This paper brings together evidence from various data sources and the most recent studies to describe what we know so far about the impacts of the COVID‐19 crisis on inequalities across several key domains of life, including employment and ability to earn, family life and health. We show how these new fissures interact with existing inequalities along various key dimensions, including socio‐economic status, education, age, gender, ethnicity and geography. We find that the deep underlying inequalities and policy challenges that we already had are crucial in understanding the complex impacts of the pandemic itself and our response to it, and that the crisis does in itself have the potential to exacerbate some of these pre‐existing inequalities fairly directly. Moreover, it seems likely that the current crisis will leave legacies that will impact inequalities in the long term. These possibilities are not all disequalising, but many are.

source: https://onlinelibrary.wiley.com/doi/full/10.1111/1475-5890.12232

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Angela Rasmussen is as outspoken a scientist as you are likely to find. And this year she spoke out a lot. One of many researchers who became celebrities during the COVID-19 pandemic, the virologist at Georgetown University was quoted in hundreds of articles, appeared as an expert on TV and radio, and took to Twitter to put news about mutations or reinfections into context—and to call radiologist and top U.S. government adviser Scott Atlas a “gaslighting motherf—er.”

But Rasmussen’s messages did not resonate with everyone—even in her own family. Split along political fault lines in the Trump era, some of her relatives no longer speak to one another, she says. When one of her aunts ended up in intensive care with COVID-19 in the summer, Rasmussen only found out because a cousin texted her, worried that others in the aunt’s household did not feel the need to quarantine and get tested. “Guess what: They all had COVID,” Rasmussen says. “But my own family, because of politics, did not reach out to the COVID expert in the family.”

A similar dynamic played out in myriad variations across the United States and the globe as the coronavirus pandemic unfolded. Researchers worked fast and furiously and achieved breakthroughs big and small. But for science’s relations with the wider world, this year marked a breakdown: in communication, in trust, in the sense of a shared reality.

The pandemic was the type of threat researchers had worried and warned about for years: a deadly animal virus, new to humans, and spread in the breath we exhale. “If you had asked me 5 years ago what would keep me most awake at night, this almost defines it perfectly,” says Jeremy Farrar, who heads the Wellcome Trust.

And this virus had help. A “syndemic” is the intersection of two epidemics—two diseases ravaging a population at the same time, exacerbating each other. HIV weakens the immune system, for instance, which makes people more likely to develop tuberculosis. The world witnessed something similar this year. We live in an ecosystem that allows viruses to cross from wildlife to humans more often and spread farther and faster than ever before—that gave us SARS-CoV-2. But the virus emerged in an information ecosystem that helps misinformation and lies spread faster than scientific evidence, weakening our ability to respond to new threats. That made the pandemic far worse.

NEWS OF A CLUSTER of pneumonia cases in China emerged on the eve of the new year. Ten days later, a full genome sequence of the virus was posted online. A diagnostic test was ready a few days later. A team of experts sent to China by the World Health Organization (WHO) in February came home with a surprising finding: China had done the impossible—halting the outbreak of a respiratory pathogen—by locking down its citizens. Science was working faster than ever before.

But the virus was faster. Carried around the world by travelers, it spread surreptitiously at first but quickly sickened and killed patients at a rate that threatened to overwhelm health care systems. As scientists, doctors, and nurses worked around the clock, countries on all continents tried to follow the Chinese example, depriving the coronavirus conflagration of the oxygen it needed: human contact.

“Science is our exit strategy,” Farrar told Science in those dark days of the first peak. And in many ways, science delivered. It launched an all-out effort to develop animal models and diagnostics, chart the pathogen’s path of destruction through the human body, find drugs, and develop vaccines. “We took out all our fancy tools and applied them to this virus,” says virologist Florian Krammer of the Icahn School of Medicine at Mount Sinai.

