COVID-19 Action Plan With Chinese Characteristics

In an op-ed in the New York Times Hong Kong epidemiologist provides a frank assessment of the status of our global efforts against COVID-19:

A formal framework is needed, with an explicit rationale grounded in science, for determining when and how and based on what factors to relax restrictions — and how to reapply some or all of them should another epidemic wave hit again.

Containment has failed everywhere. In some places — Wuhan in February; northern Italy in March — the epidemic spread so quickly that the relevant authorities had to focus mainly on mitigating its effects, on damage control. In other places, suppression has worked so far: Hong Kong, Singapore and Taiwan have not experienced sustained local epidemics. Not yet, at least.

and here’s his plan of action:

Here is a formal framework for how governments could monitor the state of this pandemic much more accurately than many seem to be doing now, and how then, acting on the evidence, they could tune their interventions quickly enough to stay ahead of the outbreak trajectory.

For starters, one needs robust data. Policy must not be determined based on the daily count of reported cases ­— the tallies you read about constantly in the news — because those are unreliable. What’s needed instead is the coronavirus’s real-time, effective reproduction number, or its actual ability to spread at a particular time. And one needs to understand that number properly, in context.

The rate at which a virus is transmitted — known as the R-naught (R0), or basic reproductive number — refers to the average number of people to whom an infected person passes on the virus in a population with no pre-existing immunity. The R0 can vary from place to place because of the population’s age structure and how frequently people come into contact with each other.

The “effective” version of that number, the Rt — or the reproductive number at time “t” — is the virus’s actual transmission rate at a given moment. It varies according to the measures to control the epidemic — quarantine and isolation protocols, travel restrictions, school closures, physical distancing, the use of face masks — that have been put in place.

Daily reported cases do not convey the true state of the virus’s spread. For one thing, there is so much heterogeneity in the per capita testing capacity of countries around the world that it would be foolhardy to try to draw any broad conclusion about the virus’s transmissibility from all that disparate data. For another, the figures for reported cases lag actual infections by at least 10 to 14 days.

That’s because the incubation period for Covid-19 is about six days. And because — partly given shortages of test kits in many countries — some people don’t ever get tested, and those who do probably don’t until they have displayed symptoms for a few days.

However, it is possible to bring the daily count of reported cases closer to the real-time Rt thanks to both statistical adjustments and digital analytics.

The School of Public Health at the University of Hong Kong has been estimating, and publishing, the real-time Rt for Hong Kong since early February. The chart is based on the epidemic curve corrected by established statistical methods to reduce the time lag between the onset of infection symptoms and the official reporting of new cases. (The result is called “nowcasting.”) We hope to soon be able to further enhance these estimates by incorporating location-based data from the Octopus card that many Hong Kongers use to pay for public transport or to shop.

In China, the location-based functions of the online payment platforms of Alibaba, Baidu and Tencent could be used to track people’s activity. In the West, data feeds from Facebook and Google could geo-code online searches and payments. Citymapper, a mapping and public transit app, follows people’s movements in major cities in real time.

Activity data mined from all these apps and platforms, as well as records from payment cards, could be used to determine how people mix — which in turn could be used to infer the likelihood of their passing the virus around. In a recent contribution to the journal Science, Caroline Buckee described how all this data could be marshaled to chart a real-time map analyzing how physical distancing policies are affecting people’s movements.

With a bit of ingenuity, existing digital tools can quickly be turned into epidemic-monitoring instruments — and without intruding into people’s lives. Those who, as a general matter, worry about invasions of privacy (and rightly so) need not in this case: The idea is to only study aggregate, and therefore anonymous, numbers — to look at big data, not at personal information or anyone’s identity.

Then, having determined what the Rt actually is, decision makers could more precisely adjust their interventions to keep that number at what is, for them and their constituencies, an acceptable level.

