Monthly Cumulative Number and Percent of Persons Who Received ≥1 Influenza Vaccination Doses, by Flu Season, Age Group, and Jurisdiction

• Influenza vaccination coverage for children and adults is assessed through U.S. jurisdictions’ Immunization Information Systems (IIS) data, submitted from jurisdictions to CDC monthly in aggregate by age group. More information about the IIS can be found at https://www.cdc.gov/vaccines/programs/iis/about.html.

• Influenza vaccination coverage estimate numerators include the number of people receiving at least one dose of influenza vaccine in a given flu season, based on information that state, territorial, and local public health agencies report to CDC. Some jurisdictions’ data may include data submitted by tribes. Estimates include persons who are deceased but received a vaccination during the current season. People receiving doses are attributed to the jurisdiction in which the person resides unless noted otherwise. Quality and completeness of data may vary across jurisdictions. Influenza vaccination coverage denominators are obtained from 2020 U.S. Census Bureau population estimates.

• Monthly estimates shown are cumulative, reflecting all persons vaccinated from July through a given month of that flu season. Cumulative estimates include any historical data reported since the previous submission. National estimates are not presented since not all U.S. jurisdictions are currently reporting their IIS data to CDC. Jurisdictions reporting data to CDC include U.S. states, some localities, and territories.

• Because IIS data contain all vaccinations administered within a jurisdiction rather than a sample, standard errors were not calculated and statistical testing for differences in estimates across years were not performed.

• Laws and policies regarding the submission of vaccination data to an IIS vary by state, which may impact the completeness of vaccination coverage reflected for a jurisdiction. More information on laws and policies are found at https://www.cdc.gov/vaccines/programs/iis/policy-legislation.html.

• Coverage estimates based on IIS data are expected to differ from National Immunization Survey (NIS) estimates for children (https://www.cdc.gov/flu/fluvaxview/dashboard/vaccination-coverage-race.html) and adults (https://www.cdc.gov/flu/fluvaxview/dashboard/vaccination-adult-coverage.html) because NIS estimates are based on a sample that may not be representative after survey weighting and vaccination status is determined by survey respondent rather than vaccine records or administrations, and quality and completeness of IIS data may vary across jurisdictions. In general, NIS estimates tend to overestimate coverage due to overreporting and IIS estimates may underestimate coverage due to incompleteness of data in certain jurisdictions.

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.

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.

Influenza, also called the flu, is one of the most infectious diseases worldwide. Its symptoms range from mild to severe, and include sore throat, cough, runny nose, fever, headache, and muscle pain, but can also cause severe illness and death among high-risk populations such as the elderly and children. During the 2022-2023 flu season, there were 31 million cases of influenza in the United States.

Influenza deaths Although influenza does not require medical attention for most people, it can be deadly, and causes thousands of deaths every year. The impact of influenza varies from year to year. The number of influenza deaths during the 2021-2022 flu season was 4,977. The vast majority of deaths attributed to influenza during the 2021-2022 flu season occurred among those aged 65 years and older.

Vaccination An annual influenza vaccination remains the most effective way of preventing influenza. During the 2021-2022 flu season, influenza vaccinations prevented an estimated 867 deaths among U.S. adults aged 65 years and older. Although, flu vaccinations are accessible and cheap, a large share of the United States population still fails to get vaccinated every year. In 2021-2022, only 37 percent of those aged 18 to 49 years received a flu vaccination, much lower compared to children and the elderly.

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.

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.

