Michelle Dunbar

A screening algorithm for HIV-associated neurocognitive disorders

HIV physicians have limited time for cognitive screening. Here we developed an extra‐brief, clinically based tool for predicting HIV‐associated neurocognitive impairment (HAND) in order to determine which HIV‐positive individuals require a more comprehensive neurological/neuropsychological (NP) assessment.

On the quantification of operational supply chain resilience

Operational disruptions impact a supply chain’s ability to match supply and demand. To remain competitive, supply chains need to be resilient and thus capable of rapidly and effectively recovering from operational disruptions. Supply chain resilience is inherently multidimensional, as it spans across multiple tiers, and thus is difficult to quantify. Extant research has measured the transient response through a single-dimension or single-organisation as a proxy for operational resilience. Whilst this greatly simplifies the analysis, it is also potentially misleading, as an erroneous selection of metric(s) may lead to an inaccurate evaluation of the transient response. This research extends the understanding of operational resilience via quantitative evaluation of multiple transient response measures across multiple tiers; the objective being to construct a multidimensional, multi-echelon operational supply chain resilience metric. The study utilises disruptions as experimental inputs for a serial supply chain simulation model; results are obtained for individual measurements of the transient response across multiple supply chain tiers. Analysis indicates that individual dimensions of resilience can adequately explain the transient response at the single-firm level, whilst aggregation of multiple resilience dimensions across multiple tiers has greater capacity to holistically capture the performance response to supply chain disruptions.

Simultaneous classification and feature selection via convex quadratic programming with application to HIV-associated neurocognitive disorder assessment ☆

Support vector machines (SVMs), that utilize a mixture of the L1-norm and the L2-norm penalties, are capable of performing simultaneous classification and selection of highly correlated features. These SVMs, typically set up as convex programming problems, are re-formulated here as simple convex quadratic minimization problems over non-negativity constraints, giving rise to a new formulation - the pq-SVM method. Solutions to our re-formulation are obtained efficiently by an extremely simple algorithm. Computational results on a range of publicly available datasets indicate that these methods allow greater classification accuracy in addition to selecting groups of highly correlated features. These methods were also compared on a new dataset assessing HIV-associated neurocognitive disorder in a group of 97 HIV-infected individuals.

Spatio-topological network analysis of hydrological infrastructure as a decision support tool for flood mitigation in coastal mega-cities

Hydrological infrastructure components such as pumps, floodgates, and flood gauges are invaluable assets for mitigating flooding, which threatens millions of lives and damages property worth billions of dollars in coastal mega-cities around the world. By improving the understanding of how these hydrological infrastructure components are both spatially and topologically connected through waterways (rivers, canals, streams, etc.) within coastal mega-cities, more precise decisions can be made regarding the most appropriate hydrological infrastructure components required to mitigate flooding during emergency conditions. This paper explores the use of graph theory to create a spatio-topological model of a real world hydrological infrastructure network for one of the most representative coastal mega-cities—Jakarta, Indonesia. The network is modeled as a directed multigraph, with hydrological infrastructure represented as network nodes and waterways as edges. The article demonstrates how the network model can be used as a real-time decision support tool for responding to flooding events by alerting decision makers to the occurrence of rising water levels in any given area and, suggesting the most appropriate infrastructure components to engage in order to prevent a given area from flooding.

A Bayesian approach for three-dimensional markerless tumor tracking using kV imaging during lung radiotherapy

