The following dashboards provide data on contagious respiratory viruses, including acute respiratory diseases, COVID-19, influenza (flu), and respiratory syncytial virus (RSV) in Massachusetts. The data presented here can help track trends in respiratory disease and vaccination activity across Massachusetts.

The FluView National Flu Activity Map is a complementary widget to the state-by-state flu map widget introduced in the 2007-2008 flu season. This interactive map allows users to see the most recent seasonal influenza activity map for the entire country as well as the activity levels from previous weeks in the current flu season.

Access available resources below such as data reports, and Public Health Council presentations.

A Spatial Multi Criteria Evaluation was applied to map a suitability index (ranging from 0: low suitability to 255: high suitability) for habitat suitability for occurrence of highly pathogenic avian influenza virus H5N1 in domestic poultry in Asia. The method developed by (Stevens et al., 2013) was applied on recent databases of poultry and human populations. Variables included in the study: 1) Domestic waterfowl density, 2) Chicken density, 3) Human population density, 4) Roads, 5) Water, 6) Crops. A full description of the methodology is presented in (Stevens et al., 2013). The present data set includes rasters (spatial resolution: ca 1 km): - the AI suitability map - the normalized criteria

Since 2020, outbreaks of high pathogenicity avian influenza (HPAI) have led to a global rise in deaths of both wild birds and poultry, as well as an increase in reported cases of HPAI detected in mammals. These outbreaks have had negative impacts on poultry producers, trade, and wild bird populations. Risk governance frameworks for emerging infectious diseases such as HPAI encourage outbreak policies to be grounded in a variety of stakeholder perspectives and for there to be effective, transparent communication between all those involved. However, the COVID-19 pandemic exemplified how collaboration is not always easy to implement, leading to potentially sub-optimal outbreak response processes. To our best knowledge, there is limited to no current research assessing the stakeholder landscape and outbreak decision-making and response processes in the United Kingdom (UK) and United States of America (USA) for the recent HPAI outbreak. In this study, 20 key stakeholders involved in outbreak decision-making and response in the United Kingdom and United States were asked to provide their insights into the structure of stakeholder landscape, communication pathways, and challenges in decision-making and response implementation for their respective countries. Semi-structured interviews were conducted with participants from the United Kingdom and United States; participants included policy advisors, veterinarians, researchers, and poultry industry representatives all involved in HPAI outbreak processes in their country. From these interviews, stakeholder maps for all those involved in HPAI decision-making and response were created for the UK and USA. This study concluded that smallholders and backyard poultry owners need to be better represented in policy-industry communication pathways and that improved information sharing at the policy-science and policy-industry interfaces is essential to ensure an efficient outbreak response.

Over 12 million people in the United States died from all causes between the beginning of January 2020 and August 21, 2023. Over 1.1 million of those deaths were with confirmed or presumed COVID-19.

Vaccine rollout in the United States Finding a safe and effective COVID-19 vaccine was an urgent health priority since the very start of the pandemic. In the United States, the first two vaccines were authorized and recommended for use in December 2020. One has been developed by Massachusetts-based biotech company Moderna, and the number of Moderna COVID-19 vaccines administered in the U.S. was over 250 million. Moderna has also said that its vaccine is effective against the coronavirus variants first identified in the UK and South Africa.

This is a dataset for the City of Somerville Infectious Illness Dashboard. This dataset combines multiple public data sources concerning COVID and flu in Massachusetts and, where possible, in the Somerville area specifically. Data sources include the Center for Disease Control, the Massachusetts Department of Public Health, and the Massachusetts Water Resources Authority.

