2009.09097

The ever-increasing competitiveness in the academic publishing market incentivizes journal editors to pursue higher impact factors. This translates into journals becoming more selective, and, ultimately, into higher publication standards. However, the fixation on higher impact factors leads some journals to artificially boost impact factors through the coordinated effort of a "citation cartel" of journals in addition to self-citations. "Citation cartel" behavior has become increasingly common in recent years, with several instances of cartels being reported. Here, we propose an algorithm---named CIDRE---to detect anomalous groups of journals that exchange citations at excessively high rates when compared against a null model that accounts for scientific communities and journal size. CIDRE detects more than half of the journals suspended by Thomson Reuters due to cartel-like behavior in the year of suspension or in advance. Furthermore, CIDRE detects a large number of additional anomalous groups, which reveal a variety of mechanisms that may help to detect citation cartels at their onset. We describe a number of such examples in detail and discuss the implications of our findings with regard to the current academic climate.

Detecting citation cartels in journal networks

7 #COVID19 deaths on my reservation as of today. Nobody knows how many have the virus bc the numbers are different depending on who you ask. Tribal, IHS, state & federal data systems are spitting out different damn #s. This is ridiculous! Frustrated & angry af. We are dying!

The rush to immunise populations against Covid-19 could lead to the rollout of a vaccine that is not very effective and risk worsening the pandemic, leading scientists have said. Politicians and commercial companies are competing to be the first to license a vaccine, but experts say the world would be better served by waiting until comprehensive results showed at least 30-50% effectiveness.

Covid vaccine rush could make pandemic worse, say scientists

10.1073/pnas.2010398117

Many countries have passed their first COVID-19 epidemic peak. Traditional epidemiological models describe this as a result of nonpharmaceutical interventions pushing the growth rate below the recovery rate. In this phase of the pandemic many countries showed an almost linear growth of confirmed cases for extended time periods. This new containment regime is hard to explain by traditional models where either infection numbers grow explosively until herd immunity is reached or the epidemic is completely suppressed. Here we offer an explanation of this puzzling observation based on the structure of contact networks. We show that for any given transmission rate there exists a critical number of social contacts, DcDc<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mrow><mml:mi>D</mml:mi></mml:mrow><mml:mrow><mml:mi>c</mml:mi></mml:mrow></mml:msub></mml:math>, below which linear growth and low infection prevalence must occur. Above DcDc<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mrow><mml:mi>D</mml:mi></mml:mrow><mml:mrow><mml:mi>c</mml:mi></mml:mrow></mml:msub></mml:math> traditional epidemiological dynamics take place, e.g., as in susceptible–infected–recovered (SIR) models. When calibrating our model to empirical estimates of the transmission rate and the number of days being contagious, we find Dc∼7.2Dc∼7.2<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mrow><mml:mi>D</mml:mi></mml:mrow><mml:mrow><mml:mi>c</mml:mi></mml:mrow></mml:msub><mml:mo>∼</mml:mo><mml:mn>7.2</mml:mn></mml:math>. Assuming realistic contact networks with a degree of about 5, and assuming that lockdown measures would reduce that to household size (about 2.5), we reproduce actual infection curves with remarkable precision, without fitting or fine-tuning of parameters. In particular, we compare the United States and Austria, as examples for one country that initially did not impose measures and one that responded with a severe lockdown early on. Our findings question the applicability of standard compartmental models to describe the COVID-19 containment phase. The probability to observe linear growth in these is practically zero.

A network-based explanation of why most COVID-19 infection curves are linear

10.3386/w26882

We extend the canonical epidemiology model to study the interaction between economic decisions and epidemics. Our model implies that people’s decision to cut back on consumption and work reduces the severity of the epidemic, as measured by total deaths. These decisions exacerbate the size of the recession caused by the epidemic. The competitive equilibrium is not socially optimal because infected people do not fully internalize the effect of their economic decisions on the spread of the virus. In our benchmark model, the best simple containment policy increases the severity of the recession but saves roughly half a million lives in the U.S.

