View the latest data about COVID-19 vaccine in Massachusetts.

Community specific data reports for vaccine administration results, updated weekly, and data from the Public Health (DPH) COVID Community Impact Survey to help target approaches.

This table will no longer be updated after 5/30/2024 given the end of the 2023-2024 viral respiratory vaccine season. This table shows the cumulative number and percentage of CT residents who have received an updated COVID-19 vaccine during the 2023-2024 viral respiratory season by age group (current age). CDC recommends that people get at least one dose of this vaccine to protect against serious illness, whether or not they have had a COVID-19 vaccination before. Children and people with moderate to severe immunosuppression might be recommended more than one dose. For more information on COVID-19 vaccination recommendations, click here. • Data are reported weekly on Thursday and include doses administered to Saturday of the previous week (Sunday – Saturday). All data in this report are preliminary. Data from the previous week may be changed because of delays in reporting, deduplication, or correction of errors. • These analyses are based on data reported to CT WiZ which is the immunization information system for CT. CT providers are required by law to report all doses of vaccine administered. CT WiZ also receives records on CT residents vaccinated in other jurisdictions and by federal entities which share data with CT Wiz electronically. Electronic data exchange is being added jurisdiction-by-jurisdiction. Currently, this includes Rhode Island and New York City but not Massachusetts and New York State. Therefore, doses administered to CT residents in neighboring towns in Massachusetts and New York State will not be included. A full list of the jurisdiction with which CT has established electronic data exchange can be seen at the bottom of this page (https://portal.ct.gov/immunization/Knowledge-Base/Articles/Vaccine-Providers/CT-WiZ-for-Vaccine-Providers-and-Training/Query-and-Response-functionality-in-CT-WiZ?language=en_US) • Population size estimates used to calculate cumulative percentages are based on 2020 DPH provisional census estimates*. • People are included if they have an active jurisdictional status in CT WiZ at the time weekly data are pulled. This excludes people who live out of state, are deceased and a small percentage who have opted out of CT WiZ. DPH Provisional State and County Characteristics Estimates April 1, 2020. Hayes L, Abdellatif E, Jiang Y, Backus K (2022) Connecticut DPH Provisional April 1, 2020, State Population Estimates by 18 age groups, sex, and 6 combined race and ethnicity groups. Connecticut Department of Public Health, Health Statistics & Surveillance, SAR, Hartford, CT.

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

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.

Reporting of Aggregate Case and Death Count data was discontinued May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. Although these data will continue to be publicly available, this dataset will no longer be updated.

This archived public use dataset has 11 data elements reflecting United States COVID-19 community levels for all available counties.

The COVID-19 community levels were developed using a combination of three metrics — new COVID-19 admissions per 100,000 population in the past 7 days, the percent of staffed inpatient beds occupied by COVID-19 patients, and total new COVID-19 cases per 100,000 population in the past 7 days. The COVID-19 community level was determined by the higher of the new admissions and inpatient beds metrics, based on the current level of new cases per 100,000 population in the past 7 days. New COVID-19 admissions and the percent of staffed inpatient beds occupied represent the current potential for strain on the health system. Data on new cases acts as an early warning indicator of potential increases in health system strain in the event of a COVID-19 surge.

Using these data, the COVID-19 community level was classified as low, medium, or high.

COVID-19 Community Levels were used to help communities and individuals make decisions based on their local context and their unique needs. Community vaccination coverage and other local information, like early alerts from surveillance, such as through wastewater or the number of emergency department visits for COVID-19, when available, can also inform decision making for health officials and individuals.

For the most accurate and up-to-date data for any county or state, visit the relevant health department website. COVID Data Tracker may display data that differ from state and local websites. This can be due to differences in how data were collected, how metrics were calculated, or the timing of web updates.

Archived Data Notes:

This dataset was renamed from "United States COVID-19 Community Levels by County as Originally Posted" to "United States COVID-19 Community Levels by County" on March 31, 2022.

