Cigarbutt

^This will become clearer over time but the data essentially rules out that the typical population who died was the medically destitute that you described. Look also at the Europe data and focus on countries which have been reporting low Covid death rates for a few months (by country, age group etc). https://www.euromomo.eu/graphs-and-maps/ So far, the incredibly high positive peaks have not been followed (or even partly matched) by lower than average death rates. So far, the data suggests that the average or median period of life lost is many years and may match the 10 to 14 avg years lost derived from other data (use age of death vs expectancy, adjusted for risk factors). i submit that this also fits anecdotally with my local and regional knowledge. For folks 'admitted' to nursing homes, there are two populations, one with limited life expectancy but the other with much longer life expectancy. Also, there is a lot of people in the community who are older and who have several risk factors but who still have many years of quality life left. An interesting feature is that the 'secondary' deaths (from the virus and/or the lockdowns; that's another discussion) (ie deaths from people foregoing care, having no access to care, late heart attacks, strokes, late cancer screening and delayed treatments, suicides, despair due to job loss economic hardship etc) should occur over a period of many months and this excess mortality is not showing up in numbers once the direct Covid death rates went down. i'm not saying those numbers don't exist, i'm only suggesting that they account for only a tiny fraction of direct Covid-related deaths. A potential misconception may lie in the fact that people assume that life expectancy in general is around 80 for the population, which is true from a specific point of view. However, somebody in the US or similar reaching age 80 have, on average, about 9 years left. Those who reach 70, about 15. People who reach 70 or 80, almost by definition, have co-morbidities. Of course, quality of life left is different from years left but that's also a different discussion. For those who suggest that this amount of years lost (older people closer to death anyways) is not significant, please send the memo to health authorities since most of healthcare dollars are spent in that category. 'We' have become quite good at adapting to chronic conditions (perhaps less good at more cost-effective treatments more upstream) and have become quite poor at quality of life cost allocation near the end. Covid deaths happen to concentrate in certain categories who only partially chose to be in those categories and the 'social contract' at this point (in the US think Medicare and Social Security and elsewhere) does not, fundamentally, include restrictions based on age and risk factors. I agree with the importance of cost-effective responses and that's why i had reservations when schools closed and why i'm in favor of maintaining school presence for children even if there appears to be a price to pay, at least in my jurisdiction, in terms of virus resurgence and eventually years lost for some.

Thank you for your post & link, cobafdek, However, the content of the link is not in any way Trump-related. But there is a link, at least from a certain perspective. The residual question is if the attempt at disinformation is intentional or not. ----- When being part of debating societies, one of the mentors used to describe the logos (appeal to reason, careful with fallacies), ethos (appeal to character and credibility, in large part based on respect for the opposing view(s)) and pathos (appeal to emotions, positive and negative). The emphasis should be on logos and ethos/pathos should only be used as adjuncts. However some try to get away (and sometimes “succeed” doing so) by only using emotions. And of course using only negative emotions is pathological. ----- The WHO, impersonated by Mr. Ryan, suggested, way back, that lockdowns should be used as an opportunity to control and suppress the virus. From last March, six key actions were recommended: 1- expand, train and deploy your healthcare and public health workforce. 2- implement a system to find every suspected case at community level. 3- ramp up production capacity and availability of testing. 4- identify, adapt and equip facilities you will use to treat and isolate patients. 5- develop a clear plan and process to quarantine contacts. 6- refocus the whole of government on suppressing and controlling COVID-19. Of course, this was suggested to build a solid framework so that, when restrictions are lifted, the virus doesn't resurge or at least the virus resurges to reasonably manageable levels, adjusted for different country circumstances. The fundamental substance behind the message was (and still is) that not dealing appropriately with a problem during lockdowns invariably puts at risk a country for further punishing lockdowns, either spontaneous at the individual level or government-imposed. Nobody here is interested in the following but the interferon system within the immunity of individuals shares the same characteristic (inability to deal with the disease acutely can end up in a cytokine storm; it is basically a failure of the body’s governance system) and I would venture to say that quantitative easing also follows the same pattern. But that’s a story for another day.

