- Welcome
- Data Overview
- How to Use These Data
- Why Are These Data Important?
- What Do These Data Show?
- What Do These Data Not Show?
- Implications for Public Health Practice
- Executive Summary
- Laboratory-Confirmed Cases of Influenza in New York State
- Visits to the Emergency Department that have an Influenza Diagnosis, New York City
- Hospitalizations from the Emergency Department that have an Influenza Diagnosis, New York City
- Influenza Mortality
- Influenza Vaccination Coverage
Published: November 8, 2022
Updated: February 06, 2026 at 06:09PM
Welcome
Welcome to New York Flu Watch, a resource you can use to follow influenza activity across New York State and New York City in near real time. This page brings together multiple open data sources to give you a comprehensive view of laboratory-confirmed influenza cases, emergency department utilization, hospitalizations, mortality, and vaccination coverage. By drawing from statewide and city-level surveillance systems, you can explore how influenza patterns differ across regions, populations, and phases of the season. Whether you are making operational decisions, planning prevention efforts, or simply trying to stay informed, the analyses presented here are intended to help you interpret current trends with clarity and context.
Data Overview
You will find that the data presented on this page represent several complementary surveillance streams, each providing a different perspective on the burden and distribution of influenza. Laboratory-confirmed case data give you insight into virologically confirmed infections, while emergency department and hospitalization data highlight how many people are seeking care for influenza-related illness. Mortality data, drawn from federal vital statistics systems, show you how influenza contributes to severe outcomes across age groups and regions. Vaccination coverage data, collected through national surveys, allow you to assess how well communities are protected as the season unfolds. Together, these datasets form a layered picture of influenza activity, although each dataset has its own structure, strengths, and limitations.
How to Use These Data
You can refer to these data to understand where influenza activity is most prominent, how quickly it is increasing or decreasing, and which groups or geographic areas may be experiencing greater burden. By reviewing the maps, time trends, and age-stratified analyses, you can identify patterns that may warrant closer attention—such as early spikes in activity, regional disparities, or notable changes in healthcare use. These displays also help you compare the current season with historical patterns, an essential step in determining whether observed activity is typical or unusually elevated. Public health departments, hospital systems, and community organizations may find these outputs useful for situational awareness, resource planning, vaccination outreach, and communication with the public. Individuals can also use these data to make informed decisions about preventive behaviors, healthcare-seeking, and vaccination timing.
Why Are These Data Important?
Understanding influenza trends is crucial for protecting both individual and community health, and these data provide you with actionable insight into the timing, spread, and severity of each flu season. The data help you see when influenza begins circulating, how rapidly it grows, and which areas or groups experience higher levels of illness. These patterns support critical planning and response actions, such as reinforcing vaccination messaging, preparing healthcare facilities for increased demand, and identifying communities where additional outreach may be needed. Because influenza varies from year to year—and because vaccination uptake, population immunity, and circulating strains change over time—having timely and transparent data is essential for making informed policy and operational decisions. By following these indicators throughout the season, you can stay aware of emerging risks and understand how influenza is affecting New Yorkers in real time.
What Do These Data Show?
The figures and tables on this page show you how influenza is manifesting across multiple dimensions, including counts, rates, severity, and geographic variation. Laboratory-confirmed case data show where reported infections are occurring and how those patterns shift across weeks, regions, and influenza types. Emergency department visit and hospitalization data highlight the clinical burden of influenza-like illness on healthcare systems, an important marker of severity and system stress that may rise even when testing is incomplete. Mortality data show how influenza contributes to severe outcomes, allowing you to explore differences across age groups and regions. Vaccination coverage data give you a sense of how well-protected various populations may be, and whether coverage levels align with seasonal risk. Taken together, these outputs present a detailed and interconnected view of the influenza landscape.
What Do These Data Not Show?
Although these data provide valuable insights, they do not reflect the full extent of influenza activity in the population. Laboratory-confirmed cases capture only individuals who sought care, were tested, and received results that were reported to surveillance systems; many mild or moderate influenza infections occur outside of healthcare settings and are therefore not counted. Emergency department and hospitalization data reflect only the subset of people who became ill enough to seek urgent or inpatient care, so they do not represent all influenza-like illness in the community. Mortality data report deaths in which influenza or related conditions were identified on death certificates, but they do not capture all influenza-associated deaths, especially in cases where influenza was not tested for or recorded. Vaccination coverage data are based on survey responses and may be subject to recall error, nonresponse bias, or sampling variability. For these reasons, you should consider these data as highly useful indicators—but not complete measures—of influenza activity.
