Residential mobility predicts COVID-19 and seasonal flu vaccination behaviors in the United States.

Aim: Vaccination is one of the most effective strategies to contain the transmission of infectious diseases; however, people's intentions and behavior for vaccination vary across different regions and countries around the world. It is not clear how socioecological factors such as residential mobility influence people's vaccination behaviors for infectious diseases. Methods: We analyzed public data on residential mobility and vaccination rates for COVID-19 and seasonal flu in the United States and explored how residential mobility in the previous year influenced vaccination rates for COVID-19 and seasonal flu (2011-2018) across 50 states of the US. The data were accessed and analyzed in 2021. Results: Study 1 demonstrated that collective-level residential mobility predicted COVID-19 vaccination rates across the United States (B = -168.162, 95% CI [-307.097, -29.227], adjusted R 2 = 0.091, p = 0.019). Study 2 corroborated this finding by documenting that collective-level residential mobility predicted vaccination rates for seasonal flu from 2011 to 2018 across the United States (B = -0.789, 95% CI = [-1.018, -0.56], adjusted R 2 = 0.222, p < 0.001). The link between residential mobility and vaccination behavior was robust after controlling relevant variables, including collectivism, cultural tightness-looseness, and sociodemographic variables. Conclusions: Our research demonstrated that residential mobility is an important socioecological factor that influences people's vaccination behaviors for COVID-19 and seasonal flu. The results enrich our understanding of the socioecological factors that influence vaccination behaviors and have implications for developing tailored interventions to promote vaccination during pandemics of infectious diseases.

The importance of vaccinated individuals to population-level evolution of pathogens.

Virus evolution shapes the epidemiological patterns of infectious disease, particularly via evasion of population immunity. At the individual level, host immunity itself may drive viral evolution towards antigenic escape. Using compartmental SIR-style models with imperfect vaccination, we allow the probability of immune escape to differ in vaccinated and unvaccinated hosts. As the relative contribution to selection in these different hosts varies, the overall effect of vaccination on the antigenic escape pressure at the population level changes. We find that this relative contribution to escape is important for understanding the effects of vaccination on the escape pressure and we draw out some fairly general patterns. If vaccinated hosts do not contribute much more than unvaccinated hosts to the escape pressure, then increasing vaccination always reduces the overall escape pressure. In contrast, if vaccinated hosts contribute significantly more than unvaccinated hosts to the population level escape pressure, then the escape pressure is maximised for intermediate vaccination levels. Past studies find only that the escape pressure is maximal for intermediate levels with fixed extreme assumptions about this relative contribution. Here we show that this result does not hold across the range of plausible assumptions for the relative contribution to escape from vaccinated and unvaccinated hosts. We also find that these results depend on the vaccine efficacy against transmission, particularly through the partial protection against infection. This work highlights the potential value of understanding better how the contribution to antigenic escape pressure depends on individual host immunity.

Mobility-Guided Estimation of COVID-19 Transmission Rates.

It is of critical importance to estimate changing disease-transmission rates and their dependence on population mobility. A common approach to this problem involves fitting daily transmission rates using a susceptible-exposed-infected-recovered-(SEIR) model (regularizing to avoid overfitting) and then computing the relationship between the estimated transmission rate and mobility. Unfortunately, there are often several very different transmission-rate trajectories that can fit the reported cases well, meaning that the choice of regularization determines the final solution (and thus the mobility-transmission rate relationship) selected by the SEIR model. Moreover, the classical approaches to regularization-penalizing the derivative of the transmission rate trajectory-do not correspond to realistic properties of pandemic spread. Consequently, models fitted using derivative-based regularization are often biased toward underestimating the current transmission rate and future deaths. In this work, we propose mobility-driven regularization of the SEIR transmission rate trajectory. This method rectifies the artificial regularization problem, produces more accurate and unbiased forecasts of future deaths, and estimates a highly interpretable relationship between mobility and the transmission rate. For this analysis, mobility data related to the coronavirus disease 2019 pandemic was collected by Safegraph (San Francisco, California) from major US cities between March and August 2020.

[The impact of a full implementation of the Action Plan on Dependency 2021-2023 on the Spanish economy]. / Impacto económico de una implantación plena del Plan de Choque en Dependencia 2021-2023 en España.

