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

This dataset contains all the code, notebooks, datasets used in the study conducted to measure the spatial accessibility of COVID-19 healthcare resources with a particular focus on Illinois, USA. Specifically, the dataset measures spatial access for people to hospitals and ICU beds in Illinois. The spatial accessibility is measured by the use of an enhanced two-step floating catchment area (E2FCA) method (Luo & Qi, 2009), which is an outcome of interactions between demands (i.e, # of potential patients; people) and supply (i.e., # of beds or physicians). The result is a map of spatial accessibility to hospital beds. It identifies which regions need more healthcare resources, such as the number of ICU beds and ventilators. This notebook serves as a guideline of which areas need more beds in the fight against COVID-19. ## What's Inside A quick explanation of the components of the zip file * COVID-19Acc.ipynb is a notebook for calculating spatial accessibility and COVID-19Acc.html is an export of the notebook as HTML. * Data contains all of the data necessary for calculations: * Chicago_Network.graphml/Illinois_Network.graphml are GraphML files of the OSMNX street networks for Chicago and Illinois respectively. * GridFile/ has hexagonal gridfiles for Chicago and Illinois * HospitalData/ has shapefiles for the hospitals in Chicago and Illinois * IL_zip_covid19/COVIDZip.json has JSON file which contains COVID cases by zip code from IDPH * PopData/ contains population data for Chicago and Illinois by census tract and zip code. * Result/ is where we write out the results of the spatial accessibility measures * SVI/contains data about the Social Vulnerability Index (SVI) * img/ contains some images and HTML maps of the hospitals (the notebook generates the maps) * README.md is the document you're currently reading! * requirements.txt is a list of Python packages necessary to use the notebook (besides Jupyter/IPython). You can install the packages with python3 -m pip install -r requirements.txt

Description Source data: https://www.chicago.gov/city/en/sites/covid-19/home/latest-data.html.

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)

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 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)

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.

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 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.cityofchicago.org/browse?limitTo=datasets&sortBy=alpha&tags=covid-19.

Data Source: Illinois' National Electronic Disease Surveillance System (I-NEDSS), Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE), U.S. Census Bureau American Community Survey

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.

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.

This study aimed to detect, analyze, and correlate the clinical characteristics, blood coagulation functions, blood calcium levels, and inflammatory factors in patients with mild and severe COVID-19 infections. The enrolled COVID-19 infected patients were from Wuhan Jin Yin-tan Hospital (17 cases, Wuhan, China), Suzhou Infectious Disease Hospital (87 cases, Suzhou, China), and Xuzhou Infectious Disease Hospital (14 cases, Xuzhou, China). After admission, basic information was collected; X-ray and chest CT images were obtained; and data from routine blood tests, liver and kidney function, myocardial enzymes, electrolytes, blood coagulation function, (erythrocyte sedimentation rate) ESR, C-reactive protein (CRP), IL-6, procalcitonin (PCT), calcitonin, and other laboratory tests were obtained. The patients were grouped according to the clinical classification method based on the pneumonia diagnosis and treatment plan for new coronavirus infection (trial version 7) in China. The measurements from mild (56 cases) and severe cases (51 cases) were compared and analyzed. Most COVID-19 patients presented with fever. Chest X-ray and CT images showed multiple patchy and ground glass opacities in the lungs of COVID 19 infected patients, especially in patients with severe cases. Compared with patients with mild infection, patients with severe infection were older (p = 0.023) and had a significant increase in AST and BUN. The levels of CK, LDH, CK-MB, proBNP, and Myo in patients with severe COVID-19 infection were also increased significantly compared to those in patients with mild cases. Patients with severe COVID-19 infections presented coagulation dysfunction and increased D-dimer and fibrin degradation product (FDP) levels. Severe COVID-19 patients had low serum calcium ion (Ca2+) concentrations and high calcitonin and PCT levels and exhibited serious systemic inflammation. Ca2+ in COVID-19 patients was significantly negatively correlated with PCT, calcitonin, D-dimer, PFDP, ESR, CRP and IL-6. D-dimer in COVID-19 patients was a significantly positively correlated with CRP and IL-6. In conclusion, patients with severe COVID-19 infection presented significant metabolic dysfunction and abnormal blood coagulation, a sharp increase in inflammatory factors and calcitonin and procalcitonin levels, and a significant decrease in Ca2+. Decreased Ca2+ and coagulation dysfunction in COVID-19 patients were significantly correlated with each other and with inflammatory factors.

