Immigrants facing Covid 19 containment in France : An ordinary hardship of disaffiliation.
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ABSTRACT:
SUBMITTER: Carillon S
PROVIDER: S-EPMC8351997 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Countries worldwide have confirmed a staggering number of COVID-19 cases, and it is now clear that no country is immune to the SARS-CoV-2 infection. Resource-poor countries with weaker health systems are struggling with epidemics of their own and are now in a more uncertain situation with this rapidly spreading infection. Frontline healthcare workers are succumbing to the infection in their efforts to save lives. There is an urgency to develop treatments for COVID-19, yet there is limited clinical data on the efficacy of potential drug treatments. Countries worldwide implemented a stay-at-home order to "flatten the curve" and relieve the pressure on the health system, but it is uncertain how this will unfold after the economy reopens. Trehalose, a natural glucose disaccharide, is known to impair viral function through the autophagy system. Here, we propose trehalose as a potential preventative treatment for SARS-CoV-2 infection and transmission.
| S-EPMC7358456 | biostudies-literature
Project description:The COVID-19 economic crash is idiosyncratic because of its virtual standstill of economic activity. We therefore ask how individual labor market experiences are related to the development of mental health complaints in the spring of 2020. As clinical data collection was compromised during the lockdowns, standardized surveys of the European labor force provide an opportunity to observe mental health complaints as the crisis unfolded. Data are representative of active members of the labor force of six European nations that contained varying levels of COVID-19 burdens in terms of mortality and lockdown measures. We document a steep occupational prestige level gradient on the probability of facing economic hardship during the lockdowns-looming job loss, income loss, and workload decline-which evidently exacerbate socioeconomic inequalities. Analyses indicate a striking positive relationship between instantaneous economic hardships during the COVID-19 lockdown and expressing feelings of depression and health anxiety. Importantly, the magnitude of the association between such hardships and indicators of mental health deterioration is highly dependent on workers' occupational standing, revealing a second layer of exacerbating inequality.
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Project description:The devastating trail of Covid-19 is characterized by one of the highest mortality-to-infected ratio for a pandemic. Restricted therapeutic and early-stage vaccination still renders social exclusion through lockdown as the key containment mode.To understand the dynamics, we propose PHIRVD, a mechanistic infection propagation model that Machine Learns (Bayesian Markov Chain Monte Carlo) the evolution of six infection stages, namely healthy susceptible (H), predisposed comorbid susceptible (P), infected (I), recovered (R), herd immunized (V) and mortality (D), providing a highly reliable mortality prediction profile for 18 countries at varying stages of lockdown. Training data between 10 February to 29 June 2020, PHIRVD can accurately predict mortality profile up to November 2020, including the second wave kinetics. The model also suggests mortality-to-infection ratio as a more dynamic pandemic descriptor, substituting reproduction number. PHIRVD establishes the importance of early and prolonged but strategic lockdown to contain future relapse, complementing futuristic vaccine impact.
| S-EPMC8173017 | biostudies-literature
Project description:BackgroundWe hypothesize that comprehensive surveillance of COVID-19 in Singapore has facilitated early case detection and prompt contact tracing and, with community-based measures, contained spread. We assessed the effectiveness of containment measures by estimating transmissibility (effective reproduction number, (Equation is included in full-text article.)) over the course of the outbreak.MethodsWe used a Bayesian data augmentation framework to allocate infectors to infectees with no known infectors and determine serial interval distribution parameters via Markov chain Monte Carlo sampling. We fitted a smoothing spline to the number of secondary cases generated by each infector by respective onset dates to estimate (Equation is included in full-text article.)and evaluated increase in mean number of secondary cases per individual for each day's delay in starting isolation or quarantine.ResultsAs of April 1, 2020, 1000 COVID-19 cases were reported in Singapore. We estimated a mean serial interval of 4.6 days [95% credible interval (CI) = 4.2, 5.1] with a SD of 3.5 days (95% CI = 3.1, 4.0). The posterior mean (Equation is included in full-text article.)was below one for most of the time, peaking at 1.1 (95% CI = 1.0, 1.3) on week 9 of 2020 due to a spreading event in one of the clusters. Eight hundred twenty-seven (82.7%) of cases infected less than one person on average. Over an interval of 7 days, the incremental mean number of cases generated per individual for each day's delay in starting isolation or quarantine was 0.03 cases (95% CI = 0.02, 0.05).ConclusionsWe estimate that robust surveillance, active case detection, prompt contact tracing, and quarantine of close contacts kept (Equation is included in full-text article.)below one.
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Project description:Responses to the COVID-19 outbreak resulted in one of the largest short-term decreases in anthropogenic emissions in modern history. To date, there has been no comprehensive assessment of the impact of lockdowns on air quality and human health. Using global satellite observations and ground measurements from 36 countries in Europe, North America, and East Asia, we find that lockdowns led to reductions in NO2 concentrations globally, resulting in ~32,000 avoided premature mortalities, including ~21,000 in China. However, we do not find corresponding reductions in PM2.5 and ozone globally. Using satellite measurements, we show that the disconnect between NO2 and ozone changes stems from local chemical regimes. The COVID-related lockdowns demonstrate the need for targeted air quality policies to reduce the global burden of air pollution, especially related to secondary pollutants.