March 31, 2022: Column name for county population was changed to “county_population”. No change was made to the data points previous released.

March 31, 2022: New column, “health_service_area_population”, was added to the dataset to denote the total population in the designated Health Service Area based on 2019 Census estimate.

March 31, 2022: FIPS codes for territories American Samoa, Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands were re-formatted to 5-digit numeric for records released on 3/3/2022 to be consistent with other records in the dataset.

March 31, 2022: Changes were made to the text fields in variables “county”, “state”, and “health_service_area” so the formats are consistent across releases.

March 31, 2022: The “%” sign was removed from the text field in column “covid_inpatient_bed_utilization”. No change was made to the data. As indicated in the column description, values in this column represent the percentage of staffed inpatient beds occupied by COVID-19 patients (7-day average).

March 31, 2022: Data values for columns, “county_population”, “health_service_area_number”, and “health_service_area” were backfilled for records released on 2/24/2022. These columns were added since the week of 3/3/2022, thus the values were previously missing for records released the week prior.

April 7, 2022: Updates made to data released on 3/24/2022 for Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands to correct a data mapping error.

April 21, 2022: COVID-19 Community Level (CCL) data released for counties in Nebraska for the week of April 21, 2022 have 3 counties identified in the high category and 37 in the medium category. CDC has been working with state officials to verify the data submitted, as other data systems are not providing alerts for substantial increases in disease transmission or severity in the state.

May 26, 2022: COVID-19 Community Level (CCL) data released for McCracken County, KY for the week of May 5, 2022 have been updated to correct a data processing error. McCracken County, KY should have appeared in the low community level category during the week of May 5, 2022. This correction is reflected in this update.

May 26, 2022: COVID-19 Community Level (CCL) data released for several Florida counties for the week of May 19th, 2022, have been corrected for a data processing error. Of note, Broward, Miami-Dade, Palm Beach Counties should have appeared in the high CCL category, and Osceola County should have appeared in the medium CCL category. These corrections are reflected in this update.

May 26, 2022: COVID-19 Community Level (CCL) data released for Orange County, New York for the week of May 26, 2022 displayed an erroneous case rate of zero and a CCL category of low due to a data source error. This county should have appeared in the medium CCL category.

June 2, 2022: COVID-19 Community Level (CCL) data released for Tolland County, CT for the week of May 26, 2022 have been updated to correct a data processing error. Tolland County, CT should have appeared in the medium community level category during the week of May 26, 2022. This correction is reflected in this update.

June 9, 2022: COVID-19 Community Level (CCL) data released for Tolland County, CT for the week of May 26, 2022 have been updated to correct a misspelling. The medium community level category for Tolland County, CT on the week of May 26, 2022 was misspelled as “meduim” in the data set. This correction is reflected in this update.

June 9, 2022: COVID-19 Community Level (CCL) data released for Mississippi counties for the week of June 9, 2022 should be interpreted with caution due to a reporting cadence change over the Memorial Day holiday that resulted in artificially inflated case rates in the state.

July 7, 2022: COVID-19 Community Level (CCL) data released for Rock County, Minnesota for the week of July 7, 2022 displayed an artificially low case rate and CCL category due to a data source error. This county should have appeared in the high CCL category.

July 14, 2022: COVID-19 Community Level (CCL) data released for Massachusetts counties for the week of July 14, 2022 should be interpreted with caution due to a reporting cadence change that resulted in lower than expected case rates and CCL categories in the state.

July 28, 2022: COVID-19 Community Level (CCL) data released for all Montana counties for the week of July 21, 2022 had case rates of 0 due to a reporting issue. The case rates have been corrected in this update.

July 28, 2022: COVID-19 Community Level (CCL) data released for Alaska for all weeks prior to July 21, 2022 included non-resident cases. The case rates for the time series have been corrected in this update.

July 28, 2022: A laboratory in Nevada reported a backlog of historic COVID-19 cases. As a result, the 7-day case count and rate will be inflated in Clark County, NV for the week of July 28, 2022.

