A new study shows that SARS-CoV-2 can linger in the air for hours and on some materials for days
We’re not going back to normal by Gideon Lichfield, MIT Tech Rev, Mar 17, 2020
Social distancing is here to stay for much more than a few weeks. It will upend our way of life, in some ways forever.
A Silent Hero of the Coronavirus Crisis project-syndicate Role of technology in tackling crisis
Leslie Davenport on staying sane amid coronavirus craziness Post Carbon Institute
I was part of saying that kids in schools are OK and kids aren't getting very sick. This article doesn't talk about permanent problems kids might suffer (elsewhere, I read long term loss of lung function). While fewer require critical care, still, 6% of children are severely affected, even more among infants. And because their symptoms are different (see end of quoted section), they may spread coronavirus faster.
>>>Largest study to date suggests infants may be vulnerable to critical illness after all -- and that children may play a ‘major role’ in spread of pathogen
In the nightmare of the coronavirus pandemic that is unfolding around the world, parents have been able to take comfort in one thing — early reports that the virus mysteriously spares children even as it often causes critical illness in the elderly.
A paper released this week in the journal Pediatrics, based on 2,143 young people in China, provides the most extensive evidence on the spread of the virus in children, and there is bad news and good news.
The study provides confirmation that coronavirus infections are in fact generally less severe in kids, with more than 90 percent having mild to moderate disease or even being asymptomatic. But it contains worrisome information about one subset — infants — and suggests that children may be a critical factor in the disease’s rapid spread...
So what does coronavirus look like in children?
According to the analysis by Shanghai Children’s Medical Center researchers Yuanyuan Dong, Xi Mo and co-authors, mild cases (52 percent) were marked by the typical symptoms of a cold — fever, fatigue, cough, sore throat, runny nose and sneezing. Some patients had no fever and only digestive symptoms such as nausea, vomiting, abdominal pain and diarrhea.
Those with moderate infection (39 percent) had pneumonia with frequent fever and cough, mostly dry cough, followed by a wetter cough. Some had wheezing but no obvious shortness of breath.
Severe cases were rare (5 percent) as were those who required critical care (0.4 percent.) The severe cases began with early respiratory symptoms which were sometimes accompanied by gastrointestinal issues. Around one week the children have more difficulty breathing. Those cases sometimes quickly progressed to critical illness with acute respiratory distress or failure which in turn sometimes led to other organ dysfunction — heart failure or kidney injury.
One boy, a 14-year-old, died on Feb. 7. No further details on the patient were revealed in the study.
Of special interest to pediatricians is a group of seven infants (11 percent of the total number of infants in the study), and two children in the age 1 to 5 range (15 percent), who progressed to critical condition. The study suggests, the authors wrote, that “young children, particularly infants, were vulnerable.”<<<
Excellent visual comparison of how COVID-19 has advanced in different countries. Anton Van Der Merwe
We can now read the Imperial College report on COVID-19 that led to the extreme measures we've seen in the US this week. Read it; it's terrifying. I'll offer a summary in this thread; please correct me if I've gotten it wrong.
The Imperial College team plugged infection and death rates from China/Korea/Italy into epidemic modeling software and ran a simulation: what happens if the US does absolutely nothing -- if we treat COVID-19 like the flu, go about our business, and let the virus take its course?
Here's what would happen: 80% of Americans would get the disease. 0.9% of them would die. Between 4 and 8 percent of all Americans over the age of 70 would die. 2.2 million Americans would die from the virus itself.
It gets worse. People with severe COVID-19 need to be put on ventilators. 50% of those on ventilators still die, but the other 50% live. But in an unmitigated epidemic, the need for ventilators would be 30 times the number available in the US. Nearly 100% of these patients die.
So the actual death toll from the virus would be closer to 4 million Americans -- in a span of 3 months. 8-15% of all Americans over 70 would die.