The main entity of this document is a taxonomy with accession number 1865945

Tabulated individual data points for data reported in the associated publication: Spackman E, Suarez DL, Lee CW, Pantin-Jackwood MJ, Lee SA, Youk S, Ibrahim S. Efficacy of inactivated and RNA particle vaccines against a North American Clade 2.3.4.4b H5 highly pathogenic avian influenza virus in chickens. Vaccine. 2023 Nov 30;41(49):7369-7376. doi: 10.1016/j.vaccine.2023.10.070. Epub 2023 Nov 4. PMID: 37932132.Description of methodsVirusesThe highly pathogenic avian influenza virus (HPAIV) isolate A/turkey/Indiana/22-003707-003/2022 H5N1 (TK/IN/22) and A/Gyrfalcon/Washington/41088/2014 H5N8 (GF/WA/14) isolate were each propagated and titrated in embryonating specific pathogen free (SPF) chicken eggs using standard procedures and titers were determined using the Reed-Muench method.VaccinesAn in-house vaccine was produced by de novo synthesizing the HA gene of TK/IN/22 that was modified to be low pathogenic (LP) and placing it in a PR8 backbone using rg methods as described . The vaccine (SEP-22-N9) contained 6 genes from PR8 and a de novo synthesized N9 NA from A/blue winged teal/Wyoming/AH0099021/2016 (H7N9). The rg virus was inactivated by treatment with 0.1% beta-propiolactone. Vaccines were produced with Montanide ISA 71 VG (Seppic Inc., Fairfield, NJ) adjuvant at ambient temperature in a L5M-A high shear mixer (Silverson Machines, Inc., East Longmeadow, MA) for 30sec at 1,000rpm, then for 3min at 4,000rpm using an emulsifying screen in accordance with the adjuvant manufacturer’s instructions.Sham vaccine was prepared in-house using sterile phosphate buffered saline as described above.Commercial vaccines were supplied by the manufacturers. The commercial inactivated vaccine (1057.R1 serial 590088) (rgH5N1) (Zoetis Inc., Parsippany, NJ) was produced with the GF/WA/14 (clade 2.3.4.4c HA gene) and the remaining 7 gene segments including the NA from PR8 (1). The Sequivity vaccine (serial V040122NCF) (RP) (Merck and Co. Inc., Rahway, NJ) is an updated version of their replication restricted alphavirus vector vaccine that expresses the TK/IN/22 H5 HA (modified to be low pathogenic LP).Challenge study designThree-week-old, mixed sex, SPF white leghorn chickens (Gallus gallus domesticus) were obtained from in-house flocks and were randomly assigned to vaccine groups.All vaccines were administered by the subcutaneous route at the nape of the neck. Commercial vaccines were given at the volumes instructed by the manufacturer (0.5ml each). In-house vaccine was given at a dose of 512 hemagglutination units per bird in 0.5ml. Three weeks post vaccination chickens were challenged with 6.7 log10 50% egg infectious doses (EID50) of TK/IN/22 in 0.1ml by the intrachoanal route.Oropharyngeal (OP) and cloacal (CL) swabs were collected from all birds at 2-, 4-, and 7-days post challenge (DPC). Swabs were also collected from dead and euthanized sham vaccinates at 1DPC.To evaluate antibody-based DIVA-VI tests, blood for serum was collected from the RP and SEP-22-N9 vaccinated groups at 7, 10 and 14DPC because the SEP-22-N9 vaccine does not elicit antibodies to N1 and the RP vaccine does not elicit antibodies to the N1 or NP proteins.Mortality and morbidity were recorded for 14DPC after which time the remaining birds were euthanized. If birds were severely lethargic or had neurological signs they were euthanized and were counted as mortality at the next observation time for mean death time calculations.Evaluation of antibody titers based on prime-boost order with the RP and inactivated vaccinesTo determine if there was a difference in antibody levels based on the order of vaccination with the RP vaccine and an inactivated vaccine, groups of 20 chickens (hatch-mates of the chickens in the challenge study) were given one dose of each vaccine three weeks apart (Supplementary Table 1). The first dose was administered at three weeks of age using the RP or SEP-22-N9 vaccine as described above. Then a second dose of either the same vaccine or the other vaccine was administered three weeks later (six weeks of age). All birds were bled for serum three weeks after the second vaccination (nine weeks of age). Antibody was quantified by hemagglutination inhibition (HI) assay as described below using the homologous antigen (TK/IN/22).Quantitative rRT-PCR (qRRT-PCR)RNA was extracted from OP and CL swabs using the MagMax (Thermo Fisher