Résumé
During the early stages of the pandemic, many households experienced a loss of employment income, which worsened food insecurity and food insufficiency across the United States. This study aimed to examine whether food assistance programs (FAPs) significantly alleviated food insufficiency (FI) among job-loss households during the pandemic. The analysis was conducted with nationally representative survey data (n = 229,668) collected across 50 states and D.C. in 2020. Spatial analysis and logistic regression were applied to analyze the patterns of FI and to assess the Supplemental Nutrition Assistance Program (SNAP), FAPs for children, and community FAPs. Approximately 18.6% of participants faced FI, 41% of whom experienced FI for the first time. SNAP significantly lowered the odds of being food insufficient by 24.5% among households who were already food insufficient before the pandemic and by 11.9% for households with children. FAPs for children slightly reduced FI among households with children, but those who participated in these programs still had higher prevalence of food insufficiency than those who did not. Community FAPs were not effective. The findings suggest increasing the benefit level of SNAP for households with lowest income, strengthening their partnership with community FAPs, and distributing more emergent assistance to the most vulnerable households.
Résumé
The coronavirus disease 2019 (COVID-19) is spreading rapidly all over the world. The transmission dynamics of the COVID-19 pandemic is still unclear, but developing strategies for mitigating the severity of the pandemic is yet a top priority for global public health. In this study, we developed a novel compartmental model, SEIR-CV(susceptible-exposed-infectious-removed with control variables), which not only considers the key characteristics of asymptomatic infection and the effects of seasonal variations, but also incorporates different control measures for multiple transmission routes, so as to accurately predict and effectively control the spread of COVID-19. Based on SEIR-CV, we predicted the COVID-19 epidemic situation in China out of Hubei province and proposed corresponding control strategies. The results showed that the prediction results are highly consistent with the outbreak surveillance data, which proved that the proposed control strategies have achieved sound consequent in the actual epidemic control. Subsequently, we have conducted a rolling prediction for the United States, Brazil, India, five European countries (the United Kingdom, Italy, Spain, Germany, and France), southern hemisphere, northern hemisphere, and the world out of China. The results indicate that control measures and seasonal variations have a great impact on the progress of the COVID-19 pandemic. Our prediction results show that the COVID-19 pandemic is developing more rapidly due to the impact of the cold season in the southern hemisphere countries such as Brazil. While the development of the pandemic should have gradually weakened in the northern hemisphere countries with the arrival of the warm season, instead of still developing rapidly due to the relative loose control measures such as the United States and India. Furthermore, the prediction results illustrate that if keeping the current control measures in the main COVID-19 epidemic countries, the pandemic will not be contained and the situation may eventually turn to group immunization, which would lead to the extremely severe disaster of about 5 billion infections and 300 million deaths globally. However, if China's super stringent control measures were implemented from 15 July, 15 August or 15 September 2020, the total infections would be contained about 15 million, 32 million or 370 million respectively, which indicates that the stringent and timely control measures is critical, and the best window period is before September for eventually overcoming COVID-19.