The Macroeconomics of Epidemics

Impact of delays on effectiveness of contact tracing strategies for COVID-19: a modelling study

10.1016/S2468-2667(20)30157-2

BackgroundIn countries with declining numbers of confirmed cases of COVID-19, lockdown measures are gradually being lifted. However, even if most physical distancing measures are continued, other public health measures will be needed to control the epidemic. Contact tracing via conventional methods or mobile app technology is central to control strategies during de-escalation of physical distancing. We aimed to identify key factors for a contact tracing strategy to be successful.MethodsWe evaluated the impact of timeliness and completeness in various steps of a contact tracing strategy using a stochastic mathematical model with explicit time delays between time of infection and symptom onset, and between symptom onset, diagnosis by testing, and isolation (testing delay). The model also includes tracing of close contacts (eg, household members) and casual contacts, followed by testing regardless of symptoms and isolation if testing positive, with different tracing delays and coverages. We computed effective reproduction numbers of a contact tracing strategy (RCTS) for a population with physical distancing measures and various scenarios for isolation of index cases and tracing and quarantine of their contacts.FindingsFor the most optimistic scenario (testing and tracing delays of 0 days and tracing coverage of 100%), and assuming that around 40% of transmissions occur before symptom onset, the model predicts that the estimated effective reproduction number of 1·2 (with physical distancing only) will be reduced to 0·8 (95% CI 0·7–0·9) by adding contact tracing. The model also shows that a similar reduction can be achieved when testing and tracing coverage is reduced to 80% (RCTS 0·8, 95% CI 0·7–1·0). A testing delay of more than 1 day requires the tracing delay to be at most 1 day or tracing coverage to be at least 80% to keep RCTS below 1. With a testing delay of 3 days or longer, even the most efficient strategy cannot reach RCTS values below 1. The effect of minimising tracing delay (eg, with app-based technology) declines with decreasing coverage of app use, but app-based tracing alone remains more effective than conventional tracing alone even with 20% coverage, reducing the reproduction number by 17·6% compared with 2·5%. The proportion of onward transmissions per index case that can be prevented depends on testing and tracing delays, and given a 0-day tracing delay, ranges from up to 79·9% with a 0-day testing delay to 41·8% with a 3-day testing delay and 4·9% with a 7-day testing delay.InterpretationIn our model, minimising testing delay had the largest impact on reducing onward transmissions. Optimising testing and tracing coverage and minimising tracing delays, for instance with app-based technology, further enhanced contact tracing effectiveness, with the potential to prevent up to 80% of all transmissions. Access to testing should therefore be optimised, and mobile app technology might reduce delays in the contact tracing process and optimise contact tracing coverage.

10.7326/M20-5126

On my second-to-last day in the hospital, I felt a sudden, unexplainable fatigue—nothing else. I finished work, asked the resident to page me with updates, and left early. The next morning, I awoke with a fever, sweats, headache, and mild cough. Not surprisingly, I tested positive for COVID-19. At the testing site, I became confused when someone asked for my address and insurance. Upon my return home, I isolated myself from my family in my bedroom. That was the last time I left my bedroom for 4 weeks.

My COVID-19

10.1016/S2214-109X(20)30384-3

The public health response to COVID-19 in India has been highly centralised, resulting in a homogenous strategy applied across a sixth of the world's population. India was placed in a nationwide lockdown on March 24, 2020, with restrictions being relaxed in three phases since June. In May 2020, the prime minister called upon the Indian people to be self-reliant. We discuss here opportunities to modify several aspects of the medical response to echo this sentiment.

COVID-19 care in India: the course to self-reliance

10.3386/w27020

The Covid-19 pandemic has motivated a myriad of studies and proposals on how economic policy should respond to this colossal shock. But in this debate it is seldom recognized that the health shock is not entirely exogenous. Its magnitude and dynamics themselves depend on economic policies, and the explicit or implicit incentives those policies provide. To illuminate the feedback loops between medical and economic factors we develop a minimal economic model of pandemics. In the model, as in reality, individual decisions to comply (or not) with virus-related public health directives depend on economic variables and incentives, which themselves respond to current economic policy and expectations of future policies. The analysis yields several practical lessons: because policies affect the speed of virus transmission via incentives, public health measures and economic policies can complement each other, reducing the cost of attaining desired social goals; expectations of expansionary macroeconomic policies during the recovery phase can help reduce the speed of infection, and hence the size of the health shock; the credibility of announced policies is key to rule out both self-fulfilling pessimistic expectations and time inconsistency problems. The analysis also yields a critique of the current use of SIR models for policy evaluation, in the spirit of Lucas (1983).

