Inferring Contagion Patterns in Social Contact Networks with Limited Infection Data

Area of Science:

• Epidemiology
• Network Science
• Computational Biology

Background:

• Infectious disease spread is a stochastic process, making real-time prediction and control difficult.
• Direct human contact diseases can be modeled using social-contact networks, representing individuals as nodes and contacts as links.

Purpose of the Study:

• To identify infection patterns in ongoing outbreaks by inferring likely infection paths.
• To develop a model for understanding disease transmission dynamics within a population.

Main Methods:

• Formulating the infection path inference problem as a bi-linear integer program.
• Developing heuristic solution methods based on efficiently solvable sub-problems.
• Evaluating heuristic performance on randomly generated networks with varying information availability.

Main Results:

• The model successfully infers the most likely set of infection-spreading contacts within a network.
• Heuristic methods demonstrate effective performance across diverse network structures and data completeness levels.

Conclusions:

• The developed model provides insights into epidemic outbreak patterns.
• Results can inform the development of targeted intervention strategies for infectious diseases.