This is categorically false.
According to estimates from the CDC, in the United States, H1N1 infected ~61M people and killed ~12,649 for a CFR of around 0.02%.
https://www.cdc.gov/h1n1flu/estimates_2009_h1n1.htm
Stats for the global H1N1 pandemic estimates estimate ~1B people infected with 500,000 deaths. That's a CFR of 0.05% worldwide.
https://en.wikipedia.org/wiki/2009_flu_pandemic
You should stop claiming that the CFR of H1N1 is larger than COVID-19, when all current data and epidemiological estimates show that it absolutely is not.
COVID-19: 0.6% CFR in South Korea (likely similar in other locations when we stop undercounting infections)
These aren't so dramatically different, are they?
But they are: H1N1 had a median death in the US of 37 years old, rather than 80+.
https://www.who.int/immunization/sage/1.Briand_epi_7th_July_...
This seems categorically false.
CDC estimates that ~61M Americans were infected with ~12,469 deaths. That would be a CFR in the US of 0.02%.
The best data sets so far are South Korea and the Princess Cruise ship, because they did widespread testing. Both show a CFR of about 0.6%.
> The fatality rate was greater than Covid-19.
No, it wasn't. The fatality rate for H1N1 was about 0.2%. The fatality rate for covid-19 is tricky to work out at the moment, but 2% is a reasonable low estimate.
https://www.who.int/immunization/sage/1.Briand_epi_7th_July_...
https://www.bmj.com/content/339/bmj.b2840
> At first sight, the data seem to imply that this new virus is relatively mild, with case fatality ratios around 0.5%, similar to the upper range of that seen for seasonal influenza2 and relatively low hospitalisation ratios. However, the case fatality ratio seems to vary substantially between countries, and deaths have occurred in much younger people than is the case for seasonal influenza.3 4
> There are many reasons why simple interpretations of these crude figures at the beginning of a pandemic may be misleading both in terms of assessing severity and in making comparisons between countries. Here, we discuss some of the important mechanisms resulting in biases, propose study designs and associated statistical methods to estimate the case fatality ratio given these limitations, and show their strengths using simulated data. The two main sources of bias in estimates of the case fatality ratio we consider stem from shifts in case ascertainment (over time, efforts may become more focused on the most severe cases, leading to an overestimation of the case fatality ratio) and from the inevitable delay between symptom onset and death, which in the early phase of the epidemic can lead to underestimation of the case fatality ratio if it is not adjusted for.
> A natural definition for the case fatality ratio is the ratio of the total number of deaths from a disease divided by the total number of cases. In a fully ascertained (and complete) epidemic, this simple method works perfectly. However, in most infectious diseases there is underascertainment of cases as people who are asymptomatic or have mild infection will be less likely to present to health care, and if they do present they will be less likely to be tested and confirmed. It is therefore likely that there will be a bias towards diagnosis of more severe cases (fig 1⇓), with the result that the case fatality ratio and other measures of severity are overestimated. Furthermore, this underascertainment will change as an epidemic matures. Initially increased awareness by patients and doctors may lead to high ascertainment, but as cases increase and systems are overwhelmed, only a proportion will be tested (potentially those with links to other confirmed cases), making it difficult to understand the scale of under-reporting.
I just want to remind everyone that H1N1 in 2009 killed between 150-600k people worldwide. Between 10 and 20% of the world population caught it at some point. The fatality rate was greater than Covid-19.
And yet, we got through it without panicking. People don't even give it a second thought today, and it was just 10 years ago.