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.

NO LONGER UPDATED. See the State Respiratory Illness Reporting site (https://www.mass.gov/info-details/respiratory-illness-reporting) for more recent information.

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.

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

The COVID-19 dashboard includes data on city/town COVID-19 activity, confirmed and probable cases of COVID-19, confirmed and probable deaths related to COVID-19, and the demographic characteristics of cases and deaths.

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

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.

Numerous studies have attempted to model the effect of mass media on the transmission of diseases such as influenza, however quantitative data on media engagement has until recently been difficult to obtain. With the recent explosion of “big data†coming from online social media and the like, large volumes of data on a population’s engagement with mass media during an epidemic are becoming available to researchers. In this study we combine an online data set comprising millions of shared messages relating to influenza with traditional surveillance data on flu activity to suggest a functional form for the relationship between the two. Using this data we present a simple deterministic model for influenza dynamics incorporating media effects, and show that such a model helps explain the dynamics of historical influenza outbreaks. Furthermore, through model selection we show that the proposed media function fits historical data better than other media functions proposed in earlier studies.

Recent avian and swine-origin influenza virus outbreaks illustrate the ongoing threat of influenza pandemics. We investigated immunogenicity and protective efficacy of a multi-antigen (MA) universal influenza DNA vaccine consisting of HA, M2, and NP antigens in cynomolgus macaques. Following challenge with a heterologous pandemic H1N1 strain, vaccinated animals exhibited significantly lower viral loads and more rapid viral clearance when compared to unvaccinated controls. The MA DNA vaccine induced robust serum and mucosal antibody responses but these high antibody titers were not broadly neutralizing. In contrast, the vaccine induced broadly-reactive NP specific T cell responses that cross-reacted with the challenge virus and inversely correlated with lower viral loads and inflammation. These results demonstrate that a MA DNA vaccine that induces strong cross-reactive T cell responses can, independent of neutralizing antibody, mediate significant cross-protection in a nonhuman primate model and further supports development as an effective approach to induce broad protection against circulating and emerging influenza strains.

This datasets displays the summary of the Geographic Spread of Influenza throughout the United States for the week ending on 5.10.2008. Each state is given a flu Status that is dependent on the spread of influenza throughout the state for the particular week. There are five levels of status that a state can obtain. They are: * No Activity: No laboratory-confirmed cases of influenza and no reported increase in the number of cases of ILI. * Sporadic: Small numbers of laboratory-confirmed influenza cases or a single laboratory-confirmed influenza outbreak has been reported, but there is no increase in cases of ILI. * Local: Outbreaks of influenza or increases in ILI cases and recent laboratory-confirmed influenza in a single region of the state. * Regional: Outbreaks of influenza or increases in ILI and recent laboratory confirmed influenza in at least 2 but less than half the regions of the state. * Widespread: Outbreaks of influenza or increases in ILI cases and recent laboratory-confirmed influenza in at least half the regions of the state. The information comes from the Centers for Disease Control and Prevention (CDC) To see more information about how this status is determined see http://www.cdc.gov/flu/weekly/fluactivity.htm for a full explanation of their system.

Estimated number of medically-attended (MA) influenza cases averted by vaccination, 2005/06–2010/11 influenza seasons (95% confidence interval in parentheses).

Information about school immunization requirements and data

The Medicare Enrolled Mass Immunizers dataset provides information on all Medicare providers that are currently enrolled as Mass Immunizers/Centralized flu billers.

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.

What is the FluPRINT database?

The FluPRINT represents fully integrated and normalized immunology measurements from eight clinical studies taken from 740 individuals undergoing influenza vaccination with inactivated or live attenuated seasonal influenza vaccines from 2007 to 2015 at the Stanford Human Immune Monitoring Center.

The FluPRINT dataset contains information on more than 3,000 parameters measured using mass cytometry, flow cytometry, phosphorylation-specific cytometry, multiplex cytokine assays, clinical lab tests (hormones and complete blood count), serological profiling and virological tests. In the dataset, vaccine protection is measured using a hemagglutination inhibition (HAI) assay, and following FDA guidelines individuals are marked as high or low responders depending on the HAI antibody titers after vaccination.

Want to know more?

To understand how the FluPRINT dataset was generated and validated, and how to use it, please refer to our open-access paper published in Scientific Data journal:

Tomic, A., Tomic, I., Dekker, C.L. et al. The FluPRINT dataset, a multidimensional analysis of the influenza vaccine imprint on the immune system. Sci Data 6, 214 (2019). https://doi.org/10.1038/s41597-019-0213-4

For additional exploration, please check out the project’s website: www.fluprint.com, where you can also explore the FluPRINT dataset on the following link: https://fluprint.com/#/database-access.

If you want to host your own FluPRINT database, please follow our instructions provided on the Github repository: https://github.com/LogIN-/fluprint.

How to use FluPRINT?

Here, you can download the entire FluPRINT database export as an SQL file, or as a CSV file. Additionally, we included the file with the SQL query to obtain those files.

Files are provided in two formats: zip and 7zip (7z). 7zip is a free and open-source file archiver available for download here: https://www.7-zip.org.

In the FluPRINT database, there are 4 tables: donor, donor_visits, experimental_data, and medical_history.
The exact description of each table is available in the FluPRINT paper.

