Wikipedia:Reference desk/Archives/Science/2020 May 2

= May 2 =

Is there a minimum practical size (thermochemically) for a blast furnace?
I know there are minimum economic sizes, with considerations for fuel and maintenance costs. I'm not asking about that.

From a thermal/chemical standpoint, there can be other considerations. For instance, you can't scale a bloomery furnace much below 30cm in inside diameter for reasons of fuel element size, air injection, and thermal loss through the (~10cm) clay walls. That means that in practice, the minimal size creates maximum bloom sizes between 2 and 20kg

I'm wondering if there are similar low-end constraints on a traditional (tall, brick, cast-iron-producing) coal+iron ore(+ lime) blast furnace. Anyone know?

Riventree (talk) 01:18, 2 May 2020 (UTC)
 * Making a miniature version of a full production-capable Blast furnace would mean sacrificing all Economies of scale and require much effort to assemble special tiny fire bricks, flues, bellows, etc., the ingredients would need to be unusually finely powdered and metered, and it would not be easy to keep a very small quantity of coal burning. However there is no lower size limit on performing the essential Smelting operation on Iron ore in a laboratory Crucible. DroneB (talk) 18:57, 2 May 2020 (UTC)

Maths question on pandemic graphs
In the pandemic graph, with time (t) on the x axis and infected people (c) on the y axis, I understand that the area under the curve remains the same if you reduce R(t) on the exponential curve. So in other words, it’s modelling the spreading out of the same number of infections over a longer period of time, in order to lower and delay the peak which I’ll call C-max. So how do you model this for countries which have eradicated this virus? The countries which locked down early seem to have reduced cases to 0 after a short period of lockdown but unless I’ve misunderstood, number of infections would have continued to rise if lockdown had not been imposed and so I don’t see how infected number of people would be the same by the end. So when you model this, which variable do you change? Or is it that this model becomes less accurate at the lower numbers of c since it will just tend towards infinity? 90.196.238.188 (talk) 13:15, 2 May 2020 (UTC)
 * Is it simply that the model assumes that in both scenarios (restrictions vs no restrictions) the same number of infections will occur which in reality wouldn’t be true? Or that R(t) gets closer and closer to 0 as the virus finds less people to infect? If this was the case, would this be so for every country? That R(t) will start tending towards 0 if the same measures are kept in place? 90.196.238.188 (talk) 13:30, 2 May 2020 (UTC)


 * We do not know enough to create a mathematical model with any certainty that it reflects the situation of the current pandemic, but an extremely simplistic model with a stationary population (ignoring demographics – so no births and no deaths from other causes) may help to clarify some things. Assumption 1 is that everyone is susceptible and may get infected if in contact with an infected person. Assumption 2 is that even vigorous contact tracing and isolation of contacts cannot entirely eliminate spreading (since they may have been infectious before they were identified and isolated). Assumption 3: no one (think people at high risk) can be kept indefinitely in isolation with zero chance of being infected. Under these assumptions, it is possible that the disease may get completely eradicated, like smallpox, before everyone is immune or deceased. But if we add assumption 4 that the disease will remain endemic with a low but non-zero lower bound on its incidence until no one is left to be infected, then eventually everyone will get infected, if not sooner then later, possibly much later. The total area under the curve of new cases against time (with linear scales and 0 at the bottom of the y-axis) is proportional to the total fraction of the population that has been infected. If that is the whole population, then all you can do is alter its shape. To explain the apparent paradox: the curve may have a thin but very long tail; its thinness is canceled out by its length. But if the disease is totally eradicated, that area may be genuinely smaller. --Lambiam 15:36, 2 May 2020 (UTC)
 * Thanks, appreciate this is a very simplified model. So what would be the best way to represent that 4th assumption in this very simplified mathematical model? In my mind, if R(t) is less than 1, the number of infections will always tend towards 0? Do you basically make the assumption that R(t) will be just below 1? I know in reality, there’s a lot more to take account of but keeping with the simplified model.  90.196.238.188 (talk) 16:05, 2 May 2020 (UTC)
 * I guess representing what you’ve described mathematically would mean that the curve would tend towards infinity? So how do you vary R(t) to model this? — Preceding unsigned comment added by 90.196.238.188 (talk) 16:08, 2 May 2020 (UTC)


 * If we are working with proportions of the total population, so that the total solution corresponds to 1 (100%), the area under the curve is bounded between 0 and 1, inclusive. The effective reproduction number $$R_\mathrm{e}$$ at time $$t$$ can be found by multiplying the basic reproduction number $$R_0$$ by the fraction $$S_t$$ of the population that is susceptible at time $$t$$. This is described at Herd immunity; it ought to be mentioned also in the article on Mathematical modelling of infectious disease.


