This dataset contains the following files for California influenza surveillance data: 1) Outpatient Influenza-like Illness Surveillance Data by Region and Influenza Season from volunteer sentinel providers; 2) Clinical Sentinel Laboratory Influenza and Other Respiratory Virus Surveillance Data by Region and Influenza Season from volunteer sentinel laboratories; and 3) Public Health Laboratory Influenza Respiratory Virus Surveillance Data by Region and Influenza Season from California public health laboratories. The Immunization Branch at the California Department of Public Health (CDPH) collects, compiles and analyzes information on influenza activity year-round in California and produces a weekly influenza surveillance report during October through May. The California influenza surveillance system is a collaborative effort between CDPH and its many partners at local health departments, public health and clinical laboratories, vital statistics offices, healthcare providers, clinics, emergency departments, and the Centers for Disease Control and Prevention (CDC). California data are also included in the CDC weekly influenza surveillance report, FluView, and help contribute to the national picture of Influenza activity in the United States. The information collected allows CDPH and CDC to: 1) find out when and where influenza activity is occurring; 2) track influenza-related illness; 3) determine what influenza viruses are circulating; 4) detect changes in influenza viruses; and 5) measure the impact influenza is having on hospitalizations and deaths.

This file contains the provisional percent of total deaths by week for COVID-19, Influenza, and Respiratory Syncytial Virus for deaths occurring among residents in the United States. Provisional data are based on non-final counts of deaths based on the flow of mortality data in National Vital Statistics System.

Effective September 27, 2023, this dataset will no longer be updated. Similar data are accessible from wonder.cdc.gov.

Deaths involving COVID-19, pneumonia, and influenza reported to NCHS by sex, age group, and jurisdiction of occurrence.

Deaths from Pneumonia and Influenza (P&I) and all deaths, by state and region, National Center For Health Statistics Mortality Surveillance System

The Chicago Department of Public Health (CDPH) receives weekly deidentified provisional death certificate data for all deaths that occur in Chicago, which can include both Chicago and non-Chicago residents from the Illinois Department of Public Health (IDPH) Illinois Vital Records System (IVRS). CDPH scans for keywords to identify deaths with COVID-19, influenza, or respiratory syncytial virus (RSV) listed as an immediate cause of death, contributing factor, or other significant condition. The percentage of all reported deaths that are attributed to COVID-19, influenza, or RSV is calculated as the number of deaths for each respective disease divided by the number of deaths from all causes, multiplied by 100. This dataset reflects death certificates that have been submitted to IVRS at the time of transmission to CDPH each week – data from previous weeks are not updated with any new submissions to IVRS. As such, estimates in this dataset may differ from those reported through other sources. This dataset can be used to understand trends in COVID-19, influenza, and RSV mortality in Chicago but does not reflect official death statistics. Source: Provisional deaths from the Illinois Department of Public Health Illinois Vital Records System.

Provisional counts of the number of death occurrences in England and Wales due to coronavirus (COVID-19) and influenza and pneumonia, by age, sex and place of death.

Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.

This file contains the complete set of data reported to 122 Cities Mortality Reposting System. The system was retired as of 10/6/2016. While the system was running each week, the vital statistics offices of 122 cities across the United States reported the total number of death certificates processed and the number of those for which pneumonia or influenza was listed as the underlying or contributing cause of death by age group (Under 28 days, 28 days - 1 year, 1-14 years, 15-24 years, 25-44 years, 45-64 years, 65-74 years, 75-84 years, and - 85 years). U:Unavailable. - : No reported cases.* Mortality data in this table were voluntarily reported from 122 cities in the United States, most of which have populations of >100,000. A death is reported by the place of its occurrence and by the week that the death certificate was filed. Fetal deaths are not included. Total includes unknown ages. More information on Flu Activity & Surveillance is available at http://www.cdc.gov/flu/weekly/fluactivitysurv.htm.

TABLE III. Deaths in 122 U.S. cities - 2014.
122 Cities Mortality Reporting System — Each week, the vital statistics offices of 122 cities across the United States report the total number of death certificates processed and the number of those for which pneumonia or influenza was listed as the underlying or contributing cause of death by age group (Under 28 days, 28 days –1 year, 1-14 years, 15-24 years, 25-44 years, 45-64 years, 65-74 years, 75-84 years, and ≥ 85 years).

FOOTNOTE:
U: Unavailable. —: No reported cases.

• Mortality data in this table are voluntarily reported from 122 cities in the United States, most of which have populations of >100,000. A death is reported by the place of its occurrence and by the week that the death certificate was filed. Fetal deaths are not included.

