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Data-driven modelling and spatial complexity supports heterogeneity-based integrative management for eliminating
Edwin Michael1, Morgan E Smith2, Brajendra K Singh2
1Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, 46556, USA. emichael@nd.edu.
Scientific Reports
|March 8, 2020
Summary
Spatial complexity challenges parasitic infection elimination. Data-driven modeling revealed locally-relevant onchocerciasis models, with vector control overcoming spatial variability for successful elimination.
Area of Science:
- Parasitology
- Epidemiology
- Mathematical Modeling
Background:
- Spatial complexity poses challenges for area-wide parasitic infection elimination.
- Heterogeneity-based approaches are proposed, but spatial scale and local model relevance require investigation.
Purpose of the Study:
- To investigate spatial scale effects and discover locally-relevant models for parasitic infection elimination.
- To understand transmission dynamics of Simulium neavei-transmitted onchocerciasis in Western Uganda.
Main Methods:
- Utilized a data-driven modeling framework with Bayesian data-model assimilation.
- Applied the framework to infection data from various monitoring sites.
Main Results:
- Successfully discovered onchocerciasis models reflecting local transmission conditions.
- Identified spatial variation in key management variables (breakpoints, intervention duration) at the focus level.
- Demonstrated that vector control effectively overcame spatial variability in elimination efforts.
Conclusions:
- Data-driven modeling with spatial datasets and model-data fusion is crucial for onchocerciasis elimination.
- Identifying scale-dependent models and heterogeneity-based options supports successful elimination of S. neavei-borne onchocerciasis.