Scientific, Waltham, MA) magnetic bead extraction kit with the modifications described by Das et al., (2). Quantitative real-time RT-PCR was conducted as described previously (3) on a QuantStudio 5 (Thermo Fisher Scientific). A standard curve was generated from a titrated stock of TK/IN/22 and was used to calculate titer equivalents using the real time PCR instrument’s software.Hemagglutination inhibition assayHemagglutination inhibition assays were run in accordance with standard procedures. All pre-challenge sera were tested against the challenge virus. Sera from birds vaccinated with the rgH5N1 vaccine were also tested against the vaccine antigen, GF/WA/14. Titers of 8 or below were considered non-specific binding, therefore negative.Commercial ELISAPre-vaccination sera from 30 chickens were tested to confirm the absence of antibodies to AIV with a commercial AIV antibody ELISA (IDEXX laboratories, Westbrook, ME) in accordance with the manufacturer’s instructions. Pre- and post-challenge sera from the RP vaccine group (the only vaccine utilized here that does not induce antibodies to the NP) were also tested with this ELISA to characterize the detection of anti-NP antibodies post-challenge.Enzyme-linked lectin assay (ELLA) and neuraminidase inhibition (NI) to detect N1 antibody in serum from challenged chickensThe ELLA assay was performed in accordance with a previously published protocol with minor modifications (4). Absorbance data were fit to a non-linear regression curve with Prism 9.5 (GraphPad Software LLC, Boston, MA) to determine the effective concentration, and the 98% effective concentration (EC98) of the N1 source virus was subsequently used for NI assays.To detect N1 antibody with the optimized N1 NA concentrations, serum samples from the sham, SEP-22-N9, and RP vaccinated groups collected pre-challenge, 7, 10 and 14DPC, were heat inactivated at 56°C for one hour and diluted 1:20 and 1:40 using sample dilution buffer. Equal volumes of the N1 NA source virus at a concentration of 2X EC98 was added to each of the diluted serum samples. Then 100µl of the serum-virus mixture was added to the fetuin coated plates after the fetuin plates were washed as described above for the NA assay. Fetuin plates with the serum-virus mixture were then incubated overnight (approximately 17-19hr) at 37°C. The NA assay protocol described above was followed for the remaining NI assay steps.The percent NI activity of individual serum samples was determined by subtracting percent NA activity from 100. To calculate the percent NA activity, the average background absorbance value was subtracted from the sample absorbance value. The result was then divided by the average value of the NA source virus only (no serum) wells then multiplying by 100. A cut-off value for NI activity for positive detection of N1 antibody from chickens post-challenge was calculated by adding three standard deviations to the mean value obtained from pre-challenge sera of corresponding vaccine group for each dilution tested (1:20 and 1:40).References1. Kapczynski DR, Sylte MJ, Killian ML, Torchetti MK, Chrzastek K, Suarez DL. Protection of commercial turkeys following inactivated or recombinant H5 vaccine application against the 2015U.S. H5N2 clade 2.3.4.4 highly pathogenic avian influenza virus. Vet Immunol Immunopathol. 2017;191:74-9. Epub 2017/09/13. doi: 10.1016/j.vetimm.2017.08.001.2. Das A, Spackman E, Pantin-Jackwood MJ, Suarez DL. Removal of real-time reverse transcription polymerase chain reaction (RT-PCR) inhibitors associated with cloacal swab samples and tissues for improved diagnosis of Avian influenza virus by RT-PCR. Journal of Veterinary Diagnostic Investigation. 2009;21(6):771-8.3. Spackman E, Senne DA, Myers TJ, Bulaga LL, Garber LP, Perdue ML, et al. Development of a real-time reverse transcriptase PCR assay for type A influenza virus and the avian H5 and H7 hemagglutinin subtypes. Journal of Clinical Microbiology. 2002;40(9):3256-60.4. Bernard MC, Waldock J, Commandeur S, Strauss L, Trombetta CM, Marchi S, et al. Validation of a Harmonized Enzyme-Linked-Lectin-Assay (ELLA-NI) Based Neuraminidase Inhibition Assay Standard Operating Procedure (SOP) for Quantification of N1 Influenza Antibodies and the Use of a Calibrator to Improve the Reproducibility of the ELLA-NI With Reverse Genetics Viral and Recombinant Neuraminidase Antigens: A FLUCOP Collaborative Study. Front Immunol. 2022;13:909297. Epub 2022/07/06.

