As many of my readers know, I have accepted the position of research group leader at the Complexity Science Hub (CSH) in Vienna (and I continue as University of Connecticut professor, so right now I am in Connecticut).
A week ago the Austrian government asked CSH to conduct research that would help formulate better policies for dealing with the Covid-19 epidemic. As an aside, I find it incredibly refreshing that a national government would actually ask scientists for help (and have a research institute ready to provide such assistance). In any case, CSH decided to put other research on hold and redirect all of its scientific power to deal with the challenges that the Corona crisis poses to our society.
As a result, last week I have been contributing to a working group that asked the following question: how effective are various public health measures in slowing down, or even stopping the spread of the Covid-19 epidemic? There is quite a lot of variation in how different countries have decided to deal with Corona, ranging from highly Draconian measures implemented by China to (at least, initially) laissez-faire approach of United Kingdom. Of particular interest could be pairwise comparisons between such similar countries as Denmark and Sweden, which have adopted very different Covid policies.
The goal of my research last week, thus, was to estimate the effects of various measures implemented by national governments to slow down and reverse the spread of the Covid-19 epidemic. A direct approach to answering this question is to track the growth rate of epidemic (with the severity of epidemic estimated by the number of “Active Cases”, that is, the number of people known to be currently infected) and then observe how various interventions affect this growth rate. For example, one could use the “basic reproductive number” (R0) — the average number of cases directly infected by a sick individual. The goal of an intervention is to bring down the reproductive number below 1, which will result in the epidemic dying out.
However, one potential problem with such direct approaches is that a large proportion (at least 50%) of people infected with Corona are “asymptomatic”, meaning that they themselves don’t know they are carrying the disease. As a result, they are not included in the disease statistics. Even worse, the proportion of known infected individuals tends to increase with time, as people become more aware of the epidemic and governments may decide to massively test asymptomatic individuals. Changes in the disease detection rate will, then, tend to mask changes in the epidemic growth rate.
So what can be done? My idea is that we should model both the dynamic process of how disease grows (and eventually declines) and how the numbers of actual infections are translated into official statistics. If we have a good process model (and for epidemics, we do) then an analysis of data based on such a mechanistic model will work better than using a purely data-driven approach. The reason is that we can build into the model what is known which enables us to efficiently use the precious data to estimate what is unknown.
For the technically minded I posted a document that describes exactly what I have done (GitHub:pturchin/Covid19). But in this blog, I will simply illustrate the approach with one specific example using non-technical language, which (hopefully) should be understandable to all readers (ask me questions in comments, if anything is unclear).
Important disclaimer: these results are quite preliminary and should be taken with a grain of salt. I often use my blog to air new ideas in order to find out any problems with them at an earlier, rather than later stage. And I certainly don’t speak for any organization (including CSH) or a government.
The illustrating example I use is the Covid-19 epidemic in South Korea. First, let’s look at the data. The chart below shows the progression of the disease, as measured by the number of “Active Cases” (people known to be infected). 
Next, let’s see how fitting a model to these data can clarify the internal mechanics of the epidemic. I use a variant of a standard epidemiological model, known as SIRD (so named for the first letters of the variables it tracks: the numbers of Susceptible, Infected, Recovered, and Dead). We want to make sure that the model does a good job approximating a variety of different angles from which an epidemic can be viewed. The next series of charts show whether the model succeeds in this. Points are the actual data, while curves depict model predictions.
In fact, the model does a very good job. This increases our confidence that it has captured the essential mechanisms driving the epidemic. And we only need to add two additional features to the basic SIRD model to do this.
The key parameter in the model is the transmission rate, which determines how fast the disease spreads from the infected population to that of susceptibles. The second important parameter is the detection rate. Both of these parameters changed during the epidemic. As is well known, once the South Korean officials realized that they have an epidemic to deal with, they massively expanded their testing program and imposed vigorous quarantine measures. These measures should have increased the detection rate and decreased the transmission rate. Building these changes into the model, we can estimate when and how much these two rates changed. Here’s what I got:
Panel (a) shows how the transmission rate (beta) changed with time. Initially the infection rate was very high, with the exponential rate of increase of around 0.4 day–1 (in other words, every day the number of infected increased by 40%). This parameter began declining after day 25 (mid-February), but reached low levels only close to day 50 (early March).