Chicago residents who are up to date with influenza vaccines by ZIP Code, based on the reported home address and age group of the person vaccinated, as provided by the medical provider in the Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE). “Up to date” refers to individuals aged 6 months and older who have received 1+ doses of influenza vaccine during the current season, defined as the beginning of July (MMWR week 27) through the end of the following June (MMWR week 26). Data Notes: Weekly cumulative totals of people up to date are shown for each combination ZIP Code and age group. Note there are rows where age group is "All ages" so care should be taken when summing rows. Weeks begin on a Sunday and end on a Saturday. Coverage percentages are calculated based on the cumulative number of people in each ZIP Code and age group who are considered up to date as of the week ending date divided by the estimated number of people in that subgroup. Population counts are obtained from the 2020 U.S. Decennial Census. For ZIP Codes mostly outside Chicago, coverage percentages are not calculated because reliable Chicago-only population counts are not available. Actual counts may exceed population estimates and lead to coverage estimates that are greater than 100%, especially in smaller ZIP Codes with smaller populations. Additionally, the medical provider may report a work address or incorrect home address for the person receiving the vaccination, which may lead to over- or underestimation of vaccination coverage by geography. All coverage percentages are capped at 99%. The Chicago Department of Public Health (CDPH) uses the most complete data available to estimate influenza vaccination coverage among Chicagoans, but there are several limitations that impact our estimates. Influenza vaccine administration is not required to be reported in Illinois, except for publicly funded vaccine (e.g., Vaccines for Children, Section 317). Individuals may receive vaccinations that are not recorded in I-CARE, such as those administered in another state, or those administered by a provider that does not submit data to I-CARE, causing underestimation of the number individuals who received an influenza vaccine for the current season. All data are provisional and subject to change. Information is updated as additional details are received and it is, in fact, very common for recent dates to be incomplete and to be updated as time goes on. At any given time, this dataset reflects data currently known to CDPH. Numbers in this dataset may differ from other public sources due to when data are reported and how City of Chicago boundaries are defined. For all datasets related to influenza, see https://data.cityofchicago.org/browse?limitTo=datasets&sortBy=alpha&tags=flu . Data Source: Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE), U.S. Census Bureau 2020 Decennial Census

Content

Location and facility information for places in New York City providing seasonal flu vaccinations.

Context

This is a dataset hosted by the City of New York. The city has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York City using Kaggle and all of the data sources available through the City of New York organization page!

• Update Frequency: This dataset is updated monthly.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

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

Healthy Chicago Equity Zones is an initiative of the Chicago Department of Public Health to organize and support hyperlocal, community-led efforts that promote health and racial equity. Chicago is divided into six HCEZs. Combinations of Chicago’s 77 community areas make up each HCEZ, based on geography. For more information about HCEZs including which community areas are in each zone see: https://data.cityofchicago.org/Health-Human-Services/Healthy-Chicago-Equity-Zones/nk2j-663f

“Up to date” refers to individuals aged 6 months and older who have received 1+ doses of influenza vaccine during the current season, defined as the beginning of July (MMWR week 27) through the end of the following June (MMWR week 26).

Data notes:

Weekly cumulative totals of people up to date are shown for each combination of race-ethnicity and age group within an HCEZ. Note that each HCEZ has a row where HCEZ is “Citywide” and each HCEZ has a row where age is "All" and race-ethnicity is “All Race/Ethnicity Groups” so care should be taken when summing rows. Weeks begin on a Sunday and end on a Saturday.

Coverage percentages are calculated based on the cumulative number of people in each population subgroup (age group by race-ethnicity within an HCEZ) who are up to date, divided by the estimated number of people in that subgroup. Population counts are from the 2020 U.S. Decennial Census. Actual counts may exceed population estimates and lead to >100% coverage, especially in small race-ethnicity subgroups of each age group within an HCEZ. All coverage percentages are capped at 99%. Summing all race/ethnicity group populations to obtain citywide populations may provide a population count that differs slightly from the citywide population count listed in the dataset. Differences in these estimates are due to how community area populations are calculated. The Chicago Department of Public Health (CDPH) uses the most complete data available to estimate influenza vaccination coverage among Chicagoans, but there are several limitations that impact our estimates. Influenza vaccine administration is not required to be reported in Illinois, except for publicly funded vaccine (e.g., Vaccines for Children, Section 317). Individuals may receive vaccinations that are not recorded in I-CARE, such as those administered in another state, or those administered by a provider that does not submit data to I-CARE, causing underestimation of the number individuals who received an influenza vaccine for the current season.

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

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

For all datasets related to influenza, see https://data.cityofchicago.org/browse?limitTo=datasets&sortBy=alpha&tags=flu .

