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Introduction: Seasonal influenza, often perceived as a common illness, carries a significant global burden, claiming hundreds of thousands of lives annually. Despite advancements in healthcare and vaccination efforts, the flu remains a formidable threat, particularly affecting vulnerable populations such as infants and the elderly. This article delves into the intricacies of influenza-related mortality, examining regional disparities, contributing factors, and the implications for public health.

The Global Landscape of Influenza Mortality: Data from the Global Pandemic Mortality Project II sheds light on the magnitude of influenza-related deaths, drawing from surveillance metrics spanning from 2002 to 2011. These estimates, while informative, underscore the challenge of accurately gauging mortality rates, especially in low-income countries where testing and mortality records may be lacking.

Respiratory Symptoms and Beyond: The conventional understanding of influenza-related fatalities primarily revolves around respiratory complications. Pneumonia and other respiratory ailments serve as prominent causes of death, contributing to the staggering toll of 400,000 lives claimed annually. However, it is imperative to acknowledge that the impact of influenza extends beyond respiratory symptoms. Complications such as strokes and heart attacks, though not explicitly captured in mortality estimates, further amplify the disease's lethality, warranting comprehensive preventive measures.

Vulnerability Across Age Groups: Influenza's lethality is not uniform across age demographics. Infants and the elderly emerge as the most susceptible cohorts, bearing the brunt of severe complications and mortality. Among individuals aged over 65, the mortality rate stands at approximately 31 per 100,000 in Europe alone, reflecting the disproportionate impact on older populations. The interplay of age-related factors, including weakened immune responses and underlying health conditions, exacerbates the severity of influenza outcomes among these groups.

Regional Disparities and Determinants: A notable aspect of influenza mortality lies in its disparate distribution across regions. While Europe and North America exhibit relatively lower death rates, countries in South America, Africa, and South Asia grapple with higher mortality burdens. This regional divide underscores the complex interplay of socio-economic factors, healthcare accessibility, and vaccination coverage. Poverty, inadequate healthcare infrastructure, and suboptimal vaccination rates converge to heighten vulnerability to influenza-related complications, amplifying mortality rates in resource-constrained settings.

Implications for Public Health: The revelation of significant regional differentials in influenza mortality necessitates a tailored approach to public health interventions. Strengthening healthcare systems, particularly in low-income regions, is paramount to bolstering surveillance, enhancing diagnostic capabilities, and facilitating timely interventions. Furthermore, targeted vaccination campaigns, coupled with education initiatives, hold promise in mitigating influenza's toll, especially among vulnerable populations. Addressing socio-economic disparities and bolstering healthcare resilience emerge as pivotal strategies in fortifying global defenses against seasonal influenza.

Conclusion: Seasonal influenza, often underestimated in its impact, exacts a substantial toll on global health each year. The multifaceted nature of influenza-related mortality underscores the need for a nuanced understanding and comprehensive mitigation strategies. By addressing regional disparities, prioritizing vulnerable populations, and fortifying healthcare systems, the global community can strive towards mitigating the burden of seasonal influenza, safeguarding lives, and fostering resilient health systems for generations to come.

Demographic Trends and Health Outcomes in the U.S

Inequalities,Risk Factors and Access to Care

By Data Society [source]

About this dataset

This dataset contains key demographic, health status indicators and leading cause of death data to help us understand the current trends and health outcomes in communities across the United States. By looking at this data, it can be seen how different states, counties and populations have changed over time. With this data we can analyze levels of national health services use such as vaccination rates or mammography rates; review leading causes of death to create public policy initiatives; as well as identify risk factors for specific conditions that may be associated with certain populations or regions. The information from these files includes State FIPS Code, County FIPS Code, CHSI County Name, CHSI State Name, CHSI State Abbreviation, Influenza B (FluB) report count & expected cases rate per 100K population , Hepatitis A (HepA) Report Count & expected cases rate per 100K population , Hepatitis B (HepB) Report Count & expected cases rate per 100K population , Measles (Meas) Report Count & expected cases rate per 100K population , Pertussis(Pert) Report Count & expected case rate per 100K population , CRS report count & expected case rate per 100K population , Syphilis report count and expected case rate per 100k popuation. We also look at measures related to preventive care services such as Pap smear screen among women aged 18-64 years old check lower/upper confidence intervals seperately ; Mammogram checks among women aged 40-64 years old specified lower/upper conifence intervals separetly ; Colonosopy/ Proctoscpushy among men aged 50+ measured in lower/upper limits ; Pneumonia Vaccination amongst 65+ with loewr/upper confidence level detail Additionally we have some interesting trend indicating variables like measures of birth adn death which includes general fertility ratye ; Teen Birth Rate by Mother's age group etc Summary Measures covers mortality trend following life expectancy by sex&age categories Vressionable populations access info gives us insight into disablilty ratio + access to envtiromental issues due to poor quality housing facilities Finally Risk Factors cover speicfic hoslitic condtiions suchs asthma diagnosis prevelance cancer diabetes alcholic abuse smoking trends All these information give a good understanding on Healthy People 2020 target setings demograpihcally speaking hence will aid is generating more evience backed policies

