https://mikehulme.org/why-governing-pandemics-using-models-is-dangerous/ 17 hours ago Why Governing Pandemics Using Models is Dangerous by Mike Hulme Do you remember the headline in The Guardian newspaper from 18 December 2021: “UK scientists: Bring in new curbs now or face up to 2 million daily infections”? And whatever happened to the scenario of “6,000 COVID deaths per day”? Reality turned out to be far removed from these ‘scenarios’ emanating from Britain’s leading epidemiologists. Plan B had already started in the week following 8 December – 10 days before The Guardian’s threatening headline – so what “new curbs” were scientists deeming necessary to avoid up to 2 million daily infections? ‘COVID deaths’ are currently running at around 250 per day, in fact around 30 per cent fewer than this if one recognises the difference between dying ‘from’ COVID and dying ‘with’ COVID. There are many things one could say about the ‘reality gap’ that epidemiological models – and promotional media outlets like The Guardian – have created around COVID. One could say that it shows how (certain) media platforms prefer headlines with dramatic numbers and how they seek to amplify the perception of COVID risks for their followers. This follows the well-established theory in social psychology called the Social Amplification of Risk Framework. One could also perhaps say that it shows that scientists’ model-based predictions of virus risk are wrong. This would be a little disingenuous, however, since most epidemiological modellers will point out the uncertainty of the assumptions that they make in their modelling and also that they talk about scenarios (possible outcomes, given certain assumptions) rather than unconditional predictions. But even if one parses the uncertainty and incompleteness of scientists’ knowledge and models, one could nevertheless point to the erosion of public trust that the appearance of such a ‘reality gap’ generates. When media reporting of COVID risks is so wildly out of alignment with reality, publics are entitled to question not just the motives of the journalists involved, but also the way in which modellers justify and communicate their results to media and politicians. Publics are also entitled to ask questions about whether the medical community in the UK are in the grip of ‘pessimism bias’ or even covert racism—when, for example, ignoring early evidence from South African scientists in early December that the Omicron variant was substantially less dangerous than the Delta variant. As it indeed has been. The above observations are all true and important. But the additional point that I emphasise here is one that I have written about several times before, both with respect to the pandemic and to managing climate change risk. Put simply it is this: ‘Do not govern risk using models’. In a very important Commentary published in Nature in June 2020, ‘Five ways to ensure that models serve society: a manifesto’, Andrea Saltelli and colleagues observed that “Mathematical models are a great way to explore questions. They are also a dangerous way to assert answers.” Too often over the past two years using epidemiological models to ‘assert answers’ is exactly what has happened. As the British statistician George Box is often quoted as saying: “Essentially, all models are wrong, but some are useful”. We should not expect models to give correct answers and it is dangerous to think they do or that they can. But in his 1987 book written with Norman Draper Box went on to pose a more important question, the practical question of “How wrong do [models] have to be to be not useful.” What we have seen repeatedly in the case of epidemiological models of COVID, and how they have been promoted by media and used by political interests, is that when models are persistently and egregiously wrong, their predictions need handling with extreme caution. In the Nature Commentary referred to above, Saltelli went on to say, “Asking models for certainty or consensus is more a sign of the difficulties in making controversial decisions than it is a solution, and can invite ritualistic use of quantification.” ‘Ritualistic use of quantification’ is what we are seeing happen with both COVID and climate change risk management. The dangers of trusting in numbers is what Ted Porter warned against in his classic 1995 book ‘Trust in Numbers: The Pursuit of Objectivity in Science and Public Life’. If models—whether climate models or epidemiological models–are used to assert answers to policy choices, or used to substitute for balanced political judgement, they will mis-direct public policy in dangerous ways. Mike Hulme, 22 January 2022
^He's right. Those models are predictions and projections from limited data. When they don't pan out and are swept under a carpet, skepticism is rightfully merited. Yet we're in a climate where skepticism implies a political bias which also amplifies resistance to change or acknowledge model inaccuracy. Totally dumb and infantile behavior. We all know the virus changes and strains will carry different characteristics. Somehow no one can be wrong or admit they're wrong because if they do, then "all the sacrifice and madness was a waste! Such incompetence merits a talk with their manager!!!" None of this is ****ing science. It's more like a game of telephone between bullies and ninnies.
I disagree about what happens when the models don't pan out. When they don't pan out, people that are ignorant on the issue hold them to claim the science can't be trusted, people worried about climate change are alarmists, and climate change isn't really a big deal. In addition there are different types of models. Some model the worst case scenario as opposed to what they predict is most likely. Others have qualifiers such as 'if there is no change', or if a certain bill or program isn't passed or is passed, etc. It isn't just climate change deniers that misuse the models. Those trying to bring more support to the cause use the 'worst case' scenario models in order to Hopefully scare people into caring and being more active. This allows opponents to use the arguments mentioned before when the models don't pan out.
