Alarm bell for the next pandemic, V.2

post by AllAmericanBreakfast · 2020-04-15T06:47:59.415Z · LW · GW · 5 comments


  Transmissibility: efficiency, intra-community spread, inter-community spread, outside view
  Danger: case fatality rates, overwhelm, economic impacts, treatment
  Spread limitations: demographics, geography
  Social effects: communications, shutdown, research, deaths
  How did it compare with other diseases?
  Quick historical case studies
  The future
In the beginning Disease divided the healthy and the sick. And the data was without form, and void, and uncertainty was upon the face of the people. And a look of concern moved upon the face of the doctors.

Three factors determine the global impact of a novel disease.

1) Danger: basic reproduction number (R0) and case fatality rate (CFR).

2) Maximum spread: for example, Malaria is limited by the range of its vector, the anopheles mosquito, but flu can travel anywhere.

3) Social response: improved accuracy of data; availability of tests, treatments, vaccines; likely efficacy of lockdowns, travel restrictions, exterminations are hopeful trends. We must weigh these against the threat of supply and staff shortages, as well as the economic consequences of disease and our response to it.

In the first few months, when determined efforts could nip a potential pandemic in the bud, we may not have reliable numbers for R0 or CFR. Maximum spread may be easier to predict. It's hard to predict social response with any precision.

We can wait until conclusive evidence comes in and rely on official pronouncements from centralized authority systems such as the CDC. But an alternative is to craft a tentative, quantified model based on what is known, and update regularly as new and better data comes in.

How do we model the course of a novel disease in early days, when a bold intervention might do the most good, but there is also maximum uncertainty about what we are facing?

Here are some of the critical factors that determine whether we're facing a new disease with potentially world-shaking consequences.

Transmissibility: efficiency, intra-community spread, inter-community spread, outside view

Danger: case fatality rates, overwhelm, economic impacts, treatment

Spread limitations: demographics, geography

Social effects: communications, shutdown, research, deaths

COVID-19 would have scored a 13 out of 14 prior to Feb. 20th 2020, just before the stock market crashed. It is marked down 1 point because does its worst damage to retirees rather than people of working age, reducing its economic impact. We will use this as the threshold for behaving as if the stock market is about to crash.

Looking at 40 years of stock market data, the crash provoked by COVID-19 was one of the three most significant drops and a clearly distinguishable feature of the graph. Articles during the crash confidently attributed the crash exclusively to coronavirus. Many significant events related to the disease were occurring at or before this time. This will be our standard for a disease-caused stock market crash.

How did it compare with other diseases?

The problem with using historical data to vet this model is that there hasn't been a historical pandemic that crashed the stock market in such an obvious way. COVID-19 really is unprecedented in this way. Hence, we can gather evidence on whether this model is too sensitive. If it doesn't suggest a crash, and a crash doesn't occur, that supports the idea that it's not overly sensitive.

Spanish flu is the only case I've found suggesting that this model might be too sensitive, but given that the disease overlapped with the end of WWI, it's hard to tell.

Without another case where the modern economy clearly did crash due to a pandemic, it's hard to test whether this model is sensitive enough. Given that the stock market has historically stayed strong through a large number of historical pandemics, I think the virtue of this model is that it tends to score most pandemics lower than their scary media coverage might have suggested.

This doesn't mean those pandemics, with their millions of lives lost, were unimportant. It simply means that they did not suddenly and severely shake the world economy, the way COVID-19 is doing right now.

Quick historical case studies

2013-2016 ebola only spread through body fluids or objects, did not achieve major global community spread, did not provoke concerns about equipment shortages in industrialized nations, did not provoke shutdowns of sufficient scale, and did not provoke serious investment by industry. Score: 9/14; no clear ebola-linked stock market crash.

2009 H1N1 swine flu had a treatment from the start, did not primarily affect working age people, did not see major shutdowns, and did not require significant new production (it just required a scale-up of existing vaccine production). Score: 10/14; no clear swine flu-linked stock market crash.

1968 H3N2 Hong Kong flu had treatments available, a CFR lower than 1%, and primarily affected people older than 65. I haven't seen reports of a major social response, but I also didn't look very hard. Even if it did meet all criteria for social response, HK flu would still only get an 11/14; so it shoudn't have crashed the stock market, and indeed it doesn't appear to have done so.

Source: Forbes. This doesn't look like a bear market caused by fear of the flu.

1918 Spanish flu would have met all applicable measures and scored a 14/14. There was a stock market contraction in 1917, which this article claims "...likely reflects the Great Influenza Epidemic, rather than World War I..." It's hard to imagine how a flu that did the vast majority of its damage in 1918 caused a contraction the year before, and these authors don't explain. In a previous article by the same authors, they also attribute the stock market contraction at the start of the 1920s to Spanish flu, which seems more plausible. However, the fact that 1918 at the peak of the Spanish flu did not see a stock market crash is a point of evidence that this model is too sensitive.