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How to use the dataset

What the Dataset Contains

This dataset contains valuable information about public health relevant to each county in the United States, broken down into 9 indicator domains: Demographics, Leading Causes of Death, Summary Measures of Health, Measures of Birth and Death Rates, Relative Health Importance, Vulnerable Populations and Environmental Health Conditions, Preventive Services Use Data from BRFSS Survey System Data , Risk Factors and Access to Care/Health Insurance Coverage & State Developed Types of Measurements such as CRS with Multiple Categories Identified for Each Type . The data includes indicators such as percentages or rates for influenza (FLU), hepatitis (HepA/B), measles(MEAS) pertussis(PERT), syphilis(Syphilis) , cervical cancer (CI_Min_Pap_Smear - CI_Max\Pap \Smear), breast cancer (CI\Min Mammogram - CI \Max \Mammogram ) proctoscopy (CI Min Proctoscopy - CI Max Proctoscopy ), pneumococcal vaccinations (Ci min Pneumo Vax - Ci max Pneumo Vax )and flu vaccinations (Ci min Flu Vac - Ci Max Flu Vac). Additionally , it provides information on leading causes of death at both county levels & national level including age-adjusted mortality rates due to suicide among teens aged between 15-19 yrs per 100000 population etc.. Furthermore , summary measures such as age adjusted percentage who consider their physical health fair or poor are provided; vulnerable populations related indicators like relative importance score for disabled adults ; preventive service use related ones ranging from self reported vaccination coverage among men40-64 yrs old against hepatitis B virus etc...

Getting Started With The Dataset

To get started with exploring this dataset first your need to understand what each column in the table represents: State FIPS Code identifies a unique identifier used by various US government agencies which denote states . County FIPS code denotes counties wi...

Highly pathogenic avian influenza virus (HPAIV) has caused extensive mortalities in wild birds, with a disproportionate impact on raptors since 2021. The population-level impact of HPAIV can be informed by telemetry studies that track large samples of initially healthy, wild birds. We leveraged movement data from 71 rough-legged hawks (Buteo lagopus) across all major North American migratory bird flyways concurrent with the 2022–2023 HPAIV outbreak and identified a total of 29 mortalities, of which 11 were confirmed, and an additional ~9 were estimated to have been caused by HPAIV. We estimated a 28% HPAIV cause-specific mortality rate among rough-legged hawks during a single year concurrent with the HPAIV outbreak in North America. Additionally, the overall annual mortality rate during the HPAIV outbreak (47%) was significantly higher than baseline annual mortality rates (3–17%), suggesting that HPAIV-caused deaths were additive above baseline mortality levels. HPAIV mortalities were c..., , # Increased mortality rates caused by highly pathogenic avian influenza virus in a migratory raptor

Dataset DOI: 10.5061/dryad.n2z34tn92

Description of the data and file structure

We leveraged movement data from GPS-tracked rough-legged hawks Buteo lagopus that coincided with the HPAIV panzootic in North America to determine its effect on annual mortality. All missing and unavailable data represented as NAÂ

Files and variables

File: AnnualMortality.csv

Description:Â spreadsheet used to analyze the HPAIV effect on annual mortality.

Variables

• index: index number
• tagid: transmitter ID (unique to an individual hawk)
• year: 12-month study period. 2020 = 1-Mar-2020 to 28-Feb-2021, etc...
• date_begin: date within the 12-month study period that tracking began
• date_end:Â date within the 12-month study period that tracking ended
• duration: tracking duration (number of days)
• fate: individual fate during the 12-month study period...,

Estimated average annual influenza excess mortality per age group and annual excess mortality rate per 100,000 individuals, 2006−2015.

