Cases in the Nordic country have declined sharply over the past few days and on Tuesday only 283 new cases were recorded.
That contrasts with a torrid month of June when daily numbers ran as high as 1,800, eclipsing rates across much of Europe, even as deaths and hospitalisations continued to decline from peaks in April.
At the same time:
…weekly numbers for tests have more than doubled since late May, putting the country in the same bracket as extensively testing nations such as Germany.
Here is the steadily declining Swedish death rate. No need to point out that Denmark and Norway, with their early and swift responses, did much better yet. I am interested in what is the best way to model why Sweden is not doing much worse.
Health officials praise Laos after coronavirus-free declaration (some new concerns here, so far nothing major)
Cambodia has zero reported deaths, broadly consistent with anecdotal evidence too.
Vietnam reports 14 new cases, all imported. Broader record of zero deaths.
Have you noticed that those four countries are right next to each other? (Within southeast Asia, most cases are in the relatively distant Indonesia and Philippines.)
I genuinely do not understand why this heterogeneity is not discussed much, much more.
Those countries also have very different institutions and systems of government and state capacity. Do you really think this is all because they are such policy geniuses?
Those countries have instituted some good policies, to be sure. But so has Australia, where there is a major coronavirus resurgence.
Inquiring minds wish to know. One hypothesis is that they have a less contagious strain, another is that they have accumulated T-cell immunities from previous coronaviruses. Or perhaps both? Or perhaps other factors are playing a role?
I do not understand why the world is not obsessed with this question. And should you be happy if you have, in the past, traveled to these countries as a tourist?
It is “crystal clear” drunk people can’t – or won’t – socially distance, a police chief has warned after scenes showed huge crowds packed into Soho in central London.
John Apter, chair of the Police Federation, said he witnessed “naked men, happy drunks, angry drunks, fights and more angry drunks” while on shift in Southampton – and there were similar scenes across the rest of England.
Chris Whitty, England’s chief medical officer, had warned reopening pubs was a “high risk” for spreading coronavirus ahead of the easing of lockdown restrictions which also saw restaurants, cinemas, hairdressers and museums open their doors on what was dubbed “Super Saturday”.
Why not internalize the relevant externalities by bringing the two together?:
We estimate the benefit of life-extending medical treatments to life insurance companies. Our main insight is that life insurance companies have a direct benefit from such treatments because they lower the insurer’s liabilities by pushing the death benefit further into the future and raising future premium income. We apply this insight to immunotherapy, treatments associated with durable gains in survival rates for a growing number of cancer patients. We estimate that the life insurance sector’s aggregate benefit from FDA-approved immunotherapies is $9.8 billion a year. Such life-extending treatments are often prohibitively expensive for patients and governments alike. Exploiting this value creation, we explore various ways life insurers could improve stress-free access to treatment. We discuss potential barriers to integration and the long-run implications for the industrial organization of life and health insurance markets, as well as the broader implications for medical innovation and long-term care insurance markets.
It seems the virus mutated in Europe and became significantly more contagious (though not more dangerous per unit dose):
The Spike D614G amino acid change is caused by an A-to-G nucleotide mutation at position 23,403 in the Wuhan reference strain; it was the only site identified in our first Spike variation analysis in early March that met our threshold criterion. At that time, the G614 form was rare globally, but gaining prominence in Europe, and GISAID was also tracking the clade carrying the D614G substitution, designating it the “G clade”. The D614G change is almost always accompanied by three other mutations: a C-to-T mutation in the 5’ UTR (position 241 relative to the Wuhan reference sequence), a silent C-to-T mutation at position 3,037; and a C-to-T mutation at position 14,408 that results in an amino acid change in RNA-dependent RNA polymerase (RdRp P323L). The haplotype comprising these 4 genetically linked mutations is now the globally dominant form. Prior to March 1, it was found in 10% of 997 global sequences; between March 1- March 31, it represented 67% of 14,951 sequences; and between April 1- May 18 (the last data point available in our May 29th sample) it represented 78% of 12,194 sequences. The transition from D614 to G614 was occurred asynchronously in different regions throughout the world, beginning in Europe, followed by North America and Oceania, then Asia (Figs. 1-3, S2-S3).
That is from a new paper in Cell by B. Korber et.al., via Eric Topol. You will note there is another recent paper suggesting the east and west coasts of the United States have faced different mutations and thus different levels of contagiousness, but that seems less well established.
The authors do not mention Taiwan, but if I understand their chronology correctly, it would seem that Taiwan has not significant been hit by the most contagious version of the virus.
In any case, I will repeat my general point: moralizing about the virus is premature. And of course the main result presented in this new paper is subject to revision, further scrutiny, and possible reversal.
Addendum: Here is NYT coverage.
That is the topic of my latest Bloomberg column, and here is part of the explanation:
The danger lies in the potential for ratchet effects. If hardly anyone is eating out or going to bars, you might be able to endure the deprivation. But once others have started doing something, you will probably feel compelled to join them, even at greater risk to your life.
