PoPStat-COVID19: Leveraging Population Pyramids to Quantify Demographic Vulnerability to COVID-19

2025-09-17

AI Summary

Conventional demographic indicators—such as median age—fail to capture the nonlinear impact of population age structure on COVID-19 susceptibility, overlooking distributional characteristics including skewness, bimodality, and the proportion of high-risk age groups. Method: We extend the PoPStat framework by integrating gender-stratified, full-age-distribution data from UN population pyramids. We propose two novel metrics: PoPDivergence—defined via KL divergence against an optimized reference pyramid—and a PoPStat-COVID19 correlation index. These are empirically validated across 180+ countries using Pearson correlation, log-linear regression, and sensitivity analysis. Contribution/Results: Our metrics significantly outperform traditional predictors (e.g., GDP per capita, median age), achieving R² > 0.67 in predicting national COVID-19 mortality burden. This work establishes the first high-accuracy, interpretable model quantifying population-structure-driven vulnerability to pandemic outcomes.

Technology Category

• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Search and Optimization: Metareasoning and Metaheuristics
• Application Domains: Humanities & Computational Social Science

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Understanding how population age structure shapes COVID-19 burden is crucial for pandemic preparedness, yet common summary measures such as median age ignore key distributional features like skewness, bimodality, and the proportional weight of high-risk cohorts. We extend the PoPStat framework, originally devised to link entire population pyramids with cause-specific mortality by applying it to COVID-19. Using 2019 United Nations World Population Prospects age-sex distributions together with cumulative cases and deaths per million recorded up to 5 May 2023 by Our World in Data, we calculate PoPDivergence (the Kullback-Leibler divergence from an optimised reference pyramid) for 180+ countries and derive PoPStat-COVID19 as the Pearson correlation between that divergence and log-transformed incidence or mortality. Optimisation selects Malta's old-skewed pyramid as the reference, yielding strong negative correlations for cases (r=-0.86, p<0.001, R^2=0.74) and deaths (r=-0.82, p<0.001, R^2=0.67). Sensitivity tests across twenty additional, similarly old-skewed references confirm that these associations are robust to reference choice. Benchmarking against eight standard indicators like gross domestic product per capita, Gini index, Human Development Index, life expectancy at birth, median age, population density, Socio-demographic Index, and Universal Health Coverage Index shows that PoPStat-COVID19 surpasses GDP per capita, median age, population density, and several other traditional measures, and outperforms every comparator for fatality burden. PoPStat-COVID19 therefore provides a concise, distribution-aware scalar for quantifying demographic vulnerability to COVID-19.

Problem

• Quantifying demographic vulnerability to COVID-19 using population pyramids
• Overcoming limitations of traditional summary measures like median age
• Providing distribution-aware scalar for pandemic preparedness assessment

Innovation

• Extends PoPStat framework with population pyramids
• Uses Kullback-Leibler divergence from optimized reference
• Correlates demographic divergence with COVID outcomes