Panel (b) shows the detection rate. It is estimated as 0 until day 30, which suggests that initially, and for quite a while, the epidemic was growing “below the radar screen”. People were getting sick in growing numbers, but the society as whole was not yet aware of it.
South Korean authorities started testing for Covid-19 in early February, and the scale of testing was massively expanded after Feb. 20, which closely corresponds to day 30 when model-predicted detection rate began increasing. Eventually it reached a very high level of nearly 70%, suggesting that aggressive testing of asymptomatic people is bearing fruit.
Overall, then, this analysis of South Korean data makes a lot of sense in light of what we know about the course of the epidemic there. There are some caveats, which I discuss in the technical document, but the model fits exceedingly well and provides us with numerical estimates of the effectiveness of the measures taken by the SK government. The intervention was highly effective.
A future post will report on the analysis I’ve done for China. The situation there was more complex, and the model fit was not as excellent as for the SK epidemic. But it still yields very interesting and instructive insights. Stay tuned.
Added (21:00 23.III.2020): I have posted the document providing technical details and the R-script on my GitHub directory
Are you going to post the results for the measusers taken in Austria as well.
As someone from Vienna I would be very interested in this
Yes, today my group at CSH was joined by two more people, and the plan is to do a lot of countries, Including, naturally, Austria,
Great, I am kind of proud that the government asked you to do this.
Maybe they once ask you about your main research as well and how to make society function better or prevent it from getting worse
Brilliant! And amazing the gov come to ask! How enlightened!
I can’t help but point out that te fit on R is not good and could be a lot better it seems. And the scatter on D is huge.
I read an analysis on the perfect control data set from a large cruise ship that was isolated and where the completeness is perfect given the sample is perfectly known. You could also use that to test/tune the model.
@G
The cruise ship provides some good data but with major caveats. The staff comprised a significant number of infected, everyone was eating food prepared and brought by staff, and it is obv a densely populated ecosystem. It makes sense that highly dense old world Northern Italy is getting slammed, and in the United States, despite Seattle being the early hot spot, New York City has surged ahead as the biggest problem zone (throw in a very late lockdown by NYC leadership.)
Seattle is a very suburban city with a small downtown, and despite the early spread, they just aren’t going to experience the transmission rate of New York City with heavy public transit use and high density (and throw in a very late lockdown.)
The temperature and humidity data is very interesting, we will see how that pans out.
I’m happy for the Austrian people (and all of us) that they are employing Peter’s talents. Godspeed!
I agree, the cruise ship is a very peculiar environment. Fortunately, we have lots of countries to fit models to, which should allow us to get at general patterns.
Peter, thank you for the life-time of preparation that enables you to undertaken this work. Thank you for sharing it. I am also thankful that Vienna is not your normal European city. Their eccentricity has long been and is again a gift to all of us.
Peter,
Have you seen Tomas Pueyo’s simulations along the same lines as yours?
Pete
https://medium.com/@tomaspueyo/coronavirus-the-hammer-and-the-dance-be9337092b56
I completely agree with people like Tomas and Yaneer Bar Yam who urge the “hammer”. Our approach is different in that we look at how different interventions worked in the past, rather than projecting various scenarios into the future. It’s retrospective, rather than prospective.
But the goal is the same — to find out what works best. And how we can do better.the next time — and there, undoubtedly will be the next time.
Hi, Peter. Very interesting. Thank you!
I would like to add that the economic consequences of the crisis will also have a significant impact on public health, sooner or later. Would it be possible to factor this into the analysis?
There is a clear trade-off between hard measures and the economy, specially when it comes to “platicurtize” the infection curve.
Best regards.
Thats true the governments need to make massively tests to asymptomatic individuals. Other problem it’s how each individual understands cleanliness. I hope someone follow the logistic in your research.
There are about 10 other research groups at CSH who are probing precisely the questions you asked. Undoubtedly they will be reporting on their results at some point.
Thats true the governments need to make massively tests to asymptomatic individuals. Other problem it’s how each individual understands cleanliness. I hope someone follow the logistic in your research.
Dear Juan,
I wonder if for the economic impact, the aggressive Asian approach is not better too. It shuts the economy down, but only for a few weeks while you hold R<<1. If you just flatten the curve of the epidemic you might save a lot of lives by not over-burdening the hospitals but the economic disruption might last months not weeks. I'd like to see an economic model coupled to an epidemiological one to investigate that possibility.
Best, Pete
Hi, Pete
Thx for your reply.
Yes, that would be great; An approach that minimizes both the direct impact of the epidemics, and the indirect impact on health of the economic problems generated by the measures, would be a clear winner!
Best regards from the very much needed of good measures Madrid.
I wonder how or if DST predicts national responses to the epidemic. For example, one might conjecture that countries in a harmonious portion of the cycle would act more quickly, decisively, and with more elite support. China, for example.
Conversely, one might expect countries in a discordant portion of the cycle to act slower, less decisively, and with elite conflict. The US, for example.
Does this approach work for other countries? For example S. Korea, Japan, Singapore exemplify countries with relatively effective reactions. Italy, Iran, and various other European countries exemplify ineffective reactions.
Interesting question, but Japan and South Korea did have very _different_ reactions. Japan, as I understand it, has had relatively few restrictions on movement and gatherings, compared to Singapore, South Korea, or China.
Of course, that’s just the governmental policy side. You may be right that in Japan people actually follow the instructions to wash hands, maintain distance, etc. better than in other countries.
@Ross
one fairly dramatic thing that Japan did do was to close all schools very early.
I think the fact that so many Japanese wear masks in normal circumstances (and now its pretty much universal) goes a long way for a virus that is primarily transmitted through the air.
If American leaders were wise they would all be wearing masks in their public appearances and encouraging the public to do the same. The safety provided by a standard surgical mask is largely (although not exclusively) to everybody else, but this basic concept is poorly understood by Western elites whether political or in the media, and they prefer to play language games anyway.
It may well be that the CDC and our elites lied to the public about the community efficacy of masks because they did such a poor job stockpiling in the event of an epidemic, and they were concerned about a lack for public health professionals.
Of course, our “just in time” supply chains, lack of slack in the health care system, and lack of any serious disaster stockpiles are just a few of the many failings of our elites. When your leaders (political donor class and puppet pols) can careen from disaster to disaster over three decades with little to no personal repercussions, you know the next crisis is right around the corner.
These are all very good questions. There is no question that Covid-19 has delivered a huge shock to social systems, including ours (the US). What will be the consequences? I don’t have answers — yet.
Coronavirus Poll: A Lack Of Trust In Trump, Federal Government’s Response : NPR https://www.npr.org/2020/03/17/816680033/poll-americans-dont-trust-what-they-re-hearing-from-trump-on-coronavirus
The coronavirus issue has fallen victim to the culture wars, with Democrats tending to think it much more dangerous than the Republicans. As part of this, President Trump started out by dismissing the virus’s dangers as the Democrats’ “new hoax” for unseating him. He later claimed that it would go away very quickly.
On the other side, Alexandria Ocasio-Cortez tweeted about non-contact greetings. She mentioned putting one’s hand on one’s heart and then smiling or nodding. Some commenters mentioned the namaste gesture.
How long before actual food production chain…farmers…field workers…processing centers… then delivery system/truck drivers get infected enough to hamper store supply/availablits for shoppers
Now for the opposite question: How (or if) does a jolt like the COVID-19 pandemic affect the progression of the DST cycle? We know it decreases the wealth that the elite segment is already at odds over in a discordant portion of the cycle. Does it affect the distribution of wealth, which would be otherwise trending toward wealth inequality in down part of the cycle, and trending toward wealth equality in an up part of the cycle? Does it hasten the trimming of an overproduced elite? Does it hasten/worsen state fiscal crisis (how could it not?)? Does it affect mass mobilization potential or other relevant measures?
The president of the United States and others have suggested that there will come a point where epidemic mitigation efforts will harm the economy, and thereby the citizenry, more than loosened Covid-19 restrictions would have. And the CSH website includes the following research focuses: “How resilient is our economy? How will we deal with this crisis?” And, “How can we ensure basic provision of food and other necessities? What are our supply chains?”
Will there be an attempt by CSH to determine if there is a point where rising mitigation costs (of all kinds, including deaths), cross a descending trend line of economic and societal well-being?
Will there be an attempt to assess mitigation costs relative to various societies, e.g., Korean vs. US?
> The president of the United States and others have suggested that there will come a point where epidemic mitigation efforts will harm the economy, and thereby the citizenry, more than loosened Covid-19 restrictions would have.
