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Innov Aging
Innov Aging
innovateage
Innovation in Aging
2399-5300 Oxford University Press US 

10.1093/geroni/igaa057.1576
igaa057.1576
Abstracts
Session 2999 (Paper)
Experiences in Long-Term Care
AcademicSubjects/SOC02600
Building, testing, and learning from network models of human aging
Rutenberg Andrew  Farrell Spencer  Mitnitski Arnold  Rockwood Kenneth  Stubbings Garrett  
Dalhousie University, Halifax, Nova Scotia, Canada
2020 
16 12 2020 
16 12 2020 
4 Suppl 1 Program Abstracts from The GSA 2020 Annual Scientific Meeting “Turning 75: Why Age Matters”487 487
© The Author(s) 2020. Published by Oxford University Press on behalf of The Gerontological Society of America.2020This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.Abstract
We have developed computational models of human aging that are based on complex networks of interactions between health attributes of individuals. Our “generic network model” (GNM) captures the population level exponential increase of mortality with age in Gompertz’s law together with the exponential decrease of health as measured by the frailty index (FI). Our GNM includes only random accumulation of damage, with no programmed aging. Our GNM allows large populations of model individuals to be quickly generated with detailed individual health trajectories. This allows us to explore individual damage propagation in detail. To facilitate comparison with observational data, we have also developed and tested new approaches to binarizing continuous-valued health data. To extract the most information out of available cross-sectional or longitudinal data, we have also reconstructed interactions from generalized network models that can predict individual health trajectories and mortality.