With acceleration came accidents. Preprint servers like bioRxiv and medRxiv became hubs for sharing information quickly, but they occasionally spread misinformation just as fast. A paper suggesting SARS-CoV-2 was an engineered virus, for instance, found a platform it did not deserve—and widespread media coverage. Peer-reviewed journals slipped up as well. The Lancet and The New England Journal of Medicine fell for fraudulent papers purportedly containing data from hundreds of hospitals around the world, collected by a tiny company few had ever heard of. Many research results, including the stunning vaccine data of the past few weeks, were communicated directly to journalists, bypassing any scientific scrutiny. “It is a pandemic by press release,” says WHO epidemiologist Maria Van Kerkhove.

Still, for those willing to learn, this year presented an unprecedented opportunity to see science at work—to hear experts explain viruses and vaccines, see them critique each other’s papers on Twitter, and understand how in science, uncertainty and self-correction are strengths rather than flaws. The process of science was rarely as visible as this year. It was like watching open-heart surgery live on TV: messy but vital and riveting.

BUT WHEN IT CAME to countering the other plague, that of disinformation and deception, the toolbox was empty. Just as videoconferencing and online shopping found massive new markets as stores, schools, and offices closed, so polarization, politicization, and a media ecosystem that elevates simple lies over complex truths were ready to take advantage of an unsettled public struggling with uncertainty. Even as hundreds of thousands died, many people downplayed the problem or refused to acknowledge its existence, no matter what the experts said. “It’s a little like watching a zombie movie in which half of the people can’t see the zombies and keep demanding to know what the fuss is about,” says epidemiologist William Hanage of the Harvard T.H. Chan School of Public Health. Politicians and some physicians began to promote drugs without evidence. The White House flouted epidemiologists’ advice about face masks and SARS-CoV-2’s propensity to spread in clusters indoors—and itself became the site of a superspreading event.

Scientists, not the virus, became the enemy for some. Top virologists needed police protection. Many other researchers reported threats and harassment, with women often subjected to the worst of it. “I used to think it only took brains, but now you need to be brave and courageous as well to do science,” Mike Ryan, executive director of WHO’s Health Emergencies Programme, said during a November press conference. No wonder many scientists did not speak out.

Conspiracy theories flourished. People burned down cellphone towers, blaming them for the pandemic. Others tried to film in hospitals they said were empty. It was all planned. It was all fake. Or maybe it was both.

Scientists themselves contributed to the confusion. French microbiologist Didier Raoult touted hydroxychloroquine based on a study with few participants and no real control group. Stanford University statistician John Ioannidis, once described as “the scourge of sloppy science,” was accused of being less than rigorous himself in studies that suggested SARS-CoV-2 was not all that deadly. Three scientists with high-profile affiliations published the Great Barrington Declaration, which advocated for shielding the most vulnerable in society while letting the virus infect everyone else to build up herd immunity, a strategy most epidemiologists considered dangerously misguided.

Such episodes played into the desire for easy solutions: a cure-all pill, a disease that was less dangerous, a quick return to life before the pandemic. Some scientists may have been driven by a healthy distrust of accepted wisdom or a contrarian spirit, but the effect was reminiscent of industry’s playbook in the fights over tobacco and climate change: Create just enough confusion about the evidence to allow people to carry on as before.

Science worked best when many researchers joined hands. Hundreds of small drug studies didn’t result in clear answers, but two big trials—the United Kingdom’s Recovery and WHO’s Solidarity—convincingly relegated hydroxychloroquine and other drugs to the dustbin while showing that dexamethasone, a cheap steroid, cut deaths by one-third. Thousands of scientists signed the John Snow Memorandum, a riposte to the Great Barrington Declaration that declared the herd immunity strategy “a dangerous fallacy.” The vaccines, too, were the product of thousands of scientists and doctors working together.

In the end, science may save the day—we’ll find out in the months and years ahead whether vaccines can defeat the virus. But the pandemic was a stress test for the scientific enterprise. Some cracks that had long been there, small enough to be ignored by many, widened into deep fissures.

FARRAR IS HOPEFUL that humanity will come away wiser after staring into the abyss. “I think we will look back after the horror of this and say, humanity is incredibly vulnerable,” he says. “This will inspire a whole generation to come into science.” Evidence, he says, will carry the day.

But a new crisis is coming that scientists have warned and worried about for years—one that is slower, yet even more menacing, and far easier to ignore or deny. “You know the biggest deal of this year?” Hanage asks. “When it comes to climate change we are totally screwed.”