If the economy is really the primary issue, then the best thing to do is nothing. Most people will then miss work for a couple of weeks, not too big a deal, and the majority of deaths will be of people who are old and sick, draining the economy more than contributing to it, and thinning that herd would be more likely economically beneficial than harmful.
Of course, the big shots who are now wanting to go that route don’t mention how they themselves will be the first to get access to protective immunoglobulin shots. Advocating risking lives for the economy is a much better deal when the lives at risk are *other* people’s lives.
> Will there be an attempt by CSH to determine if there is a point where rising mitigation costs (of all kinds, including deaths), cross a descending trend line of economic and societal well-being?
A sufficiently destroyed economy leads to increased deaths too. So a logical approach to this question would be to ask something along the lines of, “What is the amount of economic stress caused by measures to reduce deaths from the epidemic, beyond which these measures increase deaths from other causes more than they decrease deaths from COVID-19”? Now, the cost/benefit calculation is strictly in terms of deaths, thus eliminating the need to price the value of a life.
Perhaps — thanks for the reply. On the other hand, certain models, such as Pueyo’s “Hammer and Dance” previously mentioned, and Churches and Jorm’s “Shrink the Covid Curve,” ( https://newsroom.unsw.edu.au/news/health/we-can-shrink-covid-19-curve-rather-just-flatten-it) seem to offer less long-term dislocation (including reduced sickness and death among the younger, and most productive groups), than the so-far-revealed Anglophone societies’ dithering attempts at mitigation.
Yes, as I mentioned in a reply above, several groups at CSH are working on understanding the economic consequences of the epidemic.
J. Daniel: in Secular Cycles, Turchin & Nefedov show that catastrophic pandemics tend to strike around the transition between the Stagflation and Crisis phases of the Demographic Structural cycle. E.g., the Antonine Plague of 165-180 AD hit right at the start of the Crisis phase (165-197) of the Principate cycle in Rome. “Crisis” in the sense of the DS cycle, not the “Crisis of the 3rd Century,” which falls under the Depression phase of the DS cycle.
Our Stagflation phase is the neoliberal / Reagan era, after the Expansion of the Great Compression / New Deal era. Similar in many other Western nations. A pandemic — whether it’s COVID-19 or something worse in the near future — is a sign of the end of Stagflation and the beginning of Crisis.
Some of the crucial effects of a pandemic on a state at the cusp of the Crisis phase is weakening state finances, since — as we’re seeing ourselves right now — it puts a major dent in economic activity that could be taxed. Plague-stricken peasants can’t harvest the grain that’s due to their social superiors.
It also weakens a fragile state to invasion by foreigners, which the Antonine Plague did (Germanic invaders had an easy time when their enemies are dropping like flies from smallpox or whatever it was). That makes state collapse a more likely outcome than weathering the storm.
It doesn’t prune the over-production of elites directly, though, since they’re more able to escape its effects than are the commoners (usually by fleeing to sparsely populated country estates, avoiding crowd diseases). Remove a small fraction of elites and a much larger fraction of commoners, and the society becomes even more top-heavy than before.
Here’s to hoping this one is different, though, and makes the elites put as much skin in the game as the commoners.
Good summary. This is why it is so difficult to answer the question of how an epidemic, like the current one, is going to affect the secular cycles — because its effects are manifold and ramifying.
From our host’s work, the United States has had nearly 1.5 SDT integrative-disintegrative cycles. The US was in its first integrative period when the data starts, around 1800. The US reaches its first peak in the 1820’s, and the Era of Good Feelings seems aptly named. Then around 1830, it goes into a disintegrative period that lasts until around 1900. Then the US recovers in an integrative period that lasted until the 1950’s, then it entered another disintegrative period, one that we are still in.
https://en.wikipedia.org/wiki/Cyclical_theory_(United_States_history) – describes the Schlesinger liberal-conservative cycle:
* Liberal, public interest, wrongs of the many, increase democracy, human rights
* Conservative, private purpose, rights of the few, contain democracy, property rights
Shorter cycles, though they fit the SDT cycles rather well.
* First integrative: L Constitution, C Hamilton, L Jefferson, C Era of Good Feelings
* First disintegrative: L Jackson, C Slaveowner dominance, L Civil War and Reconstruction, C Gilded Age
* Second integrative: L Progressive, C Repub Restoration, L New Deal, C Eisenhower
* Second disintegrative: L Sixties, C Gilded Age II
This suggests what the path out of the current phase is likely to be. Another liberal period, and one that begins another integrative cycle. I think that we are seeing efforts to start such a period.