† Pneumonia and influenza.

§ Because of changes in reporting methods in this Pennsylvania city, these numbers are partial counts for the current week. Complete counts will be available in 4 to 6 weeks.

¶ Total includes unknown ages.

More information on Flu Activity & Surveillance is available at http://www.cdc.gov/flu/weekly/fluactivitysurv.htm.

Age-standardised mortality rates for deaths involving coronavirus (COVID-19), non-COVID-19 deaths and all deaths by vaccination status, broken down by age group.

Context

Dataset aims to facilitate a state by state comparison of potential risk factors that may heighten Covid 19 transmission rates or deaths. It includes state by state estimates of: covid 19 positives/deaths, flu/pneumonia deaths, major city population densities, available hospital resources, high risk health condition prevalance, population over 60, and means of work transportation rates.

Content

The Data Includes:

1) Covid 19 Outcome Stats:

Covid_Death : Covid Deaths by State

Covid_Positive : Covid Positive Tests by State

2) US Major City Population Density by State: CBSA_Major_City_max_weighted_density

3) KFF Estimates of Total Hospital Beds by State:

Kaiser_Total_Hospital_Beds

4) 2018 Season Flu and Pneumonia Death Stats:

FLUVIEW_TOTAL_PNEUMONIA_DEATHS_Season_2018

FLUVIEW_TOTAL_INFLUENZA_DEATHS_Season_2018

5)US Total Rates of Flu Hospitalization by Underlying Condition:

Fluview_US_FLU_Hospitalization_Rate_....

6) State by State BRFSS Prevalance Rates of Conditions Associated with Higher Flu Hospitalization Rates

BRFSS_Diabetes_Prevalance BRFSS_Asthma_Prevalance BRFSS_COPD_Prevalance
BRFSS_Obesity BMI Prevalance BRFSS_Other_Cancer_Prevalance BRFSS_Kidney_Disease_Prevalance BRFSS_Obesity BMI Prevalance BRFSS_2017_High_Cholestoral_Prevalance BRFSS_2017_High_Blood_Pressure_Prevalance Census_Population_Over_60

7)State by state breakdown of Means of Work Transpotation:

COMMUTE_Census_Worker_Public_Transportation_Rate

Acknowledgements

Links to data sources:

https://worldpopulationreview.com/states/

https://covidtracking.com/data/

https://gis.cdc.gov/GRASP/Fluview/FluHospRates.html https://www.kff.org/health-costs/issue-brief/state-data-and-policy-actions-to-address-coronavirus/#stateleveldata

https://data.census.gov/cedsci/table?q=United%20States&tid=ACSDP1Y2018.DP05&hidePreview=true&vintage=2018&layer=VT_2018_040_00_PY_D1&cid=S0103_C01_001E

Tables: ACSST1Y2018.S1811 ACSST1Y2018.S0102

https://www.census.gov/library/visualizations/2012/dec/c2010sr-01-density.html

https://gis.cdc.gov/grasp/fluview/mortality.html

Inspiration

I hope to show the existence of correlations that warrant a deeper county by county analysis to identify areas of increased risk requiring increased resource allocation or increased attention to preventative measures.

Annual: from October to the following September.Winter: from December to the following February.

Contact: PD Dr. Kaspar Staub kaspar.staub@iem.uzh.ch

For the LEAD Hub we digitized and analyzed the following historical demographic and epidemiological data for the city and the canton of Zurich the first time: Since the end of the 19th century, the Federal Health Office (Eidgenössisches Gesundheitsamt) published a weekly bulletin on vital statistics, newly reported cases of notifiable infectious diseases, and hospitalisations. For the period January 1910 to December 1970, we have digitized and transcribed the following weekly series:

Weekly deaths for residents and non-residents of the city of Zurich. The quality of these historical vital statistics is assessed to be very good in the literature, incompleteness and migration are no longer a problem as compared to earlier years. However, age-, sex- and cause-specific death numbers were not available on the weekly level.

Weekly newly reported cases of influenza-like-illness for the canton and the city of Zurich. This series begins with the introduction of the reporting obligation for influenza in the canton of Zürich in mid-July 1918. As these figures do not include mild cases not treated by a doctor and misdiagnoses, they are probably underestimates, but can still track pandemic and seasonal waves. The reporting system and obligation did not change in the observed time period.

Weekly new hospitalisation due to influenza in the canton of Zurich. This series ends in 1938.

The original data format in the weekly bulletins are printed, aggregated tables that have been converted into PDFs using a professional book scanner. Transcription of the data was performed by student assistants using a software and running extended quality-controls. The original tables were in German and French, the digitised data set was annotated in English.

