Alex Skvortsov

Monitoring and prediction of an epidemic outbreak using syndromic observations.

The paper presents a method for syndromic surveillance of an epidemic outbreak due to an emerging disease, formulated in the context of stochastic nonlinear filtering. The dynamics of the epidemic is modeled using a stochastic compartmental epidemiological model with inhomogeneous mixing. The syndromic (typically non-medical) observations of the number of infected people (e.g. visits to pharmacies, sale of certain products, absenteeism from work/study, etc.) are assumed available for monitoring and prediction of the epidemic. The state of the epidemic, including the number of infected people and the unknown parameters of the model, are estimated via a particle filter. The numerical results indicate that the proposed framework can provide useful early prediction of the epidemic peak if the uncertainty in prior knowledge of model parameters is not excessive.

Bayesian likelihood-free localisation of a biochemical source using multiple dispersion models

Localisation of a source of a toxic release of biochemical aerosols in the atmosphere is a problem of great importance for public safety. Two main practical difficulties are encountered in this problem: the lack of knowledge of the likelihood function of measurements collected by biochemical sensors, and the plethora of candidate dispersion models, developed under various assumptions (e.g. meteorological conditions, terrain). Aiming to overcome these two difficulties, the paper proposes a likelihood-free approximate Bayesian computation method, which simultaneously uses a set of candidate dispersion models, to localise the source. This estimation framework is implemented via the Monte Carlo method and tested using two experimental datasets. HighlightsWe develop a statistical method for adaptive likelihood free Bayesian estimation and model selection.The method is developed in the context of localisation of an emitting source of toxic material in the atmosphere.Three atmospheric dispersion models are considered.Two real datasets are used to assess the performance of the proposed method.

High-Resolution Monitoring of Atmospheric Pollutants Using a System of Low-Cost Sensors

Increased levels of particulate matter (PM) in the atmosphere have contributed to an increase in mortality and morbidity in communities and are the main contributing factor for respiratory health problems in the population. Currently, PM concentrations are sparsely monitored; for instance, a region of over 2200 square kilometers surrounding Melbourne in Victoria, Australia, is monitored using ten sensor stations. This paper proposes to improve the estimation of PM concentration by complementing the existing high-precision but expensive PM devices with low-cost lower precision PM sensor nodes. Our evaluation reveals that local PM estimation accuracies improve with higher densities of low-precision sensor nodes. Our analysis examines the impact of the precision of the lost-cost sensors on the overall estimation accuracy.

A reaction-diffusion model of the receptor-toxin-antibody interaction

BackgroundIt was recently shown that the treatment effect of an antibody can be described by a consolidated parameter which includes the reaction rates of the receptor-toxin-antibody kinetics and the relative concentration of reacting species. As a result, any given value of this parameter determines an associated range of antibody kinetic properties and its relative concentration in order to achieve a desirable therapeutic effect. In the current study we generalize the existing kinetic model by explicitly taking into account the diffusion fluxes of the species.ResultsA refined model of receptor-toxin-antibody (RTA) interaction is studied numerically. The protective properties of an antibody against a given toxin are evaluated for a spherical cell placed into a toxin-antibody solution. The selection of parameters for numerical simulation approximately corresponds to the practically relevant values reported in the literature with the significant ranges in variation to allow demonstration of different regimes of intracellular transport.ConclusionsThe proposed refinement of the RTA model may become important for the consistent evaluation of protective potential of an antibody and for the estimation of the time period during which the application of this antibody becomes the most effective. It can be a useful tool for in vitro selection of potential protective antibodies for progression to in vivo evaluation.