Bill Clinton seemed like he might have started a liberal period, but he failed in that. He never had good relations with Congress, not even when it was dominated by his fellow Democrats.
Barack Obama also seemed like that, but he also failed. He seemed remarkably meek about the Republicans taking over the House in 2010, he let the 2011 Wisconsin rebels hang out to dry, and he ignored the 2011 Occupy movement.
But the Left is moving into Congress, and that could make a difference.
I was delighted to see that such work was in hand. The UK (like Iran) is now operating a totally medicalised model where you don’t get tested unless you can persuade a medical practitioner that your symptoms are sufficiently severe to warrant admission to hospital. The test results seem linked the day of the week! One day in London, the day-on-day growth is 1.28 and the next it is 1.53, then back to 1.29. While Northern Ireland has 78.6 cases per million population, the Irish Republic (on a different testing regime) has 228.6 cases per million. Totally implausible. The border is open and the first known case in the island of Ireland is a patient who landed in Dublin and traveled to Belfast.
Of course, we are assuming that the likelihood of generating a severe case is uniform through the population, but that’s not so. Initial results from Wuhan suggest that the likelihood of those in the population with blood group A of presenting a case warranting hospitalisation was significantly higher than for those with blood group O (survival rate thereafter was not significantly different). Using the Wuhan data, one can calculate the relative propensity of countries to generate cases, from their blood group composition. If the world average is 100, Armenia is 114 and Chile is 74.
For Austria and its neighbours, the figures are
Country Blood group-based likelihood of severe cases (world =100)
Austria 107
Czech 110
Germany 107
Hungary 109
Italy 104
Slovenia 107
Switzerland 110
Italy has a population less at risk than its northern neighbours. UK is 105, and Ireland 96
Happy to share these figures (and quite a lot more) with anyone interested.
Very interesting
Excellent work, sir! As soon as this blew up I thought of you and your research themes, especially around the cyclical social dislocations you have discussed in other research. Are we heading into that? Clearly all the implications of the COVID crisis signal that the Tremoulis Twenties are a whole new social paradigm, and are leading us into an era with many changing expectations, and potential redrafting of social and political relationships, which will be far different from those we lived with in the preceding Tentative Tens.
I am sure you have tons of data models. But knowing you a little, you are not afraid of few more. Here is the Australian perspective from UNSW:
We can “shrink” the COVID-19 curve, rather than just flatten it. Public health interventions can do more than just flatten the COVID-19 curve, UNSW epidemiological modelling shows. By TIM CHURCHES AND LOUISA JORM.
https://newsroom.unsw.edu.au/news/health/we-can-shrink-covid-19-curve-rather-just-flatten-it
Link to Tim Church’s model…
https://timchurches.github.io/blog/
I would be curious about how (fast) the immunity system of a body builds its resistance and if this is mesured/ or there simply exists a diffential in types of body resistance in a population which creates an overall resistance in the whole community by replicating itself later on as a overall mediator.
It is said 90% of African population now carries aids, but there is no death toll. This could not have been passed by genes.
Based on the theory bodies learn from each other as well/ in stead of just spreading the disease.
This would also affect lock down results as being under or over productive. Lesser lockdown would increase the
spread/ but speed up the learning coincidental – while severe lockdown would decrease the initial risk, but leave a slower learning process which leaves open a higher chance of infections later on in smaller numbers.
Unless the virus itself has mutated into a less agressive type in the same time perod, so it would make no difference, regarding one type of lockdown or the other. Then the peak in lesser lockdown would be unproductive.
Is it possible that the 2020 forecast of uptick in death rate in Europe in political civil unrest, is somehow connected to this covid-19 cases, or maybe it is a lead up to the civil unrest that will probably occur, according to the 2020 forecast? I am referring to the political violence model, forecast for 2020.
Isn’t it fascinating to watch country after country having to pass an asabiya test? Unfortunately, my parents are stuck in Russia where the outcome will likely be the worst: abysmal asabiya, corrupt and incompetent government, idiotic public attitude, cold climate, heavy reliance on public transportation, and unreliable medical services.
It will be interesting to see what happens in Chile where blood type A is particularly rare (no idea why).
There are a few coronaviruses that routinely circulate in the population, causing something intermediate in severity between a common cold and a typical flu. They generate only short-term immunity. So the hopes for a fix-all-problems vaccine might not be justified.
