Source-linked AI summary
Enhanced vaccine control of epidemics in adaptive networks
Leah B. Shaw, Ira B. Schwartz
TL;DR
This paper examines how vaccination interacts with disease-avoidance rewiring in adaptive networks. It models Poisson vaccination of susceptibles and finds that substantially less vaccine is needed in adaptive than static networks to reduce infection and endemic-state lifetime.
Problem
How vaccination interacts with time-varying social contacts remains to be examined alongside disease spread on adaptive networks.
Source: p. 1Method
The model adds Poisson-distributed vaccination pulses of a fraction of susceptible nodes to an epidemic model with adaptive rewiring.
Source: pp. 1–2Results
About two orders of magnitude less vaccine is needed in adaptive networks to significantly reduce endemic-state lifetime, and a much smaller vaccination frequency significantly lowers infectives than in static networks.
Source: p. 2Takeaways & Limitations
In the adaptive model, rewiring leaves susceptibles with higher average degree, so random vaccination favors higher-degree nodes.
Source: p. 3Vaccination is modeled as random fixed-fraction pulses; the optimal strategy remains undetermined, and the model assumes vaccinated nodes are uncertain of infection status and rewire like susceptibles.
Source: pp. 1, 3
Abstract
from arXiv · showhide
We study vaccine control for disease spread on an adaptive network modeling disease avoidance behavior. Control is implemented by adding Poisson distributed vaccination of susceptibles. We show that vaccine control is much more effective in adaptive networks than in static networks due to an interaction between the adaptive network rewiring and the vaccine application. Disease extinction rates using vaccination are computed, and orders of magnitude less vaccine application is needed to drive the disease to extinction in an adaptive network than in a static one.