Adrien Ickowicz

Target tracking within a binary sensor network

The aim of this paper is to present a new algorithm for target tracking within a binary sensor network. The present work is based on our previous results developed in. A novel tracking method is proposed and its performance through a very classical trajectory model is evaluated. For a given target, this algorithm provides an estimation of its velocity and then of its position. The greatest improvements are made through a position correction and velocity analysis.

Identifying and detecting potentially adverse ecological outcomes associated with the release of gene-drive modified organisms

Synthetic gene drives could provide new solutions to a range of old problems such as controlling vector-borne diseases, agricultural pests and invasive species. In this paper, we outline methods to...

Modelling hospital length of stay using convolutive mixtures distributions.

Length of hospital stay (LOS) is an important indicator of the hospital activity and management of health care. The skewness in the distribution of LOS poses problems in statistical modelling because it fails to adequately follow the usual traditional distribution of positive variables such as the log-normal distribution. We present in this paper a model using the convolution of two distributions, a technique well known in the signal processing community. The specificity of that model is that the variable of interest is considered to be the resulting sum of two random variables with different distributions. One of the variables features the patient-related factors in terms of their need to recover from their admission condition, while the other models the hospital management process such as the discharging process. Two estimation procedures are proposed. One is the classical maximum likelihood, while the other relates to the expectation-maximization algorithm. We present some results obtained by applying this model to a set of real data from a group of hospitals in Victoria (Australia). Copyright

Anonymization for outputs of population health and health services research conducted via an online data center

Objective Online data centers (ODCs) are becoming increasingly popular for making health-related data available for research. Such centers provide good privacy protection during analysis by trusted researchers, but privacy concerns may still remain if the system outputs are not sufficiently anonymized. In this article, we propose a method for anonymizing analysis outputs from ODCs for publication in academic literature. Methods We use as a model system the Secure Unified Research Environment, an online computing system that allows researchers to access and analyze linked health-related data for approved studies in Australia. This model system suggests realistic assumptions for an ODC that, together with literature and practice reviews, inform our solution design. Results We propose a two-step approach to anonymizing analysis outputs from an ODC. A data preparation stage requires data custodians to apply some basic treatments to the dataset before making it available. A subsequent output anonymization stage requires researchers to use a checklist at the point of downloading analysis output. The checklist assists researchers with highlighting potential privacy concerns, then applying appropriate anonymization treatments. Conclusion The checklist can be used more broadly in health care research, not just in ODCs. Ease of online publication as well as encouragement from journals to submit supplementary material are likely to increase both the volume and detail of analysis results publicly available, which in turn will increase the need for approaches such as the one suggested in this paper.

Assessing privacy risks in population health publications using a checklist-based approach

Objective Recent growth in the number of population health researchers accessing detailed datasets, either on their own computers or through virtual data centers, has the potential to increase privacy risks. In response, a checklist for identifying and reducing privacy risks in population health analysis outputs has been proposed for use by researchers themselves. In this study we explore the usability and reliability of such an approach by investigating whether different users identify the same privacy risks on applying the checklist to a sample of publications. Methods The checklist was applied to a sample of 100 academic population health publications distributed among 5 readers. Cohens κ was used to measure interrater agreement. Results Of the 566 instances of statistical output types found in the 100 publications, the most frequently occurring were counts, summary statistics, plots, and model outputs. Application of the checklist identified 128 outputs (22.6%) with potential privacy concerns. Most of these were associated with the reporting of small counts. Among these identified outputs, the readers found no substantial actual privacy concerns when context was taken into account. Interrater agreement for identifying potential privacy concerns was generally good. Conclusion This study has demonstrated that a checklist can be a reliable tool to assist researchers with anonymizing analysis outputs in population health research. This further suggests that such an approach may have the potential to be developed into a broadly applicable standard providing consistent confidentiality protection across multiple analyses of the same data.

An Insight on Big Data Analytics

This paper discusses the opportunities big data offers decision makers from a statistical perspective. It calls for a multidisciplinary approach by computer scientists, statisticians and domain experts to providing useful big data solutions. Big data calls for us to think in new ways and communicate effectively within such teams. We make a plea for linking data-driven and model-driven analytics, and stress the role of cause-effect models for knowledge enhancement in big data analytics. We remember Kant’s statement that theory without data is blind, but facts without theories are meaningless. A case is made for each discipline to define the contribution they offer to big data solutions so that effective teams can be formed to improve inductions. Although new approaches are needed much of the past learning related to small data are valuable in providing big data solutions. Here we have in mind the long-term academic training and field experience of statisticians concerning reduction of dataset volumes, sampling in a more general setting, data depreciation and quality, model design and validation, visualisation, etc. We expect that combining the present approaches will give incentives for increasing the chances for “real big solutions”.

Estimation of an origin/destination matrix: application to a ferry transport data

The estimation of the number of passengers with an identical journey is a common problem for public transport authorities. This problem is also known as the origin–destination estimation (OD) problem and it has been widely studied for the past 30 years. However, theory is missing when observations are not limited to the passenger counts but also include station surveys. Our aim is to provide a solid framework for the estimation of an OD matrix when only a portion of the journey counts are observable. Our method consists of a statistical estimation technique for OD matrix when we have the sum-of-row counts and survey-based observations. Our technique differs from the previous studies in that it does not need a prior OD matrix which can be hard to obtain. Instead, we model the passengers behavior through the survey data, and use the diagonalization of the partial OD matrix to reduce the space parameter and derive a consistent global OD matrix estimator. We demonstrate the robustness of our estimator and apply it to several examples showcasing the proposed models and approach. We highlight how other sources of data can be incorporated in the model such as explanatory variables, e.g. rainfall, indicator variables for major events, etc, and inference made in a principled, non-heuristic way.

Track Estimation with Binary Derivative Observations

We focus in the work presented here on in the estimation of a target trajectory defined by whether a time constant parameter is a simple stochastic process or a random walk with binary observations. The binary observations come from binary derivative sensors, that is, the target is getting closer or moving away. Such a binary observation has a time property that will be used to ensure the quality of the velocity estimation, through single index model or classification for the constant velocity movement. In the second part of this work we present a new algorithm for target tracking within a binary sensor network when the target trajectory is assumed to be modelled by a random walk. For a given target this algorithm provides an estimation of its velocity and its position. The greatest improvements are made through a position correction and velocity analysis.

On the Effect of Data Contamination on Track Purity

The work presented here is concerned with performance analysis for data association, in a target tracking environment. Effects of misassociation are considered in a simple (linear) multiscan framework so as to provide closed-form expressions of the probability of correct association. We focus here on the development of explicit approximations of this probability. Via rigorous calculations the effect of dimensioning parameters (number of scans, false measurement positions or densities) is analyzed, for various modelings of the false measurements. Remarkably, it is possible to derive very simple expressions of the probability of correct association which are independent of the scenario kinematic parameters.

On the effect of data contamination for multitarget tracking, part II

This paper deals with the probabilistic data association issue in the context of multiple target tracking. In the continuation of the part I framework, we focus here on scenarios where multiple false measurements may occur. In particular, the influence of various critical parameters on the multi-tracking efficiency, i.e. the probability of correct association, is analyzed. Besides, we study the impact of the tracking scenario, including a large number of misassociations.