There will be no easy scientific fix for global warming. And if this pandemic has shown anything, it is that evidence without action is like a vaccine in a freezer: It is all potential. Scientists knew deaths would follow cases as sure as thunder follows lightning. And yet politicians and ordinary citizens alike found it hard to act until morgues were overflowing. Some refused to acknowledge reality even then. How much harder will it be to act on climate change?

The upshot of this year cannot just be more research on unknown pathogens lurking in nature. It has to be an effort to revive and strengthen the bonds between science and the rest of society.

SARS-CoV-2 did not just disrupt the world. It shattered the fragile mirror we thought of as reality. Without it, we will be defenseless in the next crisis.

source: https://vis.sciencemag.org/breakthrough2020/#/finalists/a-divisive-disease

Luca Ferretti, Senior Researcher in Statistical Genetics and Pathogen Dynamics, University of Oxford

Luca Ferretti presenta il suo nuovo paper “Early Analysis of a potential link between viral load and the N501Y mutation in the SARS-COV-2 spike protein” — come è nato, come si è sviluppato, quali risultati ha raggiunto — con un approfondimento sulla nuova “variante UK” che è molto diversa dalle altre mutazioni già individuate.

Quali sono le caratteristiche di questa mutazione? Quanto è pericolosa? Un aumento della trasmissibilità del 56% come impatterà sulla pandemia? E sui vaccini? Se avessimo eradicato il virus, questo non avrebbe avuto spazio per mutare. Quanto sono state importanti le diverse strategie di gestione della pandemia? 

Qui i link ai papers (alcuni in fase pre-print) che son stati citati:

1) “Early Analysis of a potential link between viral load and the N501Y mutation in the SARS-COV-2 spike protein” https://t.co/UvNhqdPvkN?amp=1 con Luca Ferretti

2) “Preliminary genomic characterisation of an emergent SARS-CoV-2 lineage in the UK defined by a novel set of spike mutations” https://virological.org/t/preliminary… con Andrew Rambaut

3) “Estimated transmissibility and severity of novel SARS-CoV-2 Variant of Concern 202012/01 in England” https://cmmid.github.io/topics/covid1… con Nick Davies

4) BirminghamUniversity Turnkey “S-variant SARS-CoV-2 is associated with significantly higher viral loads in samples tested by ThermoFisher TaqPath RT-PCR” https://www.medrxiv.org/content/10.11…

5) Gupta Lab Neutralising “Antibodies drive Spike mediated SARS-CoV-2 evasion” https://www.medrxiv.org/content/10.11…

6) Nervtag slides “New evidence on VUI-202012/01 and review of the public health risk assessment” https://khub.net/documents/135939561/…

7) Nextstrain “Genomic epidemiology of novel coronavirus – Europe-focused subsampling” https://nextstrain.org/ncov/ e, in particolare, la nuova variante scoperta in UK si vede chiaramente da questa visualizzazione: https://nextstrain.org/ncov/europe?f_… 

Authors: Laetitia Gauvin, Paolo Bajardi, Emanuele Pepe, Brennan Lake, Filippo Privitera and Michele Tizzoni

In the effort of fighting the COVID-19 pandemic, several governments world-wide have imposed unprecedented mobility restrictions and social distancing policies, as these – combined with contact tracing and isolation of cases – represent the most effective strategy to slow down the spread of SARS-CoV-2. Italy has been the first EU country to adopt such interventions, by imposing a national lockdown on March 12 that was lifted about 2 months later, once the epidemic curve had been suppressed. Here, we extensively investigate the socioeconomic determinants of the responses to mobility restrictions imposed in Italy during the full course of the first wave of the COVID-19 epidemic, from February until June 2020, through the analysis of human mobility patterns derived from anonymized and aggregated mobile phone data. To this aim, we monitored the timelines of mobility responses using several mobility metrics during 3 phases of the COVID-19 outbreak in Italy both at the province level and at the district level in 3 major cities. Through statistical modeling, we identified the association between mobility responses and several demographic, economic, and epidemiological covariates, across different spatial scales. The results show that behavioral responses were associated to varying socioeconomic factors in the different phases of the pandemic management cycle, and across different geographic scales, highlighting the complex landscape of the determinants of behavioral responses to non-pharmaceutical interventions.