Data Source: Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE), U.S. Census Bureau 2020 Decennial Census

Content

List of San Francisco Department of Public Health clinics offering flu vaccinations throughout the city in fall 2013.This dataset complies with the emerging Data Specification for Flu Shot Locations. For information about the specification, please see https://github.com/CityOfPhiladelphia/flu-shot-spec.For more information about SFDPH's Influenza Program www.sfcdcp.org/flu or call 311For more information about SFDPH's Open data Initiatives http://www.sfphes.org/resources/health-data or Contact Cyndy.comerford@sfdph.org

Context

This is a dataset hosted by the city of San Francisco. The organization has an open data platform found here and they update their information according the amount of data that is brought in. Explore San Francisco's Data using Kaggle and all of the data sources available through the San Francisco organization page!

• Update Frequency: This dataset is not updated.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by rawpixel on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Health and Safety Code section 1288.7(a) requires California acute care hospitals to offer influenza vaccine free of charge to all healthcare providers (HCP) or sign a declination form if a HCP chooses not to be vaccinated. Hospitals must report HCP influenza vaccination data to the California Department of Public Health (CDPH), including the percentage of HCP vaccinated. CDPH is required to make this information public on an annual basis [Health and Safety Code section 1288.8 (b)].

California acute care hospitals are required to offer free influenza vaccine to HCP. Hospital HCP must receive an annual vaccine or sign a declination form. Hospitals collect vaccination data for all HCP physically working in the hospital for at least one day during influenza season, regardless of clinical responsibility or patient contact. Hospitals report HCP vaccination rates to the California Department of Public Health (CDPH) and CDPH publishes the hospital results annually. CDPH reports data separately for hospital employees, licensed independent practitioners such as physicians, other contract staff, and trainees and volunteers (Health and Safety Code section 1288.7-1288.8).

Detailed information about the variables included in each dataset are described in the accompanying data dictionaries for the year of interest.

For general information about NHSN, surveillance definitions, and reporting requirements for HCP influenza vaccination, please visit: https://www.cdc.gov/nhsn/hps/vaccination/index.html

To link the CDPH facility IDs with those from other Departments, including OSHPD, please reference the "Licensed Facility Cross-Walk" Open Data table at: https://data.chhs.ca.gov/dataset/licensed-facility-crosswalk.

For information about healthcare personnel influenza vaccinations in California hospitals, please visit: https://www.cdph.ca.gov/Programs/CHCQ/HAI/Pages/HealthcarePersonnelInfluenzaVaccinationReportingInCA_Hospitals.aspx

This dataset shows the number of hospital admissions for influenza-like illness, pneumonia, or include ICD-10-CM code (U07.1) for 2019 novel coronavirus. Influenza-like illness is defined as a mention of either: fever and cough, fever and sore throat, fever and shortness of breath or difficulty breathing, or influenza. Patients whose ICD-10-CM code was subsequently assigned with only an ICD-10-CM code for influenza are excluded. Pneumonia is defined as mention or diagnosis of pneumonia. Baseline data represents the average number of people with COVID-19-like illness who are admitted to the hospital during this time of year based on historical counts. The average is based on the daily avg from the rolling same week (same day +/- 3 days) from the prior 3 years. Percent change data represents the change in count of people admitted compared to the previous day. Data sources include all hospital admissions from emergency department visits in NYC. Data are collected electronically and transmitted to the NYC Health Department hourly. This dataset is updated daily. All identifying health information is excluded from the dataset.

Content

List of Chicago Department of Public Health free flu clinics offered throughout the city. For more information about the flu, go to http://bit.ly/9uNhqG.

Context

This is a dataset hosted by the City of Chicago. The city has an open data platform found here and they update their information according the amount of data that is brought in. Explore the City of Chicago using Kaggle and all of the data sources available through the City of Chicago organization page!

• Update Frequency: This dataset is updated annually.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Hush Naidoo on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

The Respiratory Virus Hospitalization Surveillance Network (RESP-NET) is a network that conducts, active, population-based surveillance for laboratory confirmed hospitalizations associated with Influenza, COVID-19, and RSV. The RESP-NET platforms have overlapping surveillance areas and use similar methods to collect data. Hospitalization rates show how many people in the surveillance area are hospitalized with influenza, COVID-19, and RSV compared to the total number of people residing in that area.

Data will be updated weekly. Data are preliminary and subject to change as more data become available.

This dataset contains information from a population-based survey, which investigated human exposure to live poultry, and population psychological response and behavioral changes of the community members during two waves of influenza A(H7N9) epidemics in Southern China in 2013-2014. The dataset including 3 files. * One file named "population_wt.csv" contained population information of the studied sites; * One file named "H7N9 survey China_Que stionarie_eng.doc" was the survey questionaire; * The third file named "dataset_H7N9.csv" contained datasets acquired during the two waves of A(H7N9) epidemics,a data frame with 1657 observations on the following 44 variables. Survey ##a numeric vector: where the subject live## 1= the first wave () 2= the second wave () Place ##a numeric vector: where the subject live## 5=Guangzhou 10=Zijin County, Heyuan City SG3 ##a numeric vector: the gender of the subject## 1=Female 2=Male SG4_b ##a numeric vector: the age group of the subject, unit=years## 1=18-24 2=25-34 3=35-44 4=45-54 5=55-64 6=65+ SG6 ##a numeric vector: the marital status of the subject## 1=Single 2=Married 3=Divorced /separated 4=Widowed 5=Refuse to answer SG8 ##a numeric vector: the educational attainment of the subject## 1=Illiteracy 2=Primary school 3=Middle school 4=High school 5=College and above SG12 ##a numeric vector: the average income of the subject, unit=Chinese Yuan## 1=Less than l,000 2=1,001—2,000 3=2,001—3,000 4=3,001—4,000 5=4,001—6,000 6=6,001—8,000 7=8,001—10,000 8=10,001—2,000 9=15,001—20,000 10=20,001—30,000 11=More than 30,001 12=No income 13=Don’t know 14=Refuse to answer AX1_a ##a numeric vector: the anxiety level of the subject, I feel rested ## 1=Not at all 2=Sometimes 3=Moderately So 4=Very Much So AX1_b ##a numeric vector: the anxiety level of the subject, I feel content ## 1=Not at all 2=Sometimes 3=Moderately So 4=Very Much So AX1_c ##a numeric vector: the anxiety level of the subject, I feel comfortable ## 1=Not at all 2=Sometimes 3=Moderately So 4=Very Much So AX1_d ##a numeric vector: the anxiety level of the subject, I am relaxed ## 1=Not at all 2=Sometimes 3=Moderately So 4=Very Much So AX1_e ##a numeric vector: the anxiety level of the subject, I feel pleasant ## 1=Not at all 2=Sometimes 3=Moderately So 4=Very Much So AX1_f ##a numeric vector: the anxiety level of the subject, I feel anxious ## 1=Not at all 2=Sometimes 3=Moderately So 4=Very Much So AX1_g ##a numeric vector: the anxiety level of the subject, I feel nervous ## 1=Not at all 2=Sometimes 3=Moderately So 4=Very Much So AX1_h ##a numeric vector: the anxiety level of the subject, I am jittery ## 1=Not at all 2=Sometimes 3=Moderately So 4=Very Much So AX1_i ##a numeric vector: the anxiety level of the subject, I feel “high strung” ## 1=Not at all 2=Sometimes 3=Moderately So 4=Very Much So AX1_j ##a numeric vector: the anxiety level of the subject, I feel over-excited and “rattled” ## 1=Not at all 2=Sometimes 3=Moderately So 4=Very Much So BF4b##a numeric vector indicating the subject's rate of worriness towards H7N9 avian flu, 1 being very mild to 10 being very severe## EM1 ##a numeric vector: How often did you go to wet markets in the past year ## 1=1-2/year 2=3-5/year 3=6-11/year 4=1-3/month 5=1-2/week 6=3-5/week 7=Almost every day 8=Almost not EM2 ##a numeric vector: How often did you buy poultry in wet markets in the past year ## 1=1-2/year 2=3-5/year 3=6-11/year 4=1-3/month 5=1-2/week 6=3-5/week 7=Almost every day 8=Almost not EM3 ##a numeric vector: Did you usually pick up the poultry for examination before deciding to buy it ## 1=Yes 2=No 3=Sometime “yes”, sometime “no” EM4 ##a numeric vector: Where was the live poultry slaughtered when you bought it? ## 1=Always in wet market 2=Usually in wet market 3=Usually in my household 4=Always in my household 5=Other places EM5 ##a numeric vector: Have your habit of buying live poultry changed since the first human H7N9 case was released in the past month ## 1=Yes, not buying since then 2=No, still buying and eating live poultry 3=Still buying but less than before EM6 ##a numeric vector: Would you support permanent closure of live poultry markets in order to control avian influenza epidemics ## 1=Strongly agree 2=Agree 3=Not agree 4=Strongly disagree 5=Don’t know EM8 ##a numeric vector: Have your raised live poultry in your backyard in the past year ## 1=Yes 2=No BF1 ##a numeric vector indicating risk perception of the subject: How likely do you think it is that you will contract H7N9 avian flu over the next 1 month ## 1=Never 2=Very unlikely 3=Unlikely 4=Evens 5=Likely 6=Very likely 7=Certain BF2a ##a numeric vector indicating risk perception of the subject: What do you think are your chances of getting H7N9 avian flu over the next 1 month compared to other people outside your family of a similar age ## 1=Not at all 2=Much less 3=Less 4=Evens 5=More 6=Much more 7=Certain BF3_l ##a numeric vector indicating knowledge of the subject: H7N9 avian flu is spread by the body contact with patients ## 1=Yes 2=No 3=Don’t Know BF3_m ##a numeric vector indicating knowledge of the subject: H7N9 avian flu is spread by touching objects that have been contaminated by the virus ## 1=Yes 2=No 3=Don’t Know BF3_n ##a numeric vector indicating knowledge of the subject: H7N9 avian flu is spread by the close contact with chickens in a wet market ## 1=Yes 2=No 3=Don’t Know BF4 ##a numeric vector: If you were to develop flu-like symptoms tomorrow, would you be... ## 1=Not at all worried 2=Much less worried than normal 3=Worried less than normal 4=About same 5=Worried more than normal 6=Worried much more than normal 7=Extremely worried BF4a ##a numeric vector indicating risk perception of the subject: In the past one week, have you ever worried about catching H7N9 avian flu ## 1=No, never think about it 2=Think about it but it doesn’t worry me 3=Worries me a bit 4=Worries me a lot 5=Worry about it all the time BF5a ##a numeric vector indicating risk perception of the subject: How does H7N9 avian flu compare with seasonal flu in terms of seriousness ## 1=Much higher 2=A little higher 3=Same 4=A little lower 5=Much lower 6=Don’t Know BF5b ##a numeric vector indicating risk perception of the subject: How does H7N9 avian flu compare with H5N1 avian flu in terms of seriousness ## 1=Much higher 2=A little higher 3=Same 4=A little lower 5=Much lower 6=Don’t Know BF5c ##a numeric vector indicating risk perception of the subject: How does H7N9 avian flu compare with SARS in terms of seriousness ## 1=Much higher 2=A little higher 3=Same 4=A little lower 5=Much lower 6=Don’t Know BF7 ##a numeric vector evaluating the current performance of the national government in controlling H7N9 avian flu, (0=extremely poor, 5=moderate, 10=excellent) ## BF7a ##a numeric vector evaluating the current performance of the provincial/city government in controlling H7N9 avian flu, (0=extremely poor, 5=moderate, 10=excellent) ## PM2 ##a numeric vector indicating the preventive behavior of the subject, covering the mouth when sneeze or cough ## 1=Always 2=Usually 3=Sometimes 4=Never 5=Don’t know 6=Not applicable (no sneeze or cough) PM3 ##a numeric vector indicating the preventive behavior of the subject, washing hands after sneezing, coughing or touching nose ## 1=Always 2=Usually 3=Sometimes 4=Never 5=Don’t know 6=Not applicable (no sneeze or cough) PM3a ##a numeric vector indicating the preventive behavior of the subject,washing hands after returning home ## 1=Always 2=Usually 3=Sometimes 4=Never 5=Don’t know 6=Not applicable (never go out) PM4 ##a numeric vector indicating the preventive behavior of the subject,using liquid soap when washing hands ## 1=Always 2=Usually 3=Sometimes 4=Never 5=Don’t know PM5 ##a numeric vector indicating the preventive behavior of the subject,wearing face mask ## 1=Always 2=Usually 3=Sometimes 4=Never 5=Don’t know PM7 ##a numeric vector:If free H7N9 flu vaccine is available in the coming month, would you consider receiving it ## 1=Yes 2=No 3=Not sure 4=Don’t know

