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IISE Annual Conference and Expo 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2011555


Recent events such as natural hazards, diet trends, and the COVID-19 pandemic have shed light on several inefficiencies of the traditional fresh fruit and vegetable (FFV) supply chain (SC). Factors that contribute to this problem are the lack of coordination of the SC participants, the inaccessibility of planning tools for agricultural production, and the absence of market information to determine if a product will have a demand. Intelligent SCs are emerging to address some of these issues by using data-driven tools to aid in decision-making. Nonetheless, there has been little work to incorporate market intelligence in the new SC model to solve the lack of market information in the traditional model. It is essential to include market intelligence in the new SC model to decrease food waste, reduce losses related to low market prices and demands, and avoid scarcity events in which food availability and affordability decrease, while aiding small growers by alerting them of potential market opportunities. This work aims to develop a market intelligence framework for the FFV SC and incorporate it into the intelligent SC. A layered system approach is used with the goal of collecting relevant data to monitor and diagnose the market's state and provide recommendations to the SC participants. The layered system framework aims to decompose the overall problem into several layers with distinct goals such as data collection, processing, monitoring, diagnostics, among others. This work will focus on the monitoring aspect of the system. © 2022 IISE Annual Conference and Expo 2022. All rights reserved.

Preprint in English | EMBASE | ID: ppcovidwho-327022


Objective: COVID-19 burdens are disproportionally high in underserved and vulnerable groups in Arizona. As the pandemic progresses, it is unclear if the disparities have evolved. In this study, we aim to elicit the dynamic landscape of COVID-19 disparities at the community level and identify newly emerged vulnerable subpopulations. Materials and Methods: We compiled biweekly COVID-19 case counts of 274 zip code tabulation areas (ZCTAs) in Arizona from October 21, 2020, to November 25, 2021, during which the COVID-19 growth rate has changed significantly. Within each growth period, we detected health disparities by testing associations between the growth rate of COVID-19 cases in a ZCTA and the population composition of race/ethnicity, income, employment, and age. We then compared the associations between periods to discover temporal patterns of health disparities. Results: High percentage of Latinx or Black residents, high poverty rate, and young median age were risk factors of high cumulative COVID-19 case counts in a ZCTA. However, the impact of these factors on the growth rate of new COVID-19 cases varied. While high percentage of Black residents and young median age remained as risk factors of fast COVID-19 growth rate, high poverty rate became a protective factor. The association between the percentage of Latinx residents and the COVID-19 growth rate converted from positive to negative during summer 2021. The unemployment rate emerged as a new risk factor of fast COVID-19 growth rate after September 2021. Based on these findings, we identified 37 ZCTAs that are highly vulnerable to fast escalation of COVID-19 cases. Discussion and Conclusion: As the pandemic progresses, disadvantaged communities continue suffering from escalated risk of COVID-19 infection. But the vulnerabilities have evolved. While the disparities related to Latinx ethnicity improved gradually, those related to Black ethnicity and young communities aggravated. The struggle of financially disadvantaged communities continued, although the burden had shifted from those living under the poverty line to those with a high unemployment rate. It is necessary to adjust current resource allocations and design and deploy new interventions to address emerging needs.