The burden of influenza in the United States can vary from year to year depending on which viruses are circulating, how many people receive an influenza vaccination, and how effective the vaccination is in that particular year. During the 2023-2024 flu season, around 28,000 people lost their lives to the disease. Although most people recover from influenza without needing medical care, the disease can be deadly among young children, the elderly, and those with weakened immune systems or chronic illnesses. Deaths due to influenza Even though most people recover from influenza without medical care, influenza and pneumonia can be deadly, especially for older people and those with certain preexisting conditions. Influenza is a common cause of pneumonia and although most cases of influenza do not develop into pneumonia, those that do are often more severe and more deadly. Deaths due to influenza are most common among the elderly, with a mortality rate of around 32 per 100,000 population during the 2023-2024 flu season. In comparison, the mortality rate for those aged 50 to 64 years was 9.1 per 100,000 population. Flu vaccinations The most effective way to prevent influenza is to receive an annual influenza vaccination. These vaccines have proven to be safe and are usually cheap and easily accessible. Nevertheless, every year a large share of the population in the United States still fails to get vaccinated against influenza. For example, in the 2022-2023 flu season, only 35 percent of those aged 18 to 49 years received a flu vaccination. Unsurprisingly, children and the elderly are the most likely to get vaccinated. It is estimated that during the 2022-2023 flu season, vaccinations prevented over 929 thousand influenza cases among children aged 6 months to 4 years.

The mortality rate from influenza in the United States is by far highest among those aged 65 years and older. During the 2023-2024 flu season, the mortality rate from influenza for this age group was around 32.1 per 100,000 population. The burden of influenza The impact of influenza in the U.S. varies from season to season, but in the 2023-2024 flu season, there were an estimated 40 million cases. These cases resulted in around 470,000 hospitalizations. Although most people recover from influenza without requiring medical treatment, the disease can be deadly for young children, the elderly, and those with weakened immune systems or chronic illnesses. During the 2023-2024 flu season, around 28,000 people in the U.S. lost their lives due to influenza. Impact of vaccinations The most effective way to prevent influenza is to receive an annual vaccination at the beginning of flu season. Flu vaccines are safe and can greatly reduce the burden of the disease. During the 2022-2023 flu season, vaccinations prevented around 2,479 deaths among those aged 65 years and older. Although flu vaccines are usually cheap and easily accessible, every year a large share of the population in the U.S. still does not get vaccinated. For example, during the 2022-2023 flu season, only about 35 percent of those aged 18 to 49 years received a flu vaccination.

In 2022, the total number of deaths from influenza and pneumonia in Canada amounted to 5,985, an increase from 4,115 deaths the year before. From 2000 to 2022, Canada registered the highest number of deaths due to influenza and pneumonia in 2018, when 8,594 people died from these diseases. This statistic shows the number of deaths from influenza and pneumonia in Canada from 2000 to 2022.

In 2022, the number of deaths from the influenza virus in Spain amounted to ***** cases. The figure fluctuated from ** casualties in 2021 to a peak of around ***** fatal cases in 2018. As of 2022, the largest number of influenza deaths in Spain was among those aged 85 and older.

This dataset is a per-state amalgamation of demographic, public health and other relevant predictors for COVID-19.

Deaths, Infections and Tests by State

The COVID Tracking Project: https://covidtracking.com/data/api

Used positive, death and totalTestResults from the API for, respectively, Infected, Deaths and Tested in this dataset. Please read the documentation of the API for more context on those columns

Predictor Data and Sources

Population (2020)

Density is people per meter squared https://worldpopulationreview.com/states/

ICU Beds and Age 60+

https://khn.org/news/as-coronavirus-spreads-widely-millions-of-older-americans-live-in-counties-with-no-icu-beds/

GDP

https://worldpopulationreview.com/states/gdp-by-state/

Income per capita (2018)

https://worldpopulationreview.com/states/per-capita-income-by-state/

Gini

https://en.wikipedia.org/wiki/List_of_U.S._states_by_Gini_coefficient

Unemployment (2020)

