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Scenario #2 – Escalation in new geographies, disruption extends into the second quarter: In this scenario, new cases continue to rise in other parts of the world, before peaking by the end of May. The disruption extends into the second quarter, affecting corporate profitability in select sectors, risking the emergence of corporate credit risks. If the dislocations in asset markets also persist into the second quarter, a sharp tightening in financial conditions may mark a tipping point, exacerbating the impact on growth via weaker corporate confidence, falling capital expenditures and cutbacks in hiring. In response, policymakers around the world would step up easing measures, with fiscal policy in Asia and Europe and monetary policy in the U.S. doing the heavy lifting. Today, the Fed announced an unexpected half-point rate cut and our Chief U.S. Economist, Ellen Zentner, expects the Fed to cut rates again by a quarter-point at its April meeting, with the risk of an earlier action given the “fluidity” of the situation. In this scenario, global growth averages just 2.4% in the first half of 2020, but starts to pick up in the third quarter.

To understand how the spread of such a disease can be contained, it helps to break R0 down to its constituent parts. A simple formula that I got from Buckee is: the probability of infection given contact with an infectious person (b), multiplied by the contact rate (k), multiplied by the infectious duration (d) In some cases you can shorten the infectious duration with treatment. Quarantining people once you know they’re infected effectively shortens it, too. Variables b and k, meanwhile, are clearly dependent on behavior. The probability of infection is reduced by things like frequent hand-washing, replacing handshakes with fist bumps and such. The contact rate is reduced by staying home. By putting much of the country on lockdown, Chinese authorities reduced the contact rate enough that Covid-19’s R0 in the country fell below one. They also incurred huge economic and social costs. Now, as China begins to go back to work, the big question is whether a less-draconian approach can keep the disease in check or whether it will just start spreading again. That’s the big question in the U.S., Europe and pretty much everywhere else on earth too. It can’t be answered entirely by professional epidemiologists, either.

A study uploaded to medical pre-print server MedRxiv Monday plots recorded cases against climate conditions to suggest that there is indeed a significant correlation between outbreaks and the weather. In extreme cold and very hot and wet conditions the virus is “largely absent,” the researchers from Spain, Portugal and Finland wrote, meaning that people in tropical and polar climates are unlikely to see local transmission of cases. Arid regions will see a higher rate of infections but the worst-hit areas will be temperate countries and high-altitude areas closer to the equator. The period between June and September should see a slowing rate of infections in much of Europe and North America, they wrote, although areas closer to the poles in Scandinavia, Russia and Canada may see worsening conditions as the climate warms enough to support local infections.

Another pre-print study by four Beijing-based researchers uploaded to the arXiv server last week comes to a similar conclusion after analyzing the infection rates in 100 Chinese cities. That rate, known as the R0, is a key determinant of an infection’s propensity to spread. For Covid-19 it’s currently estimated to be around 2.2, but moving it below 1 should, if sustained, be enough to turn the current out-of-control epidemic into an outbreak that goes extinct on its own. Increasing the temperature by one degree centigrade reduces the R0 by 0.0383 and increasing humidity by 1% pushes it down by 0.0224, the researchers found. That should be particularly significant in places with hot, wet summers, they wrote: In the event the Tokyo Olympics goes ahead, the R0 in the city would likely be at extinction levels below 1, given normal summer conditions.

On when a drug might be available: If [our approved anti-inflammatory drug Kevzara] is shown to work in hospitalized patients, we have lots of the drug on hand and can make much more. The best case is that people wouldn’t progress from let’s say needing a little oxygen to going on a respirator. If we all get sick at once, it could be a disaster because we won’t have enough intensive care beds. A drug like this could really slow down the number of people who needed really intensive respiratory therapy. This could go fairly quickly. There are unfortunately a lot of people in the hospital. If we can enroll the study quickly over the next month, you should know within a few weeks of treating people how they’re doing.

