Clara Burgos

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

Working in large networks applied to epidemiological-type models has led us to design a simple but effective computed distributed environment to perform a large amount of model simulations in a reasonable time in order to study the behavior of these models and to calibrate them. Finding the model parameters that best fit the available data in the designed distributed computing environment becomes a challenge and it is necessary to implement reliable algorithms for model calibration. In this article, we have adapted the random particle swarm optimization algorithm to our distributed computing environment to be applied to the calibration of a papillomavirus transmission dynamics model on a lifetime sexual partners network. And we have obtained a good fitting saving time and calculations compared with the exhaustive searching strategy we have been using so far.

Solving random mean square fractional linear differential equations by generalized power series: Analysis and computing

Abstract This paper deals with solving the general random (Caputo) fractional linear differential equation under general assumptions on random input data (initial condition, forcing term and diffusion coefficient). Our contribution extends, in two directions, the results presented in a recent contribution by the authors. In that paper, a mean square random generalized power series solution has been constructed in the case that the fractional order, say α , of the Caputo derivative lies on the interval ] 0 , 1 ] and assuming that the diffusion coefficient belongs to a class, ℭ , of random variables that contains all bounded random variables. However, significant families of unbounded random variables, such as Gaussian and Exponential, for example, do not fall into class ℭ . Now, in this contribution we first enlarge the class of random variables to which the diffusion coefficient belongs and we prove that the constructed random generalized power series solution is mean square convergent too. We show that any bounded random variable and important unbounded random variables, including Gaussian and Exponential ones, are allowed to play the role of the diffusion coefficient as well. Secondly, we construct a mean square random generalized power series solution in the case that α parameter lies on the larger interval ] 0 , 2 ] . As a consequence, the results established in our previous contribution are fairly generalized. It is particularly enlightening, the numerical study of the convergence of the approximations to the mean and the standard deviation of the solution stochastic process in terms of α parameter and on the type of the probability distribution chosen for the diffusion coefficient.

Uncertainty Quantification for Meningococcus B Carriers Prediction

Competition among the different classes of genogroups of meningococcal and further bacteria takes place in the ecosystem they constitute within the human body environment. To the best of our knowledge, most of the epidemiological models neglect competition that could highly influence the dynamics of the meningococcal as well as its outbreak. In this paper, we propose a susceptible-carrier-susceptilbe epidemiological model in order to describe the evolution of the percentage of carriers in the population over the time. Moreover, we introduce a competition model to study the dynamics of the meningococcal genogroups in Spain among the carriers. As we are just interested in the meningococcus B, the latter model considers two subpopulations, meningococcal genogroup B and the rest. Both models depend on a number of parameters that are adjusted using real data from the distribution of the genogroups in Spain in years 2011 and 2012. Since these data survey contain sample errors, we further quantify the uncertainties associated to the model parameters taking advantage of a probabilistic fitting method that has been recently proposed by some of the authors. This allows us to give a probabilistic prediction for the evolution of the carriers of the two genogroup over the next few years by constructing a (95%) confidence interval for the percentage of carriers of meningococcus B.

Capturing the Data Uncertainty Change in the Cocaine Consumption in Spain Using an Epidemiologically Based Model

A probabilistic model is proposed to study the transmission dynamics of the cocaine consumption in Spain during the period of 1995–2011. Using the so-called probabilistic fitting technique, we study if the model is able to capture the data uncertainty coming from surveys. The proposed model is formulated through a nonlinear system of difference equations whose coefficients are treated as stochastic processes. A discussion regarding the usefulness and limitations of probabilistic fitting technique in order to capture the data uncertainty of the proposed model is presented.