Modelling influenza A(H1N1) 2009 epidemics using a random network in a distributed computing environment

Abstract

In this paper we propose the use of a random network model for simulating and understanding the epidemics of influenza A(H1N1). The proposed model is used to simulate the transmission process of influenza A(H1N1) in a community region of Venezuela using distributed computing in order to accomplish many realizations of the underlying random process. These large scale epidemic simulations have recently become an important application of high-performance computing. The network model proposed performs better than the traditional epidemic model based on ordinary differential equations since it adjusts better to the irregularity of the real world data. In addition, the network model allows the consideration of many possibilities regarding the spread of influenza at the population level. The results presented here show how well the SEIR model fits the data for the AH1N1 time series despite the irregularity of the data and returns parameter values that are in good agreement with the medical data regarding AH1N1 influenza virus. This versatile network model approach may be applied to the simulation of the transmission dynamics of several epidemics in human networks. In addition, the simulation can provide useful information for the understanding, prediction and control of the transmission of influenza A(H1N1) epidemics.

Calibrating a large network model describing the transmission dynamics of the human papillomavirus using a particle swarm optimization algorithm in a distributed computing environment

“…In the last years, we have been working on modeling the dynamics of several phenomena using large random networks (Acedo et al, 2011; Acedo et al, 2015; González-Parra et al, 2015) and we know that, under this approach, it is necessary to perform a lot of simulations for model calibration. To do so, a distributed computing environment called Sisifo was developed (Villanueva-Oller et al, 2013).…”

Mathematical Modeling and Characterization of the Spread of Chikungunya in Colombia

“…The Chikungunya mathematical model (2) gives a smoother curve due to its deterministic nature, as was expected. In order to catch the natural irregularity of the real data, it would be necessary to introduce stochastic factors to the model, which allows one to obtain a more accurate fitting [63]. However, introducing stochastic or temporal factors would require more detailed information regarding the dynamics of the population in Colombia, and the complexity of the model would increase.…”

Smart monitoring and controlling of Pandemic Influenza A (H1N1) using Social Network Analysis and cloud computing

“…In 2010, Balcan et al [32] developed a spreading model that can be used for different epidemics based on spatial temporal data. Gonzalez-Parra et al in 2011 [33], 2012 [34], and 2015 [35] used spatial-temporal data, fractional order model and complex networks in distributed environment respectively for simulating any H1N1 infection outbreak. In 2011, Van den Broeck [36] studied an effective epidemic modeling and computational tool known as GLEaMViz.…”