Why we dehumanize illegal immigrants: A US mixed-methods study.

Dehumanization is a topic of significant interest for academia and society at large. Empirical studies often have people rate the evolved nature of outgroups and prior work suggests immigrants are common victims of less-than-human treatment. Despite existing work that suggests who dehumanizes particular outgroups and who is often dehumanized, the extant literature knows less about why people dehumanize outgroups such as immigrants. The current work takes up this opportunity by examining why people dehumanize immigrants said to be illegal and how measurement format affects dehumanization ratings. Participants (N = 672) dehumanized such immigrants more if their ratings were made on a slider versus clicking images of hominids, an effect most pronounced for Republicans. Dehumanization was negatively associated with warmth toward illegal immigrants and the perceived unhappiness felt by illegal immigrants from U.S. immigration policies. Finally, most dehumanization is not entirely blatant but instead, captured by virtuous violence and affect as well, suggesting the many ways that dehumanization can manifest as predicted by theory. This work offers a mechanistic account for why people dehumanize immigrants and addresses how survey measurement artifacts (e.g., clicking on images of hominids vs. using a slider) affect dehumanization rates. We discuss how these data extend dehumanization theory and inform empirical research.

Epidemiology of COVID-19 outbreak in Ghana, 2020.

OBJECTIVE: Describe the epidemiology of COVID-19 cases detected in the first four months of the pandemic in Ghana by person, place and time to provide an understanding of the local epidemiology of the disease. METHODS: We conducted an exploratory descriptive study of all confirmed COVID-19 cases in Ghana from March 12 to June 30, 2020. Data was merged from the country's electronic databases, cleaned and summarized using medians, proportions and geospatial analysis. DESIGN: A cross-sectional study design. SETTING: Ghana. PARTICIPANTS: All confirmed COVID-19 cases in Ghana from March 12 to June 30, 2020. INTERVENTIONS: None. MAIN OUTCOME MEASURES: Epidemiological characterization of all confirmed COVID-19 cases recorded from March 12 - June 30, 2020 in Ghana by person, place and time. RESULTS: A total of 17,763 cases were recorded with median age (IQR) of 33years (One month to 85 years). Among the confirmed cases, 10,272 (57.8%) were males and 3,521 (19.8%) were symptomatic with cough recorded in 1,420 (40.3%) cases. The remaining 14,242 (80.2%) were asymptomatic. Greater Accra region recorded the highest number of confirmed cases 11,348 (63.9%). All 16 administrative regions had recorded cases of COVID-19 by June 30, 2020 due to internal migration between the hotspots and other regions. The epidemiological curve showed a propagated outbreak with 117 deaths (CFR= 0.67%) recorded. CONCLUSION: A propagated outbreak of COVID - 19 was confirmed in Ghana on March 12, 2020. Internal migration from hotspots to other regions led to the spread of the virus across the nation. Majority of cases were asymptomatic. FUNDING: The COVID-19 pandemic response and writing workshop by the Ghana Field Epidemiology and Laboratory Training Programme (GFELTP) was supported with funding from President Malaria Initiative - CDC, and Korea International Cooperation Agency (on CDC CoAg 6NU2GGH001876) through AFENET.

Assessment of COVID-19-Related Immigration Concerns Among Latinx Immigrants in the US.

