The Private Sector and HIV/AIDS in Africa: Taking Stock of Six Years of Applied Research

Background: Until recently, little was known about the costs of the HIV/AIDS epidemic to businesses in Africa and business responses to the epidemic. This paper synthesizes the results of a set of studies conducted between 1999 and 2006 and draws conclusions about the role of the private sector in Africa’s response to AIDS. Methods: Detailed human resource, financial, and medical data were collected from 14 large private and parastatal companies in South Africa, Uganda, Kenya, Zambia, and Ethiopia. Surveys of small and medium-sized enterprises (SMEs) were conducted in South Africa, Kenya, and Zambia. Large companies’ responses or potential responses to the epidemic were investigated in South Africa, Uganda, Kenya, Zambia, and Rwanda. Results: Among the large companies, estimated workforce HIV prevalence ranged from 5%¬37%. The average cost per employee lost to AIDS varied from 0.5-5.6 times the average annual compensation of the employee affected. Labor cost increases as a result of AIDS were estimated at anywhere from 0.6%-10.8% but exceeded 3% at only 2 of 14 companies. Treatment of eligible employees with ART at a cost of $360/patient/year was shown to have positive financial returns for most but not all companies. Uptake of employer-provided testing and treatment services varied widely. Among SMEs, HIV prevalence in the workforce was estimated at 10%-26%. SME managers consistently reported low AIDS-related employee attrition, little concern about the impacts of AIDS on their companies, and relatively little interest in taking action, and fewer than half had ever discussed AIDS with their senior staff. AIDS was estimated to increase the average operating costs of small tourism companies in Zambia by less than 1%; labor cost increases in other sectors were probably smaller. Conclusions: Although there was wide variation among the firms studied, clear patterns emerged that will permit some prediction of impacts and responses in the future.

Utility-Based Decision-Making in Wireless Sensor Networks

We consider challenges associated with application domains in which a large number of distributed, networked sensors must perform a sensing task repeatedly over time. For the tasks we consider, there are three significant challenges to address. First, nodes have resource constraints imposed by their finite power supply, which motivates computations that are energy-conserving. Second, for the applications we describe, the utility derived from a sensing task may vary depending on the placement and size of the set of nodes who participate, which often involves complex objective functions for nodes to target. Finally, nodes must attempt to realize these global objectives with only local information. We present a model for such applications, in which we define appropriate global objectives based on utility functions and specify a cost model for energy consumption. Then, for an important class of utility functions, we present distributed algorithms which attempt to maximize the utility derived from the sensor network over its lifetime. The algorithms and experimental results we present enable nodes to adaptively change their roles over time and use dynamic reconfiguration of routes to load balance energy consumption in the network.

Temporal Dynamics of Decision-Making during Motion Perception in the Visual Cortex

How does the brain make decisions? Speed and accuracy of perceptual decisions covary with certainty in the input, and correlate with the rate of evidence accumulation in parietal and frontal cortical "decision neurons." A biophysically realistic model of interactions within and between Retina/LGN and cortical areas V1, MT, MST, and LIP, gated by basal ganglia, simulates dynamic properties of decision-making in response to ambiguous visual motion stimuli used by Newsome, Shadlen, and colleagues in their neurophysiological experiments. The model clarifies how brain circuits that solve the aperture problem interact with a recurrent competitive network with self-normalizing choice properties to carry out probablistic decisions in real time. Some scientists claim that perception and decision-making can be described using Bayesian inference or related general statistical ideas, that estimate the optimal interpretation of the stimulus given priors and likelihoods. However, such concepts do not propose the neocortical mechanisms that enable perception, and make decisions. The present model explains behavioral and neurophysiological decision-making data without an appeal to Bayesian concepts and, unlike other existing models of these data, generates perceptual representations and choice dynamics in response to the experimental visual stimuli. Quantitative model simulations include the time course of LIP neuronal dynamics, as well as behavioral accuracy and reaction time properties, during both correct and error trials at different levels of input ambiguity in both fixed duration and reaction time tasks. Model MT/MST interactions compute the global direction of random dot motion stimuli, while model LIP computes the stochastic perceptual decision that leads to a saccadic eye movement.

Neural Models of Motion Integration, Segmentation, and Probablistic Decision-Making

When brain mechanism carry out motion integration and segmentation processes that compute unambiguous global motion percepts from ambiguous local motion signals? Consider, for example, a deer running at variable speeds behind forest cover. The forest cover is an occluder that creates apertures through which fragments of the deer's motion signals are intermittently experienced. The brain coherently groups these fragments into a trackable percept of the deer in its trajectory. Form and motion processes are needed to accomplish this using feedforward and feedback interactions both within and across cortical processing streams. All the cortical areas V1, V2, MT, and MST are involved in these interactions. Figure-ground processes in the form stream through V2, such as the seperation of occluding boundaries of the forest cover from the boundaries of the deer, select the motion signals which determine global object motion percepts in the motion stream through MT. Sparse, but unambiguous, feauture tracking signals are amplified before they propogate across position and are intergrated with far more numerous ambiguous motion signals. Figure-ground and integration processes together determine the global percept. A neural model predicts the processing stages that embody these form and motion interactions. Model concepts and data are summarized about motion grouping across apertures in response to a wide variety of displays, and probabilistic decision making in parietal cortex in response to random dot displays.