The digitized data are organized as a spreadsheet and stored in csv format. The data are organized as rows (representing reporting weeks) and columns (see variable list below). For a few weeks, information in the original sources was missing (indicated by 1 in the “interpolated” variable). In these cases, the missing values were interpolated by averaging the numbers of the week before and the week afterwards.

Codebook:

Worksheet "Data"

StartReportingPeriod = Start date of the reporting week (dd.mm.yyyy)

EndReportingPeriod = End date of reporting week (dd.mm.yyyy)

Interpolated: 1=value for this week has been interpolated; 0=not interpolated

CityDeathsTotal = Total absolute number of deaths (all-causes) in the City of Zurich (residents and non-residents)

CityDeathsResidents = Absolute number of deaths (all-causes) in the City of Zurich for residents

CityDeathsNonresidents = Absolute number of deaths (all-causes) in the City of Zurich for non-residents

CantonCases = Absolute number of reported new influenza-like-illness cases by physicians in the Canton of Zurich (including the City)

CityCases = Absolute number of reported new influenza-like-illness cases by physicians in the City of Zurich

CantonHospitalisationsFluInfections = Absolute number of new hospitalisations due to influenza-like-illness in the Canton of Zurich (including the City)

Worksheet "Population"

Yearly population numbers for the City and the Canton of Zurich (source)

This dataset represents preliminary weekly estimates of cumulative U.S. RSV-associated hospitalizations for the 2024-2025 season. Estimates are preliminary, and use reported weekly hospitalizations among laboratory-confirmed respiratory syncytial virus (RSV) infections. The data are updated week-by-week as new RSV-associated hospitalizations are reported to CDC from the RSV-NET surveillance system and include both new admissions that occurred during the reporting week, as well as those admitted in previous weeks that may not have been included in earlier reporting. Each week CDC estimates a range (i.e., lower estimate and an upper estimate) of RSV-associated hospitalizations that have occurred since October 1, 2024. For details, please refer to the publication [7].

Note: Data are preliminary and subject to change as more data become available. Rates for recent RSV-associated hospital admissions are subject to reporting delays; as new data are received each week, previous rates are updated accordingly.

Note: Preliminary burden estimates are not inclusive of data from all RSV-NET sites. Due to model limitations, sites with small sample sizes can impact estimates in unpredictable ways and are excluded for the benefit of model stability. CDC is working to address model limitations and include data from all sites in final burden estimates.

References

1. Reed C, Chaves SS, Daily Kirley P, et al. Estimating influenza disease burden from population-based surveillance data in the United States. PLoS One. 2015;10(3):e0118369. https://doi.org/10.1371/journal.pone.0118369 
2. Rolfes, MA, Foppa, IM, Garg, S, et al. Annual estimates of the burden of seasonal influenza in the United States: A tool for strengthening influenza surveillance and preparedness. Influenza Other Respi Viruses. 2018; 12: 132– 137. https://doi.org/10.1111/irv.12486
3. Tokars JI, Rolfes MA, Foppa IM, Reed C. An evaluation and update of methods for estimating the number of influenza cases averted by vaccination in the United States. Vaccine. 2018;36(48):7331-7337. doi:10.1016/j.vaccine.2018.10.026 
4. Collier SA, Deng L, Adam EA, Benedict KM, Beshearse EM, Blackstock AJ, Bruce BB, Derado G, Edens C, Fullerton KE, Gargano JW, Geissler AL, Hall AJ, Havelaar AH, Hill VR, Hoekstra RM, Reddy SC, Scallan E, Stokes EK, Yoder JS, Beach MJ. Estimate of Burden and Direct Healthcare Cost of Infectious Waterborne Disease in the United States. Emerg Infect Dis. 2021 Jan;27(1):140-149. doi: 10.3201/eid2701.190676. PMID: 33350905; PMCID: PMC7774540.
5. Reed C, Kim IK, Singleton JA,  et al. Estimated influenza illnesses and hospitalizations averted by vaccination–United States, 2013-14 influenza season. MMWR Morb Mortal Wkly Rep. 2014 Dec 12;63(49):1151-4. https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6349a2.htm 
6. Reed C, Angulo FJ, Swerdlow DL, et al. Estimates of the Prevalence of Pandemic (H1N1) 2009, United States, April–July 2009. Emerg Infect Dis. 2009;15(12):2004-2007. https://dx.doi.org/10.3201/eid1512.091413  
7. Devine O, Pham H, Gunnels B, et al. Extrapolating Sentinel Surveillance Information to Estimate National COVID-19 Hospital Admission Rates: A Bayesian Modeling Approach. Influenza and Other Respiratory Viruses. https://onlinelibrary.wiley.com/doi/10.1111/irv.70026. Volume18, Issue10. October 2024.
8. https://www.cdc.gov/covid/php/covid-net/index.html">COVID-NET | COVID-19 | CDC 
9. https://www.cdc.gov/covid/hcp/clinical-care/systematic-review-process.html 
10. https://academic.oup.com/pnasnexus/article/1/3/pgac079/6604394?login=false">Excess natural-cause deaths in California by cause and setting: March 2020 through February 2021 | PNAS Nexus | Oxford Academic (oup.com)
11. Kruschke, J. K. 2011. Doing Bayesian data analysis: a tutorial with R and BUGS. Elsevier, Amsterdam, Section 3.3.5.

