Using Infection Networks to Control Epidemics
Author Information
Author(s): Hadidjojo Jeremy, Cheong Siew Ann
Primary Institution: Nanyang Technological University, Singapore
Hypothesis
Infection networks estimated from common infections can be useful to contain epidemics of more severe diseases with the same transmission mode.
Conclusion
The study demonstrates that targeted strategies based on estimated infection networks can effectively slow down and reduce the size of epidemics.
Supporting Evidence
- Targeted immunization of key nodes reduced the average size of epidemics by 58%–88%.
- Using estimated networks, the method was effective even with low accuracy.
- Interventions applied late in the epidemic still showed significant effectiveness.
Takeaway
The researchers created fake social networks to study how diseases spread and found that by targeting key people in these networks, they could stop diseases from spreading quickly.
Methodology
The study used computer simulations to generate artificial social networks and simulated SIR epidemics to estimate infection networks and test mitigation strategies.
Limitations
The accuracy of the estimated infection networks decreases with higher censor rates and larger network sizes.
Participant Demographics
The study involved simulations based on artificial social networks with varying sizes and characteristics.
Digital Object Identifier (DOI)
Want to read the original?
Access the complete publication on the publisher's website