I don’t know about Chile. Could be simply an error, or a localised study of say Mapuche (Native Americans seem low A) or it might be right. There is archaeological evidence that Andean Native Americans were low in A before 1492.
Great iniative. Very curious to see the results for the other countries, especially the USA.
Do facemasks worn by the general public slow spread? I understand the scarcity problem for healthcare workers but homemade masks are partially as effective. I think leaders mandating or encouraging masks is the easiest step that could reduce spread. I’d love to see evidence!
Good work. Though I have some criticism of the coding. The ODE solver in it was a very primitive one, Euler’s method. There are much more accurate ones that do not need much more computation time for smooth integrand functions.
To solve dx/dt = f(t,x) where t is the independent variable, x can be an arbitrary array of dependent variables, and f is the integrand function, Euler’s method for stepsize h is t = t + h, x = x + h*f(t,x)
An easy-to-code ODE solver with a lot of improvement is the famous 4th-order Runge-Kutta method or RK4. It goes: f1 = f(t,x), f2 = f(t+h/2,x+h*f1/2), f3 = f(t+h/2,x+h*f2/2), f4 = f(t+h,x+h*f3), t = t + h, x = x + h*(f1+2*f2+2*f3+f4)/6
For dx/dt = x, over stepsize h starting at x = 1, the exact solution is exp(h). The ODE solvers’ solutions are: Euler: 1 + h, RK4: 1 + h + h^2/2 + h^3/6 + h^4/24
https://cran.r-project.org/ – R users have created a lot of software packages that one can install, packages like ODE solvers. ODE = ordinary differential equation.
Loren, I’ve been at this business (working at the interface between models and data) for nearly 40 years. Believe me, you are not saying anything that is new to me. The choice to use a discrete model was a conscious one. All models are approximations. In this case, a discrete model with one day time step is a better approximation of the reality than a continuous differential equations model. Think about the actual process of people getting infected. It’s not continuous in time (when they sleep, for example). The disease is transmitted during a particular time of the day when people are about and interacting with each other. So a discrete-time model is appropriate. Also, it’s parameters, for example, beta, are easily interpretable and the model form fits the form in which data comes (at daily intervals).
For fitting parameters to observations, there are several algorithms that one can use. Number of parameters: N
* Needs only the function to optimize:
* * Simulated annealing — O(N), random with selection
* * Genetic algorithms – like simulated annealing, but with several parameter sets and random crossover between them
* * Nelder-Mead simplex — O(N^2)
* Needs function, derivative:
* * Quasi-Newton (BFGS, DFP) — O(N^2)
* * Conjugate gradient — O(N)
* * Gradient descent — O(N)
* Needs function, first two derivatives:
* * Levenberg-Marquardt — O(N^2)
In this case, it’s best off using some derivative-less method like Nelder-Mead or simulated annealing.
The world leadership is suddenly bowing to the predictions of science, perhaps more than they ever did to the likes of Oracle of Delphi. Could we be headed for an era of the rule of science (wisdom)?
Leaders are, and have always been specially vulnerable in epidemics. Status and popularity are strongly linked. Getting things done involves contact with large numbers of people every day, The people they meet are mostly also leaders, who have recently met many others for the same reason. Contagion is focused on the person at the top. The likelihood that large numbers these people have used air travel further increases the probability of the person at the top catching Covid-19.
Curiously, only one monarch died of plague in the Black Death.The nobility head for their country estates and leave the lower orders to fend for them selves. Modern leaders of democracies are at risk, especially if they ignore their own advice and crowd together in the stairs, like the UK’s Prime Minister, Health Minister and Chief Medical Officer.
Or President Trump and some 15 officials, who all showed up together in the Oval Office for Trump’s signing of the most recent bailout bill.