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Publication available at: https://doi.org/10.1038/s41598-021-01857-4 Municipality codes for the municipalities of Sweden can be found here: https://www.scb.se/en/finding-statistics/regional-statistics/regional-divisions/counties-and-municipalities/counties-and-municipalities-in-numerical-order/ Data available according to Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license Model inputs 1. giim_kommun_graph.csv Set of frequent travel routes between the municipalities of Sweden. The graph was constructed from "Trafikanalys, 2016. Resvanor. (accessed 26.8.19). Available from: http://www.trafa.se/RVU-Sverige/." using the methodology described in the paper. Date of construction: 2018-12-01 Format: csv Structure: edge list in (kommun1;kommun2) format with rows indicating a directed link between two municipalities. Municipalities are denoted according to their official municipal code giim_casecounts.xlsx Number of new H1N1 cases in the municipalities of Sweden between 2009 and 2015. Our data set consists of all laboratory-verified cases of A(H1N1)pdm09 between May 2009 and December 2015, extracted from the SmiNet register of notifiable diseases, held by the Public Health Agency of Sweden. Due to confidentiality reasons, cases are anonymized, and addresses are aggregated at the DeSo level together with the date of diagnosis, age, and gender. We obtained ethical approval for the data acquisition. Date of construction: 2018-12-01 Format: xlsx Structure: Each tab represents a single flu season from the 2009/2010 season to the 2014/2015 season. Each tab is a matrix with rows indicating municipalities according to their official municipal code, and columns indicating epidemic weeks. Values of the matrices indicate the number of new laboratory-verified cases of A(H1N1)pdm09 giim_kommun_indicators.csv Socioeconomic and meteorological indicators are assigned to the municipalities of Sweden according to the methodology described in the paper. Indicators included are: a, mean temperature in degree Celsius, b, absolute humidity in grams per cubic metre, c, population size as the number of people living in each municipality, d, population density as the number of people per sq. km of land area, e, median income per household in thousand SEK, f, fraction of people on social aid (as a percentage), g, average number of children younger than 18 years per household. Meteorological data was obtained from the European Climate Assessment Dataset "Klein Tank A, Wijngaard J, Können G, Böhm R, Demarée G, Gocheva A, et al. Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. International Journal of Climatology: A Journal of the Royal Meteorological Society. 2002;22(12):1441–1453." Data from the dataset was converted to the municipality level according to the methodology described in the paper. Variables are mean temperature and relative humidity converted to absolute humidity for all municipalities of Sweden. Socioeconomic data was collected from Statistics Sweden between 2018 Ocotber and 2019 February. Available from: https://www.scb.se/en/. Variables are: The average household income as an economic indicator. The average number of children younger than 18 years per household to indicate family size. The fraction of people receiving social aid to represent poverty in a municipality. Population size and population density as the number of people per sq. km of land area. Date of construction: 2018-02-01 Format: csv Structure: Each row corresponds to a municipality denoted according to their official municipal code. Columns indicate socioeconomic and meteorological indicators as marked by the header row. Model outputs 1. giim_export_risk.csv Exportation risk values for all municipalities from week 37 to week 50 in the fall of 2009 computed using the methodology described in the paper. Date of construction: 2020-12-01 Format: csv Structure: Table with rows denoting Swedish municipalities according to their official municipal code, columns denoting epidemic weeks. Values indicate exportation risk values (should not be interpreted as probabilities). giim_import_risk.csv Importation risk values for all municipalities from week 37 to week 50 in the fall of 2009 computed using the methodology described in the paper. Date of construction: 2020-12-01 Format: csv Structure: Table with rows denoting Swedish municipalities according to their official municipal code, columns denoting epidemic weeks. Values indicate importation risk values (should not be interpreted as probabilities). giim_transmission_prob.csv Transmission probabilities between all municipalities from week 37 to week 50 in the fall of 2009 computed using the methodology described in the paper. Date of construction: 2020-12-01 Format: csv Structure: Edge list with multiple edge weights. Rows indicate a directed link between the two municipalities (kommun1;kommun2) in the beginning of the row. The rest of the values in each row denote the corresponding transmission probabilities for each epidemic week computed according to the methodology described in the paper.