Rates from Feb 2020 and are percentage of labor force
https://www.bls.gov/web/laus/laumstrk.htm

Sex (2017)

Ratio is Male / Female
https://www.kff.org/other/state-indicator/distribution-by-gender/

Smoking Percentage (2020)

https://worldpopulationreview.com/states/smoking-rates-by-state/

Influenza and Pneumonia Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/flu_pneumonia_mortality/flu_pneumonia.htm

Chronic Lower Respiratory Disease Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/lung_disease_mortality/lung_disease.htm

Active Physicians (2019)

https://www.kff.org/other/state-indicator/total-active-physicians/

Hospitals (2018)

https://www.kff.org/other/state-indicator/total-hospitals

Health spending per capita

Includes spending for all health care services and products by state of residence. Hospital spending is included and reflects the total net revenue. Costs such as insurance, administration, research, and construction expenses are not included.
https://www.kff.org/other/state-indicator/avg-annual-growth-per-capita/

Pollution (2019)

Pollution: Average exposure of the general public to particulate matter of 2.5 microns or less (PM2.5) measured in micrograms per cubic meter (3-year estimate)
https://www.americashealthrankings.org/explore/annual/measure/air/state/ALL

Medium and Large Airports

For each state, number of medium and large airports https://en.wikipedia.org/wiki/List_of_the_busiest_airports_in_the_United_States

Temperature (2019)

Note that FL was incorrect in the table, but is corrected in the Hottest States paragraph
https://worldpopulationreview.com/states/average-temperatures-by-state/
District of Columbia temperature computed as the average of Maryland and Virginia

Urbanization (2010)

Urbanization as a percentage of the population https://www.icip.iastate.edu/tables/population/urban-pct-states

Age Groups (2018)

https://www.kff.org/other/state-indicator/distribution-by-age/

School Closure Dates

Schools that haven't closed are marked NaN https://www.edweek.org/ew/section/multimedia/map-coronavirus-and-school-closures.html

Note that some datasets above did not contain data for District of Columbia, this missing data was found via Google searches manually entered.

The flu is estimated to cause 400,000 respiratory deaths each year on average across the world. These deaths come from pneumonia and other respiratory symptoms caused by the flu. People also die from other complications of the flu – such as a stroke or heart attack – but global estimates have not been made of their death toll. The Spanish flu caused the largest influenza pandemic in history. Yet, data on the flu is limited. With better testing, countries could improve their response to flu epidemics. It could help to rapidly identify new strains, detect epidemics early, and design better-matched vaccines to target flu strains circulating in the population.

this data set contains the vaccine coverage around the world from 2018 to 2022.

Background # Seasonal influenza virus is a common cause of acute lower respiratory infection (ALRI) in young children. In 2008, we estimated that 20 million influenza-virus-associated ALRI and 1 million influenza-virus-associated severe ALRI occurred in children under 5 years globally. Despite this substantial burden, only a few low-income and middle-income countries have adopted routine influenza vaccination policies for children and, where present, these have achieved only low or unknown levels of vaccine uptake. Moreover, the influenza burden might have changed due to the emergence and circulation of influenza A/H1N1pdm09. We aimed to incorporate new data to update estimates of the global number of cases, hospital admissions, and mortality from influenza-virus-associated respiratory infections in children under 5 years in 2018. # Methods # We estimated the regional and global burden of influenza-associated respiratory infections in children under 5 years from a systematic review of 100 studies published between Jan 1, 1995, and Dec 31, 2018, and a further 57 high-quality unpublished studies. We adapted the Newcastle-Ottawa Scale to assess the risk of bias. We estimated incidence and hospitalisation rates of influenza-virus-associated respiratory infections by severity, case ascertainment, region, and age. We estimated in-hospital deaths from influenza virus ALRI by combining hospital admissions and in-hospital case-fatality ratios of influenza virus ALRI. We estimated the upper bound of influenza virus-associated ALRI deaths based on the number of in-hospital deaths, US paediatric influenza-associated death data, and population-based childhood all-cause pneumonia mortality data in six sites in low-income and lower-middle-income countries. # Findings # In 2018, among children under 5 years globally, there were an estimated 109*5 million influenza virus episodes (uncertainty range [UR] 63*1-190*6), 10*1 million influenza-virus-associated ALRI cases (6*8-15*1); 870 000 influenza-virus-associated ALRI hospital admissions (543 000-1 415 000), 15 300 in-hospital deaths (5800-43 800), and up to 34 800 (13 200-97 200) overall influenza-virus-associated ALRI deaths. Influenza virus accounted for 7% of ALRI cases, 5% of ALRI hospital admissions, and 4% of ALRI deaths in children under 5 years. About 23% of the hospital admissions and 36% of the in-hospital deaths were in infants under 6 months. About 82% of the in-hospital deaths occurred in low-income and lower-middle-income countries. # Interpretation # A large proportion of the influenza-associated burden occurs among young infants and in low-income and lower middle-income countries. Our findings provide new and important evidence for maternal and paediatric influenza immunisation, and should inform future immunisation policy particularly in low-income and middle-income countries.

This dataset represents preliminary estimates of cumulative U.S. COVID-19 disease burden for the 2024-2025 period, including illnesses, outpatient visits, hospitalizations, and deaths. The weekly COVID-19-associated burden estimates are preliminary and based on continuously collected surveillance data from patients hospitalized with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. The data come from the Coronavirus Disease 2019 (COVID-19)-Associated Hospitalization Surveillance Network (COVID-NET), a surveillance platform that captures data from hospitals that serve about 10% of the U.S. population. Each week CDC estimates a range (i.e., lower estimate and an upper estimate) of COVID-19 -associated burden that have occurred since October 1, 2024.

Note: Data are preliminary and subject to change as more data become available. Rates for recent COVID-19-associated hospital admissions are subject to reporting delays; as new data are received each week, previous rates are updated accordingly.

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.

Basic Metadata Note: this is the combination of influenza (flu) and pneumonia combined as they often co-occur together. *Rates per 100,000 population. Age-adjusted rates per 100,000 2000 US standard population.

**Blank Cells: Rates not calculated for fewer than 5 events. Rates not calculated in cases where zip code is unknown.

***API: Asian/Pacific Islander. ***AIAN: American Indian/Alaska Native.

Prepared by: County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics Unit, 2019.

Code Source: ICD-9CM - AHRQ HCUP CCS v2015. ICD-10CM - AHRQ HCUP CCS v2018. ICD-10 Mortality - California Department of Public Health, Group Cause of Death Codes 2013; NHCS ICD-10 2e-v1 2017.

Data Guide, Dictionary, and Codebook: https://www.sandiegocounty.gov/content/dam/sdc/hhsa/programs/phs/CHS/Community%20Profiles/Public%20Health%20Services%20Codebook_Data%20Guide_Metadata_10.2.19.xlsx

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 2023-2024 flu season, there were 40 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 2023-2024 flu season was 27,965. The vast majority of deaths attributed to influenza during the 2023-2024 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 2022-2023 flu season, influenza vaccinations prevented an estimated 2,479 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 2022-2023, only 35 percent of those aged 18 to 49 years received a flu vaccination, much lower compared to children and the elderly.

**Blank Cells: Rates not calculated for fewer than 5 events. Rates not calculated in cases where zip code is unknown.

***API: Asian/Pacific Islander. ***AIAN: American Indian/Alaska Native.

Prepared by: County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics Unit, 2019.

Code Source: ICD-9CM - AHRQ HCUP CCS v2015. ICD-10CM - AHRQ HCUP CCS v2018. ICD-10 Mortality - California Department of Public Health, Group Cause of Death Codes 2013; NHCS ICD-10 2e-v1 2017.

