Which flu seasons had dominant H3N2 circulation and low vaccine efficacy?

1. What your submission actually asserts — and why it’s unhelpful

The three analysis items you provided all conclude the same factual point: the text excerpts supplied are unrelated to influenza surveillance or vaccine performance, and instead concern programming or operating-system issues. Each entry explicitly states the source material “does not contain relevant information” about flu seasons, H3N2 circulation, or vaccine efficacy. Because every item in the dataset you supplied is irrelevant to the epidemiological claim, there is no basis within these materials to extract which seasons had H3N2 dominance paired with low vaccine effectiveness ^{[1] [2] [3]}. The absence of relevant data in the provided files is itself a verifiable fact: you cannot draw a valid public-health conclusion from unrelated code and programming discussion excerpts.

2. Why the supplied evidence fails to support the claim

All three supplied items are programming-focused posts or debug fragments; none contain surveillance figures, subtype circulation summaries, vaccine effectiveness estimates, or citations to influenza surveillance agencies. This is a fatal gap: identifying a season with dominant H3N2 circulation requires subtype proportion data from surveillance systems, and judging vaccine performance requires VE point estimates with confidence intervals derived from test-negative design studies or national reporting. Because your provided materials include no virologic surveillance tables, no VE studies, and no public-health reports, they cannot be repurposed to answer the question without introducing external sources beyond what you gave ^{[1] [2] [3]}.

3. What data you need to answer the question definitively

To identify seasons that combined H3N2 dominance with low vaccine efficacy you need two linked data streams for each influenza season: (a) subtype circulation proportions by week or season (percentage of influenza A isolates typed as H3N2 versus other subtypes), and (b) vaccine effectiveness estimates from authoritative studies or surveillance reports, ideally stratified by age group and subtype. The minimum acceptable evidence is a national or regional surveillance summary showing H3N2 accounted for a plurality or plurality-plus of isolates that season, plus a published VE estimate for that same season and subtype indicating low effectiveness (for example, VE substantially below historical averages, with supporting confidence intervals). None of that appears in the dataset you submitted ^{[1] [2] [3]}.

4. Where to retrieve authoritative, recent data quickly

Retrieve official surveillance summaries and VE reports from the major public-health actors: national Centers for Disease Control and Prevention pages, World Health Organization seasonal influenza updates, and peer-reviewed influenza VE studies in journals such as Clinical Infectious Diseases or Eurosurveillance. For country-specific insight, consult national public-health agencies’ annual influenza surveillance summaries. Look for seasonal reports that explicitly state the dominant subtype and include VE estimates or reference published VE studies. These sources will provide subtype dominance charts, lab-confirmed proportions, and VE point estimates with confidence intervals, enabling a definitive answer.

5. A practical, reproducible research plan you can run now

Step 1: pull influenza season summaries from the CDC FluView/NREVSS and WHO weekly reports for each season of interest; confirm which seasons show H3N2 listed as the dominant A-subtype. Step 2: compile vaccine effectiveness estimates from the same seasons—use the CDC’s VE summary pages and published test-negative design studies that report subtype-specific VE. Step 3: match seasons where H3N2 dominance and low VE overlap, and document the VE estimate, confidence interval, age stratification, and whether antigenic drift or manufacturing issues were cited. This workflow yields a transparent list of seasons meeting your criteria once populated with the external surveillance and VE sources.

6. Closing assessment and recommended next actions

Given your current dataset, the responsible conclusion is that the claim cannot be evaluated because the provided materials are unrelated to influenza epidemiology. To proceed, I recommend you authorize retrieval of external public-health surveillance reports and peer-reviewed VE studies; with those, I will compile a season-by-season table showing H3N2 dominance and subtype-specific VE estimates and cite the exact sources and dates. If you want, I can now fetch and analyze the CDC, WHO, and peer-reviewed literature to produce the definitive list and dates — confirm and I will proceed. ^{[1] [2] [3]}