 * We can partition the total population into three compartments: $$S$$ for susceptible, $$I$$ for infectious, and $$H$$ for the rest, hopefully healed, but in any case the cumulative fraction that has at some time been infectious. At all times they sum up to the whole population: $$S_t + I_t + H_t = 1$$. Individuals can transition from $$S$$ to $$I$$, and from $$I$$ to $$H$$. The transition rate from from $$S$$ to $$I$$ is given by $$\mu R_0 S_t I_t .$$ The factor $$\mu$$ is needed because an individual's infectiousness lasts several days, and $$R_0$$ is the cumulative effect over that whole period. If we take one day as the unit of time, and we assume that an infected individual remains on the average infectious for 10 days, a reasonable value is $$\mu = 0.1$$, the reciprocal of the infectious period. The transition rate from from $$I$$ to $$H$$ should depend on how long the members of compartment $$I$$ have been there, but for simplicity let us assume the continuous equivalent of a Poisson process and set the rate at $$\mu I_t .$$ Together this gives the coupled differential equations
 * $$\frac{d}{dt}S_t = - \mu R_0 S_t I_t ;$$
 * $$\frac{d}{dt}I_t = \mu R_0 S_t I_t - \mu I_t ;$$
 * We can eliminate $$I$$ by solving the first equation for $$I$$ and substituting the solution into the second:
 * $$\frac{d}{dt} \left( \frac{1}{S_t} \frac{d}{dt}S_t \right) = - \mu \left( \frac{1}{S_t} \frac{d}{dt}S_t - R_0  \frac{d}{dt}S_t \right) .$$
 * I don't know if this can be solved analytically. I have simulated the process by a simple difference method: repeatedly replacing $$t$$ by $$t+\Delta t$$, $$S_t$$ by $$S_t+\Delta S_t$$, and $$I_t$$ by $$I_t+\Delta I_t$$, where
 * $$\Delta S_t = - \mu R_0 S_t I_t \Delta t ,$$
 * $$\Delta I_t = (\mu R_0 S_t I_t - \mu I_t) \Delta t .$$
 * I set $$\Delta t = 0.01$$; smaller values give negligeably different results. To start the epidemic, we need a value for $$I_0$$. I used $$I_0 = 0.0002$$; the final outcome is not particularly sensitive to this, except for very small values of $$R_0$$. $H_0 = 0$, so $$S_0 = 1 - I_0$$. In the end, $$I_t$$ tends to 0. I stopped the process 1000 steps (10 virtual days) after its value dropped below $$5 {\cdot} 10^{-9}$$.


 * Other than I had expected, $$H_\infty$$ was substantially lower than $$1$$ for moderate values of $$R_0$$; for example, $$R_0 = 1.2$$ resulted in $$H_\infty = 0.31448$$, reached in 1201 days. For large values of $$R_0$$, such as $$R_0 > 4$$, I saw that $$H_\infty \approx 1 - \exp (-R_0)$$. For very small $$R_0$$, on the other hand, $$H_\infty \approx I_0(1+R_0)$$. I have not attempted to see if these observations have a theoretical explanation. --Lambiam 18:06, 3 May 2020 (UTC)

Thanks for the detailed answer. I see for simplicity you used differential equations. I’m guessing in reality, such complex modelling would require the use of matrices. Do you know if any of the studies have released the source code? 90.196.238.188 (talk) 19:07, 3 May 2020 (UTC)