I am not convinced that this 1917 contraction can be attributed to 1918 Spanish flu.

The medieval Black Plague, scaling for contemporary world population and industry and interpreting some questions creatively, easily clears the bar to score a 14/14. This at least shows that a disease we'd intuit as being one of the worst does, in fact, receive the top score.

The future

Currently, there is a universal flu vaccine, FLU-v in a stage III clinical, which suggests it has a high probability of getting approved. It is possible that other universal vaccines will come to market in the near future. COVID-19 may cause major changes in our pandemic-prevention infrastructure, from a crackdown on wet markets, to scaled-up stockpiles, to more funding for research, to better public hygiene.

If those changes help us do better in the future, it may call this model into question, making it too sensitive. Hopefully, the model is robust enough to anticipate some of these developments. For example:

Imagine new flu appears, spreading throughout the world and killing thousands. Fortunately, we've stockpiled medical equipment. Also, a universal flu vaccine has already been developed, and the WHO uses this occasion to roll it out globally in the largest mass-vaccination effort humanity has ever seen. It beats back an otherwise-frightening strain before it kills too many people.

In this case, there's no threat of medical supply shortages. It's unlikely that we'll see the same mass effort to build hospitals and develop new drugs, since the vaccine would have already been produced. We'd have a clearly effective treatment. And major quarantines seem like they'd be unnecessary. Even if all other criteria are met, in this circumstance the presence of the vaccine prevents the ringing of the "alarm bell."

That's a made-up example. But it illustrates that this model does have the ability to take medical infrastructure improvements into account.


The main purpose of this model is to synthesize early evidence of the potential severity of a new pandemic. It could reduce our anxieties in the face of a less severe disease, and help us to act with appropriate force when a more dangerous virus is looming on the horizon. It would serve as a research agenda, and as a guide and focus for conversation and charitable donations.

I personally would feel comfortable selling my stock if a new disease scored a 13/14 on this model. If the model was wrong, I would likely miss a few months of moderate market growth. If it was right, I might avoid a major loss. Of course the point of this model is not to make money off the world's suffering.

Instead, the point is to get myself to take this risk seriously when it's knocking on the door. Selling stock sends a price signal to the economy, even if it's minuscule on the global scale. The model also tells a story, making it easier to communicate about why we should be more afraid of disease X than disease Y.

I continue to appreciate critiques and suggestions. If anyone knows of a model like this used by governments, NGOs, or corporations to assess the risk of a novel disease, please let me know. I'd be surprised if they don't exist.


Comments sorted by top scores.

comment by Roko · 2020-04-19T08:04:05.746Z · LW(p) · GW(p)

It would be very valuable to take a particular "Alarm" and see how many true positives, false positives, true negatives and false negatives it would have produced over the past 20 years.

comment by AllAmericanBreakfast · 2020-04-19T15:05:22.166Z · LW(p) · GW(p)

I did this with a few diseases! Two investigations got their own post, which are linked above. Others just got mentions in the v. 2 post, linked at the very top.

comment by avturchin · 2020-04-15T13:48:18.871Z · LW(p) · GW(p)

The problem here os "fog of war": we can't know for sure all R0, rout of transmission and other parameters for sure before a pandemic will reach high stages. This will result either in the frequent false alarms, or there will be no early warning.

comment by AllAmericanBreakfast · 2020-04-15T16:19:08.610Z · LW(p) · GW(p)

I updated the post to distinguish between respiratory droplets and a fully airborne route of transmission.

We still don't know whether COVID-19 (or SARS) is transmitted only through respiratory droplets, or whether it is airborne more broadly in smaller droplets beyond a range of 6' or so. If that's the key difference between a widely transmissible disease and one that's less of a threat, and there's a lot of variability among respiratory illnesses, then that would make it less likely a priori that a novel respiratory illness is highly contagious.

On the other hand, it might be that respiratory droplets vs. fully airborne is not the key difference, or of most diseases spreadable by droplets are also aerosolized.

To tell what we're dealing with, we might look for suggestive case studies. There was a choir that met in the early days of COVID-19 where none of the members were sick, they all stayed 6' apart, but half the choir still caught COVID-19 from each other. This suggests COVID-19 is fully airborne, though of course it's not hard evidence.

But in general, this model is designed to help with 'fog of war'. Since we can't know these factors for sure, we use what evidence is available to reason under uncertainty. My historical research both into COVID-19 and historical diseases suggested to me that this model is fairly well-calibrated, but there just aren't enough data points to know for sure. Even if not, though, it at least serves as guideposts for future reasoning, and I think that's valuable.

comment by ChristianKl · 2020-04-16T07:01:54.157Z · LW(p) · GW(p)
We still don't know whether COVID-19 (or SARS) is transmitted only through respiratory droplets, or whether it is airborne more broadly in smaller droplets beyond a range of 6' or so.

And if it is airbone in smaller droplets whether it is outside of specific medical procedures airbone in that way.