I made a mathematical model to predict the spread of Coronavirus given different assumptions about how we respond - you can have a look here:
The idea is to show how even a very simple model can capture the trends which are currently making the headlines.
For example the attached graph shows how "flattening the curve" through aggressive social distancing and quarantining measures would reduce the overall mortality rate by easing strain on the healthcare system.
The initial simple model is extended to look at:
(a) Social distancing,
(b) Finite capacity of the healthcare system,
(c) Specifically isolating the most vulnerable people.
You can read through how it works, but the summary of the conclusions (which agree with the real scientist's results) are:
- Very simple and intuitive mathematical models can reproduce the same overall trends as more complex epidemiological models.
- If unchecked, the virus will spread exponentially through a large fraction of the population.
- In the worst-case scenario, the total death toll in the UK alone could be in the hundreds of thousands.
- The peak load on the healthcare system might not be reached for 2-3 months.
- Widespread social distancing can help reduce the number of cases, and limit the burden on the healthcare system.
- The earlier social distancing begins the lower the number of cases will be.
- However, slacking off on the social distancing could lead to a re-emergence of the virus.
- Specifically isolating the most vulnerable segments of the population can significantly reduce the overall mortality rate.
- If possible, then the best thing to do would be to act decisively immediately, to stop the virus in its tracks.
Following the emergence of a novel coronavirus (SARS-CoV-2) and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national lockdowns.
In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number – a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of lockdown (11th March), although with a high level of uncertainty.
Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented all interventions considered in our analysis. This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March [95% credible interval 21,000-120,000]. Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-2 up to 28th March, representing between 1.88% and 11.43% of the population. The proportion of the population infected to date – the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.
Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-2 is slowing.
See also 30 March Imperial Report above.
Most countries with major outbreaks moved faster than Boris Johnson'
South Korea keeps covid-19 at bay without a total lockdown Economist [freewalled]
But the long-term outlook is still uncertain
A GROUP OF young people sit around a table in a bar, immersed in animated conversation. A man and a woman sit in the window of a coffee shop and share bites of a large slice of cream cake. A handful of people in face masks and office attire emerge from a side street and say their goodbyes before disappearing down the stairs into the subway station. In normal times such scenes would hardly merit a mention. But when a third of the world’s population is living under lockdown, the relative normality of Seoul feels surreal.
That life in South Korea has not ground to a complete halt is mostly owing to an early and aggressive response to the covid-19 pandemic. The country’s disease-control authority approved the first test kit for the virus in early February, less than a week after the application was filed. By the time the number of cases began to rise a couple of weeks later, it had the capacity to test thousands of people a day and get results within a few hours from a network of labs across the country.
This was invaluable when an enormous cluster of cases emerged in the city of Daegu, centred around a messianic cult. Members of the Shincheonji church pray crammed together for hours during their services, providing an ideal environment for the new coronavirus to spread. Of the country’s 9,661 currently confirmed cases, more than half are linked to the cult. Its founder claims to be descended from Korean kings and to possess the ability to foresee the apocalypse. Luckily for Korean officials, his group also kept meticulous records. Working off a membership list provided by the cult, the virus detectives managed to track down its more than 200,000 members, including 10,000 followers in Daegu. Those with symptoms of the virus were tested and, if positive, taken to hospital or confined to quarantine facilities.
Besides interviewing patients, officials used location data from mobile phones, credit-card transaction records and CCTV footage to trace and test people who might have crossed paths with an infected person. In many places, they published detailed maps of the movements of patients and encouraged people who thought they might have been in contact with one to seek out testing. Overall, the country has tested nearly 400,000 people for the virus in less than two months, one of the highest rates of testing in the world. In parallel, South Koreans were advised to wash their hands frequently, avoid unnecessary outings, keep away from other people when out and about and wear masks on public transport and in other closed spaces.