The table includes 1 products with the active ingredient Influenza A Virus A/massachusetts/18/2022 (h3n2) Recombinant Hemagglutinin Antigen.

These data represent the predicted (modeled) prevalence of adults (Age 18+) who received a Flu Vaccine (flu shot or a vaccine sprayed in the nose) within the past 12 months for each census tract in Colorado. The length and intensity of each annual flu season varies from year to year, and there can be large variability between age groups in terms of who is receiving the annual flu vaccine.The estimate for each census tract represents an average that was derived from multiple years of Colorado Behavioral Risk Factor Surveillance System data (2014-2017).CDPHE used a model-based approach to measure the relationship between age, race, gender, poverty, education, location and health conditions or risk behavior indicators and applied this relationship to predict the number of persons' who have the health conditions or risk behavior for each census tract in Colorado. We then applied these probabilities, based on demographic stratification, to the 2013-2017 American Community Survey population estimates and determined the percentage of adults with the health conditions or risk behavior for each census tract in Colorado.The estimates are based on statistical models and are not direct survey estimates. Using the best available data, CDPHE was able to model census tract estimates based on demographic data and background knowledge about the distribution of specific health conditions and risk behaviors.The estimates are displayed in both the map and data table using point estimate values for each census tract and displayed using a Quintile range. The high and low value for each color on the map is calculated based on dividing the total number of census tracts in Colorado (1249) into five groups based on the total range of estimates for all Colorado census tracts. Each Quintile range represents roughly 20% of the census tracts in Colorado. No estimates are provided for census tracts with a known population of less than 50. These census tracts are displayed in the map as "No Est, Pop < 50."No estimates are provided for 7 census tracts with a known population of less than 50 or for the 2 census tracts that exclusively contain a federal correctional institution as 100% of their population. These 9 census tracts are displayed in the map as "No Estimate."

This dataset has been archived and will no longer be updated as of 10/16/2024. For updated data, please refer to the ILINet State Activity Indicator Map.

Information on outpatient visits to health care providers for respiratory illness referred to as influenza-like illness (ILI) is collected through the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). ILINet consists of outpatient healthcare providers in all 50 states, Puerto Rico, the District of Columbia, and the U.S. Virgin Islands. More than 100 million patient visits were reported during the 2022-23 season. Each week, more than 3,000 outpatient health care providers around the country report to CDC the number of patient visits for ILI by age group (0-4 years, 5-24 years, 25-49 years, 50-64 years, and ≥65 years) and the total number of visits for any reason. A subset of providers also reports total visits by age group. For this system, ILI is defined as fever (temperature of 100°F [37.8°C] or greater) and a cough and/or a sore throat. Activity levels are based on the percent of outpatient visits due to ILI in a jurisdiction compared to the average percent of ILI visits that occur during weeks with little or no influenza virus circulation (non-influenza weeks) in that jurisdiction. The number of sites reporting each week is variable; therefore, baselines are adjusted each week based on which sites within each jurisdiction provide data. To perform this adjustment, provider level baseline ILI ratios are calculated for those that have a sufficient reporting history. Providers that do not have the required reporting history to calculate a provider-specific baseline are assigned the baseline ratio for their practice type. The jurisdiction level baseline is then calculated using a weighted sum of the baseline ratios for each contributing provider.