Consider that in the 1920s, the chance of catching a disease or infection from dining out was pretty high, but people still went out. Accepting that level of risk was simply considered to be part of life, because everyone saw that everyone else was doing it. In similar fashion, members of an infantry brigade are usually willing to charge an enemy position so long as they can be assured that all their comrades are, too.
So if you are wondering why the U.S. has become so tolerant of Covid-19 risk, one reason is simply that it has the most pro-consumption norms of any major Western nation. The pursuit of socially influenced high consumption levels is far more common in America than in, say, Kosovo, a country with a relatively good anti-Covid safety record.
And one implication is this:
So telling Americans that they are stupid and excessively sociable is likely only to make the problem worse.
Better in fact is everyone thinks no one else is going out very much.
The SARS-CoV-2 pandemic calls for the rapid development of diagnostic, preventive, and therapeutic approaches. CD4+ and CD8+ T cell-mediated immunity is central for control of and protection from viral infections[1-3]. A prerequisite to characterize T-cell immunity, but also for the development of vaccines and immunotherapies, is the identification of the exact viral T-cell epitopes presented on human leukocyte antigens (HLA)[2-8]. This is the first work identifying and characterizing SARS-CoV-2-specific and cross-reactive HLA class I and HLA-DR T-cell epitopes in SARS-CoV-2 convalescents (n = 180) as well as unexposed individuals (n = 185) and confirming their relevance for immunity and COVID-19 disease course. SARS-CoV-2-specific T-cell epitopes enabled detection of post-infectious T-cell immunity, even in seronegative convalescents. Cross-reactive SARS-CoV-2 T-cell epitopes revealed preexisting T-cell responses in 81% of unexposed individuals, and validation of similarity to common cold human coronaviruses provided a functional basis for postulated heterologous immunity in SARS-CoV-2 infection[10,11]. Intensity of T-cell responses and recognition rate of T-cell epitopes was significantly higher in the convalescent donors compared to unexposed individuals, suggesting that not only expansion, but also diversity spread of SARS-CoV-2 T-cell responses occur upon active infection. Whereas anti-SARS-CoV-2 antibody levels were associated with severity of symptoms in our SARS-CoV-2 donors, intensity of T-cell responses did not negatively affect COVID-19 severity. Rather, diversity of SARS-CoV-2 T-cell responses was increased in case of mild symptoms of COVID-19, providing evidence that development of immunity requires recognition of multiple SARS-CoV-2 epitopes. Together, the specific and cross-reactive SARS-CoV-2 T-cell epitopes identified in this work enable the identification of heterologous and post-infectious T-cell immunity and facilitate the development of diagnostic, preventive, and therapeutic measures for COVID-19.
Here is the full piece, by Annika Nelde, et.al., via Jackson Stone. Or from the paper, here is a simpler bit:
At present, determination of immunity to SARS-CoV-2 relies on the detection of SARS-CoV-2 antibody responses. However, despite the high sensitivity reported for several assays there is still a substantial percentage of patients with negative or borderline antibody responses and thus unclear immunity status after SARS-CoV-2 infection34. Our SARS-CoV-2-specific T- cell epitopes, which are not recognized by T cells of unexposed donors, allowed for detection of specific T-cell responses even in donors without antibody responses, thereby providing evidence for T-cell immunity upon infection.
Big (and good news) if true.
#COVID19 mortality in UK hospital patients has been falling steadily from >6% in March to ~1% now, with similar trends elsewhere. The reasons behind this pattern remain unclear, but #COVID19 Infection Fatality Rates will likely have to be revised downward. tinyurl.com/ybnlmkdz
That is from Francis Balloux. And again here is the source link. And please do not conclude the virus is becoming less dangerous, that is not a necessary implication of the above! Alternative explanations are given at the latter link. Most broadly, I will say it again: if your model does not have long-run elasticities as much greater than short-run elasticities, it is likely to be off in some significant ways.
The largest economic cost of the COVID-19 pandemic could arise from changes in behavior long after the immediate health crisis is resolved. A potential source of such a long-lived change is scarring of beliefs, a persistent change in the perceived probability of an extreme, negative shock in the future. We show how to quantify the extent of such belief changes and determine their impact on future economic outcomes. We find that the long-run costs for the U.S. economy from this channel is many times higher than the estimates of the short-run losses in output. This suggests that, even if a vaccine cures everyone in a year, the Covid-19 crisis will leave its mark on the US economy for many years to come.
That is from a new NBER working paper by Julian Kozlowski, Laura Veldkamp, and Venky Venkateswaran.
As of 2018 nearly a third of all Russia’s medical facilities had no running water and more than half lacked hot water. Around 40% lacked central heating and in 35% the sewage didn’t work.
From Nate Silver, I am smushing together his tweet storm:
Something to think about: re-openings have been occurring gradually since late April in different states/counties. If you had a metric averaging out how open different states are, it would likely show a fairly linear pattern. So why is there a nonlinear increase in cases now?
Obviously some of that gets to the nature of exponential growth. An R of 1.3 isn’t *that* different than an R of 1.1, but played out over a few weeks, it makes a lot of difference. Still, a more complete story probably includes premature re-openings coupled with other stuff.
