The Clean Your Plate Campaign: Resisting Table Food Waste in an Unstable World

The COVID-19 pandemic threatens global food security and has created an urgent need for food conservation. This article presents a review of clean plate campaigns around the world. It aims to fight food waste and reveal the factors that may influence food waste. The Clean Plate Club in the US developed during wartime and relied heavily on political power for compliance, whereas the Clean Plate movement in South Korea was based on religion. China's Clean Your Plate Campaign (CYPC) has gone through two stages: CYPC I and CYPC II. The latter occurred during the unstable period of the COVID-19 pandemic. It was large-scale and more strongly enforced than CYPC I. In China, CYPC has relied more on personal virtue than on politics or religion. Culture, policy, COVID-19, and behavior are all important social factors that influence food waste. Specifically, two cultural values are drivers of food waste in China: hospitality and face-saving (mianzi). In terms of policy, China's food waste law mainly relies on persuasion;it lacks any power of enforcement. Laws in France and Italy, by contrast, focus on re-using food and involve both coercion and incentives. COVID-19 may have led to panic purchasing and stockpiling, but, in general, it has resulted in a reduction in food waste.

Rapid Response in an Uncertain Environment: Study of COVID-19 Scientific Research Under the Parallel Model.

PURPOSE: The rapid response of COVID-19 scientific research played a significant role in pandemic prevention and control but failed to block the spread of the pandemic rapidly. Besides the complexity of the virus, the effectiveness of control and prevention measures, and other factors, the adaptation of the mode of conducting scientific research is also crucial for the prevention and control of COVID-19. In this study, a parallel model was used to explore the effects of the rapid scientific response on COVID-19 to assess why pandemics continue to spread under rapid response. ANALYSIS: This study presents the response of scientific research based on country/region and publication dimensions after analyzing COVID-19 studies in the Web of Science and PubMed databases. Co-occurrence analysis of items was used to determine the generation rate of COVID-19 research under different topics to identify the reflected innovation model. RESULTS: More manifestations on rapid response of COVID-19 research, especially compared with the linear model of SARS research, showed that the COVID-19 research followed a parallel or concurrent innovation model. CONCLUSION: Early multi-stakeholder partnership, convenient information sharing, and improved research competence promote the parallel model in COVID-19. Meanwhile, the uncertainty of the COVID-19 virus and the adverse effect of rapid response may limit the time efficiency of the parallel model. In conclusion, the rapid prevention and control of the pandemic cannot fully rely on scientific research but requires more combined effort under an uncertain global setting.

The Effect of Governmental Health Measures on Public Behaviour During the COVID-19 Pandemic Outbreak.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic resulted in radical changes in many aspects of life. To deal with this, each country has implemented continuous health measures from the beginning of the outbreak. Discovering how governmental actions impacted public behaviour during the outbreak stage is the purpose of this study. METHODS: This study uses a hybrid large-scale data visualisation method to analyse public behaviour (epidemic concerns, self-protection, and mobility trends), using the data provided by multiple authorities. Meanwhile, a content analysis method is used to qualitatively code the health measures of three countries with severe early epidemic outbreaks from different continents, namely China, Italy, and the United States. Eight dimensions are coded to rate the mobility restrictions implemented in the above countries. RESULTS: (1) Governmental measures did not immediately persuade the public to change their behaviours during the COVID-19 epidemic. Instead, the public behaviour proceeded in a three-phase rule, which is typically witnessed in an epidemic outbreak, namely the wait-and-see phase, the surge phase and the slow-release phase. (2) The strictness of the mobility restrictions of the three countries can be ranked as follows: Hubei Province in China (with an average score of 8.5 out of 10), Lombardy in Italy (7.125), and New York State in the United States (5.375). Strict mobility restrictions are more likely to cause a surge of population outflow from the epidemic area in the short term, whereas the effect of mobility restrictions is positively related to the stringency of policies in the long term. CONCLUSION: The public showed generally lawful behaviour during regional epidemic outbreaks and blockades. Meanwhile public behaviour was deeply affected by the actions of local governments, rather than the global pandemic situation. The contextual differences between the various countries are important factors that influence the effects of the different governments' health measures.

Coevolution of COVID-19 research and China's policies.

BACKGROUND: In the era of evidence-based policy-making (EBPM), scientific outputs and public policy should engage with each other in a more interactive and coherent way. Notably, this is becoming increasingly critical in preparing for public health emergencies. METHODS: To explore the coevolution dynamics between science and policy (SAP), this study explored the changes in, and development of, COVID-19 research in the early period of the COVID-19 outbreak in China, from 30 December 2019 to 26 June 2020. In this study, VOSviewer was adopted to calculate the link strength of items extracted from scientific publications, and machine learning clustering analysis of scientific publications was carried out to explore dynamic trends in scientific research. Trends in relevant policies that corresponded to changing trends in scientific research were then traced. RESULTS: The study observes a salient change in research content as follows: an earlier focus on "children and pregnant patients", "common symptoms", "nucleic acid test", and "non-Chinese medicine" was gradually replaced with a focus on "aged patients", "pregnant patients", "severe symptoms and asymptomatic infection", "antibody assay", and "Chinese medicine". "Mental health" is persistent throughout China's COVID-19 research. Further, our research reveals a correlation between the evolution of COVID-19 policies and the dynamic development of COVID-19 research. The average issuance time of relevant COVID-19 policies in China is 8.36 days after the launching of related research. CONCLUSIONS: In the early stage of the outbreak in China, the formulation of research-driven-COVID-19 policies and related scientific research followed a similar dynamic trend, which is clearly a manifestation of a coevolution model (CEM). The results of this study apply more broadly to the formulation of policies during public health emergencies, and provide the foundation for future EBPM research.

Co-evolution of COVID-19 Research and China’s Policies (preprint)

The dynamics of research are a prism that reflects interactions between science, the societal environment, and government policies. Against the backdrop of the ongoing global COVID-19 pandemic, this study explored the changes in, and development of, COVID-19 research in the period from December 30, 2019 to April 27, 2020. The study observes a salient change in research content: an earlier focus on “all patients”, “common symptoms”, and “nucleic acid test” was gradually replaced by a focus on “children”, “pregnant patients”, “severe symptoms”, and a combination of “nucleic acid test” and “antibody assay”. Some topics such as “vaccine R&D”, “knowledge, attitude, and practice” (KAP), and “mental health” were persistent throughout the recent history of China’s COVID-19 research. This study also reveals a correlation between the evolution of COVID-19 policies and the dynamic development of COVID-19 research. In the early stage of the outbreak in China, the formulation of COVID-19 policies followed a rapidly-progressing co-evolutionary model (CEM). The results of this study apply more broadly to the formulation of policies in public health emergencies, especially in the early stages.