The activity levels compare the mean reported percent of visits due to ILI during the current week to the mean reported percent of visits due to ILI during non-influenza weeks. The 13 activity levels correspond to the number of standard deviations below, at, or above the mean for the current week compared with the mean during non-influenza weeks. Activity levels are classified as minimal (levels 1-3), low (levels 4-5), moderate (levels 6-7), high (levels 8-10), and very high (levels 11-13). An activity level of 1 corresponds to an ILI percentage below the mean, level 2 corresponds to an ILI percentage less than 1 standard deviation above the mean, level 3 corresponds to an ILI percentage more than 1 but less than 2 standard deviations above the mean, and so on, with an activity level of 10 corresponding to an ILI percentage 8 to 11 standard deviations above the mean. The very high levels correspond to an ILI percentage 12 to 15 standard deviations above the mean for level 11, 16 to 19 standard deviations above the mean for level 12, and 20 or more standard deviations above the mean for level 13.

Disclaimers:

The ILI Activity Indicator map reflects the intensity of ILI activity, not the extent of geographic spread of ILI, within a jurisdiction. Therefore, outbreaks occurring in a single area could cause the entire jurisdiction to display high or very high activity levels. In addition, data collected in ILINet may disproportionally represent certain populations within a jurisdiction, and therefore, may not accurately depict the full picture of respiratory illness activity for the entire jurisdiction. Differences in the data presented here by CDC and independently by some health departments likely represent differing levels of data completeness with data presented by the health department likely being more complete.

More information is available on FluView Interactive.

By identifying influenza-related tweets, the goal is to forecast the spatiotemporal patterns of influenza outbreaks for different locations and dates.

The data is from the United States. The data comes from different states under different weeks. For each week, the task is to predict whether or not there is an influenza outbreak on the next date. More specifically, for influenza activity, there are four levels of flu activities from minimal to high according to CDC Flu Activity Map. An influenza outbreak occurrence is indicated if the activity level is high.

Variable Information

The input of the prediction task is the set of the keyword counts for all the tweets in a state in a week. The output is the occurrence of influenza outbreak for the specific state in the next week, which is zero if no event in the next week; or one, otherwise. Here are the briefs of all the variables:

'flu_locations': a list of states. 'flu_keywords': keyword list. 'flu_X_': input data for all the locations and all the weeks. 'flu_Y_': output data for all the locations and all the weeks.

525 keywords specified in the variable 'flu_keywords' in the data

License

CC-BY

Original Data Source:Influenza outbreak event prediction via Twitter

Note: After November 1, 2024, this dataset will no longer be updated due to a transition in NHSN Hospital Respiratory Data reporting that occurred on Friday, November 1, 2024. For more information on NHSN Hospital Respiratory Data reporting, please visit https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html.

Due to a recent update in voluntary NHSN Hospital Respiratory Data reporting that occurred on Wednesday, October 9, 2024, reporting levels and other data displayed on this page may fluctuate week-over-week beginning Friday, October 18, 2024. For more information on NHSN Hospital Respiratory Data reporting, please visit https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html. Find more information about the updated CMS requirements: https://www.federalregister.gov/documents/2024/08/28/2024-17021/medicare-and-medicaid-programs-and-the-childrens-health-insurance-program-hospital-inpatient. 
. This dataset represents weekly respiratory virus-related hospitalization data and metrics aggregated to national and state/territory levels reported during two periods: 1) data for collection dates from August 1, 2020 to April 30, 2024, represent data reported by hospitals during a mandated reporting period as specified by the HHS Secretary; and 2) data for collection dates beginning May 1, 2024, represent data reported voluntarily by hospitals to CDC’s National Healthcare Safety Network (NHSN). NHSN monitors national and local trends in healthcare system stress and capacity for up to approximately 6,000 hospitals in the United States. Data reported represent aggregated counts and include metrics capturing information specific to COVID-19- and influenza-related hospitalizations, hospital occupancy, and hospital capacity. Find more information about reporting to NHSN at: https://www.cdc.gov/nhsn/covid19/hospital-reporting.html