August 4, 2022: COVID-19 Community Level (CCL) data was updated on August 2, 2022 in error during performance testing. Data for the week of July 28, 2022 was changed during this update due to additional case and hospital data as a result of late reporting between July 28, 2022 and August 2, 2022. Since the purpose of this data set is to provide point-in-time views of COVID-19 Community Levels on Thursdays, any changes made to the data set during the August 2, 2022 update have been reverted in this update.

August 4, 2022: COVID-19 Community Level (CCL) data for the week of July 28, 2022 for 8 counties in Utah (Beaver County, Daggett County, Duchesne County, Garfield County, Iron County, Kane County, Uintah County, and Washington County) case data was missing due to data collection issues. CDC and its partners have resolved the issue and the correction is reflected in this update.

August 4, 2022: Due to a reporting cadence change, case rates for all Alabama counties will be lower than expected. As a result, the CCL levels published on August 4, 2022 should be interpreted with caution.

August 11, 2022: COVID-19 Community Level (CCL) data for the week of August 4, 2022 for South Carolina have been updated to correct a data collection error that resulted in incorrect case data. CDC and its partners have resolved the issue and the correction is reflected in this update.

August 18, 2022: COVID-19 Community Level (CCL) data for the week of August 11, 2022 for Connecticut have been updated to correct a data ingestion error that inflated the CT case rates. CDC, in collaboration with CT, has resolved the issue and the correction is reflected in this update.

August 25, 2022: A laboratory in Tennessee reported a backlog of historic COVID-19 cases. As a result, the 7-day case count and rate may be inflated in many counties and the CCLs published on August 25, 2022 should be interpreted with caution.

August 25, 2022: Due to a data source error, the 7-day case rate for St. Louis County, Missouri, is reported as zero in the COVID-19 Community Level data released on August 25, 2022. Therefore, the COVID-19 Community Level for this county should be interpreted with caution.

September 1, 2022: Due to a reporting issue, case rates for all Nebraska counties will include 6 days of data instead of 7 days in the COVID-19 Community Level (CCL) data released on September 1, 2022. Therefore, the CCLs for all Nebraska counties should be interpreted with caution.

September 8, 2022: Due to a data processing error, the case rate for Philadelphia County, Pennsylvania,

This interactive Vaccine Finder application was developed by the Analytics Team in order to help residents find vaccine locations and related information. Please report any problems, suggestions, or feedback to analytics@boston.gov

This open dataset shows data on Cambridge residents who have received a COVID-19 vaccine at any location (e.g., mass vaccination site, pharmacy, doctor's office). These data come from the Massachusetts Department of Public Health's weekly report on vaccine doses administered by municipality. The report is released on Thursdays. This open dataset includes data going back several weeks and complements another open dataset called "Cambridge Vaccine Demographics," which shows data for the latest week (https://data.cambridgema.gov/Public-Health/Cambridge-Vaccination-Demographics/66td-u88k)

The Moderna and Pfizer vaccines require two doses administered at least 28 days apart in order to be fully vaccinated. The J&J (Janssen) vaccine requires a single dose in order to be fully vaccinated.

The category "Residents Who Received at Least One Dose" reflects the total number of individuals in the fully and partially vaccinated categories. That is, this category comprises individuals who have received one or both doses of the Moderna/Pfizer vaccine or have received the single dose J&J (Janssen) vaccine.

The category "Fully Vaccinated Residents" comprises individuals who have received both doses of the Moderna/ Pfizer vaccine or the single-dose J&J vaccine.

The category "Partially Vaccinated Residents" comprises individuals who have received only the first dose of the Moderna/Pfizer vaccine.

Source: Weekly COVID-19 Municipality Vaccination Report. Massachusetts releases updated data each Thursday at 5 p.m.