Authors: Alberto Aleta, David Martìn-Corral, Michiel A. Bakker, Ana Pastore y Piontti, Marco Ajelli, Maria Litvinova, Matteo Chinazzi, Natalie E. Dean, M. Elizabeth Halloran, Ira M. Longini, Jr., Alex Pentland, Alessandro Vespignani, Yamir Moreno, Esteban Moro.

Detailed characterizations of SARS-CoV-2 transmission risk across different social settings
can inform the design of targeted and less disruptive non-pharmaceutical interventions (NPI),
yet these data have been lacking. Here we integrate real-time, anonymous and privacy enhanced geolocalized mobility data with census and demographic data in the New York
City and Seattle metropolitan areas to build a detailed agent-based model of SARS-CoV2 transmission. The aim is to estimate where, when, and how many transmission events happened in those urban areas during the first wave of the pandemic. We estimate that most
infections (80%) are produced by a small number of people (27%), and that about 10% of
events can be considered super-spreading events (SSEs), i.e. generating more than eight secondary infections. Although mass gatherings present an important risk for future SSEs, we
find that the bulk of transmission in the first wave occurred in smaller events at settings like workplaces, grocery stores, or food venues. We also observe that places where the majority
of transmission and SSEs happened changed during the pandemic and are different across
cities, a signal of the large underlying behavioral component underneath them. Our results demonstrate that constant real-time tracking of transmission events is needed to create,
evaluate, and refine more effective and localized measures to contain the pandemic.

https://www.medrxiv.org/content/10.1101/2020.12.15.20248273v1.full.pdf

Sars-Cov-2: i Vaccini

I vaccini per Sars-Cov-2 entreranno nella storia della medicina perché ottenuti in un periodo inusualmente breve (circa un anno, mentre la media è di 10anni) e con una tecnologia nuova (mRna) si è ottenuto un risultato strabiliante. Il primo vaccino Pfizer-BioNTech è stato approvato per emergency user e in UK – in questo momento – è stata inoculata la prima dose a circa 140.000 persone. I due vaccini Pfizer BioNtech e Moderna nei due trial hanno mostrato risultati quasi equivalenti nella riduzione degli effetti della malattia sulle persone a rischio, ovvero 95% di efficacia vaccinale nella riduzione delle conseguenze della malattia. Il secondary point richiesto, ovvero la riduzione della trasmissione del virus, non ha ancora abbastanza risultati a supporto. I vaccini sono stati ricercati per ridurre la severità della malattia e i risultati di Moderna e Pfizer-BioNTech su questo punto sono stati ritenuti efficaci. Gli scienziati dell’EMA e della FDA stanno approfondendo tutte le verifiche per approvarne la distribuzione per tutta la popolazione. Il dibattito pubblico si arricchisce ora di nuovi temi sulla sicurezza dei vaccini, sulle differenze tra i diversi vaccini ora in approvazione, sugli effetti a lungo termine, sulla pianificazione e i tempi entro i quali le popolazioni saranno sottoposte alla vaccinazione. Aureliano Stingi e Domenico Somma ci spiegano come interpretare le notizie sull’approvazione dei vaccini, il significato delle reazioni allergiche, la sicurezza dei vaccini e le informazioni su mRna e la discussione sull’obbligo di vaccinazione per la popolazione. E’ la prima volta che tutto il mondo ha gli occhi puntati sull’intero processo di nascita e sviluppo di un vaccino, un argomento che è necessariamente entrato anche nel dibattito pubblico.

@AurelianoStingi@Doom3Gloom ne discutono con @ThManfredi e @gianlucac1

The Covid-19 epidemic had an impact far from geographically homogeneous, even within most infected zones. We analyse the correlates of this heterogeneity at a very granular level, relying on a novel dataset with wide information on Italian municipalities. We first describe Covid-19 impact heterogeneities selecting a number of relevant covariates.

We find that higher mortality rates across municipalities are associated with lower income, lower education, higher share of workers in industrial sector, lower household dimension, lower service and trade employment. This suggests that these areas are mostly peripheral ones. All our covariates are severely multicollinear, cautioning on causal interpretation of the results.

