Volume 44 - Article 52 | Pages 1271–1294 Author has provided data and code for replicating results

Probabilistic forecasting of maximum human lifespan by 2100 using Bayesian population projections

By Michael Pearce, Adrian E. Raftery

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Date received:07 Jul 2020
Date published:30 Jun 2021
Word count:5164
Keywords:Bayesian population projections, human lifespan, population forecasting, supercentenarians
Additional files:readme.44-52 (text file, 1 kB)
 demographic-research.44-52 (zip file, 7 kB)


Background: We consider the problem of quantifying the human lifespan using a statistical approach that probabilistically forecasts the maximum reported age at death (MRAD) through 2100.

Objective: We seek to quantify the probability that any person attains various extreme ages, such as those above 120, by the year 2100.

Methods: We use the exponential survival model for supercentenarians (people over age 110) of Rootzén and Zholud (2017) but extend the forecasting window, quantify population uncertainty using Bayesian population projections, and incorporate the most recent data from the International Database on Longevity (IDL) to obtain unconditional estimates of the distribution of MRAD this century in a fully Bayesian analysis.

Results: We find that the exponential survival model for supercentenarians is consistent with the most recent IDL data and that projections of the population aged 110–114 through 2080 are sensible. We integrate over the posterior distributions of the exponential model parameter and uncertainty in the supercentenarian population projections to estimate an unconditional distribution of MRAD by 2100.

Conclusions: Based on the Bayesian analysis, there is a greater than 99% probability that the current MRAD of 122 will be broken by 2100. We estimate the probabilities that a person lives to at least age 126, 128, or 130 this century, as 89%, 44%, and 13%, respectively.

Contribution: We have updated the supercentenarian survival model of Rootzén and Zholud using the most recent IDL data, incorporated Bayesian population projections, and extended the forecasting window to create the first fully Bayesian and unconditional probabilistic projection of MRAD by 2100.

Author's Affiliation

Michael Pearce - University of Washington, United States of America [Email]
Adrian E. Raftery - University of Washington, United States of America [Email]

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