Impact of influenza-like illness on deaths by disease, sex, and age.

Note: On April 30, 2024, the Federal mandate for COVID-19 and influenza associated hospitalization data to be reported to CDC’s National Healthcare Safety Network (NHSN) expired. Hospitalization data beyond April 30, 2024, will not be updated on the Open Data Portal. Hospitalization and ICU admission data collected from summer 2020 to May 10, 2023, are sourced from the California Hospital Association (CHA) Survey. Data collected on or after May 11, 2023, are sourced from CDC's National Healthcare Safety Network (NHSN).

Data is from the California Department of Public Health (CDPH) Respiratory Virus State Dashboard at https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/Respiratory-Viruses/RespiratoryDashboard.aspx.

Data are updated each Friday around 2 pm.

For COVID-19 death data: As of January 1, 2023, data was sourced from the California Department of Public Health, California Comprehensive Death File (Dynamic), 2023–Present. Prior to January 1, 2023, death data was sourced from the COVID-19 case registry. The change in data source occurred in July 2023 and was applied retroactively to all 2023 data to provide a consistent source of death data for the year of 2023. Influenza death data was sourced from the California Department of Public Health, California Comprehensive Death File (Dynamic), 2020–Present.

COVID-19 testing data represent data received by CDPH through electronic laboratory reporting of test results for COVID-19 among residents of California. Testing date is the date the test was administered, and tests have a 1-day lag (except for the Los Angeles County, which has an additional 7-day lag). Influenza testing data represent data received by CDPH from clinical sentinel laboratories in California. These laboratories report the aggregate number of laboratory-confirmed influenza virus detections and total tests performed on a weekly basis. These data do not represent all influenza testing occurring in California and are available only at the state level.

Weekly statistical results of the main indicators in the time series Poisson generalized additive model#.

Estimated excess influenza-associated deaths versus nationally registered influenza deaths per age group.

BackgroundThe disease burden associated with influenza in developing tropical and subtropical countries is poorly understood owing to the lack of a comprehensive disease surveillance system and information-exchange mechanisms. The impact of influenza on outpatient visits, hospital admissions, and deaths has not been fully demonstrated to date in south China.MethodsA time series Poisson generalized additive model was used to quantitatively assess influenza-like illness (ILI) and influenza disease burden by using influenza surveillance data in Zhuhai City from 2007 to 2009, combined with the outpatient, inpatient, and respiratory disease mortality data of the same period.ResultsThe influenza activity in Zhuhai City demonstrated a typical subtropical seasonal pattern; however, each influenza virus subtype showed a specific transmission variation. The weekly ILI case number and virus isolation rate had a very close positive correlation (r = 0.774, P < 0.0001). The impact of ILI and influenza on weekly outpatient visits was statistically significant (P < 0.05). We determined that 10.7% of outpatient visits were associated with ILI and 1.88% were associated with influenza. ILI also had a significant influence on the hospitalization rates (P < 0.05), but mainly in populations 0.05). The impact of ILI on chronic obstructive pulmonary disease (COPD) was most significant (P < 0.05), with 33.1% of COPD-related deaths being attributable to ILI. The impact of influenza on the mortality rate requires further evaluation.ConclusionsILI is a feasible indicator of influenza activity. Both ILI and influenza have a large impact on outpatient visits. Although ILI affects the number of hospital admissions and deaths, we found no consistent influence of influenza, which requires further assessment.

Data is from the California Department of Public Health (CDPH) Respiratory Virus Weekly Report.

The report is updated each Friday.

Laboratory surveillance data: California laboratories report SARS-CoV-2 test results to CDPH through electronic laboratory reporting. Los Angeles County SARS-CoV-2 lab data has a 7-day reporting lag. Test positivity is calculated using SARS-CoV-2 lab tests that has a specimen collection date reported during a given week.

Laboratory surveillance for influenza, respiratory syncytial virus (RSV), and other respiratory viruses (parainfluenza types 1-4, human metapneumovirus, non-SARS-CoV-2 coronaviruses, adenovirus, enterovirus/rhinovirus) involves the use of data from clinical sentinel laboratories (hospital, academic or private) located throughout California. Specimens for testing are collected from patients in healthcare settings and do not reflect all testing for influenza, respiratory syncytial virus, and other respiratory viruses in California. These laboratories report the number of laboratory-confirmed influenza, respiratory syncytial virus, and other respiratory virus detections and isolations, and the total number of specimens tested by virus type on a weekly basis.

