Data-Driven Modeling For Social-Ecological Resilience within a One Health Framework

Technological advancements have vastly expanded the scope of questions that can be explored within health systems. The One Health framework recognizes the interconnectedness of human, animal, and environmental health systems; the concept of social-ecological resilience (SER) incorporates the social, cultural, and political factors that can also impact how health systems respond and adapt to changes into this framework. This dissertation adopts this approach to health systems to investigate the role of advanced data-driven modeling techniques in understanding resilience mechanisms. Here, social-ecological resilience within a One Health framework is explored in both disease-affected animal populations and nutrition-sensitive agricultural systems. In animal populations, social-ecological factors can be inferred through observable social behaviors that contribute to population persistence and disease resilience. Chapter 2, employ a stochastic age-structured model of a banded mongoose (Mungos mungo) population in northern Botswana affected by the fatal Mycobacterium mungi pathogen to simulate population and disease dynamics. Model simulations reveal that certain social-ecological factors such as Allee Effects and group fusions, provide buffers to extinction events and offset disease effects. The findings also reveal that environmental heterogeneity influences the population and disease dynamics. This suggests a two-way interaction between social-ecological and environmental factors; a finding critical to the understanding of SER within an OH framework. The latter chapters shift focus to agricultural systems as key contributors to nutritional resilience, particularly in Sub-Saharan Africa (SSA), where undernutrition remains prevalent. Chapter 3 presents a systematic scoping review to characterize quantitative assessments of the impact of agricultural investments and programs (AIPs) on health or nutritional outcomes in SSA. This review revealed significant gaps in the representation of SER perspectives in nutrition-focused AIPs and highlighted the importance of interdisciplinary collaboration. The gaps found in this literature review motivated the development of the modeling process framework proposed in Chapter 4. This framework provides a structured approach for incorporating data-driven models to analyze nutritional resilience, predict the nutritional impact of interventions, and evaluate data suitability for such analyses. The findings from our application of this framework reinforce the importance of robust data architecture and multisectoral collaboration to effectively and contextually mitigate undernutrition. Collectively, these chapters advance data-driven interdisciplinary methodologies for resilience research and demonstrate the value of SER perspectives in modeling approaches in both ecological and nutrition-sensitive agricultural settings.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Genetics, Bioinformatics, and Computational Biology

Department dc:contributor.department

Genetics, Bioinformatics, and Computational Biology

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2025

Author dc:creator

• Smith, Gabrielle Lee

Chairs dc:contributor.committeechair

• Rist, Cassidy
• Lewis, Bryan Leroy

Committee members dc:contributor.committeemember

• Smith, Edward J.
• Alexander, Kathleen A.
• Childs, Lauren M.

• Data-driven modeling
• stochastic modeling
• statistical modeling
• mathematical biology
• computational biology
• data science
• data analytics
• infectious diseases
• banded mongoose
• undernutrition
• nutrition-sensitive agriculture

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:43117

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/135410