OBJECTIVE: To assess the impact on the economy of the implementation of the Action Plan on Dependency (APD), devised by the Spanish government as a first measure to tackle the major shortages in Dependency Services brought to light pursuant to COVID-19 pandemic. The APD establishes as priority areas the suppression of waiting lists, the improvement of dependency services, with a focus on home-care, and the professionalization and stabilization of employment. METHOD: To achieve this goal, first, an estimate of the increased demand for benefits and services in 2023 has been carried out, supposing that all the priority measures established in the PCD in 2021 are fully implemented. Then, the impact of investment on the economy has been measured using multisector modeling. This analysis considers not only the direct economic impact on the sectors providing services to dependent population, but also the indirect and induced impact on the economy as a whole. RESULTS: The total public investment required for the plan in 2023 will reach 13,962 million Euro, which represents around 1% of the GDP. The impact on the economy in terms of production is expected to reach 41,570 million, while the impact on gross value added will be 21,046 million, together with the creation of nearly 440,000 jobs. CONCLUSIONS: The results reveal that, for the APD to be fully implemented, public funding needs to be increased way beyond the occasional allocation of funds established in the Recovery, Transformation and Resilience Plan. These investments have a positive impact not only on the social and welfare sector, but also on the country's economy.

adaPop: Bayesian inference of dependent population dynamics in coalescent models.

The coalescent is a powerful statistical framework that allows us to infer past population dynamics leveraging the ancestral relationships reconstructed from sampled molecular sequence data. In many biomedical applications, such as in the study of infectious diseases, cell development, and tumorgenesis, several distinct populations share evolutionary history and therefore become dependent. The inference of such dependence is a highly important, yet a challenging problem. With advances in sequencing technologies, we are well positioned to exploit the wealth of high-resolution biological data for tackling this problem. Here, we present adaPop, a probabilistic model to estimate past population dynamics of dependent populations and to quantify their degree of dependence. An essential feature of our approach is the ability to track the time-varying association between the populations while making minimal assumptions on their functional shapes via Markov random field priors. We provide nonparametric estimators, extensions of our base model that integrate multiple data sources, and fast scalable inference algorithms. We test our method using simulated data under various dependent population histories and demonstrate the utility of our model in shedding light on evolutionary histories of different variants of SARS-CoV-2.

The Work, Economic, and Remittance Stress and Distress of the COVID-19 Pandemic Containment Policies: The Case of Venezuelan Migrants in Argentina and Chile.

According to the social stress process model, global crises are macro-level stressors that generate physiological stress and psychological distress. However, existing research has not identified immigrants' COVID-19 containment policy stressors or examined the social stress of sending remittances amid crises. Drawing on in-depth longitudinal interviews with 46 Venezuelan immigrants-half before and half during the pandemic-in Chile and Argentina, we identified the COVID-19 containment policies' stressors. We focused on Venezuelan immigrants because they constitute one of the largest internationally displaced populations, with most migrating within South America. We found that the governmental COVID-19 containment measures in both countries generated four stressors: employment loss, income loss, devaluation of employment status, and inability to send needed remittances. Moreover, sending remittances helped some migrants cope with concerns about loved ones in Venezuela. However, sending remittances became a social stressor when immigrants struggled to simultaneously sustain their livelihoods and send financial support to relatives experiencing hardships in Venezuela. For some immigrants, these adversities generated other stressors (e.g., housing instability) and symptoms of anxiety and depression. Broadly, for immigrants, the stressors of global crises transcend international borders and generate high stress, which strains their psychological well-being.

[Life expectancy and Covid: Setting the record straight]. / Espérance de vie et Covid : remettre les pendules à l'heure - Chroniques génomiques.

Life expectancy (LE) is an objective and highly reliable marker for events affecting demography. Analysing LE changes during the Covid pandemic shows widely different situations in a sample of 29 countries, highlighting comparatively efficient management in most Western European countries, in contrast to catastrophic results in Eastern Europe and in the United States. Loss of LE is also inversely correlated with vaccination uptake, confirming the efficacy of vaccines at the population level.

Quantifying the impact of COVID-19 on immigration in receiving high-income countries.

