Message-passing approach for recurrent-state epidemic models on networks.
Phys Rev E Stat Nonlin Soft Matter Phys
; 92(2): 022821, 2015 Aug.
Article
em En
| MEDLINE
| ID: mdl-26382468
ABSTRACT
Epidemic processes are common out-of-equilibrium phenomena of broad interdisciplinary interest. Recently, dynamic message-passing (DMP) has been proposed as an efficient algorithm for simulating epidemic models on networks, and in particular for estimating the probability that a given node will become infectious at a particular time. To date, DMP has been applied exclusively to models with one-way state changes, as opposed to models like SIS and SIRS where nodes can return to previously inhabited states. Because many real-world epidemics can exhibit such recurrent dynamics, we propose a DMP algorithm for complex, recurrent epidemic models on networks. Our approach takes correlations between neighboring nodes into account while preventing causal signals from backtracking to their immediate source, and thus avoids "echo chamber effects" where a pair of adjacent nodes each amplify the probability that the other is infectious. We demonstrate that this approach well approximates results obtained from Monte Carlo simulation and that its accuracy is often superior to the pair approximation (which also takes second-order correlations into account). Moreover, our approach is more computationally efficient than the pair approximation, especially for complex epidemic models the number of variables in our DMP approach grows as 2mk where m is the number of edges and k is the number of states, as opposed to mk^{2} for the pair approximation. We suspect that the resulting reduction in computational effort, as well as the conceptual simplicity of DMP, will make it a useful tool in epidemic modeling, especially for high-dimensional inference tasks.
Buscar no Google
Temas:
ECOS
/
Financiamentos_gastos
Bases de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Epidemias
/
Modelos Biológicos
Tipo de estudo:
Health_economic_evaluation
/
Prognostic_studies
Idioma:
En
Revista:
Phys Rev E Stat Nonlin Soft Matter Phys
Assunto da revista:
BIOFISICA
/
FISIOLOGIA
Ano de publicação:
2015
Tipo de documento:
Article
País de afiliação:
Estados Unidos