Percent of tests positive for a pathogen is one of the surveillance metrics used to monitor respiratory pathogen transmission over time. The percent of tests positive is calculated by dividing the number of positive tests by the total number of tests administered, then multiplying by 100 [(# of positive tests/total tests) x 100]. These data include percent of tests positive values for the detection of severe acute respiratory virus coronavirus type 2 (SARS-CoV-2), the virus that causes COVID-19 and Respiratory syncytial virus (RSV) reported to the National Respiratory and Enteric Virus Surveillance System (NREVSS), a sentinel network of laboratories located through the US, includes clinical, public health and commercial laboratories; additional information available at: https://www.cdc.gov/surveillance/nrevss/index.html. Influenza results include clinical laboratory test results from NREVSS and U.S. World Health Organization collaborating laboratories; more details about influenza virologic surveillance are available here: https://www.cdc.gov/flu/weekly/overview.html. Data represent calculations based on laboratory tests performed, not individual people tested. RSV and COVID-19 are limited to nucleic acid amplification tests (NAATs), also listed as polymerase chain reaction tests (PCR). Participating laboratories report weekly to CDC the total number of RSV tests performed that week and the number of those tests that were positive. The RSV trend graphs display the national average of the weekly % test positivity for the current, previous, and following weeks in accordance with the recommendations for assessing RSV trends by percent (https://academic.oup.com/jid/article/216/3/345/3860464). All data are provisional and subject to change.

The California Department of Public Health (CDPH) is coordinating with wastewater utilities, local health departments, academic researchers, and laboratories in California on wastewater surveillance for infectious disease pathogens of interest to public health (such as SARS-CoV-2, the virus causing COVID-19, influenza, respiratory syncytial virus (RSV), mpox, and norovirus). Data collected from this network of participants, called the California Surveillance of Wastewaters (Cal-SuWers) Network, are submitted to the U.S. Centers for Disease Control and Prevention (CDC) National Wastewater Surveillance System (NWSS).

Collecting and analyzing wastewater samples for the presence of, and amount of (concentration), a specified pathogen target can help inform public health about circulation of that infectious disease within a community. Data from wastewater testing do not replace existing public health surveillance systems but complement them. While wastewater surveillance cannot determine the exact number of infected persons in the area being monitored, it can provide overall trends of pathogen concentration within that community.

Please note that data included in the Cal-SuWers Network and available here originate from multiple programs and laboratories. Methodologies for producing wastewater data are not currently standardized, and analyses, comparisons, and aggregations should be done with caution. Wastewater is a complex environmental sample and inherent variability in measured concentrations is expected due to environmental variability, day-to-day differences in sewershed and population dynamics, differences in the amount of shedding between people and pathogens, and laboratory and sampling variability. Please see the CDPH Cal-SuWers, CDC NWSS, and CDC Public Health interpretation and Use of Wastewater Surveillance data webpages for more information.

Historical wastewater data can be found here.

Respiratory illness activity is monitored using the acute respiratory illness (ARI) metric. ARI captures a broad range of diagnoses from emergency department visits for respiratory illnesses, from the common cold to severe infections like influenza, RSV and COVID-19. It captures illnesses that may not present with fever, offering a more complete picture than the previous influenza-like illness (ILI) metric.

Updated once per week on Fridays.