This open dataset shows data on Cambridge residents who have received a COVID-19 vaccine at any location (e.g., mass vaccination site, pharmacy, doctor's office). These data come from the Massachusetts Department of Public Health's weekly report on vaccine doses administered by municipality. The report is released on Thursdays.
The Moderna and Pfizer vaccines require two doses administered at least 28 days apart in order to be fully vaccinated. The J&J (Janssen) vaccine requires a single dose in order to be fully vaccinated.
The category "Residents Who Received at Least One Dose" reflects the total number of individuals in the fully and partially vaccinated categories. That is, this category comprises individuals who have received one or both doses of the Moderna/Pfizer vaccine or have received the single dose J&J (Janssen) vaccine.
The category "Fully Vaccinated Residents" comprises individuals who have received both doses of the Moderna/ Pfizer vaccine or the single-dose J&J vaccine.
The category "Partially Vaccinated Residents" comprises individuals who have received only the first dose of the Moderna/Pfizer vaccine.
Source: Weekly COVID-19 Municipality Vaccination Report. Massachusetts releases updated data each Thursday at 5 p.m.

The Executive Order is relative to rescinding mandatory employee COVID vaccine or weekly testing. More information: Visit the City Manager's webpage to learn more about the current City Manager and their goals, programs, and initiatives.Informing Worcester is the City of Worcester's open data portal where interested parties can obtain public information at no cost.

COVID-19 Vaccine Bottle Market Outlook

The global COVID-19 vaccine bottle market size was valued at USD 1.2 billion in 2023 and is projected to reach USD 2.1 billion by 2032, at a compound annual growth rate (CAGR) of 6.2%. The growth of this market can be attributed to the ongoing global vaccination efforts against COVID-19, emerging variants of the virus, and the continuous need for booster shots. The increasing demand for vaccine storage solutions and the strategic initiatives taken by pharmaceutical companies and governments worldwide are driving the market growth.

The foremost growth factor is the unparalleled global vaccination campaigns initiated to curb the spread of COVID-19. With billions of doses required worldwide, the demand for vaccine bottles has surged. Governments and international health organizations have launched massive immunization programs, leading to an unprecedented demand for safe and reliable storage solutions for vaccines. Additionally, the continuous emergence of new COVID-19 variants necessitates ongoing vaccination efforts, further propelling the market for vaccine bottles.

Another significant driver is the advancements in pharmaceutical packaging technology. Innovations in material science are leading to the development of more durable, temperature-resistant, and safer vaccine storage solutions. The demand for glass bottles remains high due to their inert nature and high resistance to thermal shock. However, the development of high-grade plastic alternatives is gaining traction, owing to their lightweight and shatterproof properties. Research and development investments in improving the efficacy and safety of vaccine packaging are expected to drive market growth over the forecast period.

Furthermore, the strategic partnerships and collaborations among pharmaceutical companies, governments, and logistics providers are enhancing the distribution efficiency of vaccines. These collaborations ensure the timely and safe delivery of vaccines to the most remote areas, thus increasing the demand for reliable packaging solutions. Investments in cold chain logistics and the establishment of robust distribution networks are pivotal factors augmenting the market growth of COVID-19 vaccine bottles.

Regionally, North America and Europe are expected to be the largest markets owing to their advanced healthcare infrastructure and significant investments in COVID-19 vaccination drives. Asia Pacific is projected to witness the fastest growth, driven by large population bases and increasing government initiatives for mass immunization. Latin America and the Middle East & Africa are also expected to show substantial growth due to improving healthcare systems and international support for vaccination programs.

Material Type Analysis

In the COVID-19 vaccine bottle market, glass remains the most preferred material type, particularly borosilicate glass, due to its non-reactive nature and high thermal stability. Glass bottles are essential in maintaining the integrity of the vaccines over a range of temperatures, making them ideal for the rigorous cold chain logistics required for COVID-19 vaccines. Despite the heavier weight and fragility of glass, its ability to ensure the vaccines' safety and efficacy makes it indispensable. Manufacturers are focusing on producing high-quality glass to meet the stringent safety standards set by health authorities.