 * On YouTube there are several videos explaining mathematical models and simulation models for epidemics. Looking at the thumbnail search results I saw that the toy model I developed above is actually a well-known one (not surprising, in view of its simplicity), and is known as the SIR Model, S and I as above and R for Recovered (or Removed) instead of H as I used. (I rejected the obvious R because of potential confusion with the R of reproduction number.) What is particularly unrealistic about this model is the assumption of homogeneous mixing. The major axes of refinement, needed to make this type of model more realistic, are demographic segmenting (some groups of people are more at risk than others) and spatial segmenting (network models representing different areas and travel between them, as well as different behaviour patterns inn different areas). For the long run, you need to remove the assumption that there are no R → S transitions – immunity does not last forever. You then also need to throw in demographic dynamics, including births and deaths. One problem is that for many coefficients you don't have the data, so modellers need to use guesstimates, which they should vary to get a feeling for the model's sensitivity to variations in these guesses. Another one is that they depend on national policies or guidelines which may unpredictably change. If you have enough compute power, you may want to use stochastic models instead of the (deterministic) differential models, or even, with supercomputer power, run simulations of a particle model in which individuals are represented as moving particles that interact and change in time. --Lambiam 05:58, 4 May 2020 (UTC)

Difference in assumptions in mathematical pandemic modelling?
Slightly related to my previous question, am I correct in understanding that Sweden’s model and the UK’s model simply makes different assumptions? So on the R(0) value, infection fatality ratio and whether there will be an intervention which can stop spread before it passes through the population (this last one seems to be the key one which is driving Sweden’s response)? 90.196.238.188 (talk) 21:54, 2 May 2020 (UTC)


 * The Swedish approach is based on a different strategy. Unlike what most other countries are doing, they are not aiming to stop the epidemic running its course. Rather, they are focusing on isolating the elderly from the rest of the population so that the epidemic can run its course in the non-elderly population and allowing the population to reach herd immunity. They have only taken rather mild social distancing guidelines to make sure the epidemic doesn't rip through the country too fast. They have argued that they don't need to take strong measures because their hospitals have more than enough capacity.


 * So, this is not really about a different judgement about parameters such as R0 and the infection fatality ratio, rather a judgment that a vaccine is going to come too late to stop the epidemic from running its course. This means that whatever the infection fatality risk is and whatever R0 is, all the deaths due to infecting the 1-1/R0 fraction of the population to reach herd immunity will happen anyway. There is then only the choice of limiting the infection fatality ratio by making sure the people who are at the greatest risk of dying don't get ill. And one has to make sure that hospitals should not face capacity problems.  Count Iblis (talk) 00:45, 3 May 2020 (UTC)


 * While the Swedes are busy patting themselves on the back, their total death rate per million people peaked at more than 10 deaths/million/day averaged over 7 days, putting it in the bad boys club, Belgium(28) Spain(18) France (17) Britain(14) Ireland (14) Italy(14) Sweden (11) . In terms of total deaths per million they are on the same trajectory as the Netherlands and Ireland. Other countries have done far better than that, and some of course are worse. Greglocock (talk) 02:56, 3 May 2020 (UTC)
 * The Swedes have chosen to go through the epidemic faster than other countries, therefore their death rate per unit time should be higher. What matters is whether when it's all over, the number of deaths per head of the population will be lower. Count Iblis (talk) 03:07, 3 May 2020 (UTC)


 * and as I said, they are on the same trajectory as several other countries for overall deaths per million. At this stage we don't know if flattening the curve, or not, results in lower overall deaths, but Sweden doesn't stand out for either. I see no sign that their strategy is an outlier in death-related results. Greglocock (talk) 05:35, 3 May 2020 (UTC)


 * I see. What about Japan? I note they have a softer lockdown too although they seem to be somewhere in between Sweden and the rest of Europe? Are they making similar assumptions? 90.196.238.188 (talk) 07:30, 3 May 2020 (UTC)


 * Japan is the big outlier, way down in terms of confirmed cases (a problematical measure) and deaths (a slightly less problematical measure). I suggest you spend a bit of time exploring https://ourworldindata.org/coronavirus Greglocock (talk) 21:39, 3 May 2020 (UTC)


 * An added difficulty with making these comparisons is that, by looking just at policies, one may not be accounting for variation in compliance with policies in the different populations. So, population A with a light lockdown, but near 100% compliance, may have better numbers than a population with a harder lockdown, but only 25% compliance. --OuroborosCobra (talk) 22:07, 3 May 2020 (UTC)
 * so basically the outcome depends on the social characteristics of that country as well? 90.196.238.188 (talk) 08:49, 4 May 2020 (UTC)