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.

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

NO LONGER UPDATED.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.

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 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.

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.

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

The microneedle flu vaccine market presents a compelling investment opportunity, projected to reach $2095.3 million in 2025 and exhibiting a steady Compound Annual Growth Rate (CAGR) of 5% from 2025 to 2033. This growth is fueled by several key drivers. Firstly, the inherent advantages of microneedle technology offer painless delivery, improved patient compliance, and reduced reliance on trained medical personnel for administration, making it particularly attractive for mass vaccination campaigns. Secondly, the ongoing threat of influenza outbreaks and the emergence of new strains necessitate innovative and efficient vaccine delivery systems. Microneedle patches offer a significant advancement, enabling efficient, cost-effective, and potentially self-administered vaccinations. The market segmentation reveals strong demand across various microneedle types (solid, hollow, coated, dissolving), with solid microneedles currently dominating. Application-wise, influenza A and B vaccines are leading the charge, although future growth is anticipated across broader applications leveraging the technology’s versatile nature. The geographical distribution reveals robust growth across North America and Europe, driven by high healthcare expenditure and advanced infrastructure, while the Asia-Pacific region is projected to experience significant expansion fueled by increasing population and rising disposable incomes. Competition among established pharmaceutical players such as Sanofi and GC Pharma, alongside innovative biotech companies like NanoPass Technologies and FluGen, contributes to a dynamic and evolving market landscape. The market's restraints are primarily related to the relatively nascent stage of microneedle vaccine technology compared to traditional injection methods. Regulatory hurdles in certain regions and the need for further clinical trials to establish long-term efficacy and safety profiles for diverse populations remain significant challenges. However, the ongoing research and development efforts, coupled with increasing investments in this promising technology, are expected to mitigate these restraints, leading to further market penetration. The consistent 5% CAGR projection suggests a sustained, albeit gradual, market expansion over the forecast period. This indicates that the market will gradually mature, with further market concentration likely as major players consolidate their positions and smaller players seek strategic alliances or acquisitions. The development of multi-strain and even universal flu vaccines delivered via microneedle technology represents a significant opportunity for further market growth in the long term.

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.

This MassBank record with Accession MSBNK-Eawag_Additional_Specs-ET230001 contains the MS2 mass spectrum of Fluconazole (FLU) with the InChIkey RFHAOTPXVQNOHP-UHFFFAOYSA-N.

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

1Source: National Health Interview Survey (NHIS), United States, 2006–2011. The season-cumulative vaccine coverage rates in this table are for summary description only; model employs incremental monthly age-specific values. Estimates of the cumulative monthly proportion vaccinated through end of April of each season were developed using the Kaplan-Meier product limit method for receipt of most recent reported influenza vaccination; estimates based on the NHIS may differ from estimates from other data sources (http://www.cdc.gov/flu/professionals/vaccination/vaccinecoverage.htm).2Estimates for seasons 05/06 - 08/09 represent combined estimates from available studies each season and were calculated as averages weighted by the inverse variance of each study. Sources: 2005/06 season: Belongia 2009, Ohmit 2008, Skowronski 2007. 2006/07 season: Belongia 2009, Skowronski 2009. 2007/08 season: Monto 2009, Belongia 2011, Frey 2010. 2008/09 season: Skowronski 2010, Shay 2011. 2009/10 pandemic season: Griffin et al. 2011. 2010/11 season: Treanor et al. 2012. VE estimates for the 2010/11 season are age-specific. VE estimates for seasons 2005/06-2009/10 are not age-specific, except for a downward adjustment applied to the 65+ age group as follows: VE for the 65+ age group is assumed to be 70% of the VE for the younger age groups.3Source: U.S. Census Bureau, Population Division. Annual Estimates of the Resident Population by Sex and Five-Year Age Groups for the United States.4Source: Centers for Disease Control and Prevention Emerging Infections Program (EIP) 2005–2010. The season-cumulative EIP hospitalization rates in this table are for summary description only; model employs month-specific and age-specific values.5Estimated using EIP hospitalization rates adjusted for underreporting. The underreporting adjustment multiplier was obtained from Reed 2009 and presumed constant across seasons and age categories at 2.7(CI 1.7–4.5).6Based on the estimated number of hospitalizations and age-specific case-hospitalization ratios from Reed 2009.7Based on the estimated number of cases and medically-attended (MA) ratios. The MA ratios used for the 2009/10 season are age-specific; source: Biggerstaff 2012. Age-specific and season-specific MA ratios for the five preceding seasons (2005/06–2009/10) were not available and were presumed constant at 42.0%(CI 37.9%–48.5%); source: Kamimoto 2010.