An Rt of 1 means that the epidemic is holding steady: For every person who is infected, another one becomes infected, and as the first one either recovers or dies, the second one replaces it; the size of the total pool of infected people remains the same. At a rate below 1, the epidemic will fade out. Above 1, it will grow, perhaps exponentially.

That said, an Rt of 1 or below will not do in all circumstances. Context matters, too.

An Rt of 1 might be acceptable in a place with 10 million people if, say, no more than a couple of dozen new infections are confirmed every day. But it wouldn’t be if an epidemic were raging there and several hundred or thousands of new cases occurred daily. In the face of an explosive outbreak, the authorities would first need to take a sledgehammer to the Rt to knock it down to a very low level — 0.1 or 0.2 — and maintain it there for as long as it took to bring the daily case count down to a manageable figure.

In other words: Each community must determine the real-time effective reproductive number it can accept given its own circumstances, in particular the stage of the epidemic it is at.

Still, for all communities that determination essentially requires doing the same thing: Figuring out the number of new daily infections that their health system can handle without imploding.

Imagine a city that has 1,000 beds in intensive care units. It cannot have more than 1,000 people on a respirator at any given time. If the average length of a patient’s stay in the I.C.U. is 14 days, this city cannot provide intensive care for more than about 71 new patients a day (1,000 / 14 = 71.42). Assuming that about 5 percent of all newly infected cases are so severe as to require intensive care, then the city cannot afford to have more than a total of about 1,420 new infections a day (71 x 20 = 1,420). This is the true number of infections, only a fraction of which are reflected in the officially reported count.

The authorities, having established the number of new infections the city’s emergency health facilities can support, can then determine what Rt they should aim for and tune their interventions to reach it.

Next, once it is clear what the health care system can bear, one must ask what the economy and, separately, what the people, can accept.

Even if the health care system can just about tolerate 1,420 new infections a day, would Wall Street? Would the financial markets — and, more important, the real economy — be spooked? Or react as they do during a bad flu season?

And how long can the population accept the restrictions required to maintain that level of infections? Will people stop complying? Are their mental and emotional well-being being jeopardized?

My first observation about that is that I think he’s whistling in the dark. At the present low volume of recoveries, no action to reduce spread will prevent the health care system from being overloaded. The best that can be expected is to delay that point long enough to expand the available resource and a lot of the barriers preventing that now are regulatory.

My second observation is that in the United States at least we reached the end of the line on his plan

And how long can the population accept the restrictions required to maintain that level of infections? Will people stop complying? Are their mental and emotional well-being being jeopardized?

before it even began. Compliance has never been and will never be good enough for any suppression strategy to work, at least not until the recovery time can be shortened substantially.

Now let me air a peeve. The present method being used to treat COVID-19 patients is not scientific. It’s based on commonsense and the standard of care. Does it work in the majority of cases? Nobody really knows. It certainly hasn’t been subjected to the kind of scrutiny that the medical Powers-That-Be are demanding of other alternative treatment. For one thing it would be unethical to do so.

What should we do? I have no idea but I can make some suggestions. First, enlist the American people in what they should do rather than relying on limiting what they are allowed to do. Get volunteers making masks in the tens or hundreds of millions. There are a half million 3D printers in the U. S. alone capable of making N95 masks. The limitation there will be materials. Use the Defense Production Act more aggressively to get companies producing the necessary materials, not just for masks and respirators but for all of the things that will be necessary to treat COVID-19. We don’t produce enough of those things domestically and we can’t rely on other countries for them. They all have problems of their own. Waive requirements including environmental requirements. Get all of the many levels of government pulling in the same direction. There’s a role for the federal government in that but if we depend on the federal government for it, it’s a cop out. We’ll fail.

Get the companies whose business model consists of gathering information to do what they do best—gather information. Dragoon them into it, if necessary.

We need to respond to COVID-19 in the American way which is not to endure the hardships it imposes but to refuse to endure the hardships it imposes and do something about them. Rely on passive compliance in places where that will work.