- In real-world, the Tet Offensive was a disaster for the Viet Cong and the NVA regulars. In narrative-world, though, it changed everything. North Vietnam wasn’t on the “verge of surrender”. We weren’t “winning the hearts and minds” of the Vietnamese people. What everyone knew that everyone knew about the Vietnam War changed on a dime.
The Tet Offensive changed our Common Knowledge about the Vietnam War.
We are one photograph like this from Common Knowledge about nCov2019 changing in exactly the same way.
I made it halfway through Matterhorn. I should pick it up again. It is to Full Metal Jacket and Platoon what Deadwood is to Young Guns.
I think it's too few data points to say gotcha, but the Chinese manipulate data they share with the world as a matter of course. It would be exceptional if these data were truthful. I'm not sure about this. It depends on how bad the reality is. China can hide a lot. That said if people aren't reporting back to work, they aren't reporting back to work.We are one photograph like this from Common Knowledge about nCov2019 changing in exactly the same way.
No, they argued that the best fit of reported results does not match the best fit of other pandemics. A linear fit would not correlate as well, and an exponential fit does not correlate as well, either (and an exponential fit is the expected fit based on epidemiology). China was reporting 10,000 infected while the Lancet was estimating 75,000 so skepticism about the numbers is hardly new. A death rate under 2% is unimportant if the rate of infection is low and the impacted population is small. As of two weeks ago, 35 million people in China were under quarantine. Assume that eventually, 10% of the quarantined population becomes infected. Seventy thousand people will die that wouldn't have otherwise. Ordinary flu has, for the past several years, had a mortality rate of 0.05%. https://www.livescience.com/new-coronavirus-compare-with-flu.html Presume COVID-19 is ten percent more infectious than garden-variety flu and ten percent more lethal. That's still raggedy-bad. Presume instead that the current estimates (R0 2.2 compared to flu's R0 1.3 and 2% mortality compared to flu's 0.05%). That is beyond raggedy-bad. No doubt: it ain't airborne rabies or weaponized ebola but the whole point of the argument is that there is ample reason to believe China is under-reporting.So far, the new coronavirus, dubbed 2019-nCoV, has led to more than 20,000 illnesses and 427 deaths in China, as well as more than 200 illnesses and two deaths outside of mainland China. But that's nothing compared with the flu, also called influenza. In the U.S. alone, the flu has already caused an estimated 19 million illnesses, 180,000 hospitalizations and 10,000 deaths this season, according to the Centers for Disease Control and Prevention (CDC).
So you're going to throw up a bunch of matlab? LaTEX? Mathematica? And say look everyone else is an idiot? R0 is a power function. Period. It is defined as a power function. That's not a "cartoon" that's a definition. It is a coefficient of exponential growth. And while the argument was made in the article that certainly a curve could be fit using a power function, the point was that the numbers being generated clearly weren't. As to your model, you picked a tough one to get mathy on: So even in an article where the argument R0 is tough to estimate and really only useful to discuss indigenous spread where it is measured, you didn't get within a mile of anyone's published estimates. And for the record? One of the hallmarks of the 2009 Mexican Swine Flu outbreak was uncertainty and doubt about the numbers. So much so that I opted to drive from LA to Seattle because I had been told they were going to close the airports. Indeed. I have a pretty good idea what point you're trying to make? But I can't say that you've made it.For exam-ple, among individual states in India, the reproductive number for 2009 H1N1 ranged from 1.03to 1.75; likewise, estimates in Peru spanned from 1.2 to 2.2 depending on the specific region studied. Even close geographic neighbors had disparate R0 estimates; China estimated a mean R0 of 1.68, whereas Japan initially approximated a mean of 2.3,which was later reduced to 1.21 to1.35. Correspondingly, in Canada the mean estimate was 1.31 whereas public health officials in the United States initially esti-mated between 2.2 and 2.3, which was subsequently refined to1.7 to 1.8 with additional data collection. On the other hand,not all subsequent estimates of were downwardly biased. Fraser et al. were among the first to estimate the R0 in Mexico, proposing a basic reproductive number of 1.4 to 1.6. Just several months later, another team estimated the R0 was between 2.3 and 2.9.
Looks like the Mexican Government has got some 'splainin' to do.
Let's review: Three days ago, the argument was that China was lying about the numbers. Two days ago, your argument was that your analysis proves that nobody knows anything about numbers and nobody should accuse anyone of lying about numbers. Today, your argument is that your analysis is more work than anyone else's numbers (never mind that it's spurious) and one, two, three, four, five, six paragraphs about how everyone's numbers are bullshit. "borderline racist bullshit" in fact. Meanwhile, China has been dismissing party officials and revised their count steeply upward. The argument at the outset was that China was pushing a false narrative. Three days later, China has relieved party heads and has changed their narrative.