Fortunately it appears the parameters used in that [Imperial College London] model were too negative. The experience in China is the most critical data we have. They did their "shut down" and were able to reduce the number of cases. They are testing widely so they see rebounds immediately and so far there have not been a lot. They avoided widespread infection. The Imperial model does not match this experience. Models are only as good as the assumptions put into them. People are working on models that match what we are seeing more closely and they will become a key tool. A group called Institute for Disease Modeling that I fund is one of the groups working with others on this.

Neil Ferguson and an Imperial College team . . . make structural mistakes in analyzing outbreak response. They ignore standard Contact Tracing [2] allowing isolation of infected prior to symptoms. They also ignore door-to-door monitoring to identify cases with symptoms [3]. Their conclusions that there will be resurgent outbreaks are wrong. After a few weeks of lockdown almost all infectious people are identified and their contacts are isolated prior to symptoms and cannot infect others [4]. The outbreak can be stopped completely with no resurgence as in China, where new cases were down to one yesterday, after excluding imported international travelers that are quarantined.

This evidence fiasco creates tremendous uncertainty about the risk of dying from Covid-19. Reported case fatality rates, like the official 3.4% rate from the World Health Organization, cause horror — and are meaningless. Patients who have been tested for SARS-CoV-2 are disproportionately those with severe symptoms and bad outcomes. As most health systems have limited testing capacity, selection bias may even worsen in the near future. The one situation where an entire, closed population was tested was the Diamond Princess cruise ship and its quarantine passengers. The case fatality rate there was 1.0%, but this was a largely elderly population, in which the death rate from Covid-19 is much higher. Projecting the Diamond Princess mortality rate onto the age structure of the U.S. population, the death rate among people infected with Covid-19 would be 0.125%. But since this estimate is based on extremely thin data — there were just seven deaths among the 700 infected passengers and crew — the real death rate could stretch from five times lower (0.025%) to five times higher (0.625%). It is also possible that some of the passengers who were infected might die later, and that tourists may have different frequencies of chronic diseases — a risk factor for worse outcomes with SARS-CoV-2 infection — than the general population. Adding these extra sources of uncertainty, reasonable estimates for the case fatality ratio in the general U.S. population vary from 0.05% to 1%. That huge range markedly affects how severe the pandemic is and what should be done. A population-wide case fatality rate of 0.05% is lower than seasonal influenza. If that is the true rate, locking down the world with potentially tremendous social and financial consequences may be totally irrational. It’s like an elephant being attacked by a house cat. Frustrated and trying to avoid the cat, the elephant accidentally jumps off a cliff and dies.

We were able to compare the negativation of viral carriage in patients who followed the protocol, with patients from Avignon and Nice who did not receive the treatment. Those who have not received the Plaquenil [medicament a base d'hydroxychloroquine] are still 90% of carriers of the virus after six days, while 25% are positive for those who received treatment.

At 44KW, a nightclub in Shanghai, life almost feels normal again . . . Groups sit close together without face masks on, talking and sipping their drinks . . . [at] the club, which reopened its doors last Thursday after more than a month of staying shuttered as Shanghai joined cities across China in various levels of lockdown to contain the coronavirus outbreak.

These numbers imply a fatality rate from Covid-19 orders of magnitude smaller than it appears . . . [because] the confirmed cases are likely orders of magnitude less than the true number of infections . . . We don’t know the true infection rate in the U.S. Antibody testing of representative samples to measure disease prevalence (including the recovered) is crucial. Nearly every day a new lab gets approval for antibody testing, so population testing using this technology is now feasible . . . A universal quarantine may not be worth the costs it imposes on the economy, community and individual mental and physical health. We should undertake immediate steps to evaluate the empirical basis of the current lockdowns.

We could be one to two weeks - at most a month or so - away from knowing whether [approved arthritis drug Kevzara] is really making a difference or not [in COVID-19 patients].