Computational model of a vector-mediated epidemic

Dickman, Adriana Gomes; Dickman, Ronald

doi:10.1119/1.4917164

Cited by 6 publications

(4 citation statements)

References 18 publications

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“…The SIR model exhibits a phase transition between non-spreading (NS) and spreading (S) regions7. Later, a variety of other models8910111213 were developed to cope with different specific epidemic conditions. Notable are the so-called contact models, particularly the Susceptible-Infected-Susceptible (SIS) model, whose critical properties have been widely analysed8910111415.…”

Section: Lattice Models For Epidemics and Critical Behaviormentioning

confidence: 99%

“…Later, a variety of other models 8 9 10 11 12 13 were developed to cope with different specific epidemic conditions. Notable are the so-called contact models, particularly the Susceptible-Infected-Susceptible (SIS) model, whose critical properties have been widely analysed 8 9 10 11 14 15 . Although the ODE-based approach can predict some relevant results, it does not capture the spatial structures of outbreaks.…”

Section: Lattice Models For Epidemics and Critical Behaviormentioning

confidence: 99%

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Critical behavior in a stochastic model of vector mediated epidemics

Alfinito

Beccaria

Macorini

2016

Sci Rep

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The extreme vulnerability of humans to new and old pathogens is constantly highlighted by unbound outbreaks of epidemics. This vulnerability is both direct, producing illness in humans (dengue, malaria), and also indirect, affecting its supplies (bird and swine flu, Pierce disease, and olive quick decline syndrome). In most cases, the pathogens responsible for an illness spread through vectors. In general, disease evolution may be an uncontrollable propagation or a transient outbreak with limited diffusion. This depends on the physiological parameters of hosts and vectors (susceptibility to the illness, virulence, chronicity of the disease, lifetime of the vectors, etc.). In this perspective and with these motivations, we analyzed a stochastic lattice model able to capture the critical behavior of such epidemics over a limited time horizon and with a finite amount of resources. The model exhibits a critical line of transition that separates spreading and non-spreading phases. The critical line is studied with new analytical methods and direct simulations. Critical exponents are found to be the same as those of dynamical percolation.

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Section: Lattice Models For Epidemics and Critical Behaviormentioning

confidence: 99%

Section: Lattice Models For Epidemics and Critical Behaviormentioning

confidence: 99%

Critical behavior in a stochastic model of vector mediated epidemics

Alfinito

Beccaria

Macorini

2016

Sci Rep

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“…Complex networks describe a wide range of systems in nature and society. An important aim of network modeling is to incorporate in the interaction among individuals the already known characteristics of epidemics [1] , [2] , [3] , [4] , [5] . Each new model scientifically proposed for the study of an epidemic process is an attempt to approach the reality of this epidemy or one of its characteristics through a combination between the proposed model and the own network where the epidemy is developing.…”

Section: Introductionmentioning

confidence: 99%

Critical properties of the SIS model on the clustered homophilic network

Santos

Almeida

Albuquerque

et al. 2020

Physica A: Statistical Mechanics and its Applications

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The spreading of epidemics in complex networks has been a subject of renewed interest of several scientific branches. In this regard, we have focused our attention on the study of the susceptible–infected–susceptible (SIS) model, within a Monte Carlo numerical simulation approach, representing the spreading of epidemics in a clustered homophilic network. The competition between infection and recovery that drives the system either to an absorbing or to an active phase is analyzed. We estimate the static critical exponents , and , through finite-size scaling (FSS) analysis of the order parameter and its fluctuations, showing that they differ from those associated with the contact process on a scale-free network, as well as those predicted by the heterogeneous mean-field theory.

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“…In recent years, the network epidemiological approach has become an important tool in analyzing epidemiological data to develop control and intervention measures [ 19 , 20 ]. This approach is accepted in medical epidemiology, mathematical biology, and research into predicting the potential spread of diseases and epidemics [ 21 , 22 ]. More recently, it has become valuable to network science, and, in this context, in showing the relationship between disease and the organisms most likely to be involved.…”

Section: Introductionmentioning

confidence: 99%

The Dynamics of Avian Influenza: Individual-Based Model with Intervention Strategies in Traditional Trade Networks in Phitsanulok Province, Thailand

Wilasang

Wiratsudakul

Chadsuthi

2016

Computational and Mathematical Methods in Medicine

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Avian influenza virus subtype H5N1 is endemic to Southeast Asia. In Thailand, avian influenza viruses continue to cause large poultry stock losses. The spread of the disease has a serious impact on poultry production especially among rural households with backyard chickens. The movements and activities of chicken traders result in the spread of the disease through traditional trade networks. In this study, we investigate the dynamics of avian influenza in the traditional trade network in Phitsanulok Province, Thailand. We also propose an individual-based model with intervention strategies to control the spread of the disease. We found that the dynamics of the disease mainly depend on the transmission probability and the virus inactivation period. This study also illustrates the appropriate virus disinfection period and the target for intervention strategies on traditional trade network. The results suggest that good hygiene and cleanliness among household traders and trader of trader areas and ensuring that any equipment used is clean can lead to a decrease in transmission and final epidemic size. These results may be useful to epidemiologists, researchers, and relevant authorities in understanding the spread of avian influenza through traditional trade networks.

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Computational model of a vector-mediated epidemic

Cited by 6 publications

References 18 publications

Critical behavior in a stochastic model of vector mediated epidemics

Critical behavior in a stochastic model of vector mediated epidemics

Critical properties of the SIS model on the clustered homophilic network

The Dynamics of Avian Influenza: Individual-Based Model with Intervention Strategies in Traditional Trade Networks in Phitsanulok Province, Thailand

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