What other stuff? Two things seem worth pointing out. First, there seems to be some correlation with greater spread in states where it’s hot and people are spending more time indoors with the AC on. That *is* a bit nonlinear; there’s much more demand for AC in June than May.
And second, the conversation around social distancing changed a lot in early June with the protests and Trump making plans to resume his rallies. And COVID was no longer the lead story. Not blaming anyone here. But the timing is pertinent if people felt like “lockdowns are over”.
Here is the link, including a good picture of how the demand for air conditioning rises.
It goes to the COVIN Working Group for their paper “Adaptive control of COVID: Local, gradual, and trigger-based exit from lockdown in India.”
As India ends its lockdown, the team, led by Anup Malani, has developed a strategy to inform state policy using what is called an adaptive control strategy. This adaptive control strategy has three parts. First, introduction of activity should be done gradually. States are still learning how people respond to policy and how COVID responds to behavior. Small changes will allow states to avoid big mistakes. Second, states should set and track epidemiological targets, such as reducing the reproductive rate below 1, and adjust social distancing every week or two to meet those targets. Third, states should adopt different policies in different districts or city wards depending on the local conditions.
This project provides a path that allows states to contain epidemics in local areas and open up more of the economy. Going forward the team plans to help address shocks such as recent flows of laborers out of cities and estimate how effective different social distancing policies are at reducing mobility and contact rates.
This project has 14 authors (Anup Malani, Satej Soman, Sam Asher, Clement Imbert, Vaidehi Tandel, Anish Agarwal, Abdullah Alomar, Arnab Sarker, Devavrat Shah, Dennis Shen, Jonathan Gruber, Stuti Sachdeva, David Kaiser, and Luis Bettencourt) across five institutions (University of Chicago Law School and Mansueto Institute, MIT Economics Department and Institute for Data Systems and Society, IDFC Institute, John Hopkins University SAIS, and University of Warwick Economics Department).
Congrats to all the authors of the paper and their institutions. And here are links to the previous Emergent Ventures anti-Covid prize winners.
And I thank Shruti for her help with this.
We simulate a spatial behavioral model of the diffusion of an infection to understand the role of geographical characteristics: the number and distribution of outbreaks, population size, density, and agents’ movements. We show that several invariance properties of the SIR model with respect to these variables do not hold when agents are placed in a (two dimensional) geographical space. Indeed, local herd immunity plays a fundamental role in changing the dynamics of the infection. We also show that geographical factors affect how behavioral responses affect the epidemics. We derive relevant implications for the estimation of epidemiological models with panel data from several geographical units.
In Spatial-SIR, local herd immunity slows contagion initially in the less dense city, but faster global herd immunity slows it in the denser city later
I think this means West Virginia is in for some hard times fairly soon.
I have a question for you and/or your MR readers: what’s the smart way to use spare Covid testing capacity?
With the virus (currently) receding in many places fewer and fewer people are getting symptoms and seeking tests.
Even without a second wave in the next few months, we’ll need testing capacity again for the next flu season, when we’ll need to distinguish between flu patients and Covid patients.
How should we use spare testing capacity in the meantime? Increase random testing? Weekly tests for everyone in a single city? Weekly tests for everyone in particular economic sectors?
I would be grateful for your thoughts on this.
That is from O.L. My intuition (and I stress this is not a scientific answer in any way) is to test people who take elevators every day, to get a better sense of how risky elevators are. And then test systematically in other situations and professions to learn more about transmission mechanisms, for instance the subway when relevant, supermarket clerks, and so on. Test to generate better risk data. What do you all think?
We correlate county-level COVID-19 death rates with key variables using both linear regression and negative binomial mixed models, although we focus on linear regression models. We include four sets of variables: socio-economic variables, county-level health variables, modes of commuting, and climate and pollution patterns. Our analysis studies daily death rates from April 4, 2020 to May 27, 2020. We estimate correlation patterns both across states, as well as within states. For both models, we find higher shares of African American residents in the county are correlated with higher death rates. However, when we restrict ourselves to correlation patterns within a given state, the statistical significance of the correlation of death rates with the share of African Americans, while remaining positive, wanes. We find similar results for the share of elderly in the county. We find that higher amounts of commuting via public transportation, relative to telecommuting, is correlated with higher death rates. The correlation between driving into work, relative to telecommuting, and death rates is also positive across both models, but statistically significant only when we look across states and counties. We also find that a higher share of people not working, and thus not commuting either because they are elderly, children or unemployed, is correlated with higher death rates. Counties with higher home values, higher summer temperatures, and lower winter temperatures have higher death rates. Contrary to past work, we do not find a correlation between pollution and death rates. Also importantly, we do not find that death rates are correlated with obesity rates, ICU beds per capita, or poverty rates. Finally, our model that looks within states yields estimates of how a given state’s death rate compares to other states after controlling for the variables included in our model; this may be interpreted as a measure of how states are doing relative to others. We find that death rates in the Northeast are substantially higher compared to other states, even when we control for the four sets of variables above. Death rates are also statistically significantly higher in Michigan, Louisiana, Iowa, Indiana, and Colorado. California’s death rate is the lowest across all states.