I mean Chris Christie was just released. Another fat fuck with no shortage of high risk flags...living to tell about it. Wow another person that didn't die and he is fat as hell with asthma to boot. ... Does anyone know if testing positive for covid impacts ability/rate when applying for new health insurance coverage (i.e. new job) or when applying for a new life insurance policy? Do we understand what the long term health risks are of catching covid? My guess is health insurance providers and life insurance companies will be motivated to figure this out quickly and get it priced accordingly. The following is not expert advice and you may want to fall back on anecdotal opinions formulated by celebrities or high BMI politicians. It's also work in progress. There is a lot of noise now about "long" Covid and it seems that most of it is noise and not signal. You can break down into two categories: -The simple Covid+ test or sick person not requiring a hospitalization. This group is likely to do well for all the scenarios you mention although the life insurer may use the administrative process to make sure one has recovered before the actual signature. Health insurers have done very well with Covid, on a net basis, and the new normal makes it ideal, absent major changes in the set up, for them to gain access to profitable segments. For example, United Health and Humana can continue to increase their presence in the Medicare Advantage segment and maintain their high gross margins despite (and perhaps more so with) Covid. It seems health insurers always, at least so far, end up making more money when uncertainty is high. -The more complicated case who gets hospitalized (especially if need for intensive care and respiratory support). Survivors have consistently shown longer and possibly incomplete recovery. For a large part of this group however, these issues were already relevant before Covid (risk factors). The life insurers can require a more formal evaluation in these cases and they will come up with an new integrated score quite rapidly for those who remain candidates for life insurance. If interested, this seems to be a good summary of where things stand right now although things could change at the margin with more learning: https://globalhealth.washington.edu/sites/default/files/COVID-19%20Long%20Term%20Effects%20Summary.pdf?mkt_tok=eyJpIjoiWkdFM1lXWTNOR00xTkdFNCIsInQiOiJwYXh2Qmc4WWczMTIyTURSSER2VzNKRm5oc2RSbUdJenowUitjUEMyTVR5MUk0REFCTG10MFErbG5MRWxFNGFPbndFWUlUdzVjSzNMU0FtdldKS3BQNFwvWVhBZkxRMm5KcTRwa2U3OW5pUGRcL1hQRFk3Sk96cDl2Zk5peWtxTVBrIn0%3D#overlay-context=uwmetacenter

India's death rate is quite low. But thats impossible, because its not the 6% that was coming out of China in Jan. Other countries are more successful then the US, but when doing better then the US you cant be TOO successful and not counting all the dead bodies. Lets not get ahead of ourselves. https://www.cnn.com/2020/09/11/asia/india-covid-death-rate-explainer-intl-hnk-scli/index.html There was nothing anyone could have done to prevent the virus as it was here before we knew it and case counts were way, way higher then suspected in Feb and March. Interesting look back based on some conversations from the spring. It is fair to say that health data reporting in India is not as reliable as in some other countries but their age distribution is wildly different (much younger profile). Since age is the major risk factor and since age risk rises exponentially, one can expect that their overall mortality rates will be overall much lower. -For the it was there all along and there was nothing to do about it aspect There is solid and robust evidence concurrent to the initial spread and data revealed over time that the thesis does not make sense. -concurrent work https://www.nejm.org/doi/full/10.1056/NEJMc2008646 https://www.cdc.gov/mmwr/volumes/69/wr/mm6922e1.htm?s_cid=mm6922e1_w https://threadreaderapp.com/thread/1249414291297464321.html -data over time The thesis does not make sense from a mathematical, epidemiological and logical point of view. @orthopa and Gregmal You realize that you are basically arguing that the earth is flat? Opinion: This thesis cannot be disproved but it can be rejected with a high degree of confidence. ::) The piece in the NEJM mentioned above is interesting because it helps to explain the evolution in Washington State vs what happened elsewhere (look at deaths per million, excess mortality etc and compare to potential outcomes considered prospectively. Suggesting (and maintaining the opinion that) nothing could be done sounds awfully fatalistic and is not supported by evidence.

FWIW, i continue to think that the virus overall (apart from a rise of 20-30% in government debt to GDP) is no big deal from the point of view of the underlying long term economic potential and further waves should be smaller and easier to manage. The outlook on the market (short, mid and long term) may evolve according to the health of the underlying host and i remain extremely bullish long term. Spain is a country i know to some extent and it shares many socio-demographic features with my Canadian province and so, unsurprisingly, shares a similar outcome (very poor) in relation to failures of initial containment, community spread and subsequent waves. Opinion: There is nothing to be proud of. In Spain, mainly around Madrid and Barcelona, it is estimated that about 50% of folks who died in old people's homes died without appropriate medical or even basic care, which is simply hard to reconcile with the status of a developed nation. The reference below shows rankings in terms of mortality data (my province's result is 709 as of now). https://www.statista.com/statistics/1104709/coronavirus-deaths-worldwide-per-million-inhabitants/ There are regional variations with the TX, FL, AZ and GA 'champions' focus on the Northeast as the benchmark and the US, in toto, seems to aim to improve its ranking although the competition is high up there on top. https://www.statista.com/statistics/1109011/coronavirus-covid19-death-rates-us-by-state/ An important variable to consider here is that poor performers like Spain ('champion' states also) will tend to show over time actualized results that will reveal how they underestimated mortality as the disease runs its course. Interestingly, data from countries like Belgium and the Netherlands may require some downward adjustments. Germany continues to be exceptional in this respect as they report both lower levels of mortality and accurate data. There are some quarters in Spain (like in my province) who have underlined the deep need for questioning and reform but, in both cases, it seems that the both jurisdictions lack institutional depth. It's hard to dissect 'performance' because there are a priori factors, some of which are quite fixed, geography, density etc but for Spain (and my province), there were clear failures in governance, management and coordination. In 2019, some assessed the institutional health security and one of the messages is that staying on top requires incessant renewal. When thinking of Spain and my province, in order to strengthen the host, it's important to have capacity and to willfully get your act together and both aspects will continue to rank relatively poorly. The US has enormous potential capacity but cycles are not dead.