Implications for Public Health Practice
Using these data, you can better anticipate the needs of your community, guide prevention strategies, and strengthen preparedness for periods of increased respiratory illness. Trends in laboratory-confirmed cases and emergency department utilization can help you identify when activity is accelerating and when interventions such as vaccination messaging, testing reminders, or enhanced infection prevention practices may be most effective. Regional and age-specific analyses can help you target resources to populations or areas experiencing disproportionate impact, while vaccination coverage data can support efforts to reduce disparities in protection. Mortality patterns can inform high-level planning by highlighting groups that may be at increased risk for severe outcomes. By integrating these data into ongoing monitoring efforts, you can support timely public health action, promote community resilience, and enhance the overall response to influenza across New York State.
Executive Summary1
Date: February 06, 2026
Subject: Influenza Surveillance Update: Declining Influenza A and Rising Influenza B Activity
Statewide surveillance data indicate that the intense peak of the 2025-2026 influenza season, driven by Influenza A, has passed. Laboratory-confirmed Influenza A cases have been in decline for six consecutive weeks. However, a notable emerging signal is the steady, concurrent increase in Influenza B cases, which reached a season high in the most recent reporting week. This pattern could indicate a late-season wave of influenza activity dominated by a different strain. The overall intensity of the season to date has been substantial compared to recent, non-pandemic years.
The temporal trend in laboratory-confirmed cases illustrates this transition. Influenza A cases peaked during the week ending December 20, 2025, with 71,578 cases reported statewide. Since that peak, cases have decreased by 95.6% to 3,185 cases for the week ending January 31, 2026. Conversely, Influenza B cases have consistently increased, rising from 1,014 cases during the Influenza A peak to a season high of 1,830 cases in the most recent reporting week (+80.5%). This shift in predominant strain is a critical feature of the current epidemiological landscape.
Early-season cumulative data (through October 4, 2025) showed geographic disparities, with the highest incidence rates at that time concentrated in the Long Island (2,264.7 per 100,000) and Mid-Hudson (2,067.8 per 100,000) regions. Syndromic surveillance from New York City, available through November 8, 2025, provided an early signal of increasing activity; the percentage of emergency department visits for influenza more than doubled from 0.23% to 0.47% in a single week. This increase preceded the larger statewide wave of Influenza A. More recent syndromic data are needed to assess the healthcare impact of the current rise in Influenza B. Mortality data for the current season are not yet available, but data from the prior season provide a benchmark, showing a peak of 91 influenza-associated deaths during the week ending December 24, 2022.
These transmission dynamics are occurring in the context of potentially lower population immunity. The most recent influenza vaccine coverage estimates, from February 2025, show a decline in uptake across all age groups compared to the previous season. For instance, coverage among adults aged 50-64 was estimated at 44.8%, a substantial decrease from 51.9% the prior year. Similarly, coverage for children aged 5-12 years declined from 65.0% to 59.5%. While these ecological data do not establish a direct causal link, reduced vaccine-mediated immunity may be a contributing factor to the season’s intensity. Continued close monitoring of Influenza B circulation and its impact on healthcare capacity is recommended.
Laboratory-Confirmed Cases of Influenza in New York State
Spatial Distribution
Incidence
This map displays the number of laboratory-confirmed influenza cases reported in each county during the current flu season. Counties are shaded on a gradient from light to darker color according to the total number of cases, and each county is labeled with its exact count to support quick reference. The design helps highlight geographic differences in reported influenza activity, allowing readers to see where larger or smaller numbers of confirmed cases are concentrated across the state. Because the map reflects only laboratory-confirmed infections, it should be interpreted as an indicator of reported activity rather than a full measure of all influenza illness, which may include many untested or mild cases.
Map Prepared By: Isaac H. Michaels, DrPH
Data Source: Health Data NY
Incidence Rate
This map shows influenza incidence rates—cases per 100,000 residents—calculated for each county during the current flu season. By adjusting counts for population size, the map makes it possible to compare influenza activity more fairly across counties with very different population levels. A color gradient is used to display these rates, and each county is labeled with its rate to aid interpretation. This approach highlights where the burden of influenza is proportionally highest, which can support planning for local prevention, outreach, and preparedness efforts. As with all laboratory-based surveillance, these rates depend on testing practices and healthcare use, which may differ across regions.
Map Prepared By: Isaac H. Michaels, DrPH
Data Source: Health Data NY
Longitudinal Trend
This figure shows weekly counts of laboratory-confirmed influenza cases in New York State, separated by influenza type and displayed across multiple seasons beginning in 2009. Each panel focuses on a single influenza type, allowing readers to follow long-term patterns in circulation for that type. Bars representing previous seasons are shown in one color, while bars for the current season appear in another, making it easy to distinguish this year’s activity from historical trends. Because the display spans more than a decade of surveillance, it provides context for understanding the timing, scale, and variability of influenza seasons. The interactive format includes optional tooltips showing the week ending date, influenza type, and reported case count, supporting deeper exploration of the data. As with the maps, these counts reflect laboratory-confirmed infections and do not capture all influenza illness.
