-50 in hACE2 transgenic mice infected with SARS-CoV-2. CR-50 prophylactic treatment decreased mortality and disease severity in this model. These results demonstrate a direct proinflammatory function for Reelin in COVID-19 and identify it as a drug target. This work opens translational clinical applications in severe SARS-CoV-2 infection and beyond in auto-inflammatory diseases.