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.

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.

This Project Tycho dataset includes a CSV file with COVID-19 data reported in ISRAEL: 2019-12-30 - 2021-07-31. It contains counts of cases and deaths. Data for this Project Tycho dataset comes from: "COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University", "European Centre for Disease Prevention and Control Website", "World Health Organization COVID-19 Dashboard". The data have been pre-processed into the standard Project Tycho data format v1.1.

This file contains COVID-19 death counts and rates by month and year of death, jurisdiction of residence (U.S., HHS Region) and demographic characteristics (sex, age, race and Hispanic origin, and age/race and Hispanic origin). United States death counts and rates include the 50 states, plus the District of Columbia. Deaths with confirmed or presumed COVID-19, coded to ICD–10 code U07.1. Number of deaths reported in this file are the total number of COVID-19 deaths received and coded as of the date of analysis and may not represent all deaths that occurred in that period. Counts of deaths occurring before or after the reporting period are not included in the file. Data during recent periods are incomplete because of the lag in time between when the death occurred and when the death certificate is completed, submitted to NCHS and processed for reporting purposes. This delay can range from 1 week to 8 weeks or more, depending on the jurisdiction and cause of death. Death counts should not be compared across jurisdictions. Data timeliness varies by state. Some states report deaths on a daily basis, while other states report deaths weekly or monthly. The ten (10) United States Department of Health and Human Services (HHS) regions include the following jurisdictions. Region 1: Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont; Region 2: New Jersey, New York; Region 3: Delaware, District of Columbia, Maryland, Pennsylvania, Virginia, West Virginia; Region 4: Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee; Region 5: Illinois, Indiana, Michigan, Minnesota, Ohio, Wisconsin; Region 6: Arkansas, Louisiana, New Mexico, Oklahoma, Texas; Region 7: Iowa, Kansas, Missouri, Nebraska; Region 8: Colorado, Montana, North Dakota, South Dakota, Utah, Wyoming; Region 9: Arizona, California, Hawaii, Nevada; Region 10: Alaska, Idaho, Oregon, Washington. Rates were calculated using the population estimates for 2021, which are estimated as of July 1, 2021 based on the Blended Base produced by the US Census Bureau in lieu of the April 1, 2020 decennial population count. The Blended Base consists of the blend of Vintage 2020 postcensal population estimates, 2020 Demographic Analysis Estimates, and 2020 Census PL 94-171 Redistricting File (see https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/2020-2021/methods-statement-v2021.pdf). Rate are based on deaths occurring in the specified week and are age-adjusted to the 2000 standard population using the direct method (see https://www.cdc.gov/nchs/data/nvsr/nvsr70/nvsr70-08-508.pdf). These rates differ from annual age-adjusted rates, typically presented in NCHS publications based on a full year of data and annualized weekly age-adjusted rates which have been adjusted to allow comparison with annual rates. Annualization rates presents deaths per year per 100,000 population that would be expected in a year if the observed period specific (weekly) rate prevailed for a full year. Sub-national death counts between 1-9 are suppressed in accordance with NCHS data confidentiality standards. Rates based on death counts less than 20 are suppressed in accordance with NCHS standards of reliability as specified in NCHS Data Presentation Standards for Proportions (available from: https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf.).

Covid-19 Treatment Medicine Market Outlook

The global COVID-19 treatment medicine market size was valued at approximately USD XX billion in 2023 and is expected to reach around USD XX billion by 2032, growing at a compound annual growth rate (CAGR) of X.X% from 2024 to 2032. This significant growth can be attributed to the ongoing need for effective treatments to combat the COVID-19 virus and its variants, continuous research and development in the pharmaceutical industry, and the global emphasis on healthcare infrastructure improvement.

One of the primary growth factors driving the COVID-19 treatment medicine market is the persistent emergence of new variants of the virus. These variants often exhibit different characteristics, such as increased transmissibility or resistance to previous treatments, thereby necessitating ongoing innovation in therapeutic options. Governments and health organizations worldwide are continuously investing in research and development activities to discover effective treatment options, ensuring a robust pipeline of drugs and therapies.