Importance: Despite the contentious immigration environment and disproportionate rates of COVID-19 infection among Latinx individuals in the US, immigrants' concerns about engaging in COVID-19-related testing, treatment, and contact tracing have been largely unexplored. Objective: To examine the proportions of Latinx immigrants who endorse statements about the potential negative immigration ramifications of seeking and using COVID-19-related testing and treatment services and engaging in contact tracing. Design, Setting, and Participants: In this cross-sectional survey study, 25 COVID-19-related items were incorporated into the online Spanish-language survey of an ongoing study. Data were collected between July 15 and October 9, 2020, in Chicago, Illinois; Los Angeles, California; and Phoenix, Arizona. A nonrandom sample of 379 adult, Spanish-speaking, noncitizen Latinx immigrants (with either documented or undocumented immigration status) were sent surveys. Of those, 336 individuals (88.7% participation rate) returned surveys, and 43 individuals did not. An additional 213 individuals were screened but ineligible. Descriptive statistics were computed, and mean comparisons and bivariate correlations between sociodemographic variables, indices of immigration risk, and COVID-19-related survey items were conducted. Main Outcomes and Measures: Items elicited agreement or disagreement with statements about immigrants' access to COVID-19-related testing and treatment services and the potential immigration ramifications of using these services. Willingness to identify an undocumented person during contact tracing was also assessed. Results: A total of 336 Latinx immigrants completed surveys. The mean (SD) age of participants was 39.7 (8.9) years; 210 participants (62.5%) identified as female, and 216 participants (64.3%) had undocumented immigration status. In total, 89 participants (26.5%) agreed that hospital emergency departments were the only source of COVID-19 testing or treatment for uninsured immigrants, and 106 participants (31.6%) agreed that using public testing and health care services for COVID-19 could jeopardize one's immigration prospects. A total of 96 participants (28.6%) and 114 participants (33.9%), respectively, would not identify an undocumented household member or coworker during contact tracing. Reluctance to identify an undocumented household member or coworker was associated with having had deportation experiences (r = -0.17; 95% CI, -0.06 to 0.27; P = .003) but not with the number of years lived in the US (r = 0.07; 95% CI, -0.16 to 0.17; P = .15) or immigration status (r = 0.03; 95% CI, -0.07 to 0.13; P = .56). Conclusions and Relevance: In this cross-sectional survey study, a substantial number of immigrants endorsed statements about immigrants' restricted access to COVID-19-related testing and treatment services and the potential negative immigration ramifications of using these services. These results suggest that programs for COVID-19-related testing, contact tracing, and vaccine administration that are designed to allay immigration concerns are needed.

Immigration Detention in the United States: Identifying Alternatives That Comply With Human Rights and Advance Public Health.

Under international law, the United States is obligated to uphold noncitizens' fundamental rights, including their rights to health. However, current US immigration laws-and their enforcement-not only fail to fulfill migrants' health rights but actively undermine their realization and worsen the pandemic's spread. Specifically, the US immigration system's reliance on detention, which precludes effective social distancing, increases risks of exposure and infection for detainees, staff, and their broader communities. International agreements clearly state that the prolonged, mandatory, or automatic detention of people solely because of their migration status is a human rights violation on its own. But in the context of COVID-19, the consequences for migrants' right to health are particularly acute. Effective alternatives exist: other countries demonstrate the feasibility of adopting and implementing immigration laws that establish far less restrictive, social services-based approaches to enforcement that respect human rights. To protect public health and realize its global commitments, the United States must shift away from detaining migrants as standard practice and adopt effective, humane alternatives-both amid COVID-19 and permanently.

Cross-border COVID-19 spread amidst malaria re-emergence in Venezuela: a human rights analysis.

BACKGROUND: Since 2016 Venezuela has seen a collapse in its economy and public health infrastructure resulting in a humanitarian crisis and massive outward migration. With the emergence of the novel coronavirus SARS-CoV-2 at the end of 2019, the public health emergency within its borders and in neighboring countries has become more severe and as increasing numbers of Venezuelans migrants return home or get stuck along migratory routes, new risks are emerging in the region. RESULTS: Despite clear state obligations to respect, protect and fulfil the rights to health and related economic, social, civil and political rights of its population, in Venezuela, co-occurring malaria and COVID-19 epidemics are propelled by a lack of public investment in health, weak governance, and violations of human rights, especially for certain underserved populations like indigenous groups. COVID-19 has put increased pressure on Venezuelan and regional actors and healthcare systems, as well as international public health agencies, to deal with a domestic and regional public health emergency. CONCLUSIONS: International aid and cooperation for Venezuela to deal with the re-emergence of malaria and the COVID-19 spread, including lifting US-enforced economic sanctions that limit Venezuela's capacity to deal with this crisis, is critical to protecting rights and health in the country and region.

Modelling the Potential Impact of Social Distancing on the COVID-19 Epidemic in South Africa.

The novel coronavirus (COVID-19) pandemic continues to be a global health problem whose impact has been significantly felt in South Africa. With the global spread increasing and infecting millions, containment efforts by countries have largely focused on lockdowns and social distancing to minimise contact between persons. Social distancing has been touted as the best form of response in managing a rapid increase in the number of infected cases. In this paper, we present a deterministic model to describe the impact of social distancing on the transmission dynamics of COVID-19 in South Africa. The model is fitted to data from March 5 to April 13, 2020, on the cumulative number of infected cases, and a scenario analysis on different levels of social distancing is presented. The model shows that with the levels of social distancing under the initial lockdown level between March 26 and April 13, 2020, there would be a projected continued rise in the number of infected cases. The model also looks at the impact of relaxing the social distancing measures after the initial announcement of the lockdown. It is shown that relaxation of social distancing by 2% can result in a 23% rise in the number of cumulative cases whilst an increase in the level of social distancing by 2% would reduce the number of cumulative cases by about 18%. The model results accurately predicted the number of cases after the initial lockdown level was relaxed towards the end of April 2020. These results have implications on the management and policy direction in the early phase of the epidemic.

