But recently, we have become quite good at weather predictions 7 days out, and even for 14 days are now significantly better than chance.

Disease models can be useful even in the absence of predictive power. Getting any one of a few dozens assumption wrong can throw your prediction off by orders of magnitude. But it still allows you to, for example, explore how sensitive the epidemic is to different policy alternatives.

Besides: what are the alternatives? As long as you do anything, that action is based on some “model” of how the world works, how people behave, what value you assign to competing objectives. Writing that model-in-your-Head down or implementing it in software is strictly better than not doing so: it forces you to be explicit about the assumptions you make, it allows people to cooperate, it forces them to be specific in any criticism, it is a far better tool to communicate your reasoning to people affected by it, it deals in real numbers and will quickly expose any significant oversights you might otherwise miss, it’s accuracy can be measured and thereby improved...

The article does mention most of the parameters; but we don't know what values to assign to those parameters. The last scientific writing that I was reading speculated about the R_0, but I believe we are still not sure about that. In any case, the point of the parameters is to find R_0, so we cannot expect to have an accurate R_0 without them.

We also don't know mortality rates in a general population (look at Italy vs. China, when specifying to age groups). But anyway, all I wanted to say is that our models are simplistic, but surprisingly useful.

The models can still be useful though, e.g. 'we must reduce r0 below r_critical to eliminate spontaneous spreading given herd immunity %'s' is worth knowing.

Edit: people down voting this, please look at definition and those papers. R_0 does changes.

R0 is assumed to be a constant so that the math / models work. But it really changes dramatically in practice. Maybe someone else will eventually make a model where the constant is more... constant. But until then, we will continue to use this model.

When I've looked into it, I've found it difficult to assess the degree to which we are looking at "behavior changes between seasons" vs "the season matters".

Reasons the season might matter: Thicker air (higher humidity) may reduce particulate spread. Viral lifespan may be different under different temperatures.

Yet, in researching the issue, I tend to find things like "well, in animal models, this is what we've seen". Or "we think the heat reduces the effectiveness of a protective membrane that the flu uses".

Despite some effort, I've not found sources that both (1) seem reliable, and (2) provide a clear/concise understanding of what we know and why/how we think we know these things.

They stuck a bunch of guinea pigs into a box, one box at 41F, a 2nd at 68F, and a third at 86F.

The box with 41F had the highest transmission rate. The box at 68F and 86F had lower transmission rates, even 0% transmission rate in the presence of 80% humidity.

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No such experiment exists for COVID19 yet. But because COVID19 continues to exponentially grow in the southern hemisphere (Australia in particular), there doesn't seem to be a relationship between temperature, humidity, and COVID19 like there is with the flu.

[1] https://www.theatlantic.com/technology/archive/2020/04/coron...

Sure, there is lots that can be said about COVID-19. Epidemiology is a fairly well-studied field, and we have experience with aerosol-transmitted respiratory viruses. We have a pretty good feel for the transmissibility, getting better all the time. Pretty good feel for the disease course, and getting more specific about the different phases of treatment all the time. Pretty good feel for the R0 in many conditions, and the serial interval, and so on.

"Is there anything that can be said with certainty" is a dangerous position to be pushing on the public. It is the very thing that autocrats and dictators try to induce in populations through propaganda and disinformation, so that reason and self-help are surrendered. When there is no way to know truth, truth is what the Leader says it is. And we don't want to go there.

That isn't my (outsider) understanding, could you provide a reference? By reference I mean a recent survey that demonstrates the specific limitations?

My understanding of the Imperial College model is that it uses a 30m*30m grid of the world and the expected people in the world and various additions to simulate schools and workplaces. Maybe you know better?

Like, people in this forum assume models are done by data analysts with no special additional knowledge about domain.

Then there were models that specifically searched best case scenario or worst case scenario. The assumptions were clearly stated too, so you was able to determine whether you agree with then or not.

They made predictions about asymptomatic cases before those were actually measured. They made predictions before those hit the news.

They were also pretty open about unknowns and explicitely said what they are not trying to predict.

CT Bergstrom (UW bio prof in communication with IHME team) lays it out in his rapid peer review here: https://twitter.com/CT_Bergstrom/status/1243837050253496320

The uncertainty intervals are pretty confusing even to educated users trying to understand the results in good faith. The entire model used some version of the Wuhan intervention as a prior, and only the uncertainty in fitting curves to that prior is represented in the intervals.

The problem is that the overwhelming majority of the uncertainty in actual outcome doesn't come from the curve-fitting uncertainty, but rather what prior to fit a curve to.

The IHME model seems OK for what it does, but I'm baffled as to how it become the most-cited tool that we have. It's totally inflexible, and pretty confusing in terms of what it actually represents. Its overestimates are now being used as a bludgeon against people that take the virus seriously, which IMO is a major issue and I think wrong, but difficult to refute given how inflexible and confusing the model is.

I don't mean to imply epidemiologists are bad at their jobs! Some areas of science are just like that. It's similarly hard to predict from first principles how effective a new drug will be or what properties a new material will have. But I've seen a lot of people say "we've gotta do suchandsuch because this model I saw projected it as the best option", and that's not a level of confidence these models can actually provide.

"Therefore, characteristics of mixing networks—and how these deviate from the random-mixing norm—have become important applied concerns that may enhance the understanding and prediction of epidemic patterns and intervention measures." [1]

And as a follow-on, that is why there is so much discussion about using mobile apps for contact tracing - it builds the network for you, passively.

1 - https://royalsocietypublishing.org/doi/10.1098/rsif.2005.005...

http://longnow.org/essays/richard-feynman-connection-machine...

https://en.wikipedia.org/wiki/Butterfly_effect

https://en.wikipedia.org/wiki/Monarch_Butterfly_Biosphere_Re...