In combination, those measures slowed the spread of the virus sufficiently that South Korea did not have to resort to the more coercive lockdowns that America and many European governments have implemented over the past few weeks. South Korea has registered around 100 new cases every day for the past three weeks, with only occasional spikes, and a total of 158 deaths. In contrast Spain and Italy, countries of similar population which were hit later than South Korea, have about ten times more cases and roughly 7,000 and 11,000 deaths respectively.
As a result, South Korea’s health-care system is coping. Though hospitals in Daegu were briefly overwhelmed at the height of the outbreak in late February, this was quickly resolved by treating those with mild symptoms in residential centres rather than hospitals, easing the pressure on wards.
The main reason that South Korea responded so quickly to the initial outbreak is its recent bad experience with MERS, another coronavirus, which killed 38 people in the country in 2015. “We were hit hard by MERS because we failed to supply diagnostic kits in time,” says Hong Ki-ho, director of laboratory medicine at Seoul Medical Centre and a member of the country’s covid-19 taskforce. In response, South Korea increased diagnostic capacity, hired more epidemiological investigators and changed the law to improve information-sharing between different branches of government and the health-care system and to allow fast-track approval of test kits during an outbreak.
Widespread testing will probably help the country manage its outbreak over the next few weeks, even if, as is likely, new clusters of the virus continue to emerge all over the country. But the government clearly worries that it will not be enough. Over the past few days, it has introduced more draconian measures to curb the growing number of imported cases, including compulsory tests for arrivals from Europe and America. From April 1st, all travellers arriving from abroad will be obliged to undergo two weeks of quarantine either at home or in a government facility. Only diplomats and those on official business will be exempt. Breaking quarantine carries a fine of 10m won ($8,170) and up to a year in prison for Koreans. Foreigners risk deportation. Chung Sye-kyun, the prime minister, made it clear that the point of the measures was to close off the country for the time being: he said the policy was intended to “effectively block all unnecessary arrivals”.
In the short term, the move is likely to help keep the numbers low. But eventually South Korea, like all other countries in the world, will have to think about when and how quickly to relax the current controls. For all the relative freedom its citizens currently enjoy compared with Europeans and Americans, life is hardly back to normal. Schools, museums and gyms—places where large numbers of people might gather in an enclosed space—remain closed. Churches have moved worship online. As people are still encouraged to avoid unnecessary outings, cinemas have begun to close for lack of customers, as have many bars and restaurants. If the cases multiply, the country may have to toughen social-distancing rules. Dr Hong believes that for now, continuing to trace infections aggressively remains the most important measure. “Until there is a vaccine, we have to stay vigilant.”
Testing the Efficacy of Homemade Masks: Would They Protect in an Influenza Pandemic? Davies, Anna & Thompson, Katy-Anne & Giri, Karthika & Kafatos, George & Walker, James & Bennett, Allan. (2013); Disaster medicine and public health preparedness. 7. 413-418. 10.1017/dmp.2013.43.
This study examined homemade masks as an alternative to commercial face masks. Several household materials were evaluated for the capacity to block bacterial and viral aerosols. Twenty-one healthy volunteers made their own face masks from cotton t-shirts; the masks were then tested for fit. The number of microorganisms isolated from coughs of healthy volunteers wearing their homemade mask, a surgical mask, or no mask was compared using several air-sampling techniques. The median-fit factor of the homemade masks was one-half that of the surgical masks. Both masks significantly reduced the number of microorganisms expelled by volunteers, although the surgical mask was 3 times more effective in blocking transmission than the homemade mask. Our findings suggest that a homemade mask should only be considered as a last resort to prevent droplet transmission from infected individuals, but it would be better than no protection. (Disaster Med Public Health Preparedness. 2013;0:1-6).
We identified seasonal human coronaviruses, influenza viruses and rhinoviruses in exhaled breath and coughs of children and adults with acute respiratory illness. Surgical face masks significantly reduced detection of influenza virus RNA in respiratory droplets and coronavirus RNA in aerosols, with a trend toward reduced detection of coronavirus RNA in respiratory droplets. Our results indicate that surgical face masks could prevent transmission of human coronaviruses and influenza viruses from symptomatic individuals.
