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 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

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

To gather news articles from the web that discuss the Cochrane Review, we used Altmetric Explorer from Altmetric.com and retrieved articles on August 1, 2023. We selected all articles that were written in English, published in the United States, and had a publication date prior to March 10, 2023 (according to the “Mention Date” on Altmetric.com). This date is significant as it is when Cochrane issued a statement about the "misleading interpretation" of the Cochrane Review. The collection of news articles is presented in the Altmetric_data.csv file. The dataset contains the following data that we exported from Altmetric Explorer: - Publication date of the news article - Title of the news article - Source/publication venue of the news article - URL - Country We manually checked and added the following information: - Whether the article still exists - Whether the article is accessible - Whether the article is from the original source We assigned MAXQDA IDs to the news articles. News articles were assigned the same ID when they were (a) identical or (b) in the case of Article 207, closely paraphrased, paragraph by paragraph. Inaccessible items were assigned a MAXQDA ID based on their "Mention Title". For each article from Altmetric.com, we first tried to use the Web Collector for MAXQDA to download the article from the website and imported it into MAXQDA (version 22.7.0). If an article could not be retrieved using the Web Collector, we either downloaded the .html file or in the case of Article 128, retrieved it from the NewsBank database through the University of Illinois Library. We then manually extracted direct quotations from the articles using MAXQDA. We included surrounding words and sentences, and in one case, a news agency’s commentary, around direct quotations for context where needed. The quotations (with context) are the positions in our analysis. We also identified who was quoted. We excluded quotations when we could not identify who or what was being quoted. We annotated quotations with codes representing groups (government agencies, other organizations, and research publications) and individuals (authors of the Cochrane Review, government agency representatives, journalists, and other experts such as epidemiologists). The MAXQDA_data.csv file contains excerpts from the news articles that contain the direct quotations we identified. For each excerpt, we included the following information: - MAXQDA ID of the document from which the excerpt originates; - The collection date and source of the document; - The code with which the excerpt is annotated; - The code category; - The excerpt itself.

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.

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team, except for aggregation of individual case count data into daily counts when that was the best data available for a disease and location. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretability. We also formatted the data into a standard data format. All geographic locations at the country and admin1 level have been represented at the same geographic level as in the data source, provided an ISO code or codes could be identified, unless the data source specifies that the location is listed at an inaccurate geographical level. For more information about decisions made by the curation team, recommended data processing steps, and the data sources used, please see the README that is included in the dataset download ZIP file.

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.

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.

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.

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.

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.

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.

COVID-19 is associated with diverse neurological abnormalities, which predict poor outcome in patients. However, the mechanisms whereby infection-induced inflammation could affect complex neuropathologies in COVID-19 are unclear. We hypothesized that microglia, the resident immune cells of brain, are centrally involved in this process. To study this, we developed an autopsy platform allowing the integration of molecular anatomy-, protein- and mRNA data sets in post-mortem mirror blocks of brain and peripheral organ samples from COVID-19 cases. Nanoscale microscopy, single-cell RNA sequencing and analysis of inflammatory and metabolic signatures revealed distinct mechanisms of microglial dysfunction associated with cerebral SARS-CoV-2 infection. We observed focal loss of microglial P2Y12R at sites of virus-associated vascular inflammation together with dysregulated microglia-vascular-astrocyte interactions, CX3CR1-CX3CL1 axis deficits and metabolic failure in severely affected medullary autonomic nuclei and other brain areas. Microglial dysfunction associated with mitochondrial injury and cell loss occurs at sites of excessive synapse- and myelin phagocytosis and loss of glutamatergic terminals in line with proteomic changes of synapse assembly, metabolism and neuronal injury. These changes parallel increased numbers of perivascular macrophages in the medulla. While central and systemic viral load is strongly linked in individual patients, the regionally heterogenous microglial reactivity in the brain correlated with the extent of central and systemic inflammation related to IL-1 / IL-6 via virus-sensing pattern recognition receptors (PRRs) and inflammasome activation pathways. Thus, SARS-CoV-2-induced central and systemic inflammation might lead to a primarily glio-vascular failure in the brain, which could be a common contributor to diverse COVID-19-related neuropathologies.

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.

Over the past thirty years, disaster scholars have highlighted that communities with stronger social infrastructure - including social ties that enable trust, mutual aid, and collective action - tend to respond to and recover better from crisis. However, comprehensive measurements of social capital across communities have been rare. This study adapts Kyne and Aldrich’s (2019) county-level social capital index to the census-tract level, generating social capital indices from 2011 to 2018 at the census-tract, zipcode, and county subdivision levels. To demonstrate their usefulness to disaster planners, public health experts, and local officials, we paired these with the CDC’s Social Vulnerability Index to predict the incidence of COVID-19 in case studies in Massachusetts, Wisconsin, Illinois, and New York City. We found that social capital and social vulnerability predicted as much as 95% of the variation in COVID outbreaks, highlighting their power as diagnostic and predictive tools for combating the spread of COVID.

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.