As a second exercise, we use a machine learning methodology to predict areas with a high risk of Covid-related deaths independently from spatial proximity to infection. We believe that our findings might be useful to predict which areas are at higher risk given where the first outbreak occurs.

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Immigrants can expand labor supply and compete for jobs with native-born workers. But immigrants may also start new firms, expanding labor demand. This paper uses U.S. administrative data and other data sources to study the role of immigrants in entrepreneurship.

We ask how often immigrants start companies, how many jobs these firms create, and how firms founded by native-born individuals compare. A simple model provides a measurement framework for addressing the dual roles of immigrants as founders and workers.

The findings suggest that immigrants act more as “job creators” than “job takers” and play outsized roles in U.S. highgrowth entrepreneurship.

Tribune. En Angleterre et aux Etats-Unis, on parle sans cesse du besoin de «relancer l’économie», de «faire repartir notre économie pour qu’elle puisse tourner à plein», entre autres expressions du même acabit. Ces phrases donnent l’impression que l’économie serait une espèce d’énorme turbine vrombissante qu’on a temporairement mise à l’arrêt et qui doit reprendre du service le plus vite possible. On nous incite souvent à penser l’économie en ces termes, alors même qu’on nous disait, il n’y a pas si longtemps, que la machine tournait toute seule. Elle n’avait hélas pas de bouton «pause» ou «marche-arrêt», et si elle en avait un, mieux valait en tout cas ne pas appuyer sur «arrêt», car les conséquences auraient été immédiates et désastreuses. Mais voici que nous découvrons, étonnés, que ce bouton existe bel et bien. On peut être néanmoins tenté de pousser la réflexion un peu plus loin : que désignons-nous au juste quand nous parlons d’«économie» ? Au fond, si une économie est le système permettant de faire vivre les gens, de les nourrir et de les habiller, de les loger et même de les divertir, alors, pour la plupart d’entre nous, l’économie tournait à merveille pendant le confinement. Mais si l’économie n’est précisément pas l’approvisionnement en biens et services de première nécessité, qu’est-elle donc ? https://www.liberation.fr/debats/2020/05/27/vers-une-bullshit-economy_1789579

Background: As the coronavirus pandemic rages on, 692,000 (August 7, 2020) human lives and counting have been lost worldwide to COVID-19. Understanding the relationship between short- and long-term exposure to air pollution and adverse COVID-19 health outcomes is crucial for developing solutions to this global crisis.

Objectives: To conduct a scoping review of epidemiologic research on the link between short- and long-term exposure to air pollution and COVID-19 health outcomes.

Method: We searched PubMed, Web of Science, Embase, Cochrane, MedRxiv, and BioRxiv for preliminary epidemiological studies of the association between air pollution and COVID-19 health outcomes. 28 papers were finally selected after applying our inclusion/exclusion criteria; we categorized these studies as long-term studies, short-term time-series studies, or short-term cross-sectional studies. One study included both short-term time-series and a cross-sectional study design.

Results: 27 studies of the 28 reported evidence of statistically significant positive associations between air pollutant exposure and adverse COVID-19 health outcomes; 11 of 12 long-term studies and all 16 short-term studies reported statistically significant positive associations. The 28 identified studies included various confounders, spatial and temporal resolutions of pollution concentrations, and COVID-19 health outcomes.

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To Kamala and Maya

The aim of this work is twofold. It seeks to provide a systematic interpretation and critical assessment of the main, contemporary lines of approach to a theory of accumulation and income distribution in the capitalist economy. At the same time, an attempt is made to develop an analytic reconstruction of some of the substantive problems and issues that arise in such a theory.

A basic reference point for the discussion is the system of ideas developed by the  English Classical economists and by Marx. This is a necessary point of departure, since it is in these ideas, and especially in  the work of Marx, that some of the main conceptual foundations for theoretical analysis of accumulation and distribution in the capitalist economy were laid. From this vantage point it is possible to gain both a critical understanding of contemporary approaches to that analysis and a conceptual framework for developing a more adequate theory.

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