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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. 

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.

 

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 Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.