... 3rd wave: I was wrong about the prediction about 20k daily at the end of August. I still think GA, FL, TX, NY won't have another wave. They reached similar level of total case/population and GA, FL, TX have been open for 2 months. Other states have not reached this level of total case/population so it is possible to have another wave. -on the I should have realized that different states have different situations aspect A similar confounding problem is occurring in NYC. Using antibody levels and other inputs, there remains a large variation in 'acquired' immunity levels. In some areas like the Corona (named this way before!) community (ZIP code 11368), a 48.1% percent positive rate is reported. Note: this % was at 51.6% last August when a batch of data came out, suggesting a way to evaluate the expected decrease in reported antibody immunity over time once a relative state of population (herd) immunity has been reached. In other areas (most in Manhattan), like the Upper West Side, the percent positive is now at 12.1% (it was at 12.6% last August). There are reasons for this variation (looking at Borough Park 11219 is also worthwhile to evaluate) but the point of this post is that boroughs and communities are not sealed and absent individual-based or government-based restrictions, these differences would tend to disappear (communicating vessels principle). So, depending on the degree of "openness", one would expect further spread. Different communities within NYC have different situations but the virus does not know that. References: https://www1.nyc.gov/site/doh/covid/covid-19-data-testing.page https://www.nytimes.com/2020/08/19/nyregion/new-york-city-antibody-test.html -on the have been open aspect The measure of 'openness' needs to be defined and should include, on top of the gross and bipolar ideological take: -the basic individual behavior changes (masks, distance) applied by the median individual -the general level of mobility and activity (ie Google mobility reports, retail, public transit, physical presence at work etc, credit card usage patterns) If you look above comparing the person riding his boat off the Florida marina and yelling "let's party" to the professional running in Central Park wearing a mask, you may find that there is only a marginal overall difference between the states that are "open" and those that are not. When taking a decision, Yogi Berra suggested the following: "When you come to a fork in the road, take it" and the decision may not be to go right or left; it may mostly be to decide between right and wrong.

Zerohedge has had an unusual focus on the bioweapon thesis. There have been many stories including this one: https://www.zerohedge.com/geopolitical/visualizing-secret-history-coronavirus-bioweapon Out of curiosity, in the event that the thesis is true (ie the virus is man-made and accidentally or even as part of a human plan, was released as a 'weapon'), this would have potential consequences for 'reparation'. But how does this change the defensive strategy, ie how to limit the damage, relatively speaking, in comparison to other countries for example, from a data point of view? imo, not in any respect. but the veracity of this claim is perhaps the single most important foreign policy issue today...and it is not being discussed as such, although trump has been alluding to it...put another way, how would you react to a bombing conducted by China against the US that resulted in 200k deaths? I know this is being provocative but my intent is to provoke thought. my understanding of the bioscience is quite limited, but sufficient to appreciate these claims as legitimate But i thought the 200K+ deaths were either fake news or, if real, all as a direct consequence of the "lockdowns"? ::) It is amazing how the intensity and direction of blame allocation as well as the alleged damages can change, depending on the perspective... It is completely appropriate to investigate this and to allocate adequate time and energy depending on objective developments. An external threat can be useful to help bring out the better angels of our nature and constructive comments around the concept can have unifying effects towards common goals. However the use of external threats can result in poor (and sometimes unintended) consequences. More provocative comments here and i assume that the phenomenon described after does not apply to you. 8) Assumptions: -The social distancing spectrum (from the common sense "protect the weak" to full state-imposed lockdowns; from low costs no-nonsense to sophisticated and model-based) can result in lower morbidity and mortality arising from the virus. -The median citizen is not looking to spread it indiscriminately to all and is not hiding in his or her basement. The median citizen is reasonable and will determine behaviors based on reasonable self-appreciation of facts. This means that good information has to spread freely, that messages from experts may influence the responses to some degree and that specific government mandates may modulate the individual ways to adapt. https://iaap-journals.onlinelibrary.wiley.com/doi/full/10.1111/aphw.12223 TL;DR version: One of the unintended consequences of unusual focus on external threats may fuel conspiracy theories and the phenomenon may, directly or indirectly, be correlated (even causal?) to the undermining of basic social distancing strategies useful in mitigating damage. A fascinating aspect is that the phenomenon may have an unconscious basis. Personal note: as i write this, i'm watching an objective report on national TV which is relevant for snowbirds who are considering going to Florida next winter. They are showing a segment from last summer where anti-mask groups were 'raiding' stores when excess mortality was peaking.

Zerohedge has had an unusual focus on the bioweapon thesis. There have been many stories including this one: https://www.zerohedge.com/geopolitical/visualizing-secret-history-coronavirus-bioweapon Out of curiosity, in the event that the thesis is true (ie the virus is man-made and accidentally or even as part of a human plan, was released as a 'weapon'), this would have potential consequences for 'reparation'. But how does this change the defensive strategy, ie how to limit the damage, relatively speaking, in comparison to other countries for example, from a data point of view?

The NEJM just released an editorial (very unusual) on how to assess if tests are passed and if immunity should apply. At some point, a line has to be drawn in the sand.

