Pandemic Risk: How Large are the Expected Losses? Fan, Jamison, & Summers (2018)

post by anonymous_ea · 2018-11-21T15:58:31.856Z · score: 22 (10 votes) · EA · GW · 8 comments

This is a link post for http://dcp-3.org/sites/default/files/resources/Pandemic%20Risk_WHO%20Bulletin.pdf?issu

This is a link post to Pandemic Risk: How Large are the Expected Losses? by Victoria Y Fan, Dean T Jamison, & Lawrence H Summers (2018).

Abstract (emphasis added):

There is an unmet need for greater investment in preparedness against major epidemics and pandemics. The arguments in favour of such investment have been largely based on estimates of the losses in national incomes that might occur as the result of a major epidemic or pandemic. Recently, we extended the estimate to include the valuation of the lives lost as a result of pandemic-related increases in mortality. This produced markedly higher estimates of the full value of loss that might occur as the result of a future pandemic. We parametrized an exceedance probability function for a global influenza pandemic and estimated that the expected number of influenza-pandemic-related deaths is about 720,000 per year. We calculated that the expected annual losses from pandemic risk to be about 500 billion United States dollars – or 0.6% of global income – per year. This estimate falls within – but towards the lower end of – the Intergovernmental Panel on Climate Change’s estimates of the value of the losses from global warming, which range from 0.2% to 2% of global income. The estimated percentage of annual national income represented by the expected value of losses varied by country income grouping: from a little over 0.3% in high-income countries to 1.6% in lower-middle-income countries. Most of the losses from influenza pandemics come from rare, severe events.

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comment by SimonBeard · 2018-11-22T22:50:48.026Z · score: 13 (6 votes) · EA · GW

This paper very much builds upon a more detailed working paper published by the authors in 2016 (https://www.nber.org/papers/w22137.pdf). That seemed to receive a reasonable amount of discussion at the time whilst I was working at FHI and it has certainly been one of my go to resources for pandemic influenza since, but there are definately some problems with it.

The first thing that you need to know is that when the authors talk about ‘extreme events’ this may not quite mean what you think. They basically divide all possible influenza pandemics into two classes, moderate and extreme, and their conclusion is based on the fact that they find that 95% of all costs are due to the extreme events. However, in the working paper (and hence it seems in this paper) they define an ‘extreme’ pandemic as one that increases global mortality by more than 0.01%. That is definately not to be laughed at in terms of impact relative to other pandemics , it means that such a pandemic would kill at least 750,000 which is way higher than something like ebola (though these deaths are likely to fall disproportionately on the elderly and the already sick). However the key point is that when you look at their analysis it turns out that the reason why 95% are attributable to pandemics that fall into the extreme catagory is that less severe pandemics are actually surprisingly rare. The figures they use give a return rate of 50 years for a pandemic of moderate severity but 63 years for a pandemic of extreme severity! To be honest I have not gone through the new paper in enough detail to be sure that they are doing the same thing here, but when you see the figures they actually present in the working paper it is hard not to conclude that they carved up their dataset in the wrong way and should have set the threashold for extreme pandemics higher (or else included pandemics caused by less dangerous pathogens as well).

The other key thing is that although they note that a single pandemic (The Spanish Flu of 1918) plays an extremely disproportionate role in their analysis due to the fact that it may actually have contributed the majority (or even the vast majority) of all influenza deaths over their study period of the last 250 years they show no real curiosity about the potential for even worse pandemics that may have plaid a similar disproportionate role if they had tried to extend this, say to 2,000 years (the black death and the plague of Justinian come to mind). So whilst they conclude that the costs of pandemic influenza have a fat tail distribution they don’t really offer any insight into either the real fatness or the real length of this tail. I wouldn’t fault them on this in particular, since investigating such historical events would really be pushing the bounds of epedemiology. However it would be a clear issue if a GCR scholar just took these figures and ran with them, because we probably should be concerned about 1 in 2,000 year or 1 in 200,000 year pandemics, since that is where the greatest risk is likely to come from.