This brings up something weird. Some Congressmembers resist doing remote votes. They vote in the House and Senate chambers, and they often crowd around the central part to do so. But House Majority Leader Steny Hoyer has only conceded letting in a few Congresspeople at a time. Much of Congress’s leadership is very geriatric:
President Donald Trump (R) 73
Vice President Mike Pence (R) 60
House Speaker Nancy Pelosi (D-CA-12) 80
House Democratic Leader Steny Hoyer (D-MD-05) 80
House Democratic Whip Jim Clyburn (D-SC-06) 79
House Republican Leader Kevin McCarthy (R-CA-23) 55
House Republican Whip Steve Scalise (R-LA-01) 54
Senate Democratic Leader Chuck Schumer (D-NY) 69
Senate Democratic Whip Dick Durbin (D-IL) 75
Senate Republican Leader Mitch McConnell (R-KY) 78
Senate Republican Whip John Thune (R-SD) 59
Foreign Affairs Cmte Head – Eliot Engel (D-NY-16) 73
Ways & Means Cmte Head – Richard Neal (D-MA-01) 71
Oversight & Reform Cmte Head – Carolyn Maloney (D-NY-12) 74
Transport & Infrastructure Cmte Head – Peter DeFazio (D-OR-03) 73
Financial Services Cmte Head – Maxine Waters (D-CA-43) 82
Judiciary Cmte head – Jerry Nadler (D-NY-10) 73
Intelligence Cmte Head – Adam Schiff (D-CA-28) 60
Those that cut themselves off lose touch and get plotted against, or have to use ministers who then become de facto leaders. The plotters and ministers are then more exposed to contagion, but if a couple drop out they will have successors. Leadership is inevitably a high-risk business.
The fact that Congress and the administration are full of geriatrics suggests that COVID-19 will produce a high turnover. Awareness that they themselves are specially threatened makes them more likely to accept drastic measures, but also to stop doing their job, or do it more online. Moving online will eventually select a different, younger bunch.
Well, the Senate is supposed to be the wise old men (senex) and they have a lower age limit of 35. I hadn’t realised just how successful the policy had been.
The US Constitution’s age limits:
* House of Representatives: 25
* Senate: 30
* Presidency: 35
The Senate was originally elected by state legislatures, but that was changed to the rank-and-file of voters by the 17th Amendment, ratified in 1913. The House was elected by ordinary voters since its beginning, however.
So, the truest test of any model is how well it predicts future (or at least currently unknown) data. Does the South Korea model predict future results? It seems as if, had the transmission rate actually dropped as much as it suggests, we would have started to see a slowdown in deaths by now. There is certainly a delay between transmission to death, but if the transmission rate had actually plummeted in early March, by now we should see daily deaths plummeting as well, which is not what we see (in South Korea’s case).
None of which is to say that this isn’t excellent work, and in any case you said it was just preliminary and to garner feedback, just pointing it out.
Ross, you need to think through what is the purpose of modeling. In this case, the model is not predictive. It is retrospective. Essentially, it uses an accounting system to make sense of the dynamics so far and allows to factor out the changes in the data that are due to increasing detection rate.
It’s a common mistake to think that the only true test of the model is to predict the future. In this particular case, the main influence on the future dynamics are not epidemiological rlations, but actions taken by the government and how well citizens follow the rules. That’s not what I aim to do. To repeat, I aim to quantify retrospectively how well actions taken so far have worked.
Seems the first response to a health anomaly where no treatments work is to develop a test, then use it. Taiwan, Singapore & South Korea did. Germany had a test in January. American labs applied for permission to develop a test, but denied. Seems all sciences have a problem dealing with anomalous data. Shoot the messenger is too common a reaction. We’ll have to get new money or reallocate from existing inadequate resources and worse, have to cooperate with others and potentially have some loss of identity – the true dilemma of cooperation. When have any of us been in prison having to keep stories straight? There is a necessity here for industrial strength cooperation – a Cooperation Industry Earth, since the virus impacts all, whether or not they get. This can happen again. Nature isn’t that impressed by the Human species, or any other for that matter. Community must save us.
It occurs to me that if one was looking for a measure of risk of infection in the population, there is good regional and international data (censuses and regular surveys) on:
Percentage resident in institutions
Percent in education
Percentage workers commuting by public transport which can vary from 16% of commuters in Newcastle to 54% in London – any wonder that the case rate of Covid-19 in London is four times that in the North East? (in GB, there is also a measure of how long people travel for)
You mention Denmark vs Sweden.Cases per million over the last 10 days (to 29th) have risen from 181 to 408 in Sweden and 228 to 435 in Denmark, so slower growth in Denmark.Of course, the first recorded case in Sweden was Feb 15, and in Denmark, Feb 27 so actually the Swedes have a much more mature epidemic.The Danish clampdown came on 12 March and was clearly linked to a drop in growth rate. Swedish measures on the same day were much milder, and had a lesser effect.