Interplay of social distancing and border restrictions for pandemics via the epidemic renormalisation group framework.

One of the biggest threats to humanity are pandemics. In our global society they can rage around the world with an immense toll in terms of human, economic and social impact. Forecasting the spreading of a pandemic is, therefore, paramount in helping governments to enforce a number of social and economic measures, apt at curbing the pandemic and dealing with its aftermath. We demonstrate that the epidemic renormalisation group approach to pandemics provides an effective and simple way to investigate the dynamics of disease transmission and spreading across different regions of the world. The framework also allows for reliable projections on the impact of travel limitations and social distancing measures on global epidemic spread. We test and calibrate it on reported COVID-19 cases while unveiling the mechanism that governs the delay in the relative peaks of newly infected cases among different regions of the globe. We discover that social distancing measures are more effective than travel limitations across borders in delaying the epidemic peak. We further provide the link to compartmental models such as the time-honoured SIR-like models. We also show how to generalise the framework to account for the interactions across several regions of the world, replacing or complementing large scale simulations.

Massive migration promotes the early spread of COVID-19 in China: a study based on a scale-free network.

BACKGROUND: The coronavirus disease 2019 (COVID-19) epidemic met coincidentally with massive migration before Lunar New Year in China in early 2020. This study is to investigate the relationship between the massive migration and the coronavirus disease 2019 (COVID-19) epidemic in China. METHODS: The epidemic data between January 25th and February 15th and migration data between Jan 1st and Jan 24th were collected from the official websites. Using the R package WGCNA, we established a scale-free network of the selected cities. Correlation analysis was applied to describe the correlation between the Spring Migration and COVID-19 epidemic. RESULTS: The epidemic seriousness in Hubei (except the city of Wuhan) was closely correlated with the migration from Wuhan between January 10 and January 24, 2020. The epidemic seriousness in the other provinces, municipalities and autonomous regions was largely affected by the immigration from Wuhan. By establishing a scale-free network of the regions, we divided the regions into two modules. The regions in the brown module consisted of three municipalities, nine provincial capitals and other 12 cities. The COVID-19 epidemics in these regions were more likely to be aggravated by migration. CONCLUSIONS: The migration from Wuhan could partly explain the epidemic seriousness in Hubei Province and other regions. The scale-free network we have established can better evaluate the epidemic. Three municipalities (Beijing, Shanghai and Tianjin), eight provincial capitals (including Nanjing, Changsha et al.) and 12 other cities (including Qingdao, Zhongshan, Shenzhen et al.) were hub cities in the spread of COVID-19 in China.

Mathematical Modeling of COVID-19 Control and Prevention Based on Immigration Population Data in China: Model Development and Validation.

BACKGROUND: At the end of February 2020, the spread of coronavirus disease (COVID-19) in China had drastically slowed and appeared to be under control compared to the peak data in early February of that year. However, the outcomes of COVID-19 control and prevention measures varied between regions (ie, provinces and municipalities) in China; moreover, COVID-19 has become a global pandemic, and the spread of the disease has accelerated in countries outside China. OBJECTIVE: This study aimed to establish valid models to evaluate the effectiveness of COVID-19 control and prevention among various regions in China. These models also targeted regions with control and prevention problems by issuing immediate warnings. METHODS: We built a mathematical model, the Epidemic Risk Time Series Model, and used it to analyze two sets of data, including the daily COVID-19 incidence (ie, newly diagnosed cases) as well as the daily immigration population size. RESULTS: Based on the results of the model evaluation, some regions, such as Shanghai and Zhejiang, were successful in COVID-19 control and prevention, whereas other regions, such as Heilongjiang, yielded poor performance. The evaluation result was highly correlated with the basic reproduction number (R0) value, and the result was evaluated in a timely manner at the beginning of the disease outbreak. CONCLUSIONS: The Epidemic Risk Time Series Model was designed to evaluate the effectiveness of COVID-19 control and prevention in different regions in China based on analysis of immigration population data. Compared to other methods, such as R0, this model enabled more prompt issue of early warnings. This model can be generalized and applied to other countries to evaluate their COVID-19 control and prevention.