Respiratory virus infections cause a broad and overlapping spectrum of symptoms collectively referred to as acute respiratory virus illnesses (ARIs) or more commonly the ‘common cold’. Although mostly mild, these ARIs can sometimes cause severe disease and death1. These viruses spread between humans through direct or indirect contact, respiratory droplets (including larger droplets that fall rapidly near the source as well as coarse aerosols with aerodynamic diameter >5 µm) and fine-particle aerosols (droplets and droplet nuclei with aerodynamic diameter ≤5 µm)2,3. Although hand hygiene and use of face masks, primarily targeting contact and respiratory droplet transmission, have been suggested as important mitigation strategies against influenza virus transmission4, little is known about the relative importance of these modes in the transmission of other common respiratory viruses2,3,5. Uncertainties similarly apply to the modes of transmission of COVID-19 (refs. 6,7).
Some health authorities recommend that masks be worn by ill individuals to prevent onward transmission (source control)4,8. Surgical face masks were originally introduced to protect patients from wound infection and contamination from surgeons (the wearer) during surgical procedures, and were later adopted to protect healthcare workers against acquiring infection from their patients. However, most of the existing evidence on the filtering efficacy of face masks and respirators comes from in vitro experiments with nonbiological particles9,10, which may not be generalizable to infectious respiratory virus droplets. There is little information on the efficacy of face masks in filtering respiratory viruses and reducing viral release from an individual with respiratory infections8, and most research has focused on influenza11,12.
Here we aimed to explore the importance of respiratory droplet and aerosol routes of transmission with a particular focus on coronaviruses, influenza viruses and rhinoviruses, by quantifying the amount of respiratory virus in exhaled breath of participants with medically attended ARIs and determining the potential efficacy of surgical face masks to prevent respiratory virus transmission.
Researchers at Oxford University, led by Prof Sarah Gilbert, are planning a safety trial on humans of what is expected to be the UK’s first coronavirus vaccine next month. Provided that it goes smoothly, they will move directly into a larger trial to assess how effective the vaccine is at protecting against the infection.
The same vaccine will start animal trials next week at the Public Health England (PHE) laboratory at Porton Down near Salisbury. Normally, animal work must be completed before human trials can start, but because similar vaccines have worked safely in trials for other diseases, the work has been accelerated.
- Meet the mega-brain engineers, clinicians, students and manufacturers from Oxford University and King’s College who have built a machine that could be key to the battle against COVID-19.
The hard choices covid policymakers face Economist [freewalled]
“We have no choice,” said President Donald Trump on March 30th, after announcing that federal guidelines on social distancing would remain in force until the end of April. “Modelling…shows the peak in fatalities will not arrive for another two weeks. The same modelling also shows that, by very vigorously following these guidelines, we could save more than 1 million American lives.”
Epidemiological models are not the only reason why many countries around the world, and many states in America, are now in some form of lockdown. That China, where the outbreak started, pursued such a policy with an abandon never seen before, and subsequently reported spectacular falls in the rate of new infections, is doubtless another reason. So are the grim scenes from countries where the spread of the virus was not interrupted early enough. By April 1st Italy had seen almost four times as many deaths as China.
The power of the models has been that they capture what has just been seen in these countries and provide a quantitative picture of what may be seen tomorrow—or in alternative tomorrows. They have both made clear how bad things could get and offered some sense of the respite which different interventions can offer. Faced with experts saying, quietly but with good evidence, that a lockdown will save umpty-hundred-thousand lives, it is hard for a politician to answer “At what cost?”
What is more, when the epidemiologists reply “Not our department”, the economists to whom the buck then passes are not necessarily much more help. Estimates of the costs of the interventions now in place are all large, but they vary widely (see article). A proper assessment requires knowing how well the measures will work, how long they will last and how they will be ended—thus returning the question to the realm of public-health policy.
