Ensemble-Based Estimation of Alzheimer's Disease Incidence from Dynamic Population Reconstructions

TL;DR

This paper introduces a two-stage ensemble Kalman inversion method to reconstruct Alzheimer's disease incidence over time from mortality data, enabling disease trend analysis without direct incidence data.

Contribution

The novel approach combines demographic modeling and back-calculation with ensemble Kalman inversion to infer disease incidence from mortality data.

Findings

Successfully reconstructs historical AD incidence trends.

Provides a flexible framework for disease monitoring without direct incidence data.

Integrates demographic and disease-specific hazards into a unified model.

Abstract

We present a two-stage methodology for reconstructing Alzheimer's disease (AD) incidence over time using ensemble Kalman inversion (EKI) applied to mortality data. In the first stage, we use EKI to infer temporal trends in all-cause and Alzheimer's-specific mortality by fitting an age-structured demographic model to observed death counts. This yields posterior estimates of evolving population structure and age-specific AD mortality rates. In the second stage, we apply a back-calculation procedure that uses these estimates, along with the hazard of AD-related death following disease onset, to infer time- and age-specific incidence rates. This reverse-inference framework enables the reconstruction of latent disease dynamics in the absence of direct incidence surveillance. By integrating demographic structure, disease-specific hazards, and observed mortality into a coherent inferential pipeline, our approach offers a principled and flexible tool for monitoring chronic disease trends and estimating historical disease burden.