Economic Policy Incentives to Preserve Lives and Livelihoods

We use the synthetic control method to analyze the effect of face masks on the spread of Covid-19 in Germany. Our identification approach exploits regional variation in the point in time when face masks became compulsory. Depending on the region we analyse, we find that face masks reduced the cumulative number of registered Covid-19 cases between 2.3% and 13% over a period of 10 days after they became compulsory. Assessing the credibility of the various estimates, we conclude that face masks reduce the daily growth rate of reported infections by around 40%.

Face Masks Considerably Reduce COVID-19 Cases in Germany: A Synthetic Control Method Approach

10.1016/S2468-2667(20)30117-1

BackgroundAlthough mortality due to COVID-19 is, for the most part, robustly tracked, its indirect effect at the population level through lockdown, lifestyle changes, and reorganisation of health-care systems has not been evaluated. We aimed to assess the incidence and outcomes of out-of-hospital cardiac arrest (OHCA) in an urban region during the pandemic, compared with non-pandemic periods.MethodsWe did a population-based, observational study using data for non-traumatic OHCA (N=30 768), systematically collected since May 15, 2011, in Paris and its suburbs, France, using the Paris Fire Brigade database, together with in-hospital data. We evaluated OHCA incidence and outcomes over a 6-week period during the pandemic in adult inhabitants of the study area.FindingsComparing the 521 OHCAs of the pandemic period (March 16 to April 26, 2020) to the mean of the 3052 total of the same weeks in the non-pandemic period (weeks 12–17, 2012–19), the maximum weekly OHCA incidence increased from 13·42 (95% CI 12·77–14·07) to 26·64 (25·72–27·53) per million inhabitants (p<0·0001), before returning to normal in the final weeks of the pandemic period. Although patient demographics did not change substantially during the pandemic compared with the non-pandemic period (mean age 69·7 years [SD 17] vs 68·5 [18], 334 males [64·4%] vs 1826 [59·9%]), there was a higher rate of OHCA at home (460 [90·2%] vs 2336 [76·8%]; p<0·0001), less bystander cardiopulmonary resuscitation (239 [47·8%] vs 1165 [63·9%]; p<0·0001) and shockable rhythm (46 [9·2%] vs 472 [19·1%]; p<0·0001), and longer delays to intervention (median 10·4 min [IQR 8·4–13·8] vs 9·4 min [7·9–12·6]; p<0·0001). The proportion of patients who had an OHCA and were admitted alive decreased from 22·8% to 12·8% (p<0·0001) in the pandemic period. After adjustment for potential confounders, the pandemic period remained significantly associated with lower survival rate at hospital admission (odds ratio 0·36, 95% CI 0·24–0·52; p<0·0001). COVID-19 infection, confirmed or suspected, accounted for approximately a third of the increase in OHCA incidence during the pandemic.InterpretationA transient two-times increase in OHCA incidence, coupled with a reduction in survival, was observed during the specified time period of the pandemic when compared with the equivalent time period in previous years with no pandemic. Although this result might be partly related to COVID-19 infections, indirect effects associated with lockdown and adjustment of health-care services to the pandemic are probable. Therefore, these factors should be taken into account when considering mortality data and public health strategies.

Out-of-hospital cardiac arrest during the COVID-19 pandemic in Paris, France: a population-based, observational study

After a woman with the coronavirus visited a Starbucks cafe north of Seoul this month, more than two dozen patrons tested positive days later. But the four face mask-wearing employees escaped infection.The Aug. 8 outbreak in the South Korean city of Paju is another example of how rapidly the SARS-CoV-2 virus can spread in confined, indoor spaces -- as well as ways to minimize transmission. With health authorities around the world still debating the evidence around face masks, the 27-person cluster linked to the air-conditioned coffee outlet adds more support for their mandatory use to help limit the spread of the Covid-19-causing virus.

