A framework for reconstructing transmission networks in infectious diseases
Sara Najem1,2, Stefano Monni1,3, Rola Hatoum2
1Department of Physics, American University of Beirut, Beirut, Lebanon.
Applied Network Science
|December 26, 2022
Summary
This study introduces a framework to map infectious disease spread between regions using an autoregressive model. It identifies transmission networks to guide disease control strategies.
Area of Science:
- Epidemiology
- Network Science
- Mathematical Modeling
Background:
- Infectious disease transmission involves complex spatial dynamics.
- Understanding cross-regional spread is crucial for effective public health interventions.
- Existing models often focus on forecasting rather than network structure.
Purpose of the Study:
- To develop a general framework for reconstructing cross-regional infectious disease transmission networks.
- To analyze the topology and dynamics of these networks.
- To devise intervention strategies based on network properties.
Main Methods:
- Utilized an autoregressive model to decompose infection sources (intra-locality, inter-locality, external).
- Identified the inter-locality component as a time-evolving network.
- Focused on network topology and node centrality for analysis.
Main Results:
- Successfully reconstructed the underlying cross-regional transmission network.
- Characterized the network's topology to understand disease spread dynamics.
- Identified key nodes through centrality measures for targeted interventions.
Conclusions:
- The proposed framework provides insights into infectious disease transmission networks.
- Network analysis, particularly node centrality, is valuable for disease control.
- This approach offers a method for devising targeted intervention strategies.
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