Briefly, in the table donor, each row represents an individual with information about the clinical study in which an individual was enrolled (study ID and study internal ID), gender, and race. The second table, named donor_visits describes information about the donor’s age, cytomegalovirus (CMV) and Epstein-Barr virus (EBV) status, Body Mass Index (BMI), and vaccine received on each clinical visit. Information about vaccine outcome is available as geometric mean titers (geo_mean), the difference in the geometric mean titers before and after vaccination (delta_geo_mean), and the difference for each vaccine strain (delta_single). In the last field, each individual is classified as a high and low responder (vaccine_resp). On each visit, samples were analyzed and information about which assays were performed (assay field) and value of the measured analytes (units and data) are stored in the experimental_data table. Finally, the medical_history table describes information connected with each clinical visit about the usage of statins (statin_use) and if influenza vaccine was received in the past (influenza vaccine history), if yes, how many times (total_vaccines_received). Also, we provide information on which type of influenza vaccine was received in the previous years (1 to 5 years prior to enrolment in the clinical study). Lastly, information about influenza infection history and influenza-related hospitalization is provided.

How to cite FluPRINT?

If you use FluPRINT in an academic publication, please use the following citation:

Tomic, A., Tomic, I., Dekker, C.L. et al. The FluPRINT dataset, a multidimensional analysis of the influenza vaccine imprint on the immune system. Sci Data 6, 214 (2019). https://doi.org/10.1038/s41597-019-0213-4

Contact Information

If you are interested to find out more about the FluPRINT, or if you experience any problems with downloading files, please contact us at info@adrianatomic.com.

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.

Provided by 14 state and local health departments through an online assessment, March—April 2016.

P1334d Surveys to measure the effects of campaigns for the promotion of influenza vaccination/ P1334a: questionnaires for general practitioners: organization of r's practice with respect to influenza vaccination of high risk groups/ participation in information meetings/ evaluation of, attitude to influenza vaccination campaign p1334b-g: persons belonging to high-risk groups plus control group: knowledge of the symptoms and risks of influenza and high-risk groups/ experience with influenza and influenza vaccination/ motives for vaccination, intention to get vaccination next time/ perception and appreciation of vaccination campaign. Background variables: basic characteristics/ education Date Submitted: 1997-01-01 Date Submitted: 2007-08-03

BackgroundA novel influenza virus has emerged to produce a global pandemic four times in the past one hundred years, resulting in millions of infections, hospitalizations and deaths. There is substantial uncertainty about when, where and how the next influenza pandemic will occur.MethodsWe developed a novel mathematical model to chart the evolution of an influenza pandemic. We estimate the likely burden of future influenza pandemics through health and economic endpoints. An important component of this is the adequacy of existing hospital-resource capacity. Using a simulated population reflective of Ottawa, Canada, we model the potential impact of a future influenza pandemic under different combinations of pharmaceutical and non-pharmaceutical interventions.ResultsThere was substantial variation in projected pandemic impact and outcomes across intervention scenarios. In a population of 1.2 million, the illness attack rate ranged from 8.4% (all interventions) to 54.5% (no interventions); peak acute care hospital capacity ranged from 0.2% (all interventions) to 13.8% (no interventions); peak ICU capacity ranged from 1.1% (all interventions) to 90.2% (no interventions); and mortality ranged from 11 (all interventions) to 363 deaths (no interventions). Associated estimates of economic burden ranged from CAD $115 million to over $2 billion when extended mass school closure was implemented.DiscussionChildren accounted for a disproportionate number of pandemic infections, particularly in household settings. Pharmaceutical interventions effectively reduced peak and total pandemic burden without affecting timing, while non-pharmaceutical measures delayed and attenuated pandemic wave progression. The timely implementation of a layered intervention bundle appeared likely to protect hospital resource adequacy in Ottawa. The adaptable nature of this model provides value in informing pandemic preparedness policy planning in situations of uncertainty, as scenarios can be updated in real time as more data become available. However—given the inherent uncertainties of model assumptions—results should be interpreted with caution.

RTI in NNF children with cancer during the mass gatherings and flu season

The dataset provides a list of the wild birds identified and submitted under both passive and active (targeted) surveillance programmes in Great Britain for testing for Avian Influenza by the Animal and Plant Health Agency (APHA). The main emphasis in the surveillance programmes is on patrols of designated reserves by skilled wild bird ecologists and wardens to locate “found dead” wild birds, including “mass mortality incidents”. These Warden Patrols continue all-year-round, and are also seasonally targeted in the winter and spring periods (October to March) each year. Members of the public are also asked to remain vigilant for mass bird mortality incidents occurring in any location in GB and report these to the Defra Helpline. The criteria for a “mass mortality incident” are five or more wild birds of any species at any location in England, Scotland and Wales. The dataset contains information on the identity of the bird, the date, location and which samples were taken from those birds and their test results. Please Note: The location represents a 10km x 10km square in which the bird was found. Location data is provided by the submitter and is not verified and, if no location information is available, the location of the testing laboratory may be used. For further information and explanations of the data included in this dataset, please see the data dictionary available for download alongside this dataset. Attribution statement: ©Crown Copyright, APHA 2016