The Nonlinear Library EA - Forecasting accidentally-caused pandemics by JoshuaBlake
Jan 18, 2024
07:38
Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Forecasting accidentally-caused pandemics, published by JoshuaBlake on January 18, 2024 on The Effective Altruism Forum.
Future pandemics could arise from an accident (a pathogen being used in research accidentally infecting a human). The risk from accidental pandemics is likely increasing in line with the amount of research being conducted. In order to prioritise pandemic preparedness, forecasts of the rate of accidental pandemics are needed. Here, I describe a simple model, based on historical data, showing that the rate of accidental pandemics over the next decade is almost certainly lower than that of zoonotic pandemics (pandemics originating in animals).
Before continuing, I should clarify what I mean by an accidental pandemic. By 'accidental pandemic,' I refer to a pandemic arising from human activities, but not from malicious actors. This includes a wide variety of activities, including lab-based research and clinical trials or more unusual activities such as hunting for viruses in nature.
The first consideration in the forecast is the historic number of accidental pandemics. One historical pandemic (1977 Russian flu) is widely accepted to be due to research gone wrong, with the leading hypothesis being a clinical trial. The estimated death toll from this pandemic is 700,000. The origin of the COVID-19 pandemic is disputed, and I won't go further into that argument here. Therefore, historically, there have been one or two accidental pandemics.
Next, we need to consider the amount of research that could cause such a pandemics, or the number of "risky research units" that have been conducted. No good data exists on risky research units directly.
However, we only need a measure that is proportional to the number of experiments.[1] I consider three indicators: publicly reported lab accidents, as collated by Manheim and Lewis (2022); the rate at which BSL-4 labs (labs handling the most dangerous pathogens) are being built, gathered by Global BioLabs; and the number of virology papers being published, categorised by the Web of Science database. I find a good fit with a shared rate of growth at 2.5% per year.
A plateau in the number of virology papers in the Web of Science database is plausibly visible. It is too early to tell if this trend will feed through to the number of labs or datasets, but this is a weakness of this analysis. However, a similar apparent plateau is visible in the 1990s, yet growth then appeared to restart along the previous trendline.
The final step is to extrapolate this growth in risky research units and see what it implies for how many accidental pandemics we should expect to see. Below I plot this: the average (expected) number of pandemics per year. Two scenarios are considered: where the basis is one historical accidental pandemic (1977 Russian flu) and where the basis is two historical accidental pandemics (adding COVID-19). For comparison, I include the historic long-run average number of pandemics per year, 0.25.[2]
Predictions for the ten years starting with 2024 are in the table below. This gives, for each scenario: the number of accidental pandemics that are expected, a range which the number of pandemics should fall in with at least 80% probability, and the probability of at least one accidental pandemic occurring.
Scenario
Expected number
80% prediction
Probability at least 1
1 previous
1.2
0-2
56%
2 previous
2.1
0-3
76%
Overall, the conclusion from the model is that, for the next decade, the threat of zoonotic pandemics is likely still greater. However, if lab activity continues to increase at this rate, accidental pandemics may dominate.
The model here is extremely simple, and a more complex one would very likely decrease the number forecast. In particular, this model relies on the following major assumptions.
First, the actual ...
Future pandemics could arise from an accident (a pathogen being used in research accidentally infecting a human). The risk from accidental pandemics is likely increasing in line with the amount of research being conducted. In order to prioritise pandemic preparedness, forecasts of the rate of accidental pandemics are needed. Here, I describe a simple model, based on historical data, showing that the rate of accidental pandemics over the next decade is almost certainly lower than that of zoonotic pandemics (pandemics originating in animals).
Before continuing, I should clarify what I mean by an accidental pandemic. By 'accidental pandemic,' I refer to a pandemic arising from human activities, but not from malicious actors. This includes a wide variety of activities, including lab-based research and clinical trials or more unusual activities such as hunting for viruses in nature.
The first consideration in the forecast is the historic number of accidental pandemics. One historical pandemic (1977 Russian flu) is widely accepted to be due to research gone wrong, with the leading hypothesis being a clinical trial. The estimated death toll from this pandemic is 700,000. The origin of the COVID-19 pandemic is disputed, and I won't go further into that argument here. Therefore, historically, there have been one or two accidental pandemics.
Next, we need to consider the amount of research that could cause such a pandemics, or the number of "risky research units" that have been conducted. No good data exists on risky research units directly.
However, we only need a measure that is proportional to the number of experiments.[1] I consider three indicators: publicly reported lab accidents, as collated by Manheim and Lewis (2022); the rate at which BSL-4 labs (labs handling the most dangerous pathogens) are being built, gathered by Global BioLabs; and the number of virology papers being published, categorised by the Web of Science database. I find a good fit with a shared rate of growth at 2.5% per year.
A plateau in the number of virology papers in the Web of Science database is plausibly visible. It is too early to tell if this trend will feed through to the number of labs or datasets, but this is a weakness of this analysis. However, a similar apparent plateau is visible in the 1990s, yet growth then appeared to restart along the previous trendline.
The final step is to extrapolate this growth in risky research units and see what it implies for how many accidental pandemics we should expect to see. Below I plot this: the average (expected) number of pandemics per year. Two scenarios are considered: where the basis is one historical accidental pandemic (1977 Russian flu) and where the basis is two historical accidental pandemics (adding COVID-19). For comparison, I include the historic long-run average number of pandemics per year, 0.25.[2]
Predictions for the ten years starting with 2024 are in the table below. This gives, for each scenario: the number of accidental pandemics that are expected, a range which the number of pandemics should fall in with at least 80% probability, and the probability of at least one accidental pandemic occurring.
Scenario
Expected number
80% prediction
Probability at least 1
1 previous
1.2
0-2
56%
2 previous
2.1
0-3
76%
Overall, the conclusion from the model is that, for the next decade, the threat of zoonotic pandemics is likely still greater. However, if lab activity continues to increase at this rate, accidental pandemics may dominate.
The model here is extremely simple, and a more complex one would very likely decrease the number forecast. In particular, this model relies on the following major assumptions.
First, the actual ...