Currently, day on day growth rates are in the same sort of ball park (the seven day average on 29th was Sweden – 1.18, Denmark 1.20).
Lesson seems to be start public health measures early and don’t panic. Or get into a “More rigorous than thou” race to the bottom with the neighbours.
I’ve just finished a regression of per million infection rate of UK regions after three weeks of epidemic against the proportion of commuters using public transport (which is both a measure of urbanisation and a measure of direct risk to a large fraction of the population). R squared is .72 and the equation (for what it is worth based on 10 cases) is:
Infection = Percent commuters x 10.105 + 166.75
Since Vienna has 74% commuters in public transport, and the Austrian epidemic started a week earlier than the UK epidemic, one might surmise that with levels depending on UK testing policies, a week ago, Vienna might have had 915 cases per million population or 1,736 cases. Yesterday, there were 1128 cases and 23 deaths in Vienna. Projecting back by the growth in Austrian cases, there were likely 525 confirmed cases in Vienna a week ago (30% of the surmise)
New Statesman 28 MARCH 2020 asks Why are Germany and Austria’s coronavirus death rates so low? One of the suggestions is that their public transport system is less crowded.
German public transport is not less crowded. The primary question which should be asked (but is not asked) is wether you believe shutting down public transport would or would not kill a virus. The cat in the box is dead as well as alive.
No, ofcourse not. Unlrss you could prove a virus mutates into a non agreesive type at the same rate people are using public transport.
Medics ate very funny people.
Useful to know about German public transport. In London, public transport has been reduced in line with the reduction in people travelling so that it is as crowded as ever, so the risk stays the same, but for fewer people
No, the virus falling to the ground is not the same risk anymore; humitity grades explaining outbreaks in Italy is not correlated to peoples behaviour overthere (the youtube suggestion) and a 2% group of elderly being vulnerable is not the same as people using public transport; cause and effect are separate and have very different relations.
People being vaccinated against tbc proves there is a more general relation between types of flue, non specific (not being recognized by medics) and keeping people in coma for a month under respiration is not a cure/ but the last.thing you could do to help nature cure itself.
Nor is the fall in the peak due actions being taken (keep indoor), but the result of most vulnerable people getting scarce and building resistance on the other side. Although it is usefull for panic control, to suggest actions taken were succesfull and now can be softened.
Nor is a million people loosing their jobs although incomparable less worse rhan 1000 people dying, because that’s 0,1% and the problem does not persist (as cynicle mathematical equations can be).
Stockholm not closing down offers a bright spot, although ofcourse will be ignored by mass media, because it’s not sensation and does not align with government supported panic control spread. Note the difference in controling it by spreading it :-)(
Someone says it is related to the air moisture rate locally; below or over 6 gr per K. Because a higher moisture rate would have it fall on the ground/ or otherwise keep it afloat in the air and thus spread faster.
This would correspond with my theory a virus must have a spin rate, either light or heavy, so water would chemically mitigate it into a heavier spin, changing the effect it would otherwise have.
I am a member of an organization that translates your blog into Japanese. Yesterday, we also translated this article.
https://econ101.jp/%e5%85%ac%e8%a1%86%e8%a1%9b%e7%94%9f%e6%94%bf%e7%ad%96%e3%81%afcovid-19%e3%82%92%e6%ad%a2%e3%82%81%e3%82%8b%e3%81%ae%e3%81%ab%e3%81%a9%e3%81%ae%e3%81%8f%e3%82%89%e3%81%84%e5%8a%b9%e6%9e%9c%e7%9a%84/
What do you think about the situation of this infectious disease in Japan? Outside of Japan, it has been pointed out that the state of contagion in Japan is “inexplicable” but Japanese also consider the situation in their own countries mysterious. The Japanese government does not conduct large-scale, mandatory closures of urban areas or extensive, large-scale PCR testing. The basic policy of the Japanese government is as follows.
https://www.mhlw.go.jp/content/10900000/000617686.pdf
In Japan, attention is also focused on the relationship with BCG vaccination. The following is a very popular blog post in Japanese.
https://nosumi.exblog.jp/28020527/
I would be happy if I could read your analysis of Japan.
Thank you — I really appreciate these translations. In response to your question: other members of the team are analyzing the Japan data. We’ll report the results soon (i’ll reflect them on my blog or Twitter).