In previous epidemiological studies that address adaptive vaccination decisions, individuals generally act within a single network, which models the population structure. However, in reality, people are typically members of multiplex networks, which have various community structures. For example, a disease transmission network, which directly transmits infectious diseases, does not necessarily correspond with an information propagation network, in which individuals directly or indirectly exchange information concerning health conditions and vaccination strategies. The latter network may also be used for strategic interaction (strategy adaptation) concerning vaccination. Therefore, in order to reflect this feature, we consider the vaccination dynamics of structured populations whose members simultaneously belong to two types of networks: disease transmission and information propagation. Applying intensive numerical calculations, we determine that if the disease transmission network is modeled using a regular graph, such as a lattice population or random regular graph containing individuals of equivalent degrees, individuals should base their vaccination decisions on a different type of network. However, if the disease transmission network is a degree-heterogeneous graph, such as the Barabási-Albert scale-free network, which has a heterogeneous degree according to power low, then using the same network for information propagation more effectively prevents the spread of epidemics. Furthermore, our conclusions are unaffected by the relative cost of vaccination.
All Science Journal Classification (ASJC) codes
- Statistical and Nonlinear Physics
- Physics and Astronomy(all)
- Applied Mathematics