Asian American responses to Donald Trump’s anti-Asian rhetoric and misuse of the history of Japanese American incarceration

Since 2015, Donald Trump, his administration and supporters have repeatedly abused the history of Second World War Japanese American incarceration. In contrast to preceding Presidents who recognised the miscarriage of justice authorised by Franklin Roosevelt, Trump and his administration have used this history to justify racism. All post-war presidents before Trump, regardless of political affiliation, agreed what happened under Executive Order 9066 was wrong and should never be repeated. Donald Trump and his administration have, by contrast, not only failed to condemn the incarceration but instead attempted to use test cases brought against the United States government during the war as questionable legal precedent to justify racist policies. The travel ban for those travelling to the USA from Muslim majority countries was compared to Executive Order 9066;Trump’s policy of separating migrant children from their parents and placing them in separate detention centres was disturbingly similar to the internment of orphans of Japanese parentage at Manzanar children’s village;and Trump’s use of terms like ‘China virus’ during the Covid-19 pandemic resulted in attacks on Asian Americans. This article considers Asian American responses to these three case studies of Trump’s anti-Asian rhetoric and abuse of the history of Japanese American incarceration.

Classification Schemes of COVID-19 High Risk Areas and Resulting Policies: A Rapid Review.

The COVID-19 pandemic has posed a significant global health threat since January 2020. Policies to reduce human mobility have been recognized to effectively control the spread of COVID-19; although the relationship between mobility, policy implementation, and virus spread remains contentious, with no clear pattern for how countries classify each other, and determine the destinations to- and from which to restrict travel. In this rapid review, we identified country classification schemes for high-risk COVID-19 areas and associated policies which mirrored the dynamic situation in 2020, with the aim of identifying any patterns that could indicate the effectiveness of such policies. We searched academic databases, including PubMed, Scopus, medRxiv, Google Scholar, and EMBASE. We also consulted web pages of the relevant government institutions in all countries. This rapid review's searches were conducted between October 2020 and December 2021. Web scraping of policy documents yielded additional 43 country reports on high-risk area classification schemes. In 43 countries from which relevant reports were identified, six issued domestic classification schemes. International classification schemes were issued by the remaining 38 countries, and these mainly used case incidence per 100,000 inhabitants as key indicator. The case incidence cut-off also varied across the countries, ranging from 20 cases per 100,000 inhabitants in the past 7 days to more than 100 cases per 100,000 inhabitants in the past 28 days. The criteria used for defining high-risk areas varied across countries, including case count, positivity rate, composite risk scores, community transmission and satisfactory laboratory testing. Countries either used case incidence in the past 7, 14 or 28 days. The resulting policies included restrictions on internal movement and international travel. The quarantine policies can be summarized into three categories: (1) 14 days self-isolation, (2) 10 days self-isolation and (3) 14 days compulsory isolation.

Spill-over effect of Wuhan travel ban on population flow in the outbreak stage of COVID-19 in China.

This paper investigates the imppact of COVID-19 travel restrictions on population flow in the People's Republic of China. We discover an "unreasonable" surge in population flow after the Wuhan travel ban. We further find out that such a sure of population flow is attributed to the "spill-over" effect of the Wuhan travel ban. We utilize a logistic regression model to quantify that the spill-over effect linearly decays with the travel distance to the Pandemic center city. Because of the "spill-over" effect of the travel ban policy, government authorities should design redundancy polity to simultaneously implement a travel ban for the pandemic center city and its neighboring cities to restrain human movement and pandemic transmission.

Evaluating the impact of the travel ban within mainland China on the epidemic of the COVID-19.

OBJECTIVES: The ongoing COVID-19 pandemic expanded its geographic distribution through the movement of humans and caused subsequent local outbreaks. Hence, it is essential to investigate how human mobility and travel ban affect the transmission and spatial spread while minimizing the impact on social activities and national economics. METHODS: We developed a mobility network model for spatial epidemics, explicitly taking into account time-varying inter-province and inner-province population flows, spatial heterogeneity in terms of disease transmission, as well as the impact of media reports. The model is applied to study the epidemic of the dynamic network of 30 provinces of mainland China. The model was calibrated using the publicly available incidence and movement data. RESULTS: We estimated that the second outbreak occurred approximately on February 24, 2020, and the cumulative number of cases as of March 15, 2020, increased by 290.1% (95% CI: (255.3%, 324.9%)) without a travel ban in mainland China (excluding Hubei and Tibet). We found that intra-province travel contributes more to the increase of cumulative number of cases than inter-province travel. CONCLUSION: Our quantitative and qualitative research results suggest that the strict travel ban has successfully prevented a severe secondary outbreak in mainland China, which provides solutions for many countries and regions experiencing secondary outbreaks of COVID-19.