One final quick point is that you are right that it is surpising that not much attention was previously paid to the costs of influenza in terms of lost lives. However, there is an argument to be made that this study is no less shocking in that it only assesses the direct costs of pandemics and does not consider their potential indirect costs or systemic effects. Again I am not going to fault them for doing this as these are very hard to assess. However that is clearly a missing element in this analysis and I hope that in future studies we will be able to adress these as well.

comment by anonymous_ea · 2018-11-23T22:29:21.301Z · score: 2 (2 votes) · EA · GW

Thanks for the detailed reply! Is there a better or alternative paper you would recommend for this topic?

comment by Simon_Beard · 2018-11-26T13:33:16.839Z · score: 4 (3 votes) · EA · GW

Below I paste a really brief summary of some papers you might find interesting. This is taken from a draft literature review I have been working on with others, into methods for quantifying existential risk, hence its particular format. I hope you find it useful and would be grateful to heard if anyone knows of any good papers we have missed.

My personal takeaway from this exercise with regard to the risk from pandemics is that many GCR scholars may be overstating the risks from a major pandemic at the 'spanish flu' level (and one thing I should have mentioned in the preceeding comment is that when one takes account of the systemic impacts of such a pandemic these effects may actually decrease, for instance taking account of the social and economic impacts of the black death, the overall impact of that pandemic may have been net positive, although that is contraversial). However, on the other hand, many non-GCR scholars of pandemic may be understating the likelihood of a pandemic that would be considerably worse than the Spanish flu (both from naturally occurring and engineered pathogens). These two things do not necessarily cancel each other out!

1. Source: Troy Day, Jean-Baptiste Andre & Andrew Park, 2006, “The Evolutionary Emergence of Pandemic Influenza”, Proceedings of the Royal Society – Biological Sciences, 273, pp. 2945-2953.

Probability: The Probability of a pandemic occurring in any given year is 4%. A conservative estimate of the 95% support interval for the yearly pandemic probability is 0.7–7.6%.

Methodology: This probability is derived from combining ‘anecdotal’ evidence about the number of influenza pandemics over the past 250 years with more recent data about the expected interval between pandemics emerging.[1]Evidence was combined using a well defined Bayesian formula set out in an appendix to the paper.

2. Source: Madhav, N. (2013). Modelling a modern-day Spanish flu pandemic. AIR Worldwide, February, 21, 2013.

Probability: There is a 0.5-1%annual probability of a ‘modern day Spanish Flu’ event, with similar characteristics to the 1918 pandemic including considerable excess deaths amongst young adults. Such a pandemic would likely cause between 21 and 33 million deaths worldwide.

Methodology: The AIR Pandemic Flu Model, which combines demographic and epidemiological, demographic and technological modelling to produce a complete model for pandemic influenza. This model has been extensively peer reviewed.

3. Source: Fan, V. Y., Jamison, D. T., & Summers, L. H. (2016). The inclusive cost of pandemic influenza risk. National Bureau of Economic Research. [this has now been partially published as an, V. Y., Jamison, D. T., & Summers, L. H. (2018). Pandemic risk: how large are the expected losses?. Bulletin of the World Health Organization, 96(2), 129.]

Prediction: The annual probability of a severe influenza pandemic (one that increases global mortality by at least 0.1%) is 1.6%and the average impact of such pandemics is a global mortality increase of 0.58% (±40 million fatalities). Severe flu pandemics represent 95% of the costs associated with all pandemic influenza.

Methodology: The historical record was used to estimate the total frequency and severity of all influenza pandemics and to generate likely age-specific death rates as a result of a global pandemic. The U.S.’ historical age distributions, being the most[JF1] complete, were used as the template for global age distributions. The authors then model the “expected deaths from pandemic influenza risks” with a highly fat tailed distribution of mortality meaning that the vast majority of deaths occurring from the most severe pandemics.