Test positivity for a given week is calculated by dividing the number of positive COVID-19, influenza, RSV, or other respiratory virus results by the total number of specimens tested for that virus. Weekly laboratory surveillance data are defined as Sunday through Saturday.

Hospitalization data: Data on COVID-19 and influenza hospital admissions are from Centers for Disease Control and Prevention’s (CDC) National Healthcare Safety Network (NHSN) Hospitalization dataset. The requirement to report COVID-19 and influenza-associated hospitalizations was effective November 1, 2024. CDPH pulls NHSN data from the CDC on the Wednesday prior to the publication of the report. Results may differ depending on which day data are pulled. Admission rates are calculated using population estimates from the P-3: Complete State and County Projections Dataset provided by the State of California Department of Finance (https://dof.ca.gov/forecasting/demographics/projections/). Reported weekly admission rates for the entire season use the population estimates for the year the season started. For more information on NHSN data including the protocol and data collection information, see the CDC NHSN webpage (https://www.cdc.gov/nhsn/index.html).

CDPH collaborates with Northern California Kaiser Permanente (NCKP) to monitor trends in RSV admissions. The percentage of RSV admissions is calculated by dividing the number of RSV-related admissions by the total number of admissions during the same period. Admissions for pregnancy, labor and delivery, birth, and outpatient procedures are not included in total number of admissions. These admissions serve as a proxy for RSV activity and do not necessarily represent laboratory confirmed hospitalizations for RSV infections; NCKP members are not representative of all Californians.

Weekly hospitalization data are defined as Sunday through Saturday.

Death certificate data: CDPH receives weekly year-to-date dynamic data on deaths occurring in California from the CDPH Center for Health Statistics and Informatics. These data are limited to deaths occurring among California residents and are analyzed to identify influenza, respiratory syncytial virus, and COVID-19-coded deaths. These deaths are not necessarily laboratory-confirmed and are an underestimate of all influenza, respiratory syncytial virus, and COVID-19-associated deaths in California. Weekly death data are defined as Sunday through Saturday.

Wastewater data: This dataset represents statewide weekly SARS-CoV-2 wastewater summary values. SARS-CoV-2 wastewater concentrations from all sites in California are combined into a single, statewide, unit-less summary value for each week, using a method for data transformation and aggregation developed by the CDC National Wastewater Surveillance System (NWSS). Please see the CDC NWSS data methods page for a description of how these summary values are calculated. Weekly wastewater data are defined as Sunday through Saturday.

BackgroundInfluenza and respiratory syncytial virus (RSV) associated mortality has not been well-established in tropical Africa.MethodsWe used the negative binomial regression method and the rate-difference method (i.e. deaths during low and high influenza/RSV activity months), to estimate excess mortality attributable to influenza and RSV using verbal autopsy data collected through a health and demographic surveillance system in Western Kenya, 2007–2013. Excess mortality rates were calculated for a) all-cause mortality, b) respiratory deaths (including pneumonia), c) HIV-related deaths, and d) pulmonary tuberculosis (TB) related deaths.ResultsUsing the negative binomial regression method, the mean annual all-cause excess mortality rate associated with influenza and RSV was 14.1 (95% confidence interval [CI] 0.0–93.3) and 17.1 (95% CI 0.0–111.5) per 100,000 person-years (PY) respectively; and 10.5 (95% CI 0.0–28.5) and 7.3 (95% CI 0.0–27.3) per 100,000 PY for respiratory deaths, respectively. Highest mortality rates associated with influenza were among ≥50 years, particularly among persons with TB (41.6[95% CI 0.0–122.7]); and with RSV were among <5 years. Using the rate-difference method, the excess mortality rate for influenza and RSV was 44.8 (95% CI 36.8–54.4) and 19.7 (95% CI 14.7–26.5) per 100,000 PY, respectively, for all-cause deaths; and 9.6 (95% CI 6.3–14.7) and 6.6 (95% CI 3.9–11.0) per 100,000 PY, respectively, for respiratory deaths.ConclusionsOur study shows a substantial excess mortality associated with influenza and RSV in Western Kenya, especially among children <5 years and older persons with TB, supporting recommendations for influenza vaccination and efforts to develop RSV vaccines.