Starbucks Cafe’s Covid Outbreak Spared Employees Who Wore Masks

10.3386/w27167

Uncertainty rises in recessions and falls in booms. But what is the causal relationship? We construct cross-country panel data on stock market levels and volatility and use natural disasters, terrorist attacks, and political shocks as instruments in regressions and VAR estimations. We find that increased volatility robustly lowers growth. We also structurally estimate a heterogeneous firms business cycle model with uncertainty and disasters and use this to analyze our empirical results. Finally, using our VAR results we estimate COVID-19 will reduce US GDP by 9% in 2020 based on the initial stock market returns and volatility response.

Using Disasters to Estimate the Impact of Uncertainty

The impact of coronavirus on the UK’s 400,000 care home residents has been devastating. In these fragile communities, life is very different for residents and carers. Masks are the norm, visitors banned, and gone are hairdressers, sing-songs, and communal meals.

Care homes or prisons? The coronavirus dilemma that won’t go away.

doi.org/10.1038/s41467-020-18050-2

The pressing need to restart socioeconomic activities locked-down to control the spread of SARS-CoV-2 in Italy must be coupled with effective methodologies to selectively relax containment measures. Here we employ a spatially explicit model, properly attentive to the role of inapparent infections, capable of: estimating the expected unfolding of the outbreak under continuous lockdown (baseline trajectory); assessing deviations from the baseline, should lockdown relaxations result in increased disease transmission; calculating the isolation effort required to prevent a resurgence of the outbreak. A 40% increase in effective transmission would yield a rebound of infections. A control effort capable of isolating daily  ~5.5% of the exposed and highly infectious individuals proves necessary to maintain the epidemic curve onto the decreasing baseline trajectory. We finally provide an ex-post assessment based on the epidemiological data that became available after the initial analysis and estimate the actual disease transmission that occurred after weakening the lockdown.

The geography of COVID-19 spread in Italy and implications for the relaxation of confinement measures

2008.10959

Due to the COVID-19 pandemic, many governments imposed lockdowns that forced hundred millions to stay at home. As a result of these measures, Internet traffic of residential users increased, in particular, for remote working, entertainment, commerce, and education. In turn, traffic demands in the Internet core shifted as well. In this paper, using data from a diverse set of vantage points (one ISP, three IXPs, and one metropolitan educational network), we study the effect of these lockdowns on traffic shifts. We find that the traffic volume increased by 15-20% almost within a week--while overall still modest, this constitutes a large increase within this short time period. The Internet infrastruct ure is able to handle this increase, as most traffic shifts occur outside of traditional peak hours. When looking at traffic sources, we find that while hypergiants still contribute a significant fraction of traffic, we see a higher increase in traffic of non-hypergiants. We observe traffic increases in applications such as Web conferencing, VPN, gaming, messaging that people use when at home. While many networks see increased traffic demands, in particular, residential users, others see major decreases, e.g., the in/out ratio of the educational network switched.

The Lockdown Effect: Implications of the COVID-19 Pandemic on Internet Traffic

10.1111/ejed.12417

Science is undergoing a crisis that has been referred to, since the early 21st century, as a crisis of confidence and a crisis of replication. This article reviews questions pertaining to the replication crisis; questions addressing the quality and credibility of the sciences; specifically, questions linked to what are known as false positives, null results, and questionable research practices (p‐hacking, harking, cherry‐picking). As an outcome of our review and analysis, a set of recommendations to strengthen the elaboration of reliable and valid research studies is provided. Changes are needed in order to foment meta‐research, open science practices and replication studies; notably, changes are needed in the instruction of research methods; in the use and interpretation of statistical data, as well as in research culture in general. We conclude that the replication crisis presents an opportunity to improve research practices and the quality of scientific production in all fields of research, including research in education.

Replication crisis or an opportunity to improve scientific production?

Pseudoscience and government conspiracy theories swirl on social media, though most of them stay largely confined to niche communities. In the case of COVID-19, however, a combination of anger at what some see as overly restrictive government policies, conflicting information about treatments and disease spread, and anxiety about the future has many people searching for facts...and finding misinformation. This dynamic creates an opportunity for determined people and skilled marketers to fill the void - to create content and produce messages designed to be shared widely.

