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This paper offers a reflexive analysis of an interdisciplinary and cross-race collaboration to advance equity in engineering called LATTICE (Launching Academics on the Tenure-Track: an Intentional Community in Engineering). We engage two bodies of scholarship—matters of care in feminist science and technology studies (STS) and critical race theory on counterspaces—to theorize on the data infrastructure and narrative practices that we developed when applying critical methodologies to collective action in technoscience. We discuss how our care practices conflicted with traditional ethnographic practices and thus, inspired us to innovate on methods. These methods—member-checking and polyvocal memo-ing—make transgressing the boundaries of LATTICE counterspaces for public dissemination possible by invoking caring as praxis. We conclude that using these methods to discuss the contradictions and challenges in STS collaborations is an opportunity for advancing mutual intelligibility among interdisciplinary scholars and a politics of knowledge production grounded in values of care and friendship that may contribute to equity and justice in technoscience. 

According to Feeding America, prior to the pandemic, 1 in 5 African-American/Black, 1 in 6 Hispanic, and 1 in 4 Native American households were food insecure compared to 1 in 11 White households. The pandemic is expected to exacerbate these disparities given its disproportionate economic and health impact on historically marginalized racial and ethnic populations. Food banks are non-profit organizations that work to alleviate food insecurity within their service regions by distributing donated food to households in need. Equitable distribution of donated food is an important criteria for food banks. Existing food banking operations literature primarily focus on geographic equity, i.e., where each geographic block of a food bank's service region receives food in proportion to its demand. However, hunger-relief organizations such as food banks are gradually incorporating demography-based equity in their distribution of donated food in light of the disparities that exist within different demographic groups, such as race, age, and religion. However, the notion of demographic equity has not received attention in the food banking operations literature. This study aims to fill in the gap by developing a multi-criteria optimization model to identify optimal distribution policies for a food bank considering a two-dimensional equity criterion, geographic and demographic, in the presence of effectiveness (undistributed food minimization) and efficiency (distribution cost minimization) criteria. We apply the model to our partner food bank's data to (i) explore the trade-off between geographic and demographic equity as a function of effectiveness, and efficiency, and (ii) identify policy insights. 

Our goal in this work is to build effective yet robust models to predict unreliable and inconsistent in-kind donations at both weekly and monthly levels for two food banks across coasts: the Food Bank of Central Eastern North Carolina in North Carolina and Los Angeles Regional Food Bank in California. We explore three factors: model, data length, and window type. For the model, we evaluate a series of classic time-series forecasting models against the state-of-the-art approaches such as Bayesian Structural Time Series modeling (BSTS) and deep learning models; for the data length, we vary training data from 2 weeks to 13 years; for the window type, we compare sliding vs. expanding. Our results show the effectiveness of different models heavily depends on the data length and the window type as well as characteristics of the food bank. Motivated by these findings, we investigate the effectiveness of employing an average of all predictions formed by considering all three factors at both monthly and weekly levels for both food banks. Our results show that this average of predictions significantly and consistently outperforms all classical models, deep learning, and BSTS for the donation prediction at both monthly and weekly levels for both food banks. 

BackgroundCoronavirus Disease 2019 (COVID-19) continues to cause significant hospitalizations and deaths in the United States. Its continued burden and the impact of annually reformulated vaccines remain unclear. Here, we present projections of COVID-19 hospitalizations and deaths in the United States for the next 2 years under 2 plausible assumptions about immune escape (20% per year and 50% per year) and 3 possible CDC recommendations for the use of annually reformulated vaccines (no recommendation, vaccination for those aged 65 years and over, vaccination for all eligible age groups based on FDA approval). Methods and findingsThe COVID-19 Scenario Modeling Hub solicited projections of COVID-19 hospitalization and deaths between April 15, 2023 and April 15, 2025 under 6 scenarios representing the intersection of considered levels of immune escape and vaccination. Annually reformulated vaccines are assumed to be 65% effective against symptomatic infection with strains circulating on June 15 of each year and to become available on September 1. Age- and state-specific coverage in recommended groups was assumed to match that seen for the first (fall 2021) COVID-19 booster. State and national projections from 8 modeling teams were ensembled to produce projections for each scenario and expected reductions in disease outcomes due to vaccination over the projection period.From April 15, 2023 to April 15, 2025, COVID-19 is projected to cause annual epidemics peaking November to January. In the most pessimistic scenario (high immune escape, no vaccination recommendation), we project 2.1 million (90% projection interval (PI) [1,438,000, 4,270,000]) hospitalizations and 209,000 (90% PI [139,000, 461,000]) deaths, exceeding pre-pandemic mortality of influenza and pneumonia. In high immune escape scenarios, vaccination of those aged 65+ results in 230,000 (95% confidence interval (CI) [104,000, 355,000]) fewer hospitalizations and 33,000 (95% CI [12,000, 54,000]) fewer deaths, while vaccination of all eligible individuals results in 431,000 (95% CI: 264,000–598,000) fewer hospitalizations and 49,000 (95% CI [29,000, 69,000]) fewer deaths. ConclusionsCOVID-19 is projected to be a significant public health threat over the coming 2 years. Broad vaccination has the potential to substantially reduce the burden of this disease, saving tens of thousands of lives each year. 

The United Nations Sustainable Development Goals provide a road map for countries to achieve peace and prosperity. In this study, we address two of these sustainable development goals: achieving food security and reducing inequalities. Food banks are nonprofit organizations that collect and distribute food donations to food‐insecure populations in their service regions. Food banks consider three criteria while distributing the donated food: equity, effectiveness, and efficiency. The equity criterion aims to distribute food in proportion to the food‐insecure households in a food bank's service area. The effectiveness criterion aims to minimize undistributed food, whereas the efficiency criterion minimizes the total cost of transportation. Models that assume predetermined weights on these criteria may produce inaccurate results as the preference of food banks over these criteria may vary over time, and as a function of supply and demand. In collaboration with our food bank partner in North Carolina, we develop a single‐period, weighted multi‐criteria optimization model that provides the decision‐maker the flexibility to capture their preferences over the three criteria of equity, effectiveness, and efficiency, and explore the resulting trade‐offs. We then introduce a novel algorithm that elicits the inherent preference of a food bank by analyzing its actions within a single‐period. The algorithm does not require direct interaction with the decision‐maker. The non‐interactive nature of this algorithm is especially significant for humanitarian organizations such as food banks which lack the resources to interact with modelers on a regular basis. We perform extensive numerical experiments to validate the efficiency of our algorithm. We illustrate results using historical data from our food bank partner and discuss managerial insights. We explore the implications of different decision‐maker preferences for the criteria on distribution policies. 

Abstract Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make months ahead projections of SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we find SMH performance varied widely as a function of both scenario validity and model calibration. We show scenarios remained close to reality for 22 weeks on average before the arrival of unanticipated SARS-CoV-2 variants invalidated key assumptions. An ensemble of participating models that preserved variation between models (using the linear opinion pool method) was consistently more reliable than any single model in periods of valid scenario assumptions, while projection interval coverage was near target levels. SMH projections were used to guide pandemic response, illustrating the value of collaborative hubs for longer-term scenario projections.