Source: COVID-19 hospitalization data reported to CDC’s National Healthcare Safety Network (NHSN).

• Data source description(updated October 18, 2024): As of October 9, 2024, Hospital Respiratory Data (HRD; formerly Respiratory Pathogen, Hospital Capacity, and Supply data or ‘COVID-19 hospital data’) are reported to HHS through CDC’s National Healthcare Safety Network based on updated requirements from the Centers for Medicare and Medicaid Services (CMS). These data are voluntarily reported to NHSN as of May 1, 2024 until November 1, 2024, at which time CMS will require acute care and critical access hospitals to electronically report information via NHSN about COVID-19, Influenza, and RSV, hospital bed census and capacity, and limited patient demographic information, including age. Data for collection dates prior to May 1, 2024, represent data reported during a previously mandated reporting period as specified by the HHS Secretary. Data for collection dates May 1, 2024, and onwards represent data reported voluntarily to NHSN; as such, data included represents reporting hospitals only for a given week and might not be complete or representative of all hospitals. NHSN monitors national and local trends in healthcare system stress and capacity for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN represent aggregated counts and include metrics capturing information specific to hospital capacity, occupancy, hospitalizations, and admissions. Find more information about reporting to NHSN: https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html. Find more information about the updated CMS requirements: https://www.federalregister.gov/documents/2024/08/28/2024-17021/medicare-and-medicaid-programs-and-the-childrens-health-insurance-program-hospital-inpatient.
• Data quality: While CDC reviews reported data for completeness and errors and corrects those found, some reporting errors might still exist within the data. CDC and partners work with reporters to correct these errors and update the data in subsequent weeks. Data since December 1, 2020, have had error correction methodology applied; data prior to this date may have anomalies that are not yet resolved. Data prior to August 1, 2020, are unavailable.
• Metrics and inclusion criteria: Many hospital subtypes, including acute care and critical access hospitals, are included in the metric calculations included in this dataset. Psychiatric, rehabilitation, and religious non-medical hospital types, as well as Veterans Administration, Defense Health Agency, and Indian Health Service hospitals, are excluded from calculations. For a given metric calculation, hospitals that reported those data at least one day during a given week are included.
• Find full details on NHSN hospital data reporting guidance at https://www.hhs.gov/sites/default/files/covid-19-faqs-hospitals-hospital-laboratory-acute-care-facility-data-reporting.pdf

Notes: May 10, 2024: Due to missing hospital data for the April 28, 2024 through May 4, 2024 reporting period, data for Commonwealth of the Northern Mariana Islands (CNMI) are not available for this period in the Weekly NHSN Hospitalization Metrics report released on May 10, 2024.

May 17, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), Minnesota (MN), and Guam (GU) for the May 5,2024 through May 11, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on May 1, 2024.

May 24, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), and Minnesota (MN) for the May 12, 2024 through May 18, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on May 24, 2024.

May 31, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Virgin Islands (VI), Massachusetts (MA), and Minnesota (MN) for the May 19, 2024 through May 25, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on May 31, 2024.

June 7, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Virgin Islands (VI), Massachusetts (MA), Guam (GU), and Minnesota (MN) for the May 26, 2024 through June 1, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on June 7, 2024.

June 14, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), and Minnesota (MN) for the June 2, 2024 through June 8, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on June 14, 2024.

