Multi-state epidemic processes on complex networks.
Infectious diseases are practically represented by models with multiple states and complex transition rules corresponding to, for example, birth, death, infection, recovery, disease progression, and quarantine. In addition, networks underlying infection events are often much more complex than described by meanfield equations or regular lattices. In models with simple transition rules such as the SIS and SIR models, heterogeneous contact rates are known to decrease epidemic thresholds. We analyse steady states of various multi-state disease propagation models with heterogeneous contact rates. In many models, heterogeneity simply decreases epidemic thresholds. However, in models with competing pathogens and mutation, coexistence of different pathogens for small infection rates requires network-independent conditions in addition to heterogeneity in contact rates. Furthermore, models without spontaneous neighbor-independent state transitions, such as cyclically competing species, do not show heterogeneity effects.
Masuda
Naoki
N
Laboratory for Mathematical Neuroscience, RIKEN Brain Science Institute, 2-1, Hirosawa, Wako, Saitama 351-0198, Japan. masuda@brain.riken.jp
Konno
Norio
N
D003141:Communicable Diseases / Q000453:epidemiology* / Q000635:transmission; D004196:Disease Outbreaks; D004352:Drug Resistance, Microbial; D005716:Game Theory; D006801:Humans; D008954:Models, Biological; D009154:Mutation; D014376:Tuberculosis / Q000453:epidemiology / Q000635:transmission