4. Source: Bagus, G. (2008) Pandemic Risk Modelling. Chicago Actuarial Association

Estimate: A pandemic of the scale of the Spanish Flu, causing a ±27% increase in mortality, occur around once every 420 years. More severe pandemics causing a ±42% increase in global mortality may have a return rate of 2,700 years

Methodology: An ‘actuarial model’ is constructed in the form of a severity curve based on historical data for the past 420 years of influenza outbreaks and was found to approximate an exponential curve. This was then extrapolated to estimate the probability and severity of more extreme pandemics. The model takes account of shifting demographic features over time but assumes that pandemics have equal severity across all countries.

5. Source: Klotz, L. C., & Sylvester, E. J. (2014). The Consequences of a Lab Escape of a Potential Pandemic Pathogen. Frontiers in Public Health, 2. 

Prediction:The likelihood of a pandemic, through an undetected lab-acquired infection, “could be as high as 27%” over a 10-year research period.

Methodology:The authors take the annual probability per lab of an escape of a virus through an undetected lab-acquired infection (LAI) to be 2.4%. This statistic is taken from the Department of Homeland Security’s risk assessment for a planned National Bio- and Agro-defence Facility in Manhattan, Kansa. They then assume that a research enterprise will comprise of 10 labs working for 10 years to make a virus. So, across this period, the probability of no escape through an LAI will be 0.088. Therefore, the probability of at least one escape from the enterprise through an LAI will be 91%. This is multiplied by the assumed, as a worst-case scenario,likelihood of one LAI leading to a pandemic, 30%, to give the overall prediction.

6. Source: Marc Lipsitch & Thomas V. Inglesby, “Moratorium on research intended to create novel potential pandemic pathogens”, MBio, 5, 2014, pp. 1-6.

Probability: Each laboratory-year of Gain of Function research into virulent, transmissible influenza virus might have an 0.01% to 0.1%chance of triggering a global infection via an accidental laboratory escape. Such a pandemic could be expected to kill between 2 million and 1.4 billion people.

Methodology: The risk of a global pandemic resulting from a laboratory escape of influenza is determined from multiplying two different probabilities. The first is the risk of laboratory incidents and accidental infections in biosafety level 3 laboratories in which such research may be conducted (estimated to be between 0.2%, on the basis that 4 infections have been observed over <2,044 laboratory-years of observation, and 1%, using data from the National Institute of Allergies and Infectious Diseases). The second is the probability that an accidental infection of a working lab could lead to a laboratory escape spreading widely around the world (estimated to be between 5% and 60% according to a range of simulation models, with the authors own model indicating a 10-20% risk).

Noting that “readily transmissible influenza, once widespread, has never before been controlled before it spreads globally,” the expected severity of such a pandemic is determined by multiplying the historical infection rate of influenza pandemics (24-38%) by possible values for the case-rate fatality of a novel, virulent influenza strain (1-60%). However, it is unlikely that these two figures vary independently and so simple multiplication is likely to be inappropriate.

7. Source: Ron Fouchier, 2015, “Studies on Influenza Virus Transmission between Ferrets: the Public Health Risks Revisited”, MBio, Vol. 6, No. 1, pp. 1-4.

Probability: Each laboratory-year of Gain of Function research into virulent, transmissible influenza virus might have an 2.5x10-13to 3x10-12chance of triggering a global infection via an accidental laboratory escape.

Methodology: This paper is a direct response to Lipsitch and Inglesby (2014), arguing that their estimates “were based on historical data and did not take into account the numerous risk reduction measures that are in place in the laboratories where the research is conducted.”

8. Source: Piers Millett and Andrew Snyder-Beattie, 2017, “Existential Risk and Cost-Effective Biosecurity”,Health Security, Vol. 15, No. 4, pp. 1-11.

Probability: The annual probability of an existential catastrophe arising from a global pandemic is between 8 x 10-5 and 1.6 x 10-8.