But as time goes on, “at what cost” will become easier to voice, and harder to duck. “We have no choice” will no longer be enough; as the disruptive effects of social-distancing measures and lockdowns mount there will be hard choices to make, and they will need to be justified economically as well as in terms of public health. How is that to be done?
Epidemiological models come in two types. The first seeks to capture the basic mechanisms by which diseases spread in a set of interlinked equations. In the classic version of this approach each person is considered either susceptible, exposed, infectious or recovered from the disease. The number in each group evolves with the numbers in one or more of the other groups according to strict mathematical rules (see chart 1). In simple versions of such models the population is uniform; in more elaborate versions, such as the one from Imperial College London, which has influenced policy in Britain and elsewhere, the population is subdivided by age, gender, occupation and so on.
The second type of model makes no claim to capture the underlying dynamics. They are instead based on what is essentially a sophisticated form of moving average, predicting things about next week (such as how many new infections there will be) based mostly on what happened this week, a little bit on what happened last week, and a smidgen on what happened before that. This approach is used to forecast the course of epidemics such as the seasonal flu, using patterns seen in epidemics that have already run their course to predict what will come next. Over the short term they can work pretty well, providing more actionable insights than mechanistic models. Over the long term they remain, at best, a work in progress.
All the models are beset by insufficient data when faced with covid-19. There is still a lot of uncertainty about how much transmission occurs in different age groups and how infectious people can be before they have symptoms; that makes the links between the different equations in the mechanistic models hard to define properly. Statistical models lack the data from previous epidemics that make them reliable when staying a few steps ahead of the flu.
Obedient to controlling hands'
This causes problems. The Dutch started expanding their intensive-care capacity on the basis of a model which, until March 19th, expected intensive-care stays to last ten days. Having seen what was happening in hospitals, the modellers lengthened that to 23 days, and the authorities worry about running out of beds by April 6th. Unsettling news; but better known in advance than discovered the day before.
If more data improve models, so does allowing people to look under their bonnets. The Dutch have published the details of the model they are using; so has New Zealand. As well as allowing for expert critique, it is a valuable way of building up public trust.
As models become more important and more scrutinised, discrepancies between their purported results will become apparent. One way to deal with divergence is to bring together the results of various different but comparable models. In Britain, the government convened a committee of modelling experts who weighed the collective wisdom from various models of the covid-19 epidemic. America’s task force for the epidemic recently held a meeting of modelling experts to assess the range of their results.
Another way to try to get at the combined expertise of the field is simply to ask the practitioners. Nicholas Reich of the University of Massachusetts, Amherst, and his colleague Thomas McAndrew have used a questionnaire to ask a panel of experts on epidemics, including many who make models, how they expect the pandemic to evolve. This sounds crude compared with differential equations and statistical regressions, but in some ways it is more sophisticated. Asked what they were basing their responses on, the experts said it was about one-third the results of specific models and about two-thirds experience and intuition. This offers a way to take the models seriously, but not literally, by systematically tapping the tacit knowledge of those who work with them.
In studies run over the course of two flu seasons, such a panel of experts was consistently better at predicting what was coming over the next few weeks than the best computational models. Unfortunately, like their models, the experts have not seen a covid outbreak before, which calls the value of their experience into at least a little doubt. But it is interesting, given Mr Trump’s commitment to just another month of social distancing, that they do not expect a peak in the American epidemic until May (see chart 2).
Though the models differ in various respects, the sort of action taken on their advice has so far been pretty similar around the world. This does not mean the resultant policies have been wise; the way that India implemented its lockdown seems all but certain to have exacerbated the already devastating threat that covid-19 poses there. And there are some outliers, such as the Netherlands and, particularly, Sweden, where policies are notably less strict than in neighbouring countries.
