Population surveillance approach to detect and respond to new clusters of COVID-19

Background: To maintain control of the coronavirus disease 2019 (COVID-19) epidemic as lockdowns are lifted, it will be crucial to enhance alternative public health measures. For surveillance, it will be necessary to detect a high proportion of any new cases quickly so that they can be isolated, and people who have been exposed to them traced and quarantined. Here we introduce a mathematical approach that can be used to determine how many samples need to be collected per unit area and unit time to detect new clusters of COVID-19 cases at a stage early enough to control an outbreak. Methods: We present a sample size determination method that uses a relative weighted approach. Given the contribution of COVID-19 test results from sub-populations to detect the disease at a threshold prevalence level to control the outbreak to 1) determine if the expected number of weekly samples provided from current healthcare-based surveillance for respiratory virus infections may provide a sample size that is already adequate to detect new clusters of COVID-19 and, if not, 2) to determine how many additional weekly samples were needed from volunteer sampling. Results: In a demonstration of our method at the weekly and Canadian provincial and territorial (P/T) levels, we found that only the more populous P/T have sufficient testing numbers from healthcare visits for respiratory illness to detect COVID-19 at our target prevalence level—assumed to be high enough to identify and control new clusters. Furthermore, detection of COVID-19 is most efficient (fewer samples required) when surveillance focuses on healthcare symptomatic testing demand. In the volunteer populations: the higher the contact rates; the higher the expected prevalence level; and the fewer the samples were needed to detect COVID-19 at a predetermined threshold level. Conclusion: This study introduces a targeted surveillance strategy, combining both passive and active surveillance samples, to determine how many samples to collect per unit area and unit time to detect new clusters of COVID-19 cases. The goal of this strategy is to allow for early enough detection to control an outbreak.

Measuring the Efficiency of Automation-Aided Performance in a Simulated Baggage Screening Task

Objective The present study replicated and extended prior findings of suboptimal automation use in a signal detection task, benchmarking automation-aided performance to the predictions of several statistical models of collaborative decision making. Background Though automated decision aids can assist human operators to perform complex tasks, operators often use the aids suboptimally, achieving performance lower than statistically ideal. Method Participants performed a simulated security screening task requiring them to judge whether a target (a knife) was present or absent in a series of colored X-ray images of passenger baggage. They completed the task both with and without assistance from a 93%-reliable automated decision aid that provided a binary text diagnosis. A series of three experiments varied task characteristics including the timing of the aid’s judgment relative to the raw stimuli, target certainty, and target prevalence. Results and Conclusion Automation-aided performance fell closest to the predictions of the most suboptimal model under consideration, one which assumes the participant defers to the aid’s diagnosis with a probability of 50%. Performance was similar across experiments. Application Results suggest that human operators’ performance when undertaking a naturalistic search task falls far short of optimal and far lower than prior findings using an abstract signal detection task.

When to switch from simple random to probability-based sampling in mapping and monitoring of rare habitats and species?

Many habitats and species of conservation concern are too rare to be adequately represented in a simple random sample of observation units, e.g., for monitoring purposes. Here, we explore possibilities and limitations of a promising alternative approach, probability-based sampling, by which the probability of being sampled is a function of the predicted probability of occurrence in a potential sampling unit. We compare probability-based vs. random sampling for rare and common target phenomena by simulating variables at three nested sample levels allowing investigation of, e.g., presence or absence of a habitat, presence or abundance of a species in the habitat, and properties of this species, and by deriving theoretical limits for the different sampling designs based on a priori knowledge of the properties of the system. We show that the lower limit for target prevalence, allowing for reliable estimation of its properties, can be expressed as a function of the acceptable precision, the sampling effort and variable parameters. The simulations confirm these theoretically derived lower prevalence limits. As expected, lower demands on precision and higher sampling effort allow investigation of rarer and less predictable phenomena. Probability-based sampling gives sufficiently precise estimates for phenomena with prevalence several orders of magnitude lower than simple random sampling, as well as more precise estimates for common phenomena. This suggests a substantial unrealized potential for the use of probability-based sampling in biodiversity and conservation studies. We demonstrate how our results can be applied in sampling design for veteran oaks with many rare and threatened beetles.

Distracting Objects Induce Early Quitting in Visual Search

Task-irrelevant objects can sometimes capture attention and increase the time it takes an observer to find a target. However, less is known about how these distractors impact visual search strategies. Here, I found that salient distractors reduced rather than increased response times on target-absent trials (Experiment 1; N = 200). Combined with higher error rates on target-present trials, these results indicate that distractors can induce observers to quit search earlier than they otherwise would. These effects were replicated when target prevalence was low (Experiment 2; N = 200) and with different stimuli that elicited shallower search slopes (Experiment 3; N = 75). These results demonstrate that salient distractors can produce at least two consequences in visual search: They can capture attention, and they can cause observers to quit searching early. This novel finding has implications both for understanding visual attention and for examining distraction in real-world domains where targets are often absent, such as medical image screening.

Learning in the Target Prevalence Effect

Rare or low prevalence targets are detected less well than counterparts that occur with higher probability. It stands to reason, though, that before such a deficit is apparent, information about a given target’s probability of occurrence must be apprehended. In this study, we investigated how much target experience is necessary for target probabilities to be fully acquired and established within mental task representations. A central finding was that different target probability values required approximately the same amount of target sampling to learn. This was true whether learning about target probabilities from a naive start-point (Experiment 1) or when recalibrating from one probability value to another (Experiment 2). We discuss these findings in relation to how mental task representations are modified when new task-relevant information is received and the attentional consequences of such changes.