Not sure that's true if you vaccinate the 99.9% that would not have died from it anyway or the 98% that would not have contracted the virus anyway v the 0.01% on which you will have to spend for the ICU stays, infusions and what not. It does come down to an NPV calculation. The basic inputs involve comparing health costs related to testing, hospitalization, intensive care costs etc + adjusted quality of life years lost with the cost of vaccines at large. An interesting feature with the evolution of the virus is that younger cohorts (45-69) have been involved more over time and even if only a small fraction of them need costly care, contrary to the older cohorts where it becomes rapidly clear who is going to make it or not, younger cohorts will tend to 'fight' and become chronically lodged in an ICU bed. Again, a small number of cases may end up costing more than all the rest. But the challenge is to integrate indirect costs and some of them are based on values. Good luck with that, especially these days. You may want to note that applying your train of thought requires to ask very difficult questions about the present status of healthcare. For example, using your template, the accepted practice to use yearly recurrent influenza vaccines should be abandoned. Also, concerning new antibiotics and new anti-cancer agents and many other areas, the new drugs coming to market have, for decades now, brought very marginal improvements (if any) and have come with high costs. Interestingly, the sponsors (typically private and profit-motivated) looking to harvest a 'reasonable' return on investments will calculate very optimistic NPVs, including for vaccines. Also, if you want to use a cold, rational and cost-based approach, you may want to consider that simultaneously going for Covid vaccines while aiming for herd immunity in a population unlikely to adopt vaccines at a large scale is a negative NPV proposition. Early vaccines were clear value propositions. Over time, as with many things, it looks like a large part of forward returns have been pulled into the present. Anyways, calculating NPVs using negative interest rates is becoming the norm so who cares?

^In terms of what's related to investing, the way stocks have traded (both down and up) has been quite spectacular but this technical aspect is difficult to handicap. The virus (in the grand scheme of things and from a long term point of view) is truly a blip in the chart and dealing with this really is not rocket science. But i never thought this topic would become so interesting, including from the point of view of human nature. FL, AZ, TX, GA are not that different from NY, NJ, MA or even California from the outcome point of view, it's more along fifty shades of grey. The excess mortality (if that's a variable you consider in your value system) shows very poor patterns in the four former listed states and a strong case can be built that most of the poor aspect of the record is due to a series of unforced errors that happened during the community spread phase. The NY data that Investor20 referred to is quite unreliable from a population point of view, especially the first results. The antibodies do tend to wane and not all 'cases' seroconvert (although most do) so the better numbers (more tests) happened later on and may even have underestimated the 'true' level of seroconversion in the general population (Spekulatius referred to a better study (sampling problems too but much more robust) involving dialysis patients and the study suggested antibody levels at 33.6% for the NY area). Still, the antibody levels varied very significantly by ZIP code areas within NYC and this may contribute to increasing cases in some places presently (clusters with high and rising positivity rates in some neighborhoods). It's hard to discuss herd immunity when people use different definitions (sometimes wildly so). The strict definition based on the initial theorem is not useful as it assumes a homogeneous population. Covid transmits heterogeneously and in clusters so a lower percentage than the theoretical number is expected. This aspect has been validated quite nicely when insufficient stock of vaccines is available for a given population. The more relaxed definition, the more a population is exposed to continued community spread and excess mortality over time. In fact, the definition of herd immunity used by some here would imply that herd immunity has been achieved for influenza (the number of cases that were much higher in the early 20th century have come down and have pretty much stabilized for the last 50 years or so). A vaccine, as a tool, is used for influenza but the effect of the vaccine could be allocated to the virus mutating (something that CV is less likely to do). An interesting thing about the flu is that this year's numbers appear to be unusually low and it's reasonable to suggest that behavior changes have something to do with it... An interesting feature is that those who have opinions against various forms of lockdowns (reasonable debate) also tend to resist basic aerosol hygiene measures (mask, distance etc) or the use of vaccines which are very low cost solutions at the population level. It is puzzling. Another interesting feature is that those who push for (and say we have reached, in a binary way) herd immunity suggest the New York example (one can forget who to blame for this part of the argument and let's assume that NY results are what they are). Whatever caused NY results, in order to reach similar levels of immunity elsewhere, this would imply additional deaths over time in areas of lower immunity or lower virus penetration in the population. It's hard to figure out how to reach similar levels of immunity elsewhere and not having significantly more deaths given the imbricated and dynamic nature of our modern societies, even if the excess burden could be alleviated partially somehow through thoughtful and effective policies. Even if herd immunity needs to be reached somehow on a global basis, it has become clear that the price to pay leading to this path, at least so far, has decreased over time, suggesting that time is money in this case.

^Are you doing this on purpose 'cause it feels like fighting a barbarian invasion with a rot a the core? On April 11th, they reported a 61.3% antibody+ rate on the tests they performed. On April 11th, they performed 1131 tests, which is about 0.01% of the NYC population. On April 11th, i spoke to people on the field and the last thing they cared were irrelevant data points. On April 11th, NY reported 783 additional deaths of COVID-19 (daily count), bringing the death toll to 8,627. On April 11th, NY reported total Covid-19 cases increased by 9,946, to a total of 180,458. That day, the national death toll was at 18,578. You've got to focus on the relevant numbers.

