56 research outputs found
Asymptotic Control for a Class of Piecewise Deterministic Markov Processes Associated to Temperate Viruses
We aim at characterizing the asymptotic behavior of value functions in the
control of piece-wise deterministic Markov processes (PDMP) of switch type
under nonexpansive assumptions. For a particular class of processes inspired by
temperate viruses, we show that uniform limits of discounted problems as the
discount decreases to zero and time-averaged problems as the time horizon
increases to infinity exist and coincide. The arguments allow the limit value
to depend on initial configuration of the system and do not require dissipative
properties on the dynamics. The approach strongly relies on viscosity
techniques, linear programming arguments and coupling via random measures
associated to PDMP. As an intermediate step in our approach, we present the
approximation of discounted value functions when using piecewise constant (in
time) open-loop policies.Comment: In this revised version, statements of the main results are gathered
in Section 3. Proofs of the main results (Theorem 4 and Theorem 7) make the
object of separate sections (Section 5, resp. Section 6). The biological
example makes the object of Section 4. Notations are gathered in Subsection
2.1. This is the final version to be published in SICO
Controllability Metrics on Networks with Linear Decision Process-type Interactions and Multiplicative Noise
This paper aims at the study of controllability properties and induced
controllability metrics on complex networks governed by a class of (discrete
time) linear decision processes with mul-tiplicative noise. The dynamics are
given by a couple consisting of a Markov trend and a linear decision process
for which both the "deterministic" and the noise components rely on
trend-dependent matrices. We discuss approximate, approximate null and exact
null-controllability. Several examples are given to illustrate the links
between these concepts and to compare our results with their continuous-time
counterpart (given in [16]). We introduce a class of backward stochastic
Riccati difference schemes (BSRDS) and study their solvability for particular
frameworks. These BSRDS allow one to introduce Gramian-like controllability
metrics. As application of these metrics, we propose a minimal
intervention-targeted reduction in the study of gene networks
Border Avoidance: Necessary Regularity for Coefficients and Viscosity Approach
Motivated by the result of invariance of regular-boundary open sets in
\cite{CannarsaDaPratoFrankowska2009} and multi-stability issues in gene
networks, our paper focuses on three closely related aims. First, we give a
necessary local Lipschitz-like condition in order to expect invariance of open
sets (for deterministic systems). Comments on optimality are provided via
examples. Second, we provide a border avoidance (near-viability) counterpart of
\cite{CannarsaDaPratoFrankowska2009} for controlled Brownian diffusions and
piecewise deterministic switched Markov processes (PDsMP). We equally discuss
to which extent Lipschitz-continuity of the driving coefficients is needed.
Finally, by applying the theoretical result on PDsMP to Hasty's model of
bacteriophage (\cite{hasty\_pradines\_dolnik\_collins\_00},
\cite{crudu\_debussche\_radulescu\_09}), we show the necessity of explicit
modeling for the environmental cue triggering lysis
Mathematical Methods, Modelling and Applications
This volume deals with novel high-quality research results of a wide class of mathematical models with applications in engineering, nature, and social sciences. Analytical and numeric, deterministic and uncertain dimensions are treated. Complex and multidisciplinary models are treated, including novel techniques of obtaining observation data and pattern recognition. Among the examples of treated problems, we encounter problems in engineering, social sciences, physics, biology, and health sciences. The novelty arises with respect to the mathematical treatment of the problem. Mathematical models are built, some of them under a deterministic approach, and other ones taking into account the uncertainty of the data, deriving random models. Several resulting mathematical representations of the models are shown as equations and systems of equations of different types: difference equations, ordinary differential equations, partial differential equations, integral equations, and algebraic equations. Across the chapters of the book, a wide class of approaches can be found to solve the displayed mathematical models, from analytical to numeric techniques, such as finite difference schemes, finite volume methods, iteration schemes, and numerical integration methods
Spatio-temporal modelling of climate-sensitive disease risk: towards an early warning system for dengue in Brazil
The transmission of many infectious diseases is affected by climate variations, particularly for diseases spread by arthropod vectors such as malaria and dengue. Previous epidemiological studies have demonstrated statistically significant associations between infectious disease incidence and climate variations. Such research has highlighted the potential for developing climate-based epidemic early warning systems. To establish how much variation in disease risk can be attributed to climatic conditions, non-climatic confounding factors should also be considered in the model parameterisation to avoid reporting misleading climate-disease associations. This issue is sometimes overlooked in climate related disease studies. Due to the lack of spatial resolution and/or the capability to predict future disease risk (e.g. several months ahead), some previous models are of limited value for public health decision making.
This thesis proposes a framework to model spatio-temporal variation in disease risk using both climate and non-climate information. The framework is developed in the context of dengue fever in Brazil. Dengue is currently one of the most important emerging tropical diseases and dengue epidemics impact heavily on Brazilian public health services. A negative binomial generalised linear mixed model (GLMM) is adopted which makes allowances for unobserved confounding factors by including spatially structured and unstructured random effects. The model successfully accounts for the large amount of overdispersion found in disease counts. The parameters in this spatio-temporal Bayesian hierarchical model are estimated using Markov Chain Monte Carlo (MCMC). This allows posterior predictive distributions for disease risk to be derived for each spatial location and time period (month/season). Given decision and epidemic thresholds, probabilistic forecasts can be issued, which are useful for developing epidemic early warning systems.
The potential to provide useful early warnings of future increased and geographically specific dengue risk is investigated. The predictive validity of the model is evaluated by fitting the GLMM to data from 2001-2007 and comparing probabilistic predictions to the most recent out-of-sample data in 2008-2009. For a probability decision threshold of 30% and the pre-defined epidemic threshold of 300 cases per 100,000 inhabitants, successful epidemic alerts would have been issued for 94% of the 54 microregions that experienced high dengue incidence rates in South East Brazil, during February - April 2008.Leverhulme Trus
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