Luís Mateus

Dynamic noise, chaos and parameter estimation in population biology

We revisit the parameter estimation framework for population biological dynamical systems, and apply it to calibrate various models in epidemiology with empirical time series, namely influenza and dengue fever. When it comes to more complex models such as multi-strain dynamics to describe the virus–host interaction in dengue fever, even the most recently developed parameter estimation techniques, such as maximum likelihood iterated filtering, reach their computational limits. However, the first results of parameter estimation with data on dengue fever from Thailand indicate a subtle interplay between stochasticity and the deterministic skeleton. The deterministic system on its own already displays complex dynamics up to deterministic chaos and coexistence of multiple attractors.

Carnival or football, is there a real risk for acquiring dengue fever in Brazil during holidays seasons?

More than 600,000 football fans, coming from all over the world, were expected to visit Brazil during the FIFA World Cup 2014. International travel can become a public health problem when the visitors start to become sick, needing medical intervention and eventually hospitalization. The occurrence of dengue fever infections in Brazil is persistent and has been increasing since the 1980s, and the health authorities were expected to take preventive measures and to warn the visitors about the risks during the tournament period. Before the World Cup started, studies have been published stating that dengue could be a significant problem in some of the Brazilian cities hosting the games. These conclusions were taken after a brief observation of the available data, analyzing its mean and standard deviation only, or based on seasonal climate forecasts, causing alarm for the world cup in Brazil. Here, with a more careful data analysis, we show that the seasonality of the disease plays a major role in dengue transmission. The density of dengue cases in Brazil is residual during winter in the Southern hemisphere (mid June to mid September) and the fans of football were not likely to get dengue during the tournament period.

Stochastic models in population biology: from dynamic noise to Bayesian description and model comparison for given data sets

We study some stochastic models from population biology, namely epidemiology, which can be solved analytically. From the solution of the stochastic processes we construct the likelihood function and compare the maximum likelihood parameter estimation procedure with the Bayesian approach of obtaining an explicit probability for the parameters given the available data. Finally, we compare one model against the other in the Bayesian framework, both models performing on the same simulated data set. In some cases of data obtained under one model with specific parameter values, the model comparison favours the model not underlying the simulated data. This apparently paradoxical situation arises in parameter regions which do not easily give sufficient information to the simulated data to reject simpler models.

Towards increased BIM usage for existing building interventions

Purpose – Building information modeling (BIM) is most often used for the construction of new buildings. By using BIM in such projects, collaboration among stakeholders in an architecture, engineering and construction project is improved. To even further improve collaboration, there is a move toward the production and usage of BIM standards in various countries. These are typically national documents, including guides, protocols, and mandatory regulations, that introduce guidelines about what information should be exchanged at what time between which partners and in what formats. If a nation or a construction team agrees on these guidelines, improved collaboration can come about on top of the collaboration benefits induced by the mere usage of BIM. This scenario might also be targeted for interventions in existing buildings. The paper aims to discuss these issues. Design/methodology/approach – In this paper, the authors investigate the general content and usage of existing BIM standards for new constructio...

Understanding dengue fever dynamics: a study of seasonality in vector-borne disease models

Dengue fever dynamics show seasonality, with the disease transmission being higher during the warmer seasons. In this paper, we analyse seasonally forced epidemic models with and without vector dynamics. We assume small seasonal effects and obtain approximations for the real response of each state variable and also for the corresponding amplitude and phase via decomposition of the sinusoidal forcing into imaginary exponential functions. The analysis begins with the simplest susceptible-infected-susceptible (SIS) model, followed by the simplest model with vector dynamics, susceptible-infected-susceptible for hosts and uninfected-vector (SISUV). Finally, we compare the more complex susceptible-infected-recovered (SIR) and susceptible-infected-recovered for hosts and uninfected-vector (SIRUV) models and conclude that the models give basically the same information when we replace, in the SIR model, the human infectivity by a function of both human and mosquito infectivities.

Parameter Estimation in Epidemiology: from Simple to Complex Dynamics

We revisit the parameter estimation framework for population biological dynamical systems, and apply it to calibrate various models in epidemiology with empirical time series, namely influenza and dengue fever. When it comes to more complex models like multi‐strain dynamics to describe the virus‐host interaction in dengue fever, even most recently developed parameter estimation techniques, like maximum likelihood iterated filtering, come to their computational limits. However, the first results of parameter estimation with data on dengue fever from Thailand indicate a subtle interplay between stochasticity and deterministic skeleton. The deterministic system on its own already displays complex dynamics up to deterministic chaos and coexistence of multiple attractors.

Modelling spatial connectivity in epidemiological systems, dengue fever in Thailand on networks from radiation models

We model the connectivity between Thai provinces in terms of human mobility via a radiation model in order to describe dengue fever spreading in Thailand, for which long term epidemiological data are available.

Stochastic Hopf and torus bifurcations in population biology

In population biological systems often bifurcation sequences via period doubling are observed, especially in simplest models of epidemiology like the susceptible-infected-recovered (SIR) model with seasonal forcing in the infection rate. But other routes to complex behaviour can as easily be found, for example in multi-strain models of SIR-type without forcing, here after a Hopf bifurcation a torus bifurcation leads into chaos. However, these models are already relatively high dimensional. One of the simplest models in population biology, now in ecology, is the Rosenzweig-MacArthur model, displaying a Hopf bifurcation and with forcing also a torus bifurcation leading to more complex behaviour subsequently. Here we investigate an only slightly extended version which again can be interpreted as a stochastic process of a population dynamical model. Such stochastic models give insight into what can be observed ultimately in empirical data of the systems under investigation.

Corrigendum: Carnival or football, is there a real risk for acquiring dengue fever in Brazil during holidays seasons?

Corrigendum: Carnival or football, is there a real risk for acquiring dengue fever in Brazil during holidays seasons?

Planar Surfaces Recognition in 3D Point Cloud Using a Real-Coded Multistage Genetic Algorithm

Most frequent surface shapes of man-made constructions are planar surfaces. Discovering those surfaces is a big step toward extracting as-built/-is construction information from 3D point cloud. In this paper, a real-coded genetic algorithm (GA) formulation for planar surfaces recognition in 3D point clouds is presented. The algorithm developed based on a multistage approach; thereby, it finds one planar surface (part of solution) at each stage. In addition, the logarithmically proportional objective function that is used in this approach can adapt itself to scale and spatial density of the point cloud. We tested the proposed application on a synthetic point cloud containing several planar surfaces with different shapes, positions, and with a wide variety of sizes. The results obtained showed that the proposed method is capable to find all plane’s configurations of flat surfaces with a minor distance to the actual configurations.