Impact of influenza-like illness and influenza on hospitalizations.

NNDSS - Table II. West Nile virus disease - 2015.In this Table, provisional cases of selected notifiable diseases (≥1,000 cases reported during the preceding year), and selected low frequency diseases are displayed.The Table includes total number of cases reported in the United States, by region and by states, in accordance with the current method of displaying MMWR data. Data on United States exclude counts from US territories. Note:These are provisional cases of selected national notifiable diseases, from the National Notifiable Diseases Surveillance System (NNDSS). NNDSS data reported by the 50 states, New York City, the District of Columbia, and the U.S. territories are collated and published weekly as numbered tables printed in the back of the Morbidity and Mortality Weekly Report (MMWR). Cases reported by state health departments to CDC for weekly publication are provisional because of ongoing revision of information and delayed reporting. Case counts in this table are presented as they were published in the MMWR issues. Therefore, numbers listed in later MMWR weeks may reflect changes made to these counts as additional information becomes available. Footnotes:C.N.M.I.: Commonwealth of Northern Mariana Islands. U: Unavailable. -: No reported cases. N: Not reportable. NN: Not Nationally Notifiable. NP: Nationally notifiable but not published. Cum: Cumulative year-to-date counts. Med: Median. Max: Maximum. * Three low incidence conditions, rubella, rubella congenital, and tetanus, have been moved to Table 2 to facilitate case count verification with reporting jurisdictions. ��� Case counts for reporting year 2015 are provisional and subject to change. For further information on interpretation of these data, see http://wwwn.cdc.gov/nndss/document/ProvisionalNationaNotifiableDiseasesSurveillanceData20100927.pdf. Data for TB are displayed in Table IV, which appears quarterly. �� Updated weekly from reports to the Division of Vector-Borne Diseases, National Center for Emerging and Zoonotic Infectious Diseases (ArboNET Surveillance). Data for California serogroup, Chikungunya virus, eastern equine, Powassan, St. Louis, and western equine diseases are available in Table I. �� Not reportable in all states. Data from states where the condition is not reportable are excluded from this table, except starting in 2007 for the domestic arboviral diseases, influenza-associated pediatric mortality, and in 2003 for SARS-CoV. Reporting exceptions are available at http://wwwn.cdc.gov/nndss/downloads.html.

Age-specific mean annual excess mortality rate associated with influenza in Western Kenya, 2007–2013.

The global outbreak of clade 2.3.4.4b H5N1 highly pathogenic avian influenza (HPAI) virus has caused tremendous losses in poultry. Although turkeys are a smaller sector in poultry production compared to chickens, they tend to be affected more severely by HPAI virus because they can usually be infected with a lower dose of influenza A virus than chickens (i.e., they are more susceptible). Exposure to non-replicating proteins may help control HPAI, however data with turkeys are somewhat limited regarding how well they work and approaches to modifying surveillance have not been developed. Here, an H5N9 non-replicating protein comprised of a clade 2.3.4.4b H5 hemagglutinin from A/turkey/Indiana/22-003707-003/2022 (TK/IN/22) and a North American wild bird lineage N9 was evaluated in commercial broad breasted white turkeys by challenge to live virus. Turkeys were divided into three groups, where each group was exposed to the non-replicating protein once at 3 (3wk), 7 (7wk), or 9 (9wk) weeks of age. All birds were challenged at 10 weeks of age with TK/IN/22 HPAIV. There was 100% survival in all groups except the sham exposure group which had 100% mortality. A significant decrease in viral shedding was observed in all exposed groups compared to the shams, although the 9wk group shed significantly higher quantities by the cloacal route at seven days post challenge (DPC) compared to the 3wk group. The neuraminidase inhibition-enzyme linked lectin assay (NI-ELLA) was used as a serological test that was able to detect antibody in birds that had been infected after exposure and challenge based on antibodies to the NA protein of the challenge virus (N1 NA) in serum collected 7, 10 and 14DPC. Between 50 and 90% of turkeys, depending on age at exposure, were positive by NI-ELLA at 7DPC and 100% were positive at 14DPC regardless of age at exposure.