The ability to monitor tumor motion without implanted markers can potentially enable broad access to more accurate and precise lung radiotherapy. A major challenge is that kilovoltage (kV) imaging based methods are rarely able to continuously track the tumor due to the inferior tumor visibility on 2D kV images. Another challenge is the estimation of 3D tumor position based on only 2D imaging information. The aim of this work is to address both challenges by proposing a Bayesian approach for markerless tumor tracking for the first time. The proposed approach adopts the framework of the extended Kalman filter, which combines a prediction and measurement steps to make the optimal tumor position update. For each imaging frame, the tumor position is first predicted by a respiratory-correlated model. The 2D tumor position on the kV image is then measured by template matching. Finally, the prediction and 2D measurement are combined based on the 3D distribution of tumor positions in the past 10 s and the estimated uncertainty of template matching. To investigate the clinical feasibility of the proposed method, a total of 13 lung cancer patient datasets were used for retrospective validation, including 11 cone-beam CT scan pairs and two stereotactic ablative body radiotherapy cases. The ground truths for tumor motion were generated from the the 3D trajectories of implanted markers or beacons. The mean, standard deviation, and 95th percentile of the 3D tracking error were found to range from 1.6-2.9 mm, 0.6-1.5 mm, and 2.6-5.8 mm, respectively. Markerless tumor tracking always resulted in smaller errors compared to the standard of care. The improvement was the most pronounced in the superior-inferior (SI) direction, with up to 9.5 mm reduction in the 95th-percentile SI error for patients with  >10 mm 5th-to-95th percentile SI tumor motion. The percentage of errors with 3D magnitude  <5 mm was 96.5% for markerless tumor tracking and 84.1% for the standard of care. The feasibility of 3D markerless tumor tracking has been demonstrated on realistic clinical scenarios for the first time. The clinical implementation of the proposed method will enable more accurate and precise lung radiotherapy using existing hardware and workflow. Future work is focused on the clinical and real-time implementation of this method.

Constrained optimization of average arrival time via a probabilistic approach to transport reliability

To achieve greater transit-time reduction and improvement in reliability of transport services, there is an increasing need to assist transport planners in understanding the value of punctuality; i.e. the potential improvements, not only to service quality and the consumer but also to the actual profitability of the service. In order for this to be achieved, it is important to understand the network-specific aspects that affect both the ability to decrease transit-time, and the associated cost-benefit of doing so. In this paper, we outline a framework for evaluating the effectiveness of proposed changes to average transit-time, so as to determine the optimal choice of average arrival time subject to desired punctuality levels whilst simultaneously minimizing operational costs. We model the service transit-time variability using a truncated probability density function, and simultaneously compare the trade-off between potential gains and increased service costs, for several commonly employed cost-benefit functions of general form. We formulate this problem as a constrained optimization problem to determine the optimal choice of average transit time, so as to increase the level of service punctuality, whilst simultaneously ensuring a minimum level of cost-benefit to the service operator.

Zoonotic Transmission of Waterborne Disease: A Mathematical Model.

Waterborne parasites that infect both humans and animals are common causes of diarrhoeal illness, but the relative importance of transmission between humans and animals and vice versa remains poorly understood. Transmission of infection from animals to humans via environmental reservoirs, such as water sources, has attracted attention as a potential source of endemic and epidemic infections, but existing mathematical models of waterborne disease transmission have limitations for studying this phenomenon, as they only consider contamination of environmental reservoirs by humans. This paper develops a mathematical model that represents the transmission of waterborne parasites within and between both animal and human populations. It also improves upon existing models by including animal contamination of water sources explicitly. Linear stability analysis and simulation results, using realistic parameter values to describe Giardia transmission in rural Australia, show that endemic infection of an animal host with zoonotic protozoa can result in endemic infection in human hosts, even in the absence of person-to-person transmission. These results imply that zoonotic transmission via environmental reservoirs is important.

Toll Pricing with Elastic Demand and Heterogeneous Users

The authors consider a general transportation network where travelers are categorized into classes according to their value-of-time (VOTs). The travel demand of each class in each origin-destination (OD) pair is a known decreasing function of the generalized travel cost to reflect different values of the trips. The authors give an economic interpretation of elastic demand with user heterogeneity. The authors further consider the first-best pricing problem with elastic demand with heterogeneous users. The authors show that such a nonnegative toll always exists, and finding such a toll is simple in that only a convex optimization problem needs to be solved.