Data Guide, Dictionary, and Codebook: https://www.sandiegocounty.gov/content/dam/sdc/hhsa/programs/phs/CHS/Community%20Profiles/Public%20Health%20Services%20Codebook_Data%20Guide_Metadata_10.2.19.xlsx

Complications and deaths among patients hospitalized with clinically diagnosed and laboratory-confirmed influenza by age group and influenza type, from 2010/2011–2018/2019 seasons.

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, means of work transportation rates, housing characteristics (ie number of large apartment complexes/seniors living alone), and industry information.

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

8) State by state breakdown of Housing Characteristics

9) State by State breakdown of Industry Information

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

Census Tables: ACSST1Y2018.S1811 ACSST1Y2018.S0102 ACSST1Y2018.S2403 ACSST1Y2018.S2501 ACSST1Y2018.S2504

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.

NNDSS - TABLE 1R. Hepatitis C, perinatal infection to Influenza-associated pediatric mortality - 2019. In this Table, provisional cases* of notifiable diseases are displayed for United States, U.S. territories, and Non-U.S. residents.

Note: This table contains provisional cases of national notifiable diseases from the National Notifiable Diseases Surveillance System (NNDSS). NNDSS data from the 50 states, New York City, the District of Columbia and the U.S. territories are collated and published weekly on the NNDSS Data and Statistics web page (https://wwwn.cdc.gov/nndss/data-and-statistics.html). Cases reported by state health departments to CDC for weekly publication are provisional because of the time needed to complete case follow-up. Therefore, numbers presented in later weeks may reflect changes made to these counts as additional information becomes available. The national surveillance case definitions used to define a case are available on the NNDSS web site at https://wwwn.cdc.gov/nndss/. Information about the weekly provisional data and guides to interpreting data are available at: https://wwwn.cdc.gov/nndss/infectious-tables.html.

Footnotes: U: Unavailable — The reporting jurisdiction was unable to send the data to CDC or CDC was unable to process the data. -: No reported cases — The reporting jurisdiction did not submit any cases to CDC. N: Not reportable — The disease or condition was not reportable by law, statute, or regulation in the reporting jurisdiction. NN: Not nationally notifiable — This condition was not designated as being nationally notifiable. NP: Nationally notifiable but not published — CDC does not have data because of changes in how conditions are categorized. Cum: Cumulative year-to-date counts. Max: Maximum — Maximum case count during the previous 52 weeks. * Case counts for reporting years 2018 and 2019 are provisional and subject to change. Cases are assigned to the reporting jurisdiction submitting the case to NNDSS, if the case's country of usual residence is the US, a US territory, unknown, or null (i.e. country not reported); otherwise, the case is assigned to the 'Non-US Residents' category. For further information on interpretation of these data, see https://wwwn.cdc.gov/nndss/document/Users_guide_WONDER_tables_cleared_final.pdf. † Previous 52 week maximum and cumulative YTD are determined from periods of time when the condition was reportable in the jurisdiction (i.e., may be less than 52 weeks of data or incomplete YTD data). § Since [INSERT DATE], XXX influenza-associated pediatric deaths occurring during the 2017-18 season have been reported.

This dataset represents preliminary estimates of cumulative U.S. RSV –associated disease burden estimates for the 2024-2025 season, including outpatient visits, hospitalizations, and deaths. Real-time estimates are preliminary and based on continuously collected surveillance data from patients hospitalized with laboratory-confirmed respiratory syncytial virus (RSV) infections. The data come from the Respiratory Syncytial Virus Hospitalization Surveillance Network (RSV-NET), a surveillance platform that captures data from hospitals that serve about 8% of the U.S. population. Each week CDC estimates a range (i.e., lower estimate and an upper estimate) of RSV-associated disease burden estimates that have occurred since October 1, 2024.

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.

Baseline characteristics stratified by mortality and survival of influenza patients in Iran, 2016–2018.

3.84 (per 100,000 inhabitants) in 2018.