Spatiotemporal Dynamics of the Epidemic Transmission in a Predator-Prey System

Su, M.; Hui, Cang; Zhang, Yanyu; Li, Zizhen

doi:10.1007/s11538-008-9340-3

Cited by 25 publications

(15 citation statements)

References 71 publications

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“…My model of learning is based on nonlocal cellular automata (CA) approach known from physics [30][31][32][33][34], with a wide range of applications [35][36][37][38][39][40][41][42][43]. The first statistical mechanics of opinion formation in groups of individuals was proposed by Lewenstein et al [44] on the class of models that were based on probabilistic cellular automata and social impact theory introduced by Latane [45,46].…”

Section: B Br Ra Ai In Ns St To or Rm MI In Ng G: : C Ce El Ll Lu Ul mentioning

confidence: 99%

Mean-Field Theory of Meta-learning

Plewczyński

2012

Encyclopedia of the Sciences of Learning

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A Ab bs st tr ra ac ct tWe discuss here the mean-field theory for a cellular automata model of meta-learning. The metalearning is the process of combining outcomes of individual learning procedures in order to determine the final decision with higher accuracy than any single learning method. Our method is constructed from an ensemble of interacting, learning agents, that acquire and process incoming information using various types, or different versions of machine learning algorithms. The abstract learning space, where all agents are located, is constructed here using a fully connected model that couples all agents with random strength values. The cellular automata network simulates the higher level integration of information acquired from the independent learning trials. The final classification of incoming input data is therefore defined as the stationary state of the meta-learning system using simple majority rule, yet the minority clusters that share opposite classification outcome can be observed in the system. Therefore, the probability of selecting proper class for a given input data, can be estimated even without the prior knowledge of its affiliation. The fuzzy logic can be easily introduced into the system, even if learning agents are build from simple binary classification machine learning algorithms by calculating the percentage of agreeing agents.

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Section: B Br Ra Ai In Ns St To or Rm MI In Ng G: : C Ce El Ll Lu Ul mentioning

confidence: 99%

Mean-Field Theory of Meta-learning

Plewczyński

2012

Encyclopedia of the Sciences of Learning

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“…To this end, it is reasonable to speculate that these life-history traits will also affect the spatial distribution of pathogens and their hosts, especially when pathogenic infection occurs through direct contacts of infected and susceptible individuals (Rand et al, 1995;Keeling, 1999). The spatial structure in this system could even lead to pathogen-driven extinction (Webb et al, 2007) and consequently, the spatial structure has been explicitly taken into account when examining the transmission dynamics and the evolution of virulence (Boots and Sasaki, 1999;Haraguchi and Sasaki, 2000;Boots et al, 2004;Webb et al, 2007Webb et al, , 2013Su et al, 2008aSu et al, , 2009a). …”

Section: Introductionmentioning

confidence: 99%

Effects of the transmissibility and virulence of pathogens on intraguild predation in fragmented landscapes

Hui

Zhen-shan

2015

Biosystems

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It is well known that pathogenic infection can have a profound effect on the outcome of competition and predation, however the role of pathogenic infection in systems where predators and prey also compete for other resources is yet to be explored (i.e. in systems of intraguild predation). Using a cellular automaton model, we here explore the effect of pathogenic infection on the spatial dynamics of species that also engage in intraguild predation (IGP) in a fragmented landscape. First, the shared pathogen by the predator and prey can enhance species coexistence in the IGP system, consistent with results for non-spatial IGP systems. Second, equilibrium population sizes of the predator and prey depend crucially on the pathogen virulence to the predator but are insensitive to the change in the virulence to the prey. This asymmetric response to virulence change is due to the fact that the predator species has to juggle between predation, resource competition and pathogenic infection. Finally, the response of the pathogen to habitat fragmentation is largely determined by its life-history strategy (transmissibility and virulence) and the trophic level of its host. These results enrich our understanding on the role of pathogens in the ecosystem functioning of eco-epidemiological systems.

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“…In 2002, Stephoen Wolfram studied CA deeply and creatively named the CA as a novel scientific filed [4]. Currently, there have been wide applications of CA in different areas, including sociology, biology, ecology, information science, computer science, mathematics, physics, chemistry, geography, environment, military, infectious diseases and so on [5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%