Infection of influenza A virus in mammals induces hyper lung pneumonia, which often causes lethal diseases. FasL is a specific ligand of Fas, which is a type-I transmembrane protein to induce cell death. Previously, it has been reported that the hyper induction of gene expression associated with Fas signal is observed in lethal influenza A virus infection. More importantly, it was also reported that functional mutation of the FasL gene protects the host against influenza A virus infection. These observations suggest that induction of FasL signal is functionally associated with the severity of influenza. However, regulation of the induction of FasL or Fas by influenza A virus infection is still unknown. Here, we demonstrated that FasL is induced after the viral infection, and inhibition of the Fas/FasL signal by treatment with a recombinant decoy receptor for FasL (Fas-Fc) increases the survival rate of mice after lethal infection of influenza A virus as well as functional mutation of the FasL gene in gld/gld mice. In addition, the induction level of FasL gene expression in the lung was correlated with the severity of influenza. We also showed that a variety of types of cells in the lung express FasL after the viral infection. Furthermore, type-I interferon induced by the viral infection was shown to be critical for induction of FasL protein expression in the lung. These findings suggested that expression of FasL protein induced by type-I IFN on the lung cell surface is critical to determine the severity of influenza.

NNDSS - Table I. infrequently reported notifiable diseases - 2017. In this Table, provisional cases of selected infrequently reported notifiable diseases (<1,000 cases reported during the preceding year) are displayed.

Note: These are provisional cases of selected national notifiable diseases, from the National Notifiable Diseases Surveillance System (NNDSS). NNDSS data reported by the 50 states, New York City, the District of Columbia, and the U.S. territories are collated and published weekly as numbered tables printed in the back of the Morbidity and Mortality Weekly Report (MMWR). Cases reported by state health departments to CDC for weekly publication are provisional because of ongoing revision of information and delayed reporting.

Case counts in these tables are presented as they were published in the MMWR issues. Therefore, numbers listed in later MMWR weeks may reflect changes made to these counts as additional information becomes available.

Footnote: —: No reported cases. N: Not reportable. NA: Not available. NN: Not Nationally Notifiable. NP: Nationally notifiable but not published. Cum: Cumulative year-to-date counts.

• Case counts for reporting year 2017 are provisional and subject to change. Data for years 2012 through 2016 are finalized. For further information on interpretation of these data, see http://wwwn.cdc.gov/nndss/document/ProvisionalNationaNotifiableDiseasesSurveillanceData20100927.pdf.

† This table does not include cases from the U.S. territories. Three low incidence conditions, rubella, rubella congenital, and tetanus, are in Table II to facilitate case count verification with reporting jurisdictions.

§ Calculated by summing the incidence counts for the current week, the 2 weeks preceding the current week, and the 2 weeks following the current week, for a total of 5 preceding years. Additional information is available at http://wwwn.cdc.gov/nndss/document/5yearweeklyaverage.pdf.

¶ Updated weekly reports from the Division of Vector-Borne Diseases, National Center for Emerging and Zoonotic Infectious Diseases (ArboNET Surveillance). Data for West Nile virus are available in Table II.

** Not reportable in all jurisdictions. Data from states where the condition is not reportable are excluded from this table, except for the arboviral diseases and influenza-associated pediatric mortality. Reporting exceptions are available at http://wwwn.cdc.gov/nndss/downloads.html.

†† Data for Haemophilus influenzae (all ages, all serotypes) are available in Table II.

§§ In 2016, the nationally notifiable condition ‘Hepatitis B Perinatal Infection’ was renamed to ‘Perinatal Hepatitis B Virus Infection’ and reflects updates in the 2016 CSTE position statement for Perinatal Hepatitis B Virus Infection.

¶¶ Please refer to the MMWR publication for weekly updates to the footnote for this condition.

*** Please refer to the MMWR publication for weekly updates to the footnote for this condition.

††† Data for meningococcal disease (all serogroups) are available in Table II.

§§§ Novel influenza A virus infections are human infections with influenza A viruses that are different from currently circulating human seasonal influenza viruses. With the exception of one avian lineage influenza A (H7N2) virus, all novel influenza A virus infections reported to CDC since 2011 have been variant influenza viruses. Total case counts are provided by the Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD).

¶¶¶ Updated weekly from reports to the Division of STD Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention.

