Pilot performance: assessing how scan patterns & navigational assessments vary by flight expertise.

INTRODUCTION: Helicopter overland navigation is a cognitively complex task that requires continuous monitoring of system and environmental parameters and many hours of training to master. This study investigated the effect of expertise on pilots' gaze measurements, navigation accuracy, and subjective assessment of their navigation accuracy in overland navigation on easy and difficult routes. METHODS: A simulated overland task was completed by 12 military officers who ranged in flight experience as measured by total flight hours (TFH). They first studied a map of a route that included both easy and difficult route sections, and then had to 'fly' this simulated route in a fixed-base helicopter simulator. They also completed pre-task estimations and post-task assessments of the navigational difficulty of the transit to each waypoint in the route. Their scan pattern was tracked via eye tracking systems, which captured both the subject's out-the-window (OTW) and topographical map scan data. RESULTS: TFH was not associated with navigation accuracy or root mean square (RMS) error for any route section. For the easy routes, experts spent less time scanning out the window (p = 0.61) and had shorter OTW dwell (p = -0.66). For the difficult routes, experts appeared to slow down their scan by spending as much time scanning out the window as the novices while also having fewer Map fixations (p = -0.65) and shorter OTW dwell (p = -0.69). However, TFH was not significantly correlated with more accurate estimates of route difficulty. DISCUSSION: This study found that TFH did not predict navigation accuracy or subjective assessment, but was correlated with some gaze parameters.

Subsewershed SARS-CoV-2 Wastewater Surveillance and COVID-19 Epidemiology Using Building-Specific Occupancy and Case Data.

To evaluate the use of wastewater-based surveillance and epidemiology to monitor and predict SARS-CoV-2 virus trends, over the 2020-2021 academic year we collected wastewater samples twice weekly from 17 manholes across Virginia Tech's main campus. We used data from external door swipe card readers and student isolation/quarantine status to estimate building-specific occupancy and COVID-19 case counts at a daily resolution. After analyzing 673 wastewater samples using reverse transcription quantitative polymerase chain reaction (RT-qPCR), we reanalyzed 329 samples from isolation and nonisolation dormitories and the campus sewage outflow using reverse transcription digital droplet polymerase chain reaction (RT-ddPCR). Population-adjusted viral copy means from isolation dormitory wastewater were 48% and 66% higher than unadjusted viral copy means for N and E genes (1846/100 mL to 2733/100 mL/100 people and 2312/100 mL to 3828/100 mL/100 people, respectively; n = 46). Prespecified analyses with random-effects Poisson regression and dormitory/cluster-robust standard errors showed that the detection of N and E genes were associated with increases of 85% and 99% in the likelihood of COVID-19 cases 8 days later (incident-rate ratio (IRR) = 1.845, p = 0.013 and IRR = 1.994, p = 0.007, respectively; n = 215), and one-log increases in swipe card normalized viral copies (copies/100 mL/100 people) for N and E were associated with increases of 21% and 27% in the likelihood of observing COVID-19 cases 8 days following sample collection (IRR = 1.206, p < 0.001, n = 211 for N; IRR = 1.265, p < 0.001, n = 211 for E). One-log increases in swipe normalized copies were also associated with 40% and 43% increases in the likelihood of observing COVID-19 cases 5 days after sample collection (IRR = 1.403, p = 0.002, n = 212 for N; IRR = 1.426, p < 0.001, n = 212 for E). Our findings highlight the use of building-specific occupancy data and add to the evidence for the potential of wastewater-based epidemiology to predict COVID-19 trends at subsewershed scales.

Some methodological issues in biosurveillance.

This paper briefly summarizes a short course I gave at the 12th Biennial Centers for Disease Control and Prevention (CDC) and Agency for Toxic Substances and Disease Registry (ATSDR) Symposium held in Decatur, Georgia on April 6, 2009. The goal of this short course was to discuss various methodological issues of biosurveillance detection algorithms, with a focus on the issues related to developing, evaluating, and implementing such algorithms.

Covid-19: One year on .

Ron Fricker assesses the impact of the pandemic in the United States by calculating the number of "excess deaths".

Covid-19 cases around the world.

Ron Fricker plots the course that the coronavirus pandemic has taken since January 2020.

Investigating a disease outbreak.

Teams of epidemiological and medical "detectives" are working to get a coronavirus pandemic under control. Ronald D. Fricker, Jr and Steven E. Rigdon walk us through a typical investigation.

Comparing syndromic surveillance detection methods: EARS' versus a CUSUM-based methodology.

This paper compares the performance of three detection methods, entitled C1, C2, and C3, that are implemented in the early aberration reporting system (EARS) and other syndromic surveillance systems versus the CUSUM applied to model-based prediction errors. The cumulative sum (CUSUM) performed significantly better than the EARS' methods across all of the scenarios we evaluated. These scenarios consisted of various combinations of large and small background disease incidence rates, seasonal cycles from large to small (as well as no cycle), daily effects, and various types and levels of random daily variation. This leads us to recommend replacing the C1, C2, and C3 methods in existing syndromic surveillance systems with an appropriately implemented CUSUM method.