Graph Prepared By: Isaac H. Michaels, DrPH
Data Source: Health Data NY
Seasonality
This figure displays weekly counts of laboratory-confirmed influenza cases across multiple seasons, separated into panels by influenza type. Each line represents a complete flu season, with the current season distinguished through color, alpha level, and point size to make it visually identifiable without obscuring historical context. By aligning all seasons on the CDC week calendar, the figure provides a consistent structure that supports comparisons of timing and relative magnitude across influenza types. The interactive tooltips provide season-specific details when a user hovers over any point, making it easier to connect visual elements with precise numeric values. Overall, the design helps public health practitioners quickly situate current-season activity within a long-term historical framework.
Graph Prepared By: Isaac H. Michaels, DrPH
Data Source: Health Data NY
By Region
This graphic displays influenza case counts across CDC weeks, separated simultaneously by region and influenza type. Organizing the output into a grid allows each region to be read horizontally while influenza types appear in vertical groupings, helping users navigate spatial and virologic differences without mixing scales. Visual distinctions such as color, alpha, and point size differentiate the current season from previous seasons while avoiding clutter in panels that contain many overlapping lines. Axis limits are allowed to vary independently across panels so that smaller regions and less common influenza types are not visually compressed. This structure is useful for identifying regional operational needs, ensuring that quieter panels remain interpretable and that areas with higher activity are not minimized by statewide scaling.
Graph Prepared By: Isaac H. Michaels, DrPH
Data Source: Health Data NY
Regions include the following counties:
Capital Region: Albany, Columbia, Greene, Saratoga, Schenectady, Rensselaer, Warren, Washington
Central New York: Cayuga, Cortland, Madison, Onondaga, Oswego
Finger Lakes: Genesee, Livingston, Monroe, Ontario, Orleans, Seneca, Wayne, Wyoming, Yates
Long Island: Nassau, Suffolk
Mid-Hudson: Dutchess, Orange, Putnam, Rockland, Sullivan, Ulster, Westchester
Mohawk Valley: Fulton, Herkimer, Montgomery, Oneida, Otsego, Schoharie
New York City: Bronx, Kings, New York, Richmond, Queens
North Country: Clinton, Essex, Franklin, Hamilton, Jefferson, Lewis, St. Lawrence
Southern Tier: Broome, Chemung, Chenango, Delaware, Schuyler, Steuben, Tioga, Tompkins
Western New York: Allegany, Cattaraugus, Chautauqua, Erie, Niagara
Cases in New York City, by Age Group
This figure presents weekly case counts for New York City, organized by age group in a single-row grid. Using bars rather than lines emphasizes absolute counts and the week-to-week shifts commonly reviewed in surveillance operations. Placing each age group in its own panel prevents larger age groups from dominating the scale and helps readers evaluate patterns within each demographic category. Aligning the x-axes allows for straightforward temporal comparison across groups without requiring them to share a single y-axis. The design supports age-specific operational planning and communication by presenting counts in a format that can be scanned quickly.
Graph Prepared By: Isaac H. Michaels, DrPH
Data Source: New York City Department of Health and Mental Hygiene
County-Level Data
This table brings together county-level influenza indicators in a format that supports both detailed review and high-level comparison. Columns include counts, incidence rates, and population denominators, along with sparklines that summarize current and historical trends in a compressed, visual form. Color shading highlights counties with higher case counts or rates, while grouped sections under tab spanners organize the table into meaningful conceptual blocks such as cumulative incidence and trend history. Sparklines are particularly useful for spotting unusual seasonal trajectories that might not be immediately apparent from the numeric fields alone. The table’s structure enables users to explore geographic variation efficiently while retaining access to underlying numerical detail.