@Investor20 This is basic statistics. If you want to generalize the sample to population, there are basic rules to follow. The antibody testing that is being reported is using serial samples for which inclusion criteria did not tightly apply to the general population and for which rules (mostly self-selection) changed over time.. Look at the 'public' side of the question: https://www1.nyc.gov/site/coronavirus/get-tested/antibody-testing.page Quite frankly, the "strategy" behind the antibody testing here is perplexing. It's not as if the area doesn't have other challenges to address. If you look at the numbers in and around NYC, herd immunity is playing a peripheral role although it seems that they (collectively, as a result of individual and non-coalesced decisions) use an approach that will guarantee success, if herd immunity is the way you define success.. Even if the antibody data is crude and poorly informative, the picture suggests that that when the virus will have run out of easy targets in the Bronk, Queens etc, it will materially make its way into Manhattan. What will happen in the NYC area is the key question and the picture will continue to be dynamic (and the key variables other than herd immunity are pointing to better outcomes) but the exercise is much more mathematical than ideological.

^In simple terms, the Treasury increased the cash held in its General Account by 1,4T, which, by definition, drained reserves in the banking system by the same amount. The question is why? Was it planned as a primary purpose or was it only a side effect? It's possible that 'cooperative' discussions occurred, especially during the last days of March (the repo and reverse repo market showed remarkable swings) and the content of those discussions may never reach the ordinary citizen but there are several problems with the need to drain reserves hypothesis: -Just like many private parties, including many corporations, available credit lines were used to build a cash position based on a very uncertain outcome, in terms of future cashflows necessary to get through the episode. Many corporations will adjust and have and will use cash to repay at least part of the credit line and the Treasury simply needs to lower debt issue for a while and spend the cash in the Account for its own general corporate-like purposes. The next few quarters will help to find out. -Cash in the Fed Treasury General Account has gone up since the GFC but it was well below 100B when excess reserves reached their pre-Covid maximum in mid 2014 (excess reserves at 2.6 to 2.7T). There was no need to drain reserves then. We live now in an abundant excess reserves world. A new normal low point was reached somewhere around 2019 but maximum levels are only tied to theoretical constraints and Japan has showed that excess reserves can reach essentially the GDP level of the country. -The Account can be seen as a checking account with a complicated overdraft procedure so it's normal for the Treasury to raise its cash level in this account in correlation to its relentlessly growing needs and as a function of the uncertainty it is facing. We can expect variations in this Account (up and down) in accordance with timing issues but i maintain that we ain't seen nothing yet in terms of debt issues over time by the Federal government.

^i'd say this thread is unlikely to get traction if we start to argue about the mechanics of the Treasury Account at the Fed. :) Your perspective, as always, is thought-provoking. Your perspective seems to include that the Treasury is in the driver's seat (which i agree with), so what is the Treasury trying to achieve here? To help the Fed? During and around the Great Depression and WW2 (extreme example but valid and maybe we're in a war, an unusual war in which we don't see (or don't want to see) the enemy), the Treasury needed to expand and to pay for unplanned expenditures so they entered into an agreement with the Fed to keep rates low. So, was the Treasury into government-sponsored social- and war-related expenditures or into Fed's reserves-management help? A major objection to the Fed help point of view is that the Treasury took advantage of a flight-to-safety mood at whatever level of interest rates to issue debt enough to increase reserves (through central bank purchases as a side effect) AND to raise the Treasury Account (in cash or printed money) to unusually high levels. This cash reflects the planned expenditures (have you read the Cares Act and what it means for federal outlays going forward?), will eventually enter the system but is meant essentially to match the output gap and some of that will become reserves as part of the Fed's plan to keep rates low for as far as the eye can see. If the Treasury's intent was to help the Fed, why would it issue debt to the extent that a lot of it ended up parked at the Fed? Let's say you're the CFO of a bankrupt airline in an emerging country and the IATA, somehow, becomes in charge of the terms of DIP financing and decides to keep the terms very loose. Isn't it possible then that the distressed airline would 'secure' too much debt for its ultimate productive capacity? The market 'works' because it does (when it's allowed to). Or would the airline's goal be to help IATA in search of reserves?

