Geographically dependent individual-level models for infectious diseases transmission
M D Mahsin1, Rob Deardon1, Patrick Brown2
1Department of Mathematics and Statistics and Faculty of Veterinary Medicine, University of Calgary, 2500 University Dr NW, Calgary AB T2N 1N4, Canada.
Biostatistics (Oxford, England)
|March 3, 2020
Summary
This study introduces geographically dependent individual-level models (ILMs) to better understand infectious disease spread. These enhanced models incorporate spatial risk factors and unobserved spatial structure for improved public health insights.
Area of Science:
- Epidemiology
- Biostatistics
- Geographic Information Systems (GIS)
Background:
- Infectious disease models are crucial for understanding disease spread and guiding public health policy.
- Current discrete-time individual-level models (ILMs) do not account for the importance of specific spatial locations in disease transmission.
- There is a need to incorporate geographically varying risk factors and unobserved spatial patterns into infectious disease models.
Purpose of the Study:
- To generalize discrete-time individual-level models (ILMs) to a new class of geographically dependent ILMs.
- To evaluate the impact of spatially varying risk factors (e.g., education, social deprivation, environmental) on infectious disease transmission.
- To incorporate unobserved spatial structure using a conditional autoregressive (CAR) model.
Main Methods:
- Development of a new class of geographically dependent individual-level models (ILMs).
- Integration of a conditional autoregressive (CAR) model to account for unobserved spatial structure.
- Application of a Bayesian statistical framework with Markov chain Monte Carlo (MCMC) methods for model fitting.
- Validation using simulated epidemic data and Alberta seasonal influenza outbreak data (2009).
Main Results:
- The new models allow for the evaluation of spatially varying risk factors and unobserved spatial effects on disease transmission.
- Demonstrated the flexibility of the models in formulating etiological hypotheses.
- Identified geographical regions with unusually high risk for targeted preventive action.
Conclusions:
- Geographically dependent ILMs provide a more nuanced understanding of infectious disease dynamics by incorporating spatial context.
- These models enhance the ability to identify high-risk areas and inform targeted public health interventions.
- The developed framework offers a flexible approach for analyzing infectious disease spread in relation to geographic factors.
Keywords:
Alberta seasonal influenza outbreakCAR modelGeographically dependent ILMsIndividual-level models (ILMs)Markov chain Monte Carlo (MCMC)Stochastic models in infectious diseasesMore Related Videos
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