Strategy maps: Generalised giving-up densities for optimal foraging.

Finding a common currency for benefits and hazards is a major challenge in optimal foraging theory, often requiring complex computational methods. We present a new analytic approach that builds on the Marginal Value Theorem and giving-up densities while incorporating the nonlinear effect of predation risk. We map the space of all possible environments into strategy regions, each corresponding to a discrete optimal strategy. This provides a generalised quantitative measure of the trade-off between foraging rewards and hazards. This extends a classic optimal diet choice rule-of-thumb to incorporate the hazard of waiting for better resources to appear. We compare the dynamics of optimal decision-making for three foraging life-history strategies: One in which fitness accrues instantly, and two with delays before fitness benefit is accrued. Foragers with delayed-benefit strategies are more sensitive to predation risk than resource quality, as they stand to lose more fitness from a predation event than instant-accrual foragers.

A minimal model of learning: quantifying the cost and benefit of learning in changing environments.

Many organisms have the ability to learn, but the costs and benefits of learning are difficult to quantify. We construct a minimal mathematical model of learning in which a forager attempts to maximize the amount of resources (food) it collects in a changing environment. Our model has two learning parameters: α, corresponding to the duration of the forager's memory, and [Formula: see text], corresponding to how much the forager explores the environment to learn more about it. We analyse the effect of different regimes of environmental change on the optimal memory and exploration parameters [Formula: see text]. By comparing the fitness outcomes from learning foragers to the outcomes from foragers following fixed strategies, we explicitly quantify the fitness benefit (or cost) of learning as a function of environmental change. We find that in many environments, the marginal benefit of learning is surprisingly small. In every environment, it is possible to implement learning in such a way that performance is as bad or worse than following a fixed strategy. In some environments, even the best implementations of our minimal model of learning perform worse than the best fixed strategy. Finally, we find that variance in resource values negatively biases foragers' estimates for those values, potentially explaining experimental results showing that animals prefer less variable resources.

Coordinated Strategy for a Model-Based Decision Support Tool for Coronavirus Disease, Utah, USA.

The coronavirus disease pandemic has highlighted the key role epidemiologic models play in supporting public health decision-making. In particular, these models provide estimates of outbreak potential when data are scarce and decision-making is critical and urgent. We document the integrated modeling response used in the US state of Utah early in the coronavirus disease pandemic, which brought together a diverse set of technical experts and public health and healthcare officials and led to an evidence-based response to the pandemic. We describe how we adapted a standard epidemiologic model; harmonized the outputs across modeling groups; and maintained a constant dialogue with policymakers at multiple levels of government to produce timely, evidence-based, and coordinated public health recommendations and interventions during the first wave of the pandemic. This framework continues to support the state's response to ongoing outbreaks and can be applied in other settings to address unique public health challenges.