Attempts to argue that the costs of such action could be far greater than the cost of letting the disease run its course have, on the other hand, failed to gain much traction. When looking for intellectual support, their proponents have turned not to epidemiologists but to analyses by scholars in other fields, such as Richard Epstein, a lawyer at the Hoover Institute at Stanford, and Philip Thomas, a professor of risk management at the University of Bristol. These did not convince many experts.
April is the cruellest month'
Even if they had, it might have been in vain. The argument for zeal in the struggle against covid-19 goes beyond economic logic. It depends on a more primal politics of survival; hence the frequent comparison with total war. Even as he talked of saving a million lives, Mr Trump had to warn America of 100,000 to 200,000 deaths—estimates that easily outstrip the number of American troops lost in Vietnam. To have continued along a far worse trajectory would have been all but impossible.
What is more, a government trying to privilege the health of its economy over the health of its citizenry would in all likelihood end up with neither. In the absence of mandated mitigation policies, many people would nonetheless reduce the time they spend out of the home working and consuming in order to limit their exposure to the virus. (Cinemas in South Korea, where the epidemic seems more or less under control, have not been closed by the government—but they are still short of customers.) There would be effects on production, too, with many firms hard put to continue business as usual as some workers fell ill (as is happening in health care today) and others stayed away (as isn’t).
This is one reason why, in the acute phase of the epidemic, a comparison of costs and benefits comes down clearly on the side of action along the lines being taken in many countries. The economy takes a big hit—but it would take a hit from the disease too. What is more, saving lives is not just good for the people concerned, their friends and family, their employers and their compatriots’ sense of national worth. It has substantial economic benefits.
Michael Greenstone and Vishan Nigam, both of the University of Chicago, have studied a model of America’s covid-19 epidemic in which, if the government took no action, over 3m would die. If fairly minimal social distancing is put in place, that total drops by 1.7m. Leaving the death toll at 1.5m makes that a tragically underpowered response. But it still brings huge economic benefits. Age-adjusted estimates of the value of the lives saved, such as those used when assessing the benefits of environmental regulations, make those 1.7m people worth about $8trn: nearly 40% of gdp.
Those sceptical of the costs of current policies argue that they, too, want to save lives. The models used to forecast gdp on the basis of leading indicators such as surveys of sentiment, unemployment claims and construction starts are no better prepared for covid-19 than epidemiological models are, and their conclusions should be appropriately salinated. But even if predictions of annualised gdp losses of 30% over the first half of the year in some hard-hit economies prove wide of the mark, the abrupt slowdown will be unprecedented.
Lost business activity will mean lost incomes and bankrupt firms and households. That will entail not just widespread misery, but ill health and death. Some sceptics of mitigation efforts, like George Loewenstein, an economist at Carnegie Mellon University, in Pittsburgh, draw an analogy to the “deaths of despair”—from suicide and alcohol and drug abuse—in regions and demographic groups which have suffered from declining economic fortunes in recent decades.
The general belief that increases in gdp are good for people’s health—which is true up to a point, though not straightforwardly so in rich countries—definitely suggests that an economic contraction will increase the burden of disease. And there is good reason to worry both about the mental-health effects of lockdown (see article) and the likelihood that it will lead to higher levels of domestic abuse. But detailed research on the health effects of downturns suggests that they are not nearly so negative as you might think, especially when it comes to death. Counterintuitive as it may be, the economic evidence indicates that mortality is procyclical: it rises in periods of economic growth and declines during downturns.
And the profit and loss
A study of economic activity and mortality in Europe between 1970 and 2007 found that a 1% increase in unemployment was associated with a 0.79% rise in suicides among people under the age of 65 and a comparable rise in deaths from homicide, but a decline in traffic deaths of 1.39% and effectively no change in mortality from all causes (see chart 3). A study published in 2000 by Christopher Ruhm, now at the University of Virginia, found that in America a 1% rise in unemployment was associated with a 1.3% increase in suicides, but a decline in cardiovascular deaths of 0.5%, in road deaths of 3.0%, and in deaths from all causes of 0.5%. In the Great Depression, the biggest downturn in both output and employment America has ever witnessed, overall mortality fell.