Dynamic governance decisions on multi-modal inter-city travel during a large-scale epidemic spreading.

Within half a year, COVID-19 spreads to most countries in the world, as well as posed a great threat to the public health of human beings. The implementation of non-pharmaceutical intervention (NPI), including travel ban, proved to be an effective way for controlling the epidemic spreading, e.g., the ban of inter-city transportation stops transporting virus through passengers between cities. However, travel ban could significantly impact many industries, e.g. tourism and logistics, thus jeopardizing the regional economy. This paper focus on assisting the national or regional government to make dynamic decisions on restricting and recovering intercity multi-modal travel services. Our model can characterize impacts of inter-city traffic on the spread of the COVID-19, as well as on the regional economy. By applying a reinforcement learning approach, we develop an online optimization model to identify the modal-specific travel banning strategy that can balance the epidemic control as well as the negative impacts on regional economy. The numerical study based on a network of multiple cities in China shows that the proposed approach can generate better strategies compared with some existing methods.

Novel coronavirus (2019-nCoV) early-stage importation risk to Europe, January 2020.

As at 27 January 2020, 42 novel coronavirus (2019-nCoV) cases were confirmed outside China. We estimate the risk of case importation to Europe from affected areas in China via air travel. We consider travel restrictions in place, three reported cases in France, one in Germany. Estimated risk in Europe remains high. The United Kingdom, Germany and France are at highest risk. Importation from Beijing and Shanghai would lead to higher and widespread risk for Europe.

Using traveller-derived cases in Henan Province to quantify the spread of COVID-19 in Wuhan, China.

Wuhan shutdown was implemented on January 23 and the first level response to public health emergencies (FLRPHE) was launched over the country, and then China got the outbreak of COVID-19 under control. A mathematical model is established to study the transmission of COVID-19 in Wuhan. This research investigates the spread of COVID-19 in Wuhan and assesses the effectiveness of control measures including the Wuhan city travel ban and FLRPHE. Based on the dynamical analysis and data fitting, the transmission of COVID-19 in Wuhan is estimated and the effects of control measures including Wuhan city travel ban and FLRPHE are investigated. According to the assumptions, the basic reproduction number for COVID-19 estimated that for Wuhan equal to 7.53 and there are 4.718 × 10 4 infectious people in Wuhan as of January 23. The interventions including the Wuhan city travel ban and FLRPHE reduce the size of peak and the cumulative number of confirmed cases of COVID-19 in Wuhan by 99%. The extraordinary efforts implemented by China effectively contain the transmission of COVID-19 and protect public health in China.

Delaying the COVID-19 epidemic in Australia: evaluating the effectiveness of international travel bans.

OBJECTIVE: Following the outbreak of novel Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), and the disease named COVID-19, in Wuhan, China in late 2019, countries have implemented different interventions such as travel bans to slow the spread of this novel virus. This brief report evaluates the effect of travel bans imposed to prevent COVID-19 importation in the Australian context. METHODS: We developed a stochastic meta-population model to capture the global dynamics and spread of COVID-19. By adjusting our model to capture the travel bans imposed globally and in Australia, the predicted COVID-19 cases imported to Australia were evaluated in comparison to observed imported cases. RESULTS: Our modelling results closely aligned with observed cases in Australia and elsewhere. We observed a 79% reduction in COVID-19 importation and a delay of the COVID-19 outbreak in Australia by approximately one month. Further projection of COVID-19 to May 2020 showed spread patterns depending on the basic reproduction number. CONCLUSION: Imposing the travel ban was effective in delaying widespread transmission of COVID-19. However, strengthening of the domestic control measures is needed to prevent Australia from becoming another epicentre. Implications for public health: This report has shown the importance of border closure to pandemic control.

Impact of population movement on the spread of 2019-nCoV in China.

Since Dec 2019, China has experienced an outbreak caused by a novel coronavirus, 2019-nCoV. A travel ban was implemented for Wuhan, Hubei on Jan 23 to slow down the outbreak. We found a significant positive correlation between population influx from Wuhan and confirmed cases in other cities across China (R2 = 0.85, P < 0.001), especially cities in Hubei (R2 = 0.88, P < 0.001). Removing the travel restriction would have increased 118% (91%-172%) of the overall cases for the coming week, and a travel ban taken three days or a week earlier would have reduced 47% (26%-58%) and 83% (78%-89%) of the early cases. We would expect a 61% (48%-92%) increase of overall cumulative cases without any restrictions on returning residents, and 11% (8%-16%) increase if the travel ban stays in place for Hubei. Cities from Yangtze River Delta, Pearl River Delta, and Capital Economic Circle regions are at higher risk.