| S-EPMC8139585 | biostudies-literature
Project description:Across three experiments (N = 1565), we investigated how forecasts about the spread of COVID 19 are impacted by data trends, and whether patterns of misestimation predict adherence to social-distancing guidelines. We also investigated how mode of data presentation influences forecasting of future cases by showing participants data on the number of COVID-19 cases from a 5-week period in either graphical, tabular, or text-only form. We consistently found that people shown tables produced more accurate forecasts compared to people shown line-graphs of the same data; yet people shown line-graphs were more confident in their estimates. These findings suggest that graphs engender false-confidence in the accuracy of forecasts, that people's forecasts of future cases have important implications for their attitudes concerning social distancing, and that tables may be better than graphs for informing the public about the trajectory of COVID-19.
| S-EPMC8960688 | biostudies-literature
Project description:BackgroundThe coronavirus disease 2019 (COVID-19) pandemic has led to a public health crisis. Only limited data are available on the characteristics and outcomes of patients hospitalized for COVID-19 in France.AimsTo investigate the characteristics, cardiovascular complications and outcomes of patients hospitalized for COVID-19 in France.MethodsThe Critical COVID-19 France (CCF) study is a French nationwide study including all consecutive adults with a diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) infection hospitalized in 24 centres between 26 February and 20 April 2020. Patients admitted directly to intensive care were excluded. Clinical, biological and imaging parameters were systematically collected at hospital admission. The primary outcome was in-hospital death.ResultsOf 2878 patients included (mean±SD age 66.6±17.0 years, 57.8% men), 360 (12.5%) died in the hospital setting, of which 7 (20.7%) were transferred to intensive care before death. The majority of patients had at least one (72.6%) or two (41.6%) cardiovascular risk factors, mostly hypertension (50.8%), obesity (30.3%), dyslipidaemia (28.0%) and diabetes (23.7%). In multivariable analysis, older age (hazard ratio [HR] 1.05, 95% confidence interval [CI] 1.03-1.06; P<0.001), male sex (HR 1.69, 95% CI 1.11-2.57; P=0.01), diabetes (HR 1.72, 95% CI 1.12-2.63; P=0.01), chronic kidney failure (HR 1.57, 95% CI 1.02-2.41; P=0.04), elevated troponin (HR 1.66, 95% CI 1.11-2.49; P=0.01), elevated B-type natriuretic peptide or N-terminal pro-B-type natriuretic peptide (HR 1.69, 95% CI 1.0004-2.86; P=0.049) and quick Sequential Organ Failure Assessment score ≥2 (HR 1.71, 95% CI 1.12-2.60; P=0.01) were independently associated with in-hospital death.ConclusionsIn this large nationwide cohort of patients hospitalized for COVID-19 in France, cardiovascular comorbidities and risk factors were associated with a substantial morbi-mortality burden.
| S-EPMC7923854 | biostudies-literature
Project description:Due to its excessive capacity for human-to-human transmission, the 2019 coronavirus disease (COVID-19) has now been declared a global public health emergency. Here we propose a simple model based on exponential infectious growth, but with a time-varying, largely damping, transmission rate. This model provides an excellent fit to the existing data for 46 countries with 10,000+ cases by 16 May 2020, five continents and the entire world. Hence, the model has largely captured the transmission patterns of the COVID-19 outbreak under a variety of intervention and control measures. The damping rate ranged from -0.0228 to 0.1669 d-1 globally (a negative damping rate represents acceleration in spread) and can greatly affect the duration of the outbreak and the eventual number of infections. Our model suggests that it is possible to defeat the COVID-19 pandemic by the end of 2020 through achieving a high damping rate (0.0615 d-1). However, the global damping rate is rather low (0.0504 d-1 before 26 April) and has dropped even further since late April (0.0168 d-1). Easing currently implemented control measures in countries with weak or no damping in transmission could lead to an exponential rebound of COVID-19 spread.
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Project description: Not available
| S-BSST563 | biostudies-other
Project description:Since the appearance of the first case of COVID-19 in Morocco on March, 02, 2020, the cumulative number of reported infectious cases continues to increase and, up to date, the peak-time of infection is not reached yet. In this study, we propose a Susceptible-Asymptomatic-Infectious deterministic model to evaluate the impact of compulsory containment imposed in Morocco on March, 21 on the spread of COVID-19 epidemic across the country. The model takes account of the unconfined individuals that continue to work or to leave their home for urgent needs and the existence of infectious asymptomatic and unreported individuals within susceptible population. Furthermore, the model is able to predict the peak-size, peak-time, final size and epidemic duration according to different rates of containment. Advanced knowledge of these details will be of great interest to establish an optimal plan-of-action to control or eradicate the epidemic. Indeed, mitigating and delaying the epidemic peak allow the official health authorities to anticipate and control the spread of COVID-19. Moreover, prediction of the epidemic duration can help the government to predict the end time of containment to avoid consequent social-economic damages as well. Using our model, the basic reproduction number R 0 is estimated to be 2.9949, with CI(2.6729-3.1485), reflecting a high speed of spread of the epidemic. The model shows that the compulsory containment can be efficient if more than 73% of population are confined. In the absence of other efficient measure of control, even with 90% of containment, the end-time is estimated to happen on July, 4, 2020 with 7558 final cumulative cases. Furthermore, a threshold value of containment rate, below which the epidemic duration is postponed, has been determined. Finally, the sensitivity analysis is performed and showed that the COVID-19 dynamics strongly depends on the asymptomatic duration as well as the contact and containment rates. Our previsions can help the government to adjust its plan-of-action to fight the disease and to face the social-economic shock induced by the containment.
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