Acoustic performance of gratings of cylindrical voids in a soft elastic medium with a steel backing

An approximate analytical model is presented to investigate sound transmission, reflection and absorption of a rubber-like medium comprising a single layer of periodic cylindrical voids attached to a steel backing. The layer of voids is modelled as a homogeneous medium with effective material and geometric properties. A numerical model based on the finite element method is developed to validate results from the homogenization model, as well as to show further insights into the physical mechanisms associated with the system acoustic performance. Monopole resonance of the voids is shown to reduce sound transmission through the voided medium due to increased reflection, resulting in poor sound absorption around this frequency. Peaks of high sound absorption are attributed to Fabry-Pérot resonance with the frequency of the first peak derivable by a lumped spring-mass analogy. Sound absorption for a single layer of voids in a soft elastic medium with a steel backing is shown to be similar to the sound absorption in the same elastic medium but without the steel backing, for a single layer of voids and its mirror image in the direction of sound propagation.

Predicting the progress and the peak of an epidemic

The problem is statistical prediction of the number of people that will be infected with a contagious illness in a closed population over time. The prediction is based on the Susceptible-Infectious-Recovered (SIR) model of epidemic dynamics with inhomogeneous population mixing. The paper presents a theoretical analysis of the predictive accuracy based on the Cramér-Rao lower bound (CRLB). The CRLB provides a tool that enables us to quantify the prediction accuracy of a scale of an epidemic as a function of the prior uncertainty of SIR model parameters, measurement accuracy of the number of infected people and the amount of data available for processing. A verification of the theoretical analysis is carried out by Monte Carlo simulations.

A study of cognitive strategies for an autonomous search

A mobile autonomous agent searching for an emitting source of unknown strength.Emitted substance transported by diffusion and advection.Three cognitive search strategies compared by simulations and using real data.Under persistent sensory cues, the distribution of search time is inverse Gaussian.Search time depends on the ratio between the search area and the sensing area. Cognitive search is a collective term for search strategies based on information theoretic rewards required in sequential decision making under uncertainty. The paper presents a comparative study of cognitive search strategies for finding an emitting source of unknown strength using sparse sensing cues in the form of occasional non-zero sensor measurements. The study is cast in the context of an emitting source of particles transported by turbulent flow. The search algorithm, which sequentially estimates the source parameters and the reward function for motion control, has been implemented using the sequential Monte Carlo method. The distribution of the search time has been explained by the inverse Gaussian distribution.

Networks of chemical sensors: A simple mathematical model for optimisation study

The paper presents an analytical study of the effects of dynamic collaboration in a network of chemical sensors using a simple population and physics based model. The approach is based on the known analogy between the information spread in a sensor network and the epidemics propagation across a population. In this framework we derive analytical expressions which relate the parameters of the network (e.g. number of sensors, their density, sensing time etc), with the network performance parameters (probability of detection, response time of a network) and the parameters of the external challenge (the chemical pollutant and environment). The paper also presents the numerical simulation results in support of analytic expressions.

A Simple Model for Assessment of Anti-Toxin Antibodies

The toxins associated with infectious diseases are potential targets for inhibitors which have the potential for prophylactic or therapeutic use. Many antibodies have been generated for this purpose, and the objective of this study was to develop a simple mathematical model that may be used to evaluate the potential protective effect of antibodies. This model was used to evaluate the contributions of antibody affinity and concentration to reducing antibody-receptor complex formation and internalization. The model also enables prediction of the antibody kinetic constants and concentration required to provide a specified degree of protection. We hope that this model, once validated experimentally, will be a useful tool for in vitro selection of potentially protective antibodies for progression to in vivo evaluation.

Autonomous Search for a Diffusive Source in an Unknown Structured Environment

The paper presents a framework for autonomous search for a diffusive emitting source of a tracer (e.g., aerosol, gas) in an environment with an unknown map of randomly placed and shaped obstacles. The measurements of the tracer concentration are sporadic, noisy and without directional information. The search domain is discretised and modelled by a finite two-dimensional lattice. The links in the lattice represent the traversable paths for emitted particles and for the searcher. A missing link in the lattice indicates a blocked path due to an obstacle. The searcher must simultaneously estimate the source parameters, the map of the search domain and its own location within the map. The solution is formulated in the sequential Bayesian framework and implemented as a Rao-Blackwellised particle filter with entropy-reduction motion control. The numerical results demonstrate the concept and its performance.