Correlation of Cognitive Scores and the Onset of Hypoxia.

INTRODUCTION: The negative effects of hypoxia on human cognitive function have been well documented. In this study we assess the correlation of performance in the SynWin cognitive Multi-Task Battery (MTB) and the onset of hypoxia and describe the use of cognitive assessment scores for real-time hypoxia detection.METHODS: We performed a correlation analysis between MTB scores (Arithmetic, Memory, Audio Monitoring, Video Monitoring tasks) and blood oxygen saturation levels to discover if the scores are good candidates to detect hypoxia. Since this analysis showed positive correlation, we proceeded to develop a parallel decision fusion system that uses these cognitive scores for real-time hypoxia detection using the Neyman-Pearson criterion.RESULTS: We demonstrate that MTB scores have considerable hypoxia detection potential and can be used (if measurable passively) in a real-time detection framework. Analysis of receiver operating characteristic (ROC) curves established a hierarchy of importance of the various MTB modules. The Arithmetic task module had the most significant contribution toward correct hypoxia detection (improvement of ∼13.5% and ∼13.9% in detection accuracy under global false alarms of 0.1 and 0.05, respectively), followed by the Memory and Audio Monitoring modules. Fusion of multiple cognitive assessment scores resulted in significantly higher detection accuracy (>86%) than using any one of the scores by itself.DISCUSSION: When available, cognitive assessment scores can be a useful tool for real-time hypoxia detection. Since these MTB tests also assess neuropsychological functioning, study of distributed detection systems based on MTB scores could help in designing tests that are more useful for detecting hypoxic symptoms.Rajasekar A, Acharya S, Shender BS, Rorres C, Hrebien L, Kam M. Correlation of cognitive scores and the onset of hypoxia. Aerosp Med Hum Perform. 2019; 90(5):429-439.

Contact tracing for the control of infectious disease epidemics: Chronic Wasting Disease in deer farms.

Contact tracing is a crucial component of the control of many infectious diseases, but is an arduous and time consuming process. Procedures that increase the efficiency of contact tracing increase the chance that effective controls can be implemented sooner and thus reduce the magnitude of the epidemic. We illustrate a procedure using Graph Theory in the context of infectious disease epidemics of farmed animals in which the epidemics are driven mainly by the shipment of animals between farms. Specifically, we created a directed graph of the recorded shipments of deer between deer farms in Pennsylvania over a timeframe and asked how the properties of the graph could be exploited to make contact tracing more efficient should Chronic Wasting Disease (a prion disease of deer) be discovered in one of the farms. We show that the presence of a large strongly connected component in the graph has a significant impact on the number of contacts that can arise.

Quantitative methods of identifying the key nodes in the illegal wildlife trade network.

Innovative approaches are needed to combat the illegal trade in wildlife. Here, we used network analysis and a new database, HealthMap Wildlife Trade, to identify the key nodes (countries) that support the illegal wildlife trade. We identified key exporters and importers from the number of shipments a country sent and received and from the number of connections a country had to other countries over a given time period. We used flow betweenness centrality measurements to identify key intermediary countries. We found the set of nodes whose removal from the network would cause the maximum disruption to the network. Selecting six nodes would fragment 89.5% of the network for elephants, 92.3% for rhinoceros, and 98.1% for tigers. We then found sets of nodes that would best disseminate an educational message via direct connections through the network. We would need to select 18 nodes to reach 100% of the elephant trade network, 16 nodes for rhinoceros, and 10 for tigers. Although the choice of locations for interventions should be customized for the animal and the goal of the intervention, China was the most frequently selected country for network fragmentation and information dissemination. Identification of key countries will help strategize illegal wildlife trade interventions.

Models of highly pathogenic avian influenza epidemics in commercial poultry flocks in Nigeria and Ghana.

State-scale and premises-scale gravity models for the spread of highly pathogenic avian influenza (H5N1) in Nigeria and Ghana were used to provide a basis for risk maps for future epidemics and to compare and rank plausible culling and vaccination strategies for control. Maximum likelihood methods were used to fit the models to the 2006-2007 outbreaks. The sensitivity and specificity of the state-scale model-generated probabilities that any given state would be involved in an epidemic were each 57 %. The premises-based model indicated that reactive, countrywide vaccination strategies, in which the order in which flocks are vaccinated was strictly determined by known risk factors for infection, were more effective in reducing the final size of the epidemic and the epidemic impact than vaccinating flocks at random or ring vaccination. The model suggests that an introduction of highly pathogenic avian influenza (H5N1) into Ghana had a high chance (84 %) of causing a major outbreak. That this did not happen was most probably a result of the very swift Ghanaian response to news of the first introductions.

Stochastic modeling of animal epidemics using data collected over three different spatial scales.

A stochastic, spatial, discrete-time, SEIR model of avian influenza epidemics among poultry farms in Pennsylvania is formulated. Using three different spatial scales wherein all the birds within a single farm, ZIP code, or county are clustered into a single point, we obtain three different views of the epidemics. For each spatial scale, two parameters within the viral-transmission kernel of the model are estimated using simulated epidemic data. We show that simulated epidemics modeled using data collected on the farm and ZIP-code levels behave similar to the actual underlying epidemics, but this is not true using data collected on the county level. Such analyses of data collected on different spatial scales are useful in formulating intervention strategies to control an ongoing epidemic (e.g., vaccination schedules and culling policies).

Estimating the kernel parameters of premises-based stochastic models of farmed animal infectious disease epidemics using limited, incomplete, or ongoing data.

Three different estimators are presented for the types of parameters present in mathematical models of animal epidemics. The estimators make use of the data collected during an epidemic, which may be limited, incomplete, or under collection on an ongoing basis. When data are being collected on an ongoing basis, the estimated parameters can be used to evaluate putative control strategies. These estimators were tested using simulated epidemics based on a spatial, discrete-time, gravity-type, stochastic mathematical model containing two parameters. Target epidemics were simulated with the model and the three estimators were implemented using various combinations of collected data to independently determine the two parameters.