You work with the best data you have. CDC and FDA (and the rest of the world) have never operated under this emergency mode this fast and for this long. They will get things wrong. Everyone has. Is that dangerous? It could be but it's much safer than operating without data or worse, made-up data. Politicians and folks that do not have honest care about public health will politicize this to their advantage and the detriment of public health. Folks that do have honest care about public health (many) will criticize the FDA, CDC, and similar organization in the world - what you will find is the criticism ranges from being too slow and too conservative to being too fast. Either way, these public servants will always be faulted - it's just the nature of their work. That's fine and all and should be welcomed when there is constructive criticism by folks that care. For others that just like to politicize, fk off. ps. I thought the UK models were way too aggressive
This is a big problem not just with COVID but with many things. It's also a cultural problem and a problem of human nature. People want simple answers and they want certainty when with something as big and complicated as a global pandemic from a novel virus there aren't simple answers or certainty. Stuff like this requires patience and understanding. Two things as a society we are very lacking in.
if a family member gets a cold (Omicron) in New Zealand, you have to lock yourself in your house for a month
Right, the US's Covid deaths per capita are 26000% higher than New Zealand's. But, let's say we don't believe any of those numbers, almost all the covid deaths are manipulated and probably aren't actually from Covid right? New Zealand's life expectancy has grown over the last 2 years, due to negative excess deaths (fewer deaths than normal, than expected). The US's life expectancy decreased substantially - down a stunning 1.8 years due to an explosion of excess deaths. Americans' average life expectancy are now 77 years old... a whopping 5.5 years lower than the average New Zealanders life expectancy. Y'know, I tell yah, as a full-blooded don't tread on me American, as a leave it to the state kind of guy, as a pro-lifer, as Christian, as a democracy lover ... it just breaks my heart to see this little country on the other side of the world choose to take these measures, and if I had the power to make this right, well I'd make this democratically elected (OCT 2020) government stop all this evil-doing at once so the people of New Zeland could be ... free.
NZ is only a handful of mass shootings away from having their average life expectancy drop to the gold standard … the USA’s. Cheer up!
If you're acknowledging the reality of there being different models wiith wide range of tolerances and confidences, then isn't it reasonable to question our heavy reliance upon these models and make its ground assumptions more accessible? The writer Mike Hulme mentioned that models aren't necessarily evil, but how we're using it can end up being borderline fraudulent. I'm fine with initial estimates claiming at its high of 2.2 million deaths during 2020, but if we continue to act like it's going to take 2.2 million Americans at the end of the year (not that current models say it will), that's the real problem. These replies about, "well they gotta Stay Strong against the deniers and heathen unvaxxed!!"...that's not ****ing science! We're not in a stadium either, they don't care if we're cheering them on or not. The outsized narratives from charts turns people off and amplifies doubt. I can't control what the deniers say and believe, but I rather still feel like I can trust these modelers and scientists. Ideally, public servants would publish what the models they based their decision at that time, then publish the new models with new facts,assumptions or where they got things wrong. "We thought infection rates would be lower. Why...because some nice old lady said so and wanted to please the president. This year our model changed. We kicked the old lady out, cable news excoriated her orange taint, and new assumptions were made." Here are the abstracts for 2021 and 2022 that are 5 pages each. Well, something more scientific and reasonable but that abstract would likely help many Americans understand rather than say "News said economy go up. Stocks go up up up! Me say Nah, economy still bad." Because the economy really is terrible. If it were great, we'd be having way more babies and there'd be less tone deaf threads about smash n grabs and train heists with weird ps5 references. Bullshit can only last so long until it all goes off the rails.
Just an acceptable trade off for our correct freedom to have a country deeply saturated with guns and poverty. Y’know we should really mandate our correct version of freedom globally so everybody can be free the way I want them to be.
Covid affects different people differently. I got it in March 2020, got vaccinated and just got it again. I’ve been sicker than I’ve ever been (assuming it is omicron). I did think it was mild prior to getting it butbthats what generalizing does
BA.2 is overtaking Omicron variant Various media across Scandinavia and the UK are reporting the emergence of a new Covid variant that is so infectious and spreading so fast that nearly half of all cases in Denmark are now the new mutation, named BA.2, with more than 400 confirmed infections across the UK. The new mutation has reportedly also popped up in Norway, Sweden, Singapore and India. Reuters reports that UK health authorities are investigating 426 confirmed cases of BA.2 in Britain, while officials in Denmark said that just over 45 per cent of all new infections in the country are now the new variant. WHO representatives have rushed to Copenhagen to investigate BA.2, nicknamed 'stealth Omicron' in Danish media as the mutation seems to be pushing the Omicron variant aside fairly quickly.
I think we should separate modelers, scientists from public figures (policymakers, experts). Modelers, scientists publish their data and assumptions. But policymakers and even experts do not always explain them well and the typical person does not understand these models. What the typical person sees is a representation or even a snapshot of it by public officials. They don’t see the ranges, the assumptions, the limitations, and so on. And heck, which one of them cares to see that? The problem is often not with the data but how it is presented. Public officials, who often IMO dumb it down too much, make weird assumptions of public reactions, and don't explain their reasoning well causes these issues. I prefer them to just lay it all out and don't worry too much about how the public reacts.