What you describe is the hot potato effect on a large scale. By actively suppressing interest rates that were already brought down by deep secular forces, the Fed’s only significant effect (apart from unintended consequences) is to swap the maturity profile of Treasury ‘debt’ issued (bonds, bills to fiat) and the effect of the maturity profile recycling has been, through asset price inflation, to bring all forward returns closer to zero (cash) or closer to what the Fed is paying on excess reserves. This is why an argument can be made today that to-be realized returns from growth stocks like Apple, from old-economy stocks like oil-related and financials, from razor-thin spread corporate bonds and from government bonds to bills have been compressed to a very narrow band. ----- Active suppression of interest rates (ie as the last example of this secular trend: the Fed absorbed most of the debt issued so far peri-Covid) has made it easier to issue debt (this point should not be controversial). Is this a good thing or is this the ideal choice? Some perspective from previous CBO ‘forecasts’: So, the Treasury has taken advantage of the fact that interest rates on its debt (which is almost yield-curve-controlled at this point) has remained low and in fact gone towards zero (and more?). Retrospectively thinking in terms of alternatives has limited usefulness because of very obvious limitations (cannot change one variable without considering second-order or domino effects), but it is obvious that the Treasury could not effectively ‘function’ now if they had been right on their forecasts of the direction of interest rates (they just followed the consensus and conventional view that growth would revert back to historical levels and more and that this would steepen the yield curve; but growth has not bounced back to historical trends (to the contrary) and the curve is ‘steep’ but the 30-yr rate is at 1.49% (!) at the time of this writing (who thought this would be possible just a few months ago?)). Just for fun though, let’s see the result of this impossible scenario (what CBO predicted during this last 'recovery' in terms of rates with everything else unchanged). -Typical historical range of net interest payments on debt as % of federal outlays: 7-15% now: about 9% -Typical historical range of interest (in fed outlays) as % GDP: 1.2-3.1% now: about 1.75% -Average interest rate on US gov. marketable debt securities: 2000: 6.41% 2007: 4.93% now: 1.66% (!) So assuming 2007 to 2000 avg rates apply now, this would mean: -net int. as % of outlays: 27-35% (!) -interest as % of GDP: 5.2-6.8% (!) Obviously this could not have happened without something (structural reform, focus on productive growth) but the message is that this easing has put the US on a path that looks like a debt trap (Canada and others look the same). There is hope to get out of this pattern by spontaneous reversion to the mean and to grow out of it but it seems that this is ‘easy’ (or even MMT-like) magical thinking that is over and above what typical animal spirits could accomplish under present circumstances. This set up of easing rates looks more and more like a liquidity trap self-fulfilling prophecy.

^Yes, excess mortality has been useful for analysis and it contributes now to a gradually clearer picture, helpful for constructive 'blame'..(assuming limiting mortality in a cost-effective manner is the objective). For disclosure, my area (province), using this benchmark, has done very poorly (close to Spain). The problem around here is not really ideological but more competence-related (previous decisions and those taken in the heat of the moment) but it's a real head-scratcher to see that the most prevalent mode of attitude is "it is what it is", which makes me wonder if my area is ready for more collective challenges (debt, socialized risk etc). Anyways, the following is interesting when evaluating the value of excess mortality data. Yesterday, in the US, the percent positive rate was at 5.7% (5.7% in October 2020!, no wonder the virus is not only trickling down), so you can expect (like it has happened recurrently along the virus spread evolution) reporting lags to cause the updated excess recent mortality curve to be adjusted upwards for some time because there continues to be a low grade excess mortality going on (in October 2020!) and it looks like this will carry on for a while. Interestingly, from many sources, it looks like the average years lost per mortality is between 10 and 15 years. https://weinbergerlab.github.io/excess_pi_covid/ You also may be interested in the following. Last May, they looked at some aspects of the meaning of this data and, recently, have looked at data more globally. https://voxeu.org/article/excess-mortality-england-european-outlier-covid-19-pandemic https://voxeu.org/article/us-excess-mortality-rate-covid-19-substantially-worse-europe-s An interesting way to look at this: Opinion: There is so much to learn and yet so little appetite to do so where it is most needed (both backward and forward looking).

Italy is an interesting case. Herd immunity, apart from some regions and sub-regions where antibody prevalence reached relatively high levels, does not materially apply as most regions elsewhere showed relatively low exposure. Their initial outbreaks were quite traumatic (social mood) and behavioral changes happened in a big way, even given the cultural inclination for close contacts and multi-generational proximity. https://www.msn.com/en-ca/news/canada/1st-in-europe-to-be-devastated-by-covid-19-italy-redoubled-its-efforts-and-they-re-now-paying-off/ar-BB19Bca7?ocid=msedgntp TL;DR: A lot remains to be explained but their extensive and community-based test and contact tracing scheme, with a backward component, may have played an important role, when combined with other measures (that may have been too extreme, in retrospect). https://science.sciencemag.org/content/early/2020/09/29/science.abd7672 TL;DR: About 8% of confirmed cases explain about 2/3 of subsequent cases and 71% of confirmed cases did not spread. A variation of the Pareto principle at work. In terms of 'spread' analysis, some may be interested in: https://en.wikipedia.org/wiki/The_Tipping_Point https://www.theatlantic.com/health/archive/2020/09/k-overlooked-variable-driving-pandemic/616548/ TL;DR: There remain many unanswered questions but the cluster effect may not have been given enough consideration. The models used for Covid have been mostly deterministic, which makes sense for an overall assessment and when community spread reaches high levels (Covid and the flu behave similarly then, in terms of contagiousness and transmission). There is room for stochastic models. For those interested in this and in how insurance reserves are determined, there are deterministic models being used and these models have been improved (ie Bornhuetter–Fergusson Technique) by including a Bayesian component but, for some lines of business, deterministic models don't work very well (latent claims etc) and stochastic models can be incorporated to improve the visualization of potential outcomes (and for a virus, to define and apply cost-effective strategies to contain spread). Keeping community levels low and backward tracing can be self-feeding. An interesting aspect is that the stochastic and cluster-based approaches help to explain how good and bad luck may have a compounding effect, help to explain what Sweden, relatively, did right and did poorly and how Japan sort of got it right, given their historical path-dependent circumstances.