Some research suggests that the procyclical link between strong economic growth and higher mortality has weakened in recent decades. But that is a long way from finding that it has reversed. What is more, the effects of downturns on health seem contingent on policy. Work published by the oecd, a group of mostly rich countries, found that some worsening health outcomes seen in the aftermath of the financial crisis were due not to the downturn, but to the reductions in health-care provision that came about as a result of the government austerity which went with it. Increased spending on programmes that help people get jobs, on the other hand, seems to reduce the effect of unemployment on suicides. The fact that some of the people now arguing that the exorbitant costs of decisive action against covid-19 will lead to poorer public health in the future were, after the financial crisis, supporters of an austerity which had the same effect is not without its irony.
But if the argument that the cure might be worse than the disease has not held up so far, the story still has a long way to go. The huge costs of shutting down a significant fraction of the economy will increase with time. And as the death rates plateau and then fall back, the trade-offs—in terms of economics, public health, social solidarity and stability and more—that come with lockdowns, the closure of bars, pubs and restaurants, shuttered football clubs and cabin fever will become harder to calculate.
It is then that both politicians and the public are likely to begin to see things differently. David Ropeik, a risk-perception consultant, says that people’s willingness to abide by restrictions depends both on their sense of self-preservation and on a sense of altruism. As their perception of the risks the disease poses both to themselves and others begins to fall, seclusion will irk them more.
It is also at this point that one can expect calls to restart the economy to become clamorous. In Germany, where the curve of the disease has started to flatten, Armin Laschet, the premier of North Rhine-Westphalia, Germany’s largest and second-most-covid-afflicted state, has said it should no longer be out of bounds to talk about an exit strategy. Angela Merkel, the chancellor—a role Mr Laschet is keen to inherit—said on March 26th there should be no discussion of such things until the doubling time for the number of cases in the country had stretched beyond ten days. When she was speaking, it was four days. Now it is close to eight.
When the restrictions are lessened it will not be a simple matter of “declaring victory and going home”, the strategy for getting out of the Vietnam war advocated by Senator Richard Russell. One of the fundamental predictions of the mechanistic models is that to put an epidemic firmly behind you, you have to get rid of the susceptible part of the population. Vaccination can bring that about. Making it harder for the disease to spread, as social distancing does, leaves the susceptible population just as vulnerable to getting exposed and infected as it was before when restrictions are lifted.
This does not mean that countries have to continue in lockdown until there is a vaccine. It means that when they relax constraints, they must have a plan. The rudiments of such a plan would be to ease the pressure step by step, not all at once, and to put in place a programme for picking up new cases and people who have been in contact with them as quickly as possible. How countries trace cases will depend, in part, on how low they were able to get the level of the virus in the population and how able, or inclined, they are to erode their citizens’ privacy. How they relax constraints will depend to some extent on modelling.
Cécile Viboud of America’s National Institutes of Health argues that if you can make mechanistic models sufficiently fine-grained they will help you understand the effectiveness of different social-distancing measures. That sounds like the sort of knowledge that governments considering which restrictions to loosen, or tighten back up, might find valuable. The ability to compare the outcomes in countries following different strategies could also help. David Spiegelhalter, a statistician at the University of Cambridge, says the differences between Norway, which is conforming to the lockdowns seen in most of the rest of Europe, and Sweden, which is not, provide a “fantastic experiment” with which to probe the various models.
But the fact that it is possible to build things like how much time particular types of people spend in the pub into models does not necessarily mean that the models will represent the world better as a result. For what they say on such subjects to be trustworthy the new parameters on pubs and such like must be calibrated against the real world; and the more parameters are in play, the harder that is. People can change so many behaviours in response to restrictions imposed and removed that the uncertainties will “balloon” over time, says Mr Reich.