Previous studies have examined the impact of COVID-19 on mortality and fertility. However, little is known about the effect of the pandemic on constraining international migration. We use Eurostat and national statistics data on immigration and ARIMA time-series models to quantify the impact of COVID-19 on immigration flows in 15 high-income countries by forecasting their counterfactual levels in 2020, assuming no pandemic, and comparing these estimates with observed immigration counts. We then explore potential driving forces, such as stringency measures and increases in unemployment moderating the extent of immigration change. Our results show that immigration declined in all countries, except in Finland. Yet, significant cross-national variations exist. Australia (60%), Spain (45%) and Sweden (36%) display the largest declines, while immigration decreased by between 15% and 30% in seven countries, and by less than 15% in four nations where results were not statistically significant. International travel restrictions, mobility restrictions and stay-at-home requirements exhibit a relatively strong relationship with declines in immigration, although countries with similar levels of stringency witnessed varying levels of immigration decline. Work and school closings and unemployment show no relationship with changes in immigration.

Increasing Representation of Epidemiologists From Around the World in the Society for Epidemiologic Research: The Case of Mexico.

Mexico has a population of 129 million and is considered one of the most unequal countries in the world, suffering from widespread health disparities. There is a pressing need to strengthen epidemiologic capacity in Mexico, to help solve the complex health problems the country faces and to reduce health inequities. However, the representation of Mexican epidemiologists in the largest epidemiologic society in North America is low, despite the short distance to the United States. In this commentary, we discuss the barriers to higher representation of Mexican epidemiologists within the Society for Epidemiologic Research (SER), including language barriers, costs, and regional necessities. We also discuss opportunities to expand Mexican SER representation and collaboration. Overall, we hope that this is a call towards expanding SER global participation and starting a conversation on a common agenda for epidemiologic research.

The global polarisation of remote work.

The Covid-19 pandemic has led to the rise of digitally enabled remote work with consequences for the global division of labour. Remote work could connect labour markets, but it might also increase spatial polarisation. However, our understanding of the geographies of remote work is limited. Specifically, in how far could remote work connect employers and workers in different countries? Does it bring jobs to rural areas because of lower living costs, or does it concentrate in large cities? And how do skill requirements affect competition for employment and wages? We use data from a fully remote labour market-an online labour platform-to show that remote platform work is polarised along three dimensions. First, countries are globally divided: North American, European, and South Asian remote platform workers attract most jobs, while many Global South countries participate only marginally. Secondly, remote jobs are pulled to large cities; rural areas fall behind. Thirdly, remote work is polarised along the skill axis: workers with in-demand skills attract profitable jobs, while others face intense competition and obtain low wages. The findings suggest that agglomerative forces linked to the unequal spatial distribution of skills, human capital, and opportunities shape the global geography of remote work. These forces pull remote work to places with institutions that foster specialisation and complex economic activities, i. e. metropolitan areas focused on information and communication technologies. Locations without access to these enabling institutions-in many cases, rural areas-fall behind. To make remote work an effective tool for economic and rural development, it would need to be complemented by local skill-building, infrastructure investment, and labour market programmes.

[The influence of COVID-19 pandemic on migratory flows towards Italy and on integration paths of immigrants]. / L'influenza della pandemia di COVID-19 sui flussi migratori verso l'Italia e sui percorsi di integrazione degli immigrati.

The text describes the effects of the pandemic on the dynamics of migration flows from abroad to Italy in 2020. The composition and demographic structure of the population of foreign origin are described in order to understand which areas of immigration and integration are most affected by the pandemic.It has been observed that, although the downward trend in migration flows predates the spread of COVID-19, the pandemic has led to a drastic decrease in migration flows towards our country, where a stable foreign presence has settled over the years. The transformation of Italy into a country of stable integration is also witnessed by the growing number of foreigners who become Italian through the acquisition of citizenship, a process that not even the pandemic has slowed down. Instead, the possible effects of the pandemic on economic and labour integration and inclusion, particularly of the many children and young people of foreign origin living in our country, are worrying.

Indonesian Migrant Workers: The Migration Process and Vulnerability to COVID-19.

Indonesia is one of the largest sources of migrant workers in Southeast Asia. Presently, these workers are vulnerable to COVID-19 due to the prolonged migration process, which requires them to relocate from their villages to another country and back to Indonesia on completion of their working contract. Therefore, this study describes and discusses the vulnerability of Indonesian migrant workers (IMWs) to the pandemic at various phases of the migration process. It is related to the implementation and practice of health protocols, ignorance and indifference to the dangers and transmission of the virus, and also to the national vaccination program. The analysis is based on the review of literature studies, such as studies related to the topic, international and national regulations on migrant workers, and official data and statistics published by the Indonesian government. The materials and data were collected from search engines such as Google Search and Google Scholar and also relevant published reports available. Several policies have been implemented by the government of Indonesia and other destination countries where the prospective IMWs intend to work, to protect and prevent the transmission of COVID-19. However, there is still a contagion among IMWs willing to leave abroad and those returning home after completing their employment contract. Therefore, both countries need to be responsible for each migration process, specifically related to providing health protection, increasing awareness of the danger and transmission of the virus, and applying polymerase chain reaction (PCR) tests and COVID-19 vaccination for migrant workers.