Another significant growth factor is the global vaccination campaign, which although crucial, has not completely eradicated the need for treatment options. Breakthrough cases and vaccine hesitancy in certain populations have made it clear that treatment options remain necessary. Additionally, the long-term effects of COVID-19, or "long COVID," have created a further demand for effective therapeutic solutions, as patients seek relief from chronic symptoms.

Moreover, advancements in biotechnology and pharmaceutical research have accelerated the development of novel treatment methodologies. The integration of artificial intelligence and machine learning in drug discovery processes has shortened the time required to develop new treatments. This technological integration not only speeds up the R&D phase but also enhances the efficacy of treatments, thereby driving market growth.

Regionally, North America and Europe are leading the market due to their advanced healthcare infrastructure, substantial investments in R&D, and the presence of major pharmaceutical companies. However, emerging markets in Asia Pacific and Latin America are expected to witness significant growth due to increasing healthcare expenditure, improving medical infrastructure, and the rising prevalence of COVID-19 cases. The Middle East & Africa region also shows promise with investments in healthcare and proactive government initiatives.

Drug Class Analysis

The COVID-19 treatment medicine market is segmented by drug class into antivirals, monoclonal antibodies, corticosteroids, immunomodulators, and others. Among these, antiviral drugs have held a significant share of the market due to their role in directly targeting the virus and inhibiting its replication. Remdesivir, for instance, was one of the first antiviral drugs to receive emergency use authorization (EUA) and has been widely used in treating COVID-19.

Monoclonal antibodies have also gained prominence, especially for their role in treating patients with mild to moderate symptoms and preventing hospitalization. These antibodies are engineered to mimic the immune system's ability to fight off harmful pathogens such as viruses. Drugs like Regeneron's casirivimab and imdevimab have shown effectiveness in reducing viral load and have been pivotal in treatment protocols.

Corticosteroids, such as dexamethasone, have been essential in managing severe cases of COVID-19. These drugs help to reduce inflammation and mitigate the immune system's overreaction, which can lead to severe complications like acute respiratory distress syndrome (ARDS). The effectiveness of corticosteroids in reducing mortality rates among critically ill patients has cemented their role in treatment protocols.

Immunomodulators, which modify the immune response, have also found a place in COVID-19 treatment. Drugs like tocilizumab, an interleukin-6 (IL-6) inhibitor, have been used to manage cytokine storms in severe patients. The use of these drugs is often tailored to the patient's immune response, making them a critical part of personalized medicine approaches in COVID-19 treatment.

Other treatment options include a variety of supportive care medications and emerging therapies that are still under investigation. The ongoing research in this segment aims to broaden the arsenal of effective treatment options, thereby ensuring that healthcare providers hav

This record contains data related to article "Thyrotoxicosis in patients with COVID-19: the THYRCOV study"

Abstract

Objective: This study assessed thyroid function in patients affected by the coronavirus disease-19 (COVID-19), based on the hypothesis that the cytokine storm associated with COVID-19 may influence thyroid function and/or the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may directly act on thyroid cells, such as previously demonstrated for SARS-CoV-1 infection.

Design and methods: This single-center study was retrospective and consisted in evaluating thyroid function tests and serum interleukin-6 (IL-6) values in 287 consecutive patients (193 males, median age: 66 years, range: 27-92) hospitalized for COVID-19 in non-intensive care units.

Results: Fifty-eight patients (20.2%) were found with thyrotoxicosis (overt in 31 cases), 15 (5.2%) with hypothyroidism (overt in only 2 cases), and 214 (74.6%) with normal thyroid function. Serum thyrotropin (TSH) values were inversely correlated with age of patients (rho -0.27; P < 0.001) and IL-6 (rho -0.41; P < 0.001). In the multivariate analysis, thyrotoxicosis resulted to be significantly associated with higher IL-6 (odds ratio: 3.25, 95% confidence interval: 1.97-5.36; P < 0.001), whereas the association with age of patients was lost (P = 0.09).

Conclusions: This study provides first evidence that COVID-19 may be associated with high risk of thyrotoxicosis in relationship with systemic immune activation induced by the SARS-CoV-2 infection.