The human engine waits
Some will see this as a reason to push ahead with calibration and other improvements. Others may see it as a reason to put off the risks associated with letting the virus out of the bag for as long as possible. Longer restrictions would give governments more time to put in place measures for testing people and tracking contacts. If they force many companies into bankruptcy, they will give others time to find workarounds and new types of automation that make the restrictions less onerous as time goes by.
Advocates of keeping things in check for as long as possible can point to a new paper by Sergio Correia, of the Federal Reserve Board, Stephan Luck, of the Federal Reserve Bank of New York, and Emil Verner, of mit, which takes a city-by-city look at the effects of the flu pandemic of 1918-19 on the American economy. They find that the longer and more zealously a city worked to stem the flu’s spread, the better its subsequent economic performance. A new analysis by economists at the University of Wyoming suggests much the same should be true today.
The flu, though, mostly killed workers in their prime, and the service industries which dominate the modern economy may not respond as the manufacturing industries of a century ago. What is more, in some places the pressure to get the economy moving again may be irresistible. According to Goldman Sachs, a bank, Italy’s debts could reach 160% of gdp by the end of the year—the sort of number that precedes panics in bond markets. The euro zone could forestall such a crisis by turning Italian debt into liabilities shared all its members—something the European Central Bank is already doing, to a limited extent, by buying Italian bonds. But resistance from Germany and the Netherlands is limiting further movement in that direction. There could come a time when Italy felt forced to relax its restrictions to someone else’s schedule rather than leave the euro.
There is also a worry that, the longer the economy is suppressed, the more long-lasting structural damage is done to it. Workers suffering long bouts of unemployment may find that their skills erode and their connections to the workforce weaken, and that they are less likely to re-enter the labour force and find good work after the downturn has ended. Older workers may be less inclined to move or retrain, and more ready to enter early retirement. Such “scarring” would make the losses from the restrictions on economic life more than just a one-off: they would become a lasting blight. That said, the potential for such scarring can be reduced by programmes designed to get more people back into the labour force.
In the end, just as lockdowns, for all that their virtues were underlined by the modellers’ grim visions, spread around the world largely by emulation, they may be lifted in a similar manner. If one country eases restrictions, sees its economy roar back to life and manages to keep the rate at which its still-susceptible population gets infected low, you can be sure that others will follow suit.
Don’t take my word for it; this information comes from leading medical journal The Lancet.
He points out that the current strategy of “suppression” was recommended by The Lancet on January 24. That’s now eight weeks ago.
That’s the strategy followed by China, South Korea, Taiwan and other countries that have been successful in reducing the rate of infection. China is now reporting no new cases at all.
And it was also recommended by the World Health Organisation (WHO).
The Conservative government is saying the science has changed, citing a report by Imperial College which states that Boris Johnson’s former policy of “mitigation” – allowing the virus to spread to create “herd immunity” – would lead to 250,000 deaths, both from the virus and because people with other illnesses would be denied treatment by an overwhelmed NHS.
But the information in the Imperial College report was already available when Johnson was forming his “mitigation” policy.
Writing in Byline Times, Mike Buckley stated that the government was right to think “herd immunity” was worth having – but ignored the fact that it has never been achieved through mass infection; it has only been managed via vaccination.
“To attempt to create herd immunity through mass infection for a disease with at least a 1% mortality rate would lead to an unacceptable numbers of deaths, all the more so in a country with a comparatively low number of intensive care beds, ventilators and specialist staff where access to care will be at a premium,” he wrote.
Rishi Sunak has given us wartime finance fit for wartime economic conditions by Will Hutton in The Guardian
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Sterling Professor of Social & Natural Science at Yale. Physician. Author of Blueprint: The Evolutionary Origins of a Good Society.
In this thread, I collect the threads about #COVID19 #SARSCoV2 that I have prepared on various aspects of the coronavirus pandemic. Please note that the situation is fluid and knowledge may change and be updated. Feel free to suggest topics in response to this tweet. 1/
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