Methodology: The authors construct a toy model to assess this risk, citing a Gryphon Scientific report (2015) as suggesting that the annual probability of a global pandemic arising from an accident with research into Potentially Pandemic Pathogens (PPP) in the US is 0.002% to 0.1%.[2]Next, they note that: “The Gryphon report also concluded that risks of deliberate misuse were about as serious as the risks of an accidental outbreak, suggesting a twofold increase in risk. Assuming that 25% of relevant research is done in the US as opposed to elsewhere in the world, gives us a further fourfold increase in risk. In total, this eightfold increase in risk gives us a 0.016% to 0.8% chance of a pandemic in the future each year.”

Next, the authors directly estimate the probability that a pandemic will cause an existential catastrophe and combine with this with the previous probability: “For the purposes of this model, we assume that for any global pandemic arising from this kind of research, each has only a one in ten thousand chance of causing an existential risk.”[3]

9. Source: Piers Millett and Andrew Snyder-Beattie, 2017, “Existential Risk and Cost-Effective Biosecurity”,Health Security, Vol. 15, No. 4, pp. 1-11.

Probability: The annual probability of an existential catastrophe resulting from biowarfare or bioterrorism is0.0000019 (or 1.9x10-6).

Methodology: The authors assume that the casualty numbers from terrorism and warfare follow a power law distribution. Previous studies have determined the power law exponent for terrorism using chemical or biological weapons to be -0.5. This means that for every order of magnitude increase in casualties from a terrorist attack, the probability of that attack occurring is multiplied by a factor 10-0.5, which is approximately 1/3. Assuming one attack per year, the annual probability that an attack kills more than 5 billion people will be (5 billion)-0.5, which is 0.000014 or 1.4x10-5. Historical data gives the power law exponent for warfare to be 0.41 and the authors assume 1 new war every other year and that bioweapons are used in 10% of wars. Therefore, the annual probability that a war involving biological weapons kills more than 5 billion people is 0.5x0.1x(5 billion)-0.41, which is 0.000005 or 5x10-6. The authors assume that of all wars or terrorist attacks that kill more than 5 billion people, 10% of these would lead to extinction. Therefore, the authors reach an annual probability of existential catastrophe from biowarfare or bioterrorism of 1.9x10-6.

10.Source: Sandberg, A. & Bostrom, N. (2008): “Global Catastrophic Risks Survey”, Technical Report #2008-1, Future of Humanity Institute, Oxford University: pp. 1-5.

Prediction: 2%chance of human extinction being caused by an engineered pandemic and 0.05%chance of it being caused by a natural pandemic.

Method: Median response of an informal survey of 13 participants at the 2008 Oxford Conference on Global Catastrophic Risk. Participants were surveyed on their estimate of human extinction, the death of more than 1 billion people and the death of more than 1 million people from a list of 8 specific threats. However, this list was not taken to be exhaustive.

11. Source: Dennis Pamlin, & Stuart Armstrong, 2015, Global Challenges: 12 Risks that Threaten Human Civilisation, Global Challenges Foundation.

Probability: “Based on available assessments the best current estimate of a global pandemic in the next 100 years is: 5%for infinite threshold [and] 0.0001%for infinite impact” (p. 150).[4]

‘Infinite impact’ refers to the state where civilization collapses and does not recover, or a situation where all human life ends. ‘Infinite threshold’ refers to a scenario that has the potential to lead to such a collapse, dependent upon other factors (Dennis & Armstrong, 2015: 11).

Methodology: This is one of a series of risk specific predictions that resulted from a large, informal, structured expert elicitation exercise conducted by the Global Challenges Foundation. This constituted an “expert review” of the relevant literature for each risk following which “[two] workshops were arranged where the selection of challenges was discussed, one with risk experts in Oxford at the Future of Humanity Institute and the other in London with experts from the financial sector.” Based on all the evidence that was gathered, probability estimates were produced for each risk (p. 12).

[1]The authors back up this claim with evidence from the following sources: Robert G. Webster, 1998, Influenza: An Emerging Disease”, Emerging Infectious Diseases, Vol. 4, No. 3, pp. 436-441, p. 437, and Ann H. Reid, Jeffery. Taubenberger & Thomas G. Fanning, 2004, “Evidence of an Absence: the Genetic Origins of the 1918 Pandemic Influenza Virus”, Nature Reviews Microbiology, 2, pp. 909-914.

