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NO LONGER UPDATED.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.

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.

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.

Protein phosphorylation is a common post-translational modification in eukaryotic cells and has a wide range of functional effects. Here, we used mass spectrometry to search for phosphorylated residues in all the proteins of influenza A and B viruses – to the best of our knowledge, the first time such a comprehensive approach has been applied to a virus. We identified 36 novel phosphorylation sites, as well as confirming 3 previously-identified sites. N-terminal processing and ubiquitination of viral proteins was also detected. Phosphorylation was detected in the polymerase proteins (PB2, PB1 and PA), glycoproteins (HA and NA), nucleoprotein (NP), matrix protein (M1), ion channel (M2), non-structural protein (NS1) and nuclear export protein (NEP). Many of the phosphorylation sites detected were conserved between influenza virus genera, indicating the fundamental importance of phosphorylation for all influenza viruses. Their structural context indicates roles for phosphorylation in regulating viral entry and exit (HA and NA); nuclear localisation (PB2, M1, NP, NS1 and, through NP and NEP, of the viral RNA genome); and protein multimerisation (NS1 dimers, M2 tetramers and NP oligomers). Using reverse genetics we show that for NP of influenza A viruses phosphorylation sites in the N-terminal NLS are important for viral growth, whereas mutating sites in the C-terminus has little or no effect. Mutating phosphorylation sites in the oligomerisation domains of NP inhibits viral growth and in some cases transcription and replication of the viral RNA genome. However, constitutive phosphorylation of these sites is not optimal. Taken together, the conservation, structural context and functional significance of phosphorylation sites implies a key role for phosphorylation in influenza biology. By identifying phosphorylation sites throughout the proteomes of influenza A and B viruses we provide a framework for further study of phosphorylation events in the viral life cycle and suggest a range of potential antiviral targets.

The data regarding the demographics of SARS-CoV-2 in the pediatric population has been published based on several single-center experiences or on metanalyses over short time frames. This article reports data on the demographics of pediatric patients with COVID-19 on a global scale using the TriNetX COVID-19 Research Network. In addition, we examined the risk of COVID-19 infection in relation to the body mass index (BMI) category and the protective value of influenza and COVID-19 immunization against COVID-19 infection. The incidence of COVID-19 infection was higher in the younger age group (≤6 years old), but no gender differences. The incidence of COVID-19 infection was higher among African Americans/Black race (28.57%) White race (27.10%), and obese patients; across all age groups, all genders, all races, and ethnicities (p 

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 dataset 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.

Explore the Mass Vaccination Clinic Software Market's rapid growth, driven by essential public health needs. Discover market size, CAGR, key drivers, and trends impacting vaccination management solutions.

The microneedle flu vaccine market is poised for significant growth, reaching $2095.3 million by 2025 and expanding at a 5% CAGR. Discover key trends, drivers, and restraints shaping this innovative vaccine delivery system, including leading companies and regional market analysis. Learn about the potential of microneedle technology for influenza A, B, and future vaccine applications.

To determine the sequences of the Flu HA1 and HA2 proteins, we performed intact mass LC-MS analysis. By reducing disulfide bridges two peaks corresponding to the HA1 and HA2 components were detected. Mass profiles were obtained for both glycosylated and deglycosylated HA proteins.

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.

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.

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

To determine the sequences of the Flu HA1 and HA2 proteins, we performed intact mass LC-MS analysis. By reducing disulfide bridges two peaks corresponding to the HA1 and HA2 components were detected. Mass profiles were obtained for both glycosylated and deglycosylated HA proteins.

BackgroundInfluenza viruses with pandemic potential and possible burden of post-viral sequelae are a global concern. To prepare for future pandemics and the development of improved vaccines, it is vital to identify the immunological changes underlying influenza disease severity.MethodsWe combined unsupervised high-dimensional single-cell mass cytometry with gene expression analyses, plasma CXCL13 measurements, and antigen-specific immune cell assays to characterize the immune profiles of hospitalized patients with severe and moderate seasonal influenza disease during active infection and at 6-month follow-up. We used age-matched healthy donors as controls.ResultsSevere disease was associated with a distinct immune profile, including lower frequencies of ICOS+ mucosal-associated invariant T (MAIT) cells, and CXCR5+ memory B and CD4+CXCR5+CD95+ICOS+ and CD8+CXCR3+CD95+PD-1+TIGIT+ memory T cells, as well as lower CD4 gene expression. Higher frequencies of CD16+CD161+ NK cells, CD169+ monocytes, CD123+/− dendritic cells, and CD38high plasma cells and high CXCL13 plasma levels were also associated with severe disease. Alterations in immune cell subpopulations persisted at convalescence for the severely ill patients only.ConclusionsOur results indicated a reduction in regulatory MAIT cells and memory T and B cells and an increase in the inhibitory subpopulations of monocytes and NK cells in severe influenza that persisted at convalescence. These immune cell alterations were associated with higher age and the presence of several underlying conditions that may contribute to frailty. This study illustrates the power and sensitivity of high-dimensional single-cell analyses in identifying potential cellular biomarkers for disease severity after influenza infection.

Information about school immunization requirements and data

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. {"references": ["Tomic, A., I. Tomic, C. L. Dekker, H. T. Maecker, and M. M. Davis. 2019. The FluPRINT dataset, a multidimensional analysis of the influenza vaccine imprint on the immune system. BioRxiv Preprint https://doi.org/10.1101/564062."]}

Seasonal epidemics caused by influenza viruses are a major public health concern that result in mild to severe respiratory infections in humans. There is significant morbidity and mortality associated with influenza, particularly among adults aged 65 or older. While three types of effective vaccines (inactivated, live attenuated, and recombinant hemagglutinin antigen (HA) vaccines) have been developed to help improve health outcomes, vaccine response remains poor, particularly in older populations. In this study, we use a combination of transcriptomics and untargeted metabolomics to define signatures of high and low antibody response after vaccination against influenza in a cohort of young and older adults. Through this multi-omics approach, we identify age-related molecular markers associated with influenza vaccine response. Uncovering these molecular profiles will help to identify populations at risk of influenza vaccine failure, as well as to inform design of vaccine development trials to improve vaccine efficacy.