June 21, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), West Virginia (WV), Massachusetts (MA), American Samoa (AS), Guam (GU), Virgin Islands (VI), and Minnesota (MN) for the June 9, 2024 through June 15, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on June 21, 2024.

June 28, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the June 16, 2024 through June 22, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on June 28, 2024.

July 5, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), West Virginia (WV), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the June 23, 2024 through June 29, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on July 5, 2024.

July 12, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), West Virginia (WV), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the June 30, 2024 through July 6 , 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on July 12, 2024.

July 19, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the July 7, 2024 through July 13, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on July 19, 2024.

July 26, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the July 13, 2024 through July 20, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on July 26, 2024.

August 2, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), West Virginia (WV), and Minnesota (MN) for the July 21, 2024 through July 27, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on August 2, 2024.

August 9, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), Guam (GU), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the July 28, 2024 through August 3, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on August 9, 2024.

August 16, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the August 4, 2024 through August 10, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics report released on August 16, 2024.

August 23, 2024: Data for Commonwealth of the Northern Mariana Islands (CNMI), Massachusetts (MA), American Samoa (AS), Virgin Islands (VI), and Minnesota (MN) for the August 11, 2024 through August 17, 2024 reporting period are not available for the Weekly NHSN Hospitalization Metrics

Data are the individual group values for oral and cloacal virus shedding and antibody titers for reach treatment group from: Mo et al., The pathogenicity and transmission of live bird market H2N2 avian influenza viruses in chickens, Pekin ducks, and guinea fowl. Vet Mic 260:109180, 2021. https://doi.org/10.1016/j.vetmic.2021.109180 Methods: Six H2N2 low pathogenic avian influenza viruses from US LBMs were selected based on recency and to represent the different genotypes present in the live birds markets during the time period (i.e., the presence or absence of a NA stalk deletion): A/duck/PA/14-030488-5/2014 (Dk/PA/14), A/chicken/NY/16-032621-2/2016 (Ck/NY/16), A/chicken/CT/17-008911-4/2017 (Ck/CT/17), A/chicken/NY/18-002471-4/2018 (CK/NY/02471/18), A/chicken/NY/18-042097-3/2018 (Ck/NY/042097/18) and A/chicken/NY/19-012787-1/2019 (Ck/NY/19). Isolates were evaluated in White Leghorn chickens (Gallus gallus), guinea fowl (Numida meleagris) and Pekin ducks (Anas platyrhynchos). Chickens and guinea fowl were challenged at 4 weeks of age and Pekin ducks were challenged at 2 weeks of age with 6log10 of virus by the intra-choanal route. “Contact” birds, which were hatch-mates of the inoculated birds, were co-housed with the inoculated birds 24hrs post inoculation to evaluate transmission. Viral loads in OP and CL swabs collected at 2, 4, 7, 10, and 14 days post inoculation were determined by quantitative real-time reverse-transcriptase polymerase chain reaction (qRT-PCR). RNA was extracted from swabs using the MagMAX96 Viral RNA Isolation Kit (Thermo Fisher Scientific, Waltham, MA) and the KingFisher Flex Magnetic Particle Processing System (Thermo Fisher Scientific), with an additional wash step to remove inhibitors (Das et al., 2009). The qRT‐PCR for AIV detection was conducted based on the standard USDA M gene AIV qRT‐PCR procedure (Spackman et al., 2002) using an Applied Biosystems® 7500 Fast Real‐Time PCR system (Thermo Fisher Scientific). Cycle threshold (Ct) values were determined by the 7500 Fast Software v2.3. For relative quantification, Ct values were converted to titer equivalents based on the standard curve method (Larionov et al., 2005). Values were established from ten-fold dilutions of the same titrated stock of the virus used to challenge the birds. The limit of detection was determined to be 0.8Log10 per reaction. Serological testing for antibodies to the virus utilized the hemagglutination inhibition (HI) assays using homologous antigens were performed to quantify antibody responses with serum collected from chickens, guinea fowl and Pekin ducks at 14 dpi based on the standard protocol (OIE, 2019). HI titers were reported as reciprocal log2 titers, and titers greater than 3 log2 (1:8) were considered positive. Resources in this dataset:Resource Title: H2N2 influenza pathobiology data for avian species. File Name: H2N2 data for Archive.xlsxResource Description: Data by day post exposure for birds exposed to low pathogenic H2N2 avian influenza virus.