[2]There is no explicit reference to these particular probabilities in the original report.

[3]The authors state that this figure is a “conservative guess”. It is not precisely clear whether the authors mean that one in ten thousand pandemics are predicted to causeextinction, or whether one in ten pandemics will have a risk of extinction. The latter reading is implausible because surely there is at least a risk, however small, that any global pandemic would cause extinction.

[4]Stated sources include: Bagus, Ghalid (2008): Pandemic Risk Modeling http://www.chicagoactuarialassociation.org/CAA_PandemicRiskModelingBagus_Jun08.pdf; Broekhoven, Henk van, Hellman, Anni (2006): Actuarial reflections on pandemic risk and its consequences http://actuary.eu/documents/pandemics_web.pdf; Brockmann, Dirk and Helbing, Dirk (2013): The Hidden Geometry of Complex, Network-Driven Contagion Phenomena SCIENCE VOL 342 http://rocs.hu-berlin.de/resources/HiddenGeometryPaper.pdf, W. Bruine de Bruin, B. Fischhoff; L. Brilliant and D. Caruso (2006): Expert judgments of pandemic influenza risks, Global Public Health, June 2006; 1(2): 178193http://www.cmu.edu/dietrich/sds/docs/fischhoff/AF-GPH.pdf; Khan K, Sears J, Hu VW, Brownstein JS, Hay S, Kossowsky D, Eckhardt R, Chim T, Berry I, Bogoch I, Cetron M.: Potential for the International Spread of Middle East Respiratory Syndrome in Association with Mass Gatherings in Saudi Arabia. PLOS Currents utbreaks. 2013 Jul 17. http://currents.plos.org/outbreaks/article/assessing-riskfor-the-international-spread-of-middle-east-respiratorysyndrome-in-association-with-mass-gatherings-insaudi-arabia/; Murray, Christopher JL, et al.: Estimation of potential global pandemic influenza mortality on the basis of vital registry data from the 1918–20 pandemic: a quantitative analysis. The Lancet 368.9554 (2007): 2211-2218. http://www.thelancet.com/journals/lancet/article/PIIS0140-6736(06)69895-4/fulltextand Sandman , Peter M. (2007): Talking about a flu pandemic worst-case scenario, http://www.cidrap.umn.edu/newsperspective/2007/03/talking-about-flu-pandemic-worstcase-scenario

comment by anonymous_ea · 2018-11-26T19:50:14.705Z · score: 2 (2 votes) · EA · GW

Thanks for posting this! A quick note: Millett & Snyder-Beatie (2017) is repeated here (it's both no. 7 and no. 8). I look forward to reading/hearing about your lit review!

comment by SimonBeard · 2018-11-26T22:50:10.224Z · score: 1 (1 votes) · EA · GW

Ha, your right! Millet and Snyder Beattie use two seperate methods to make two similar claims, however whilst I have listed both methods here I have accidently linked them to the same claim. I’ll correct this tomorrow.

comment by Simon_Beard · 2018-11-30T14:00:06.621Z · score: 2 (2 votes) · EA · GW

I hope that this is now fixed at last, although I stress that this is very much a work in progress

comment by Davidmanheim · 2018-12-16T09:41:08.108Z · score: 1 (1 votes) · EA · GW

You might be interested in my recent paper, "Questioning EStimates of Natural Pandemic Risk" - https://www.liebertpub.com/doi/pdf/10.1089/hs.2018.0039

comment by aarongertler · 2018-11-21T19:03:56.209Z · score: 4 (3 votes) · EA · GW

Thanks for posting this!

I'd be curious to know how closely this estimate tracks the estimates of Open Phil, the Future of Humanity Institute, and other organizations that think about.

Also, it seems extremely strange that most economic analyses don't include the cost of... well, dying, especially since that was a central feature of Obama-era cost-benefit analysis. (Paper with examples of how statistical mortality was used to evaluate regulatory policy.) Does anyone with an economics background know whether this is becoming more common? Maybe only in certain sub-fields?