**** Prior to 2015, CDC's National Notifiable Diseases Surveillance System (NNDSS) did not receive electronic data about incident cases of specific viral hemorrhagic fevers; instead data were collected in aggregate as "viral hemorrhagic fevers". Beginning in 2015, NNDSS has been updated to receive data for each of

All-cause deaths and laboratory-confirmed influenza cases recorded in the Philippines, 2006−2015.

BackgroundHuman infection caused by the avian influenza A H7N9 virus has a case-fatality rate of over 30%. Systematic study of the pathogenesis of avian H7N9 isolate and effective therapeutic strategies are needed.MethodsBALB/c mice were inoculated intranasally with an H7N9 virus isolated from a chicken in a wet market epidemiologically linked to a fatal human case, (A/chicken/Zhejiang/DTID-ZJU01/2013 [CK1]), and with an H7N9 virus isolated from a human (A/Anhui/01/2013 [AH1]). The pulmonary viral loads, cytokine/chemokine profiles and histopathological changes of the infected mice were compared. The therapeutic efficacy of a non-steroidal anti-inflammatory drug (NSAID), celecoxib, was assessed.ResultsWithout prior adaptation, intranasal inoculation of 106 plaque forming units (PFUs) of CK1 caused a mortality rate of 82% (14/17) in mice. Viral nucleoprotein and RNA expression were limited to the respiratory system and no viral RNA could be detected from brain, liver and kidney tissues. CK1 caused heavy alveolar inflammatory exudation and pulmonary hemorrhage, associated with high pulmonary levels of proinflammatory cytokines. In the mouse lung cell line LA-4, CK1 also induced high levels of interleukin-6 (IL-6) and cyclooxygenase-2 (COX-2) mRNA. Administration of the antiviral zanamivir did not significantly improve survival in mice infected with CK1, but co-administration of the non-steroidal anti-inflammatory drug (NSAID) celecoxib in combination with zanamivir improved survival and lung pathology.ConclusionsOur findings suggested that H7N9 viruses isolated from chicken without preceding trans-species adaptation can cause lethal mammalian pulmonary infection. The severe proinflammatory responses might be a factor contributing to the mortality. Treatment with combination of antiviral and NSAID could ameliorate pulmonary inflammation and may improve survival.

Zoonotic transmission of H5N1 highly pathogenic avian influenza virus (HPAIV) into the human population is an increasing global threat. The recent 2022 HPAIV outbreak significantly highlighted this possibility, increasing concern in the general population. The clinical outcomes of H5N1 influenza virus exposure can be determined by an individual’s primary influenza virus infection (imprinting) or vaccination status. Immunological imprinting with Group 1 - (H1N1, H2N2, and H2N3) increases survival rates following H5N1 viral infection compared to Group 2 - (H3N2) imprinted individuals. Vaccination against H5N1 influenza viruses can offer protection to at-risk populations; however, stockpiled inactivated H5N1 influenza vaccines are not readily available to the public. We hypothesize that the immunological response to vaccination and subsequent clinical outcome following H5N1 influenza virus infection is correlated with the immunological imprinting status of an individual. To test this hypothesis, our lab established a ferret pre-immune model of disease. Naïve ferrets were intranasally inoculated with seasonal influenza viruses and allowed to recover for 84 days prior to H5N1 virus infection. Ferrets imprinted following H1N1 and H2N3 virus infections were completely protected against lethal H5N1 influenza virus challenge (100% survival), with few to no clinical symptoms. In comparison, H3N2 influenza virus-imprinted ferrets had severe clinical symptoms, delayed disease progression, and a sublethal phenotype (40% mortality). Consecutive infections with H1N1 influenza viruses followed by an H3N2 influenza virus infection did not abrogate the immune protection induced by the original H1N1 influenza virus infection. In addition, ferrets consecutively infected with H1N1 and H2N3 viruses had no clinical symptoms or weight loss. H3N2 pre-immune ferrets were vaccinated with a broadly reactive H5 HA-based or H1 NA-based vaccine (Hu-CO 2). These ferrets were protected against H5N1 influenza virus challenge, whereas ferrets vaccinated with the H1N1 wild-type CA/09 rHA vaccine had similar phenotypes as non-vaccinated H3N2-imprinted ferrets with 40% survival. Overall, Group 2 imprinted ferrets, which were vaccinated with heterologous Group 1 HA vaccines, had redirected immune responses to Group 1 influenza viral antigens and rescued a sublethal phenotype to complete protection.