Plastic bottles, although less prevalent than glass, are gaining traction due to their durability and lightweight properties. Advances in medical-grade plastics have led to the development of options that are shatterproof and suitable for vaccine storage. The use of plastics like cyclic olefin copolymer (COC) and cyclic olefin polymer (COP) is growing, as they offer excellent transparency, barrier properties, and chemical resistance. The demand for plastic bottles is particularly notable in regions with less stringent cold chain facilities, as they are easier to handle and transport.

The 'Others' category includes emerging materials such as eco-friendly and biodegradable options that are being researched and developed. Although still in the nascent stages, these materials are expected to gain prominence as sustainability becomes a more critical concern. The push towards reducing the environmental impact of medical waste is leading to innovations in biodegradable and recyclable materials, which could eventually capture a significant share of the market.

The competition between glass and plastic is expected to con

The COVID-19 vaccine market, while experiencing a significant surge during the initial pandemic years, is now facing a period of contraction. The market's rapid expansion was driven by urgent global health needs, mass vaccination campaigns, and substantial government investment. However, the deceleration of the CAGR to -37.4% reflects a shift towards a more stable, albeit smaller, market. This downturn is attributable to several factors. Firstly, a large portion of the global population has already received primary vaccination courses, reducing immediate demand. Secondly, the emergence of new variants and the waning efficacy of initial vaccines have led to booster campaigns, but these are less extensive than the initial rollout. Furthermore, ongoing efforts towards developing next-generation vaccines, including those targeting newer variants or offering broader protection, are influencing the market landscape. The market segmentation by type (mRNA, viral vector, etc.) and application (primary vaccination, booster doses) will continue to be crucial factors influencing the market's evolution, with ongoing research and development likely driving future growth in specific segments. The competitive landscape remains highly consolidated with major players like Pfizer, Moderna, Johnson & Johnson, and others holding significant market share. These companies are actively engaged in expanding their vaccine portfolios, securing supply agreements, and exploring new market opportunities, particularly in emerging economies and for long-term vaccination programs. The geographical distribution of the market remains regionally diverse. North America and Europe, with their advanced healthcare systems and early adoption of vaccines, historically held the largest market shares. However, as vaccination programs progress in other regions, we can expect a gradual shift in market share distribution towards Asia Pacific and other developing regions as these areas increase vaccination rates and investment in related healthcare infrastructure. The long-term market will be driven by factors such as the emergence of new variants, the development of updated vaccines, the need for booster shots, and the potential for seasonal COVID-19 vaccination programs. Continued government support and private investment will be crucial for ensuring the long-term viability and sustainability of the COVID-19 vaccine market. Given the potential for future pandemics, the market is likely to remain relevant though significantly reduced compared to its peak during the initial pandemic phases.

Sociodemographic, severe illness risk, and vaccine-type characteristics of study participants.

Inhaled Covid-19 Vaccine Market Outlook

The global inhaled COVID-19 vaccine market size stood at approximately $1.2 billion in 2023 and is projected to reach around $5.8 billion by 2032, with a compound annual growth rate (CAGR) of 19.5% during the forecast period. The rapid growth in this market is primarily driven by the need for more effective and accessible vaccination methods to combat the ongoing pandemic. Additionally, the ease of administration and the potential for better mucosal immunity provided by inhaled vaccines are significant growth factors.

One of the major growth factors for the inhaled COVID-19 vaccine market is the continuous advancements in vaccine technology. Traditional needle-based vaccines often face challenges such as needle-phobia, especially among pediatric and geriatric populations. Inhaled vaccines provide a non-invasive alternative that can enhance patient compliance and thereby increase vaccination rates. Moreover, the ability of inhaled vaccines to induce a stronger mucosal immune response, which is crucial for respiratory pathogens like SARS-CoV-2, makes them highly effective.

