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COVID-19 still looms as the largest risk to the agriculture, energy, and health sectors, threatening sustainable global economic development. The literature shows that the COVID-19 pandemic can divert governments' attention away from climate change, renewable energy, and food security challenges that are necessary to address for sustainable economic growth. The COVID-19 pandemic has consistently influenced environmental behaviors, as it has primarily decreased income levels and disrupted food systems worldwide. This study examined the impacts of COVID-19 on food consumption patterns, food diversity, and income challenges and explored the factors affecting food consumption patterns during the pandemic. The data collected through an online survey from 1537 Chinese households were analyzed through a paired t-test, a mixed-design ANOVA, and a logistic regression analysis. The results revealed that the consumption of the majority of individual food commodities decreased during the COVID-19 pandemic. Among the individual food items, the consumption of pork witnessed the greatest decrease during the COVID-19 pandemic compared to the normal period. The decrease in food diversity was higher for the households whose income was affected compared to the households whose income was not affected during the COVID-19 pandemic. Furthermore, the consumption quantities of various food groups declined more for highly income-affected households than for medium and slightly affected households during the pandemic. Households that adopted a dissaving income-stabilizing strategy were 47% points more likely to maintain their food consumption patterns during the pandemic. Farmers were 17% points and 19% points less likely to suffer worsened food consumption compared to self-employed and wage workers, respectively, during the pandemic. Thus, self-production methods such as kitchen gardening can assist households to maintain and improve their consumption of food commodities during the COVID-19 pandemic. © 2022 by the authors.
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BACKGROUND: Food delivery has emerged as a major need during the COVID-19 pandemic due to exacerbated socioeconomic insecurity and quarantine precautions. Efficient coordination, however, is often hampered by fragmentation and varying resource availability among health and food services in a city. The purpose of this study was to describe the rapid-cycle development and early implementation of Food Access Support Technology (FAST), a centralized digital platform that pairs health systems with community-based food and delivery partners to facilitate food access. METHODS: Using FAST, providers and staff can post requests for food delivery on patients' behalf, which are reviewed and claimed by eligible CBOs that can meet dietary criteria (e.g., low-sodium). Depending on CBO capacity, the delivery arm of the request may be completed by the same CBO or a different delivery partner, also matched via FAST. The design process engaged key stakeholders city-wide, including health systems, CBOs, and the Philadelphia Department of Public Health. Iterative, rapid-cycle innovation underpinned the development and scaling of FAST, with focus groups, user interviews, and weekly teamassessments driving programmatic changes.As of December 2021, FAST has onboarded 2 health systems and 10 CBOs. The platform tracked process measures, including request status and time between changes in request status. RESULTS: Between March and December 2021, 149 requests for food delivery were posted to FAST, representing 117 unique patients in 37 distinct postal codes. Of these requests, 117 (79%) were completed by 10 different food and delivery partners. The remaining were either in the process of completion (10%), cancelled (8%), or unfulfilled because patients were unreachable (3%). About 34% of requests were initiated from a health system, with the rest initiated directly from a food CBO for delivery only. Most requests (53%) were for one week's worth of food, though requests were completed for as much as 8 weeks' worth of food. The median time from post to delivery was 1 (IQR 0-4) day. Specifically, posted requests were usually claimed by a food and/or delivery partner in less than a day (IQR 0-0), and a median of 1 (IQR 0-4) day elapsed from claim by a delivery partner to actual delivery. Requests for prepared meals took longer to complete (7 days, IQR 0- 34) than requests for unprepared food (4 days, IQR 1-12). CONCLUSIONS: The early implementation of FAST suggests that centralized platforms for food delivery can benefit both patients and organizations by streamlining partnerships between health systems and CBOs - as well as facilitating the real-time coordination and sharing of resources among CBOs - to efficiently and effectively meet the food needs of patients. As calls mount for health systems to address the social determinants of health, FAST offers a rapid-cycle, community-engagedmodel for efficient resource coordination that may be increasingly crucial to respond to social needs and promote patient health.
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The popularity of YouTube provides an effective way to propagate epidemic prevention knowledge by analyzing the video preferences of viewers from different locations. However, it is challenging to analyze video preferences due to the dispersed geographical locations of the YouTube viewers and the indistinguishable video categories and subcategories. This paper combines linear regression and neural networks to unravel both geographical and categorical difficulties and improve the accuracy of task-solving models. First, the YouTube dataset and extract variables are preprocessed, including categories, subcategories, countries, number of subscribers, and view counts of each YouTubers. Then, linear regression and neural networks are trained to classify and find the correlation between these variables. Finally, Matplotlib, google chart, and Tableau are utilized to visualize the result based on video categories and geographical locations. The accuracies of linear regression and neural network models are verified through the R-squared estimation. Both linear regression and neural network models show the trending types of videos and a positive correlation between the number of viewers and subscribers. The experimental results show a remarkable user's tendency of watching films and listening to music, a concentration of YouTube users from India and the U.S., and propose targeted Covid-19 prevention propaganda based on the above two characteristics. © 2022 IEEE.