Perhaps you both are right - it might help these red states "fail upwards"... I mean, Texas and Florida will need more productive citizens if they ever hope to match NY & CA's national GDP contribution of 22%, up from the relatively paltry 14% that they currently contribute. Further, perhaps states which care for their citizens' health may be a factor in the above GDP contribution. What's the old saying, "Health is wealth" ? UNH State health ranking: https://assets.americashealthrankings.org/app/uploads/ahr_2019annualreport.pdf New york: 11th Cali: 12th Florida: 33rd Texas: 34th Touting a state's success by GDP == Touting a company's success based on large revenue. It doesn't matter how much cash it is burning and how many employees want to leave the company. Look at the revenue! We are a huge success! FL and TX's share of popn = 15%, share of GDP 14% CA and NY's share of popn =18%, share of GDP 23% and the divergence is growing despite the net population migration to the above 2. GDP per capita and other related measures (education levels, health indicators etc) are correlated and are widely considered to be a reflection of overall quality of life. http://gppreview.com/2020/02/21/growing-divide-red-states-vs-blue-states/ Advancing standards of living, on a net basis, come with increasing costs of living (housing etc) which has and will tend to cause a reversion to the mean, assuming no unusual resistance. The evolution over time of GDP per capita in California compared to FL and TX is interesting: https://www.opendatanetwork.com/entity/0400000US12-0400000US06/Florida-California/economy.gdp.per_capita_gdp?year=2018 https://www.opendatanetwork.com/entity/0400000US48-0400000US06/Texas-California/economy.gdp.per_capita_gdp?year=2018#:~:text=GDPAPI&text=The%20last%20measured%20GDP%20per,Texas%20was%20%2459%2C674%20in%202018. Capacity to grow GDP per capita and trends in NY indicate that policy decisions need to be competently made and based on a cost-effective rational process, irrespective of other ideological factors. The virus entered the NY State at the same time as on the West Coast. The relative performance has been largely negative for the NY area. The NY area has natural disadvantages (population density, high proportion of mass transit use, close to major international and high-volume airports, poorer and high risk-factors health segments, more unequal distribution of wealth and health coverage, relatively poor public hospital network) but the major issue were policy mistakes involving a delayed application of cost-effective measures and an initial mixed and confused message. On a net basis, i would say there continues to be a lot to learn from California (wealth, quality of life, handling of coronavirus etc)..

From a policy and data angle: California and Florida are not that different although marginal differences tend to add up and some people like to focus on the (growing) differences. What can we learn from each other? California has been slow with guidelines for theme park re-opening as there was enough objective data (including Florida partially re-opening theirs, with no known material outbreaks) to justify it. Of note is that 2/3 of laid off employees were part-time and Google mobility reports comparing California to Florida only show marginal differences. The logical conclusion is that there will be a difference in economic output due to differential policies but the difference will only be marginal and should take into account the long term benefit of cost-effective policies on long term growth. There is a group of States that have tended to combine more convincing GDP per capita growth and more consistent growth in productivity with a healthier and living gradually longer population and there is a group of States that have tended to do the opposite. Those differences are slowly but surely crystalizing and polarizing and it seems the latter group of States are looking for extrinsic causes, rooted in resentment. Interestingly, strategies put in place to contain the virus (for the variables that can be controlled) are also correlated to this growing disparity. Using basic numbers reported and simple calculations, here are would-be scenarios, assuming policy differentials explain the outcome differences when comparing California and Florida: -number of lives saved in Florida with California-type policies in place: 5550, including 1000 aged less than 65 -number of extra-deaths in California with Florida-type policies in place: 10400, including 2800 aged less than 65 From a long term and high level perspective, the above mortality numbers are just a blip and the above does not describe the less-well seen costs of policies. This sets the stage for an interesting debate, if done respectfully and constructively, i continue to assume that it's possible to learn from each other. Some contend that what happens outside one's borders is not relevant but i'll submit the following anyways (in the spirit of potential vicarious learning). California has 60% more deaths than Germany as a whole and half the population. Florida has 50% more deaths than Germany as a whole and a quarter of the population. These findings prompted me to read more about Rhine capitalism as i expect that more chickens will be coming home to roost. @Muscleman This is not central to the discussion and may, in fact, derail it but i submit that you mischaracterized the intended message in the Dinner for Few short movie. The movie includes stuff from Bertolt Brecht for whom food insecurity was a big theme. For example, Brecht could not understand why a worker (and related household) in a meat-packing plant would literally die of hunger. The movie is about greed and selfishness, themes which are not related to the right-left spectrum but related to injustices coming from inappropriate concentrations of power. In the case of the movie and along Brecht's lines, they were not referring to the same pigs that you suggested.