The Flu Pandemic of 1918-1919 was one of the most disastrous events in history, killing over 100 million people across the world and 50,000 in Canada.

This story map invites users to explore how the Spanish Flu Pandemic transformed Canada through a collection of short stories that illustrate the disease's impact and legacy on Canadians. Each collection touches on a specific aspect of the pandemic, from its impact on urban and indigenous communities across the country to the efforts of Canadian scientists during and after the pandemic. For more information on the Spanish flu and other exceptional moments of Canadian history, visit Defining Moments Canada's website.

This story map was a collaborative effort between Defining Moments Canada and the Huron History Community Centre and was completed in the summer of 2019 by Research Fellow Tom Lang.

Thumbnail photo: Telephone operators in High River, Alberta wearing masks to protect themselves from influenza, Glenbow Archives, NA-3452-2.

Includes location, open days, open times, age eligibility information where applicable, and sponsorship information. More information about flu vaccination clinics can be found on the San Mateo County Health System site: http://smchealth.org/flu

BackgroundInfluenza virus is one of the most common pathogens that cause viral pneumonia. During pneumonia, host immune inflammation regulation involves microbiota in the intestine and glycolysis in the lung tissues. In the clinical guidelines for pneumonia treatment in China, Ma Xing Shi Gan Decoction (MXSG) is a commonly prescribed traditional Chinese medicine formulation with significant efficacy, however, it remains unclear whether its specific mechanism of action is related to the regulation of intestinal microbiota structure and lung tissue glycolysis.ObjectiveThis study aimed to investigate the mechanism of action of MXSG in an animal model of influenza virus-induced pneumonia. Specifically, we aimed to elucidate how MXSG modulates intestinal microbiota structure and lung tissue glycolysis to exert its therapeutic effects on pneumonia.MethodsWe established a mouse model of influenza virus-induced pneumoni, and treated with MXSG. We observed changes in inflammatory cytokine levels and conducted 16S rRNA gene sequencing to assess the intestinal microbiota structure and function. Additionally, targeted metabolomics was performed to analyze lung tissue glycolytic metabolites, and Western blot and enzyme-linked immunosorbent assays were performed to assess glycolysis-related enzymes, lipopolysaccharides (LPSs), HIF-1a, and macrophage surface markers. Correlation analysis was conducted between the LPS and omics results to elucidate the relationship between intestinal microbiota and lung tissue glycolysis in pneumonia animals under the intervention of Ma Xing Shi Gan Decoction.ResultsMXSG reduced the abundance of Gram-negative bacteria in the intestines, such as Proteobacteria and Helicobacter, leading to reduced LPS content in the serum and lungs. This intervention also suppressed HIF-1a activity and lung tissue glycolysis metabolism, decreased the number of M1-type macrophages, and increased the number of M2-type macrophages, effectively alleviating lung damage caused by influenza virus-induced pneumonia.ConclusionMXSG can alleviate glycolysis in lung tissue, suppress M1-type macrophage activation, promote M2-type macrophage activation, and mitigate inflammation in lung tissue. This therapeutic effect appears to be mediated by modulating gut microbiota and reducing endogenous LPS production in the intestines. This study demonstrates the therapeutic effects of MXSG on pneumonia and explores its potential mechanism, thus providing data support for the use of traditional Chinese medicine in the treatment of respiratory infectious diseases.

Detailed description per virus of histopathologic changes and antigen expression by immunohistochemistry. (DOC)