Another significant driver of market growth is the logistical advantage of inhaled vaccines. Unlike injectable vaccines, inhaled vaccines do not require cold chain storage, which simplifies distribution and reduces costs. This is particularly beneficial in low and middle-income countries where cold chain infrastructure can be a limiting factor. Furthermore, the ease of self-administration of inhaled vaccines can alleviate the burden on healthcare facilities, making it easier to conduct mass immunization campaigns more efficiently.

The increasing number of collaborations and partnerships between pharmaceutical companies and research institutions is also a catalyst for market expansion. These collaborations are focused on accelerating the development and commercialization of inhaled COVID-19 vaccines. Government initiatives aimed at ensuring equitable access to COVID-19 vaccines are also playing a crucial role in supporting the market. For instance, many countries are investing significantly in the research and development of alternative vaccination methods, including inhaled vaccines, to tackle the pandemic more effectively.

The development of Respiratory Virus Vaccines has become increasingly important in the context of the COVID-19 pandemic. These vaccines are designed to target viruses that primarily affect the respiratory system, providing a critical line of defense against infections like COVID-19. The focus on respiratory virus vaccines is driven by the need to address the unique challenges posed by airborne pathogens, which can spread rapidly and cause widespread outbreaks. By enhancing mucosal immunity, these vaccines offer a promising approach to prevent the transmission of respiratory viruses, thereby playing a crucial role in public health strategies aimed at controlling pandemics.

From a regional perspective, North America and Europe are expected to dominate the market due to their advanced healthcare infrastructure and significant investments in research and development. However, the Asia Pacific region is anticipated to witness the highest CAGR during the forecast period, driven by large populations, increasing healthcare awareness, and supportive government initiatives. The Middle East & Africa and Latin America are also expected to contribute to the market growth, albeit at a slower pace, due to improving healthcare facilities and growing investments in healthcare infrastructure.

Vaccine Type Analysis

The inhaled COVID-19 vaccine market can be segmented based on vaccine type, including live attenuated, protein subunit, viral vector, mRNA, and others. Live attenuated vaccines are considered highly effective due to their ability to induce a strong and long-lasting immune response. These vaccines mimic natural infections, thereby providing robust immunity. However, the development of live attenuated vaccines requires sophisticated technology and extensive safety testing, which can be time-consuming and costly.

Protein subunit vaccines have emerged as a popular choice due to their safety profile. These vaccines contain harmless pieces of the virus (often protein or pieces of protein) which stimulate the immune system without causing disease. The production of protein subunit vaccines is relatively easier and can be scaled up quickly, making them a viable option for mass i

The geometric mean titer of anti-SARS-CoV-2 RBD-IgG antibody after two doses of vaccine against COVID-19 in vaccinees with seroconversion.

Univariate linear regression analysis of willingness to undergo vaccination and information source reliability.

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.

Multivariate linear regression analysis of willingness to undergo vaccination, predictive factors.

This dataset represents preliminary estimates of cumulative U.S. RSV –associated disease burden estimates for the 2024-2025 season, including outpatient visits, hospitalizations, and deaths. Real-time estimates are preliminary and based on continuously collected surveillance data from patients hospitalized with laboratory-confirmed respiratory syncytial virus (RSV) infections. The data come from the Respiratory Syncytial Virus Hospitalization Surveillance Network (RSV-NET), a surveillance platform that captures data from hospitals that serve about 8% of the U.S. population. Each week CDC estimates a range (i.e., lower estimate and an upper estimate) of RSV-associated disease burden estimates that have occurred since October 1, 2024.

Note: Data are preliminary and subject to change as more data become available. Rates for recent RSV-associated hospital admissions are subject to reporting delays; as new data are received each week, previous rates are updated accordingly.

Note: Preliminary burden estimates are not inclusive of data from all RSV-NET sites. Due to model limitations, sites with small sample sizes can impact estimates in unpredictable ways and are excluded for the benefit of model stability. CDC is working to address model limitations and include data from all sites in final burden estimates.

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.