i agree with you directionally. It's the extent we can rely on natural immunity that is risky (IMO). Have you looked at the dialysis seroprevalence study that Spekulatius submitted? The study has limitations but the limitations are listed and discussed. It is a source of some powerful information. Relevant for this discussion: -There was a remarkable variation in seroprevalence by state in the sampled participants, with early pandemic hotspots such as New York (33·6%, 95% CI 31·7–35·6), Louisiana (17·6%, 10·8–28·7), and Illinois (17·5%, 15·2–20·2) recording substantially higher seroprevalence than their respective neighbouring states of Pennsylvania (6·4%, 4·7–8·8), Arkansas (1·9%, 1·0–3·5), and Missouri (1·9%, 0·9–3·8). -The study also estimated substantially higher seroprevalence in residents of predominantly Hispanic (11·3%, 95% CI 9·8–12·9), non-Hispanic Black (13·9%, 12·1–16·0), and Hispanic and Black (16·3%, 14·3–18·5) neighbourhoods compared with predominantly non-Hispanic white neighbourhoods (4·8%, 4·1–5·5), when standardised to the US adult population. There are large segments in the population who have risk factors (including relative lower levels of innate or cell-mediated immunity) and who happen to live in crowded conditions with people living in the same household who can't simultaneously work at home and "protect". The remarkable variation in prevalence of antibody-mediated immunity and the heterogeneity of the population makes the concept of a unique % number of antibody levels relatively risky, in its application.

Why is it that herd immunity would be the only factor? The household transmission rates are interesting. If herd immunity is the factor, how do you explain the following: -These and similar studies show that transmission rates for COVID in the household are way higher than for MERS or SARS, does that mean that herd immunity had been reached for those previous viral episodes? -Proximity seems to play a role (spouses vs others etc), so why not consider increased in-house basic precautions and basic distancing as relevant and important co-factors? -The study mentioned that self-quarantine (behavior) made a huge difference in transmission outcomes, irrespective of any theoretical herd, natural or innate immunity, so how does that fit in the mono-factor immunity theory? -Many places in the world have reached antibody levels higher than 30, 40, 50 or even 60%. If cell-mediated immunity is such a critical variable, why doesn't it prevent antibody levels from reaching such high levels? Look at the following. The study has significant weaknesses but it was made in a relatively controlled environment where herd immunity took a while to kick in: https://www.medrxiv.org/content/10.1101/2020.09.16.20194787v1 Personal note: my area recorded one of the highest prevalence of disease last spring. And now the same area shows a significant rise in cases, although, for a variety of reasons and including also a partial level (IMO) of herd immunity, this "second" wave should result in less morbidity and mortality. But despite this higher herd immunity obtained due to previous institutional weakness and basic competence issues, it seems that my area will do worse (because of persistent and widespread community spread) than most parts of Canada who, it seems, made a conscious step to not bet on the herd.

Think of the return on equity invested in insurance operations as a function of 1-underwriting gains, 2-investment income from fixed income and 3-'income' from investments in equities, net of financial leverage. 1-includes the insurance losses and the underwriting expenses required to maintain operations and maintain or build float. Once a steady-state is reached, the float is typically a multiple of equity (2-3:1). Look at the following (pages 8 and 27): https://www.tilsonfunds.com/BRK.pdf -BRK has produced low but consistent underwriting gains (negative cost of float contributing to return). -BRK has produced low returns from fixed income (size more or less balances float reserves) because of low interest rates and a staunch resistance to reach for yield. -BRK has produced market-like return on the equity portfolio side (not shown in pages 8 or 27). So, overall, the return on equity on the insurance side has been satisfactory (especially compared to P+C industry peers). The magic here is the growth in underlying operations. Since 2004, float reserves have have compounded at 21.3% and premiums earned at 23.7%. It can be a powerful combination and BRK is positioned to benefit in pretty much any scenario. Hope this helps.

^ -Treasury share issuance has been "reserved" for share-based compensation and it would be hard to issue those shares to the market (to raise capital) without appropriate disclosure. -It looks like Q3 points to a major change in trend concerning the net result of buyback (to cancel and treasury) vs issue to employees. For the issue to employee part, the result on risk-based capital measures is essentially neutral. Treasury shares are deducted from regulatory capital and when they are issued to employees, in the case of Fairfax, the net result on total equity is about zero as the increase in equity capital (through decrease in the treasury contra-account) is more or less balanced by the intra-equity share-based payment reserves account. -Reserve development is also something to look for.

Policy design and application during Covid has been tricky. As usual, it's about achieving some kind of tradeoff between the primitive and individual instincts, critical for survival and the unalienable rights to prevent the strong from exploiting the weak, critical for civilized survival. https://www.aier.org/article/coase-and-covid-the-spectrum/ TL;DR: As usual, the individual option should be the default option, even by instinct. https://www.cdc.gov/mmwr/volumes/69/wr/mm6939e1.htm?s_cid=mm6939e1_e&ACSTrackingID=DM38812&ACSTrackingLabel=MMWR%20Early%20Release%20-%20Vol.%2069%2C%20September%2023%2C%202020&deliveryName=DM38812&utm_source=newsletter&utm_medium=email&utm_campaign=newsletter_axiosam&stream=top#F2_down TL;DR: In some places, spread in the younger group (20-49) involved frontline people where exposure is not necessarily benign and that spread preceded spread in older cohorts, suggesting a very obvious free market failure. It will be interesting to see how this plays out in the event of economic hardship. In terms of raw material that can feed reflection, the following contains updated and objective data about excess mortality. Even if policy was applied by instinctual design, there is nothing that says we can't learn from the episode. as this will continue to be work in progress. https://www.ft.com/content/a2901ce8-5eb7-4633-b89c-cbdf5b386938