When I looked at the same dataset, albeit processing the data quite differently, I found that a truncated or cutoff power-law appeared to be a good fit. This gives a much lower value for extreme probabilities using the best-fit parameters. In particular, there were too few of the most severe pandemics in the dataset (COVID-19 and 1918 influenza) otherwise; this issue is visible in fig 1 of Marani et al. Could you please add the data to your tail distribution plot to assess how good a fit it is?
A final note, I think you’re calculating the probability of extinction in a single year but the worst pandemics historically have lasted multiple years. The total death toll from the pandemic is perhaps the quantity most of interest.
Agreed. In my analysis of conflict deaths, for the method where I used fitter:
The 5th and 95th percentile annual probability of a conflict causing human extinction are 0 and 5.02 % [depending on the distribution]
When I looked at the same dataset, albeit processing the data quite differently, I found that a truncated or cutoff power-law appeared to be a good fit. This gives a much lower value for extreme probabilities using the best-fit parameters. In particular, there were too few of the most severe pandemics in the dataset (COVID-19 and 1918 influenza) otherwise; this issue is visible in fig 1 of Marani et al. Could you please add the data to your tail distribution plot to assess how good a fit it is?
I did not get what you would like me to add to my tail distribution plot. However, I added here the coefficients of determination (R^2) of the regressions I did.
A final note, I think you’re calculating the probability of extinction in a single year but the worst pandemics historically have lasted multiple years. The total death toll from the pandemic is perhaps the quantity most of interest.
Focussing on the annual deaths as a fraction of the global population is useful because it being 1 is equivalent to human extinction. In contrast, total epidemic/pandemic deaths as a fraction of the global population in the year in which the epidemic/pandemic started being equal to 1 does not imply human extinction. For example, a pandemic could kill 1 % of the population each year for 100 years, but population remain constant due to births being equal to the pandemic deaths plus other deaths.
However, I agree interventions should be assessed based on standard cost-effectiveness analyses. So I believe the quantity of most interest which could be inferred from my analysis is the expected annual epidemic/pandemic deaths. These would be 2.28 M (= 2.87*10^-4*7.95*10^9) multiplying:
My annual epidemic/pandemic deaths as a fraction of the global population based on data from 1900 to 2023. Earlier years are arguably not that informative.
The population in 2021.
The above expected death toll would rank as 6th in 2021.
For reference, based on my analysis of conflicts, I get an expected death toll of conflicts based on historical data from 1900 to 2000 (also adjusted for underreporting), and the population in 2021 of 3.83 M (= 2.87*10^-4*7.95*10^9), which would rank above as 5th.
Here is a graph with the top 10 actual causes of death and expected conflict and epidemic/pandemic deaths:
Could you please expand on why you think a Pareto distribution is appropriate here? Tail probabilities are often quite sensitive to the assumptions here, and it can be tricky to determine if something is truly power-law distributed.
When I looked at the same dataset, albeit processing the data quite differently, I found that a truncated or cutoff power-law appeared to be a good fit. This gives a much lower value for extreme probabilities using the best-fit parameters. In particular, there were too few of the most severe pandemics in the dataset (COVID-19 and 1918 influenza) otherwise; this issue is visible in fig 1 of Marani et al. Could you please add the data to your tail distribution plot to assess how good a fit it is?
A final note, I think you’re calculating the probability of extinction in a single year but the worst pandemics historically have lasted multiple years. The total death toll from the pandemic is perhaps the quantity most of interest.
Thanks for the relevant points, Joshua. I strongly upvoted your comment.
I did not mean to suggest a Pareto distribution is appropriate, just that it is worth considering.
Agreed. In my analysis of conflict deaths, for the method where I used fitter:
I did not get what you would like me to add to my tail distribution plot. However, I added here the coefficients of determination (R^2) of the regressions I did.
Focussing on the annual deaths as a fraction of the global population is useful because it being 1 is equivalent to human extinction. In contrast, total epidemic/pandemic deaths as a fraction of the global population in the year in which the epidemic/pandemic started being equal to 1 does not imply human extinction. For example, a pandemic could kill 1 % of the population each year for 100 years, but population remain constant due to births being equal to the pandemic deaths plus other deaths.
However, I agree interventions should be assessed based on standard cost-effectiveness analyses. So I believe the quantity of most interest which could be inferred from my analysis is the expected annual epidemic/pandemic deaths. These would be 2.28 M (= 2.87*10^-4*7.95*10^9) multiplying:
My annual epidemic/pandemic deaths as a fraction of the global population based on data from 1900 to 2023. Earlier years are arguably not that informative.
The population in 2021.
The above expected death toll would rank as 6th in 2021.
For reference, based on my analysis of conflicts, I get an expected death toll of conflicts based on historical data from 1900 to 2000 (also adjusted for underreporting), and the population in 2021 of 3.83 M (= 2.87*10^-4*7.95*10^9), which would rank above as 5th.
Here is a graph with the top 10 actual causes of death and expected conflict and epidemic/pandemic deaths: