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.
Abstract:
In this paper, we propose a general framework for the reconstruction of the underlying cross-regional transmission network contributing to the spread of an infectious disease. We employ an autoregressive model that allows to decompose the mean number of infections into three components that describe: intra-locality infections, inter-locality infections, and infections from other sources such as travelers arriving to a country from abroad. This model is commonly used in the identification of spatiotemporal patterns in seasonal infectious diseases and thus in forecasting infection counts. However, our contribution lies in identifying the inter-locality term as a time-evolving network, and rather than using the model for forecasting, we focus on the network properties without any assumption on seasonality or recurrence of the disease. The topology of the network is then studied to get insight into the disease dynamics. Building on this, and particularly on the centrality of the nodes of the identified network, a strategy for intervention and disease control is devised.
Insights
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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