Quantifying the importance and location of SARS-CoV-2 transmission events in large metropolitan areas.

Detailed characterization of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission across different settings can help design less disruptive interventions. We used real-time, privacy-enhanced mobility data in the New York City, NY and Seattle, WA metropolitan areas to build a detailed agent-based model of SARS-CoV-2 infection to estimate the where, when, and magnitude of transmission events during the pandemic's first wave. We estimate that only 18% of individuals produce most infections (80%), with about 10% of events that can be considered superspreading events (SSEs). Although mass gatherings present an important risk for SSEs, we estimate that the bulk of transmission occurred in smaller events in settings like workplaces, grocery stores, or food venues. The places most important for transmission change during the pandemic and are different across cities, signaling the large underlying behavioral component underneath them. Our modeling complements case studies and epidemiological data and indicates that real-time tracking of transmission events could help evaluate and define targeted mitigation policies.

U.S. bound journey of migrant peoples InTransit across Dante's Inferno and Purgatory in the Americas.

Rapid rise of population migration is a defining feature of the 21st century due to the impact of climate change, political instability, and socioeconomic downturn. Over the last decade, an increasing number of migrant peoples travel across the Americas to reach the United States seeking asylum or cross the border undocumented in search of economic opportunities. In this journey, migrant people experience violations of their human rights, hunger, illness, violence and have limited access to medical care. In the 'Divine Comedy', the Italian poet Dante Alighieri depicts his allegorical pilgrimage across Hell and Purgatory to reach Paradise. More than 700 years after its publication, Dante's poem speaks to the present time and the perilious journey of migrant peoples to reach safehavens. By exploring the depths and heights of the human condition, Dante's struggles resonate with the multiple barriers and the unfathomable experiences faced by migrant peoples in transit across South, Central, and North America to reach the United States. Ensuring the safety of migrant peoples across the Americas and elsewhere, and attending to their health needs during their migratory paths represent modern priorities to reduce social injustices and achieving health equity.

The COVID-19 Pandemic: Health Impact on Unaccompanied Migrant Children.

From the point of apprehension by U.S. Customs and Border Protection at the U.S.-Mexican border to their reunification with sponsors in U.S. communities, unaccompanied children (UC) face political, social, and economic conditions, heightening their risk for mental and physical health burdens that may be exacerbated during the COVID-19 pandemic. Such risk underscores the importance of social work practice and advocacy for the improved treatment and experiences of UC. This article uses a structural vulnerability conceptual lens to summarize the existing literature regarding UC and argues that UC's liminal immigration status, economic precarity, and lack of healthcare access place this group at high structural vulnerability during the pandemic. Further, this article identifies and describes three contexts of structural vulnerability of UC that are important points of social work intervention: (1) at the border, where migrant children are denied their legal right to seek protection; (2) in detention and shelter facilities; and (3) during reunification with sponsors. This article concludes with important practice and policy opportunities for social workers to pursue to obtain social justice for an important and highly vulnerable migrant child population.

Residential Mobility of a Cohort of Homeless People in Times of Crisis: COVID-19 Pandemic in a European Metropolis.

Most vulnerable individuals are particularly affected by the COVID-19 pandemic. This study takes place in a large city in France. The aim of this study is to describe the mobility of the homeless population at the beginning of the health crisis and to analyze its impact in terms of COVID-19 prevalence. From June to August 2020 and September to December 2020, 1272 homeless people were invited to be tested for SARS-CoV-2 antibodies and virus and complete questionnaires. Our data show that homeless populations are sociologically different depending on where they live. We show that people that were living on the street were most likely to be relocated to emergency shelters than other inhabitants. Some neighborhoods are points of attraction for homeless people in the city while others emptied during the health crisis, which had consequences for virus circulation. People with a greater number of different dwellings reported became more infected. This first study of the mobility and epidemiology of homeless people in the time of the pandemic provides unique information about mobility mapping, sociological factors of this mobility, mobility at different scales, and epidemiological consequences. We suggest that homeless policies need to be radically transformed since the actual model exposes people to infection in emergency.