| Laboratory-Confirmed Influenza in New York State | |||||||||
| Data through week ending: January 31, 2026 | |||||||||
| County |
Current Season Cumulative Incidence |
Current Season Incidence Rate |
Trends |
||||||
|---|---|---|---|---|---|---|---|---|---|
| Influenza A | Influenza B | Influenza Unspecified | Total Cases | Population | Total Cases per 100,000 Population | Current Season (2025-2026) | Previous Seasons (2009-2010 through 2024-2025) | ||
| Capital Region | Albany | 2,686 | 290 | 56 | 3,032 | 319,964 | 947.61 | ||
| Columbia | 485 | 32 | 4 | 521 | 60,299 | 864.03 | |||
| Greene | 508 | 53 | 2 | 563 | 46,903 | 1,200.35 | |||
| Rensselaer | 1,355 | 173 | 59 | 1,587 | 160,749 | 987.25 | |||
| Saratoga | 2,100 | 828 | 82 | 3,010 | 240,360 | 1,252.29 | |||
| Schenectady | 1,970 | 286 | 24 | 2,280 | 162,261 | 1,405.14 | |||
| Warren | 507 | 91 | 60 | 658 | 65,288 | 1,007.84 | |||
| Washington | 516 | 149 | 29 | 694 | 59,839 | 1,159.78 | |||
| Central New York | Cayuga | 1,011 | 27 | 17 | 1,055 | 74,567 | 1,414.83 | ||
| Cortland | 612 | 31 | 1 | 644 | 45,945 | 1,401.68 | |||
| Madison | 685 | 55 | 49 | 789 | 67,072 | 1,176.35 | |||
| Onondaga | 6,005 | 491 | 10 | 6,506 | 469,812 | 1,384.81 | |||
| Oswego | 2,588 | 54 | 2 | 2,644 | 118,305 | 2,234.90 | |||
| Finger Lakes | Genesee | 669 | 7 | 0 | 676 | 57,604 | 1,173.53 | ||
| Livingston | 722 | 14 | 0 | 736 | 61,561 | 1,195.56 | |||
| Monroe | 10,947 | 297 | 2 | 11,246 | 752,202 | 1,495.08 | |||
| Ontario | 1,492 | 95 | 1 | 1,588 | 113,012 | 1,405.16 | |||
| Orleans | 437 | 6 | 0 | 443 | 39,686 | 1,116.26 | |||
| Seneca | 360 | 3 | 0 | 363 | 32,650 | 1,111.79 | |||
| Wayne | 1,293 | 18 | 0 | 1,311 | 90,757 | 1,444.52 | |||
| Wyoming | 337 | 6 | 0 | 343 | 39,588 | 866.42 | |||
| Yates | 171 | 4 | 0 | 175 | 24,387 | 717.60 | |||
| Long Island | Nassau | 30,092 | 1,314 | 2,109 | 33,515 | 1,392,438 | 2,406.93 | ||
| Suffolk | 29,061 | 1,146 | 2,595 | 32,802 | 1,535,909 | 2,135.67 | |||
| Mid-Hudson | Dutchess | 4,212 | 151 | 391 | 4,754 | 299,963 | 1,584.86 | ||
| Orange | 8,660 | 232 | 15 | 8,907 | 411,767 | 2,163.12 | |||
| Putnam | 2,581 | 43 | 144 | 2,768 | 98,409 | 2,812.75 | |||
| Rockland | 6,258 | 217 | 7 | 6,482 | 348,144 | 1,861.87 | |||
| Sullivan | 1,784 | 11 | 5 | 1,800 | 80,450 | 2,237.41 | |||
| Ulster | 1,822 | 109 | 72 | 2,003 | 182,977 | 1,094.67 | |||
| Westchester | 21,874 | 599 | 1,022 | 23,495 | 1,006,447 | 2,334.45 | |||
| Mohawk Valley | Fulton | 755 | 58 | 0 | 813 | 52,073 | 1,561.27 | ||
| Herkimer | 761 | 31 | 0 | 792 | 59,585 | 1,329.19 | |||
| Montgomery | 748 | 91 | 0 | 839 | 49,648 | 1,689.90 | |||
| Oneida | 3,852 | 144 | 47 | 4,043 | 228,347 | 1,770.55 | |||
| Otsego | 852 | 30 | 0 | 882 | 60,524 | 1,457.27 | |||
| Schoharie | 419 | 30 | 2 | 451 | 30,151 | 1,495.80 | |||
| New York City | Bronx | 29,945 | 700 | 21 | 30,666 | 1,384,724 | 2,214.59 | ||
| Kings | 41,735 | 909 | 139 | 42,783 | 2,617,631 | 1,634.42 | |||
| New York | 19,565 | 667 | 154 | 20,386 | 1,660,664 | 1,227.58 | |||
| Queens | 40,433 | 996 | 1,058 | 42,487 | 2,316,841 | 1,833.83 | |||
| Richmond | 8,611 | 272 | 909 | 9,792 | 498,212 | 1,965.43 | |||
| North Country | Clinton | 579 | 14 | 17 | 610 | 77,871 | 783.35 | ||
| Essex | 318 | 12 | 4 | 334 | 36,744 | 908.99 | |||
| Franklin | 533 | 22 | 0 | 555 | 47,086 | 1,178.69 | |||
| Hamilton | 34 | 1 | 0 | 35 | 5,082 | 688.71 | |||
| Jefferson | 1,547 | 35 | 18 | 1,600 | 113,140 | 1,414.18 | |||