COVID-19 in nursing homes: Geographic diffusion and regional risk factors from January 1 to July 26, 2020 of the pandemic.

Background: COVID-19 deaths in nursing homes accounted for 30.2% of all COVID-19 deaths in the United States during the early weeks (1-January to 26-July, 2020) of the pandemic. This study presents the geographic diffusion of COVID-19 cases and deaths in nursing homes during this time period, while also providing explanation of regional risk factors.

Spatial accessibility and travel to pharmacy by type in the Detroit region.

Background: Community pharmacies are a critical part of the health care provision system. Yet less is understood about the spatial accessibility to pharmacies and how people travel to reach these services.

Objectives: This study compared spatial accessibility and actual travel to different types of pharmacies among selected neighborhoods in the Detroit region.

How do farmers adapt to agricultural risks in northern India? An agent-based exploration of alternate theories of decision-making.

Agricultural decision-making processes occur in complex and dynamic environments and are highly contextual. Despite evidence to the contrary, utility maximization is often the implicit theoretical assumption underlying agricultural decision-making processes. This study undertakes an exploratory approach to test alternative theories of human decision-making on the process of agricultural adaptation of farmers in India by synthesizing multiple sources of social and environmental data.

The dynamics of food shopping behavior: Exploring travel patterns in low-income Detroit neighborhoods experiencing extreme disinvestment using agent-based modeling.

Only a handful of studies have leveraged agent-based models (ABMs) to examine public health outcomes and policy interventions associated with uneven urban food environments. While providing keen insights about the role of ABMs in studying urban food environments, these studies underutilize real-world data on individual behavior in their models. This study provides a unique contribution to the ABM and food access literature by utilizing survey data to develop an empirically-rich spatially-explicit ABM of food access.

A Pilot Study of Pokémon Go and Players' Physical Activity.

Objective: Pokémon Go is a mobile game released in 2016 that gained great popularity. The goals of this pilot study were to investigate player's game-related behavior pattern, to evaluate Pokémon Go's impact on players' physical activity (PA) and game enjoyment, and to examine the influence of neighborhood environment on game behavior.

Materials And Methods: Forty-seven valid online surveys were collected.

Strategic directions for agent-based modeling: avoiding the YAAWN syndrome.

In this short communication, we examine how agent-based modeling has become common in land change science and is increasingly used to develop case studies for particular times and places. There is a danger that the research community is missing a prime opportunity to learn broader lessons from the use of agent-based modeling (ABM), or at the very least not sharing these lessons more widely. How do we find an appropriate balance between empirically rich, realistic models and simpler theoretically grounded models? What are appropriate and effective approaches to model evaluation in light of uncertainties not only in model parameters but also in model structure? How can we best explore hybrid model structures that enable us to better understand the dynamics of the systems under study, recognizing that no single approach is best suited to this task? Under what circumstances - in terms of model complexity, model evaluation, and model structure - can ABMs be used most effectively to lead to new insight for stakeholders? We explore these questions in the hope of helping the growing community of land change scientists using models in their research to move from 'yet another model' to doing better science with models.

The Volatility of Data Space: Topology Oriented Sensitivity Analysis.

Despite the difference among specific methods, existing Sensitivity Analysis (SA) technologies are all value-based, that is, the uncertainties in the model input and output are quantified as changes of values. This paradigm provides only limited insight into the nature of models and the modeled systems. In addition to the value of data, a potentially richer information about the model lies in the topological difference between pre-model data space and post-model data space.

Using uncertainty and sensitivity analyses in socioecological agent-based models to improve their analytical performance and policy relevance.

Agent-based models (ABMs) have been widely used to study socioecological systems. They are useful for studying such systems because of their ability to incorporate micro-level behaviors among interacting agents, and to understand emergent phenomena due to these interactions. However, ABMs are inherently stochastic and require proper handling of uncertainty.