Edge-based modelling for disease transmission on random graphs: an application to mitigate a syphilis outbreak

Area of Science:

• Epidemiology
• Network Science
• Public Health

Background:

• Traditional epidemiological models often oversimplify disease transmission dynamics.
• Social heterogeneity significantly influences the spread of infectious diseases like syphilis.
• Edge-based network models provide a tractable approach to incorporate complex social structures.

Purpose of the Study:

• To apply an edge-based network model to syphilis transmission in a specific Canadian region.
• To compare the predictive power of network-based SIR models versus mass action SIR models.
• To evaluate the potential impact of rapid syphilis testing and treatment interventions.

Main Methods:

• Utilized an edge-based network susceptible-infectious-recovered (SIR) model.
• Modeled syphilis spread in the Kingston, Frontenac and Lennox & Addington region.
• Compared network model outputs with traditional mass action SIR model results.

Main Results:

• The network model produced significantly different predictions compared to the mass action model.
• A substantially lower final epidemic size was estimated using the network model.
• The model assessed the potential effectiveness of a rapid syphilis point-of-care testing intervention.

Conclusions:

• Edge-based network models provide a more nuanced understanding of syphilis transmission.
• Network models are crucial for accurate epidemic size estimation and intervention planning.
• Intervention strategies like rapid testing can be effectively evaluated using network modeling.