IntroductionInfluenza A virus (IAV) infection is a global respiratory disease, which annually leads to 3-5 million cases of severe illness, resulting in 290,000-650,000 deaths. Additionally, during the past century, four global IAV pandemics have claimed millions of human lives. The epithelial lining of the trachea plays a vital role during IAV infection, both as point of viral entry and replication as well as in the antiviral immune response. Tracheal tissue is generally inaccessible from human patients, which makes animal models crucial for the study of the tracheal host immune response.MethodIn this study, pigs were inoculated with swine- or human-adapted H1N1 IAV to gain insight into how host adaptation of IAV shapes the innate immune response during infection. In-depth multi-omics analysis (global proteomics and RNA sequencing) of the host response in upper and lower tracheal tissue was conducted, and results were validated by microfluidic qPCR. Additionally, a subset of samples was selected for histopathological examination.ResultsA classical innate antiviral immune response was induced in both upper and lower trachea after infection with either swine- or human-adapted IAV with upregulation of genes and higher abundance of proteins associated with viral infection and recognition, accompanied by a significant induction of interferon stimulated genes with corresponding higher proteins concentrations. Infection with the swine-adapted virus induced a much stronger immune response compared to infection with a human-adapted IAV strain in the lower trachea, which could be a consequence of a higher viral load and a higher degree of inflammation.DiscussionCentral components of the JAK-STAT pathway, apoptosis, pyrimidine metabolism, and the cytoskeleton were significantly altered depending on infection with swine- or human-adapted virus and might be relevant mechanisms in relation to antiviral immunity against putative zoonotic IAV. Based on our findings, we hypothesize that during host adaptation, IAV evolve to modulate important host cell elements to favor viral infectivity and replication.

IntroductionRespiratory viral pathogens are a major cause of morbidity and mortality, and there is a need to understand how to prevent their transmission.MethodsWe performed a scoping review to assess the amount and scope of published research literature on environmental factors, including meteorological factors and pollution, that affect the transmission of respiratory viral pathogens. We used Joanna Briggs Institute methodology for conducting a scoping review. We searched the electronic databases: MEDLINE, Register of Controlled Trials (Cochrane CENTRAL), TRIP database, WHO Covid-19 Database, Global Index Medicus, LitCovid, medRxiv, and Google Scholar. We included studies on environmental exposures and transmission of respiratory viruses (including but not restricted to: influenza, respiratory syncytial virus (RSV), human coronaviruses, viral pneumonia).ResultsThe searches identified 880 studies for screening; after screening we included 481 studies, including 395 primary studies and 86 reviews. Data were extracted by one reviewer (ES) and independently checked by a second reviewer for accuracy (AP). All primary studies were observational, mostly using an ecological design; 2/395 primary studies were prospective cohorts. Among the primary studies, 241/395 were on SARS-CoV-2/COVID-19; 95 focussed on influenza; the remaining 59 reported on RSV, other coronaviruses, and other respiratory viruses. Exposures were most commonly temperature (306 primary studies) and humidity (201 primary studies); other commonly reported exposures were air pollution, wind speed, precipitation, season, and UV radiation. It was frequently reported, but not consistently, that temperature, humidity and air pollution were positively correlated with COVID-19 cases/deaths; for influenza, season/seasonality was commonly reported to be associated with cases/deaths.DiscussionThe majority of studies reported on SARS-CoV-2/COVID-19 and were of ecological design. Few prospective cohort studies have been done for any respiratory virus and environmental exposures. Understanding the role of environmental factors on transmission is limited by the lack of prospective cohort studies to inform decision making.Systematic Review Registrationhttps://osf.io/ntdjx/, identifier: 10.17605/OSF.IO/NTDJX.