
AbstractEpidemic spreading phenomena are ubiquitous in nature and society. Examples include the spreading of diseases, information and computer viruses. Epidemics can spread by local spreading, where infected nodes can only infect a limited set of direct target nodes and global spreading, where an infected node can infect every other node. In reality, many epidemics spread using a hybrid mixture of both types of spreading. In this study we develop a theoretical framework for studying hybrid epidemics and examine the optimum balance between spreading mechanisms in terms of achieving the maximum outbreak size. We show the existence of critically hybrid epidemics where neither spreading mechanism alone can cause a noticeable spread but a combination of the two spreading mechanisms would produce an enormous outbreak. Our results provide new strategies for maximising beneficial epidemics and estimating the worst outcome of damaging hybrid epidemics.
Social and Information Networks (cs.SI), FOS: Computer and information sciences, Physics - Physics and Society, Computer Science - Artificial Intelligence, Populations and Evolution (q-bio.PE), FOS: Physical sciences, Computer Science - Social and Information Networks, Physics and Society (physics.soc-ph), Models, Theoretical, Communicable Diseases, Article, Disease Outbreaks, thermodynamics and nonlinear dynamics, Phase transitions and critical phenomena, Artificial Intelligence (cs.AI), FOS: Biological sciences, Disease Transmission, Infectious, Humans, Statistical physics, Quantitative Biology - Populations and Evolution, Epidemics
Social and Information Networks (cs.SI), FOS: Computer and information sciences, Physics - Physics and Society, Computer Science - Artificial Intelligence, Populations and Evolution (q-bio.PE), FOS: Physical sciences, Computer Science - Social and Information Networks, Physics and Society (physics.soc-ph), Models, Theoretical, Communicable Diseases, Article, Disease Outbreaks, thermodynamics and nonlinear dynamics, Phase transitions and critical phenomena, Artificial Intelligence (cs.AI), FOS: Biological sciences, Disease Transmission, Infectious, Humans, Statistical physics, Quantitative Biology - Populations and Evolution, Epidemics
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 12 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
