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Existing methodology on food accessibility predominately focuses on on-premise services, that is, dine-in and shopping at stores, which assumes a linear distance decay property (the closer, the higher accessibility). Access to delivery services is fundamentally different from that to on-premise stores. Stores with close proximity (within an inner boundary) are less desirable for delivery due to delivery fees, and there is an outer boundary beyond which deliveries are unavailable, both challenging the assumption of increasing impediment with distance. These two boundaries form a donut shape for delivery services. We propose a modified 2-step floating catchment area method that incorporates the donut shape, accounts for both demand and supply, and examines the diversity of food options. Using Seattle as a case study, our results show that delivery services increase restaurant and fast-food accessibility in areas where there is already good accessibility (e.g., downtown Seattle for restaurants and South Seattle for fast-food). Given South Seattle is where low-income and low-access households concentrate, the increase in accessibility to fast-food may not be desired. Interestingly, with delivery services, more low-income or low-access households (those who live far from grocery stores) have better accessibility to fresh produce from grocery stores compared to the rest of the population. And the newly created Supplemental Nutrition Assistance Program (SNAP) online program appears to miss low-access households. These findings have important implications for policymakers and stakeholders seeking to improve food accessibility in urban areas through delivery services.

 

When disasters isolate communities from external support, their members must turn to each other for mutual assistance. This study explores (1) resource management, (2) information sharing, and (3) community leadership and civic participation as dimensions of peer-to-peer sharing for more efficient distribution of local resources under “islanded” conditions. Interviews with members and leaders of three neighborhood-scale communities in Washington state revealed concerns about household preparedness and stockpiling of resources, but also the potential to lever individuals’ community knowledge, social networks, and willingness to participate. Future interventions might include enhancing place-based social infrastructure for resource and information sharing; online local databases and applications that normally maintain privacy but “unlock” important household information for community use in emergencies; and programs that help individuals access and adopt leadership and participation roles. Satisfying these requirements for successful disaster prepared ness also aligns with the goals of everyday community-building and strengthening of collective capacity 

When large-scale disasters occur, people often are left on their own to seek critical resources: food, water, medications, and other important items. Historically, government agencies have developed disaster preparedness strategies focused primarily on either the level of the individual or household or on the ability of government agents to provide relief to affected areas. Such approaches do not consider the potential for community members to share needed resources with one another—a crucial factor in survival when earthquakes, floods, landslides, and other disruptions to transportation and communications cut off whole communities from external aid. In this study, we used a simple random sample survey to measure households’ actual and perceived preparedness and assess individuals’ willingness to share essential resources following a large disaster using survey data gathered from three communities in the Pacific Northwest of the US (N 1⁄4 638; overall response rate 20.1%) and Nagoya, Japan (N 1⁄4 1,043; response rate 13.6%), two regions that expect to experience a magnitude 9.0 megaquake. Analysis of the survey data using an ordered response probit model found that the strength of social ties and levels of social trust strongly influence willingness to share in both regions. Differences between the Japanese and American responses suggest different dependencies on and roles for government agencies in the two societies, as well as differences in the types of resources that community members are willing to share, and with whom. Trust emerges as the most important factor across both study regions and for all resources. Willingness to share may be enhanced through trust-building interventions and should be regarded as an effective focus for preparedness efforts, especially if it is shown to be beneficial for a variety of social purposes. 

This research aims to investigate the well-being implications of changes in activity-travel and time-use patterns brought about by the COVID-19 pandemic. The study uses American Time Use Survey (ATUS) data from 2019 and 2020 to assess changes in activity-travel and time-use patterns. It applies two methods—a well-being scoring method and a time-poverty analysis method—to evaluate the impacts of these changes on society. The results show that individuals experienced diminished well-being during the pandemic even when their time-poverty statistics showed an improvement; this is because the pandemic did not allow individuals to pursue activities in a way that would enhance well-being. In general, well-being is positively associated with the pursuit of discretionary activities in the company of others in favored out-of-home locations. This explains why people have rapidly embraced traveling again in a post-pandemic era. At the same time, people desire more discretionary time (less time poverty); because the elimination of the commute contributes to this, workers are reluctant to return fully to the workplace. Planning processes need to account for a new normal in which activity-travel patterns will be increasingly shaped by the human desire to accumulate positive life experiences. 

The discovery of new drugs is a time consuming and expensive process. Methods such as virtual screening, which can filter out ineffective compounds from drug libraries prior to expensive experimental study, have become popular research topics. As the computational drug discovery community has grown, in order to benchmark the various advances in methodology, organizations such as the Drug Design Data Resource have begun hosting blinded grand challenges seeking to identify the best methods for ligand pose-prediction, ligand affinity ranking, and free energy calculations. Such open challenges offer a unique opportunity for researchers to partner with junior students (e.g., high school and undergraduate) to validate basic yet fundamental hypotheses considered to be uninteresting to domain experts. Here, we, a group of high school-aged students and their mentors, present the results of our participation in Grand Challenge 4 where we predicted ligand affinity rankings for the Cathepsin S protease, an important protein target for autoimmune diseases. To investigate the effect of incorporating receptor dynamics on ligand affinity rankings, we employed the Relaxed Complex Scheme, a molecular docking method paired with molecular dynamics-generated receptor conformations. We found that Cathepsin S is a difficult target for molecular docking and we explore some advanced methods such as distance-restrained docking to try to improve the correlation with experiments. This project has exemplified the capabilities of high school students when supported with a rigorous curriculum, and demonstrates the value of community-driven competitions for beginners in computational drug discovery.

 

Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020.