Virality Project (US): Marketing meets Misinformation

RT @MaartenvSmeden: The lack of rapid sharing of COVID19 patient level data https://t.co/rmEGf6xRXj

In your opinion, what have been the biggest travesties of science during COVID?

10.1038/d41586-020-02402-5

Genomics could reveal details about the source of the country’s first outbreak in more than 100 days, says epidemiologist Amanda Kvalsvig.

‘We felt we had beaten it’: New Zealand’s race to eliminate the coronavirus again

10.3386/w27059

We develop a quantitative framework for exploring how individuals trade off the utility benefit of social activity against the internal and external health risks that come with social interactions during a pandemic. We calibrate the model to external targets and then compare its predictions with daily data on social activity, fatalities, and the estimated effective reproduction number R(t) from the COVID-19 pandemic in March-June 2020. While the laissez- faire equilibrium is consistent with much of the decline in social activity that we observed in US data, optimal policy further imposes immediate and highly persistent social distancing. Notably, neither equilibrium nor optimal social distancing is extremely restrictive, in the sense that the effective reproduction number never falls far below 1. The expected cost of COVID-19 in the US is substantial, $12,700 in the laissez-faire equilibrium and $8,100 per person under an optimal policy. Optimal policy generates this large welfare gain by shifting the composition of costs from fatalities to persistent social distancing.

Internal and External Effects of Social Distancing in a Pandemic

10.3386/w26954

This paper describes a weekly economic index (WEI) developed to track the rapid economic developments associated with the response to the novel Coronavirus in the United States. The WEI shows a strong and sudden decline in economic activity starting in the week ending March 21, 2020. In the most recent week ending March 28, the WEI indicates economic activity has fallen further to -6.19% scaled to 4 quarter growth in GDP.

U.S. Economic Activity During the Early Weeks of the SARS-Cov-2 Outbreak

10.3386/w26982

This paper measures the role of the diffusion of high-speed Internet on an individual's ability to self-isolate during a global pandemic. We use data that tracks 20 million mobile devices and their movements across physical locations, and whether the mobile devices leave their homes that day. We show that while income is correlated with differences in the ability to stay at home, the unequal diffusion of high-speed Internet in homes across regions drives much of this observed income effect. We examine compliance with state-level directives to avoid leaving your home. Devices in regions with either high-income or high-speed Internet are less likely to leave their homes after such a directive. However, the combination of having both high income and high-speed Internet appears to be the biggest driver of propensity to stay at home. Our results suggest that the digital divide---or the fact that income and home Internet access are correlated---appears to explain much inequality we observe in people's ability to self-isolate.

Social Distancing, Internet Access and Inequality

10.3386/w26987

The outbreak of covid19 has significantly disrupted the economy. This note attempts to quantify the macroeconomic impact of costly and deadly disasters in recent US history, and to translate these estimates into an analysis of the likely impact of covid19. A costly disaster series is constructed over the sample 1980:1-2019:12 and the dynamic impact of a costly disaster shock on economic activity and on uncertainty is studied using a VAR. Unlike past natural disasters, covid19 is a multi-month shock that is not local in nature, disrupts labor market activities rather than destroys capital, and harms the social and physical well being of individuals. Calibrating different shock profiles to reflect these features, we find that the effects of the event last from two months to over a year, depending on the sector of the economy. Even a conservative calibration of a 3-month, 60 standard deviation shock is forecast to lead to a cumulative loss in industrial production of 12.75% and in service sector employment of nearly 17% or 24 million jobs over a period of ten months, with increases in macro uncertainty that last five months.

Covid19 and the Macroeconomic Effects of Costly Disasters

10.3386/w26929

We analyze the repercussions of the 1918 Influenza Pandemic on demographic measures, human capital formation, and productivity markers in the state of Sao Paulo, Brazil's financial center and the most populous city in South America today. Leveraging temporal and spatial variation in district-level estimates of influenza-related deaths for the period 1917-1920 combined with a unique database on socio-economic, health and productivity outcomes constructed from historical and contemporary documents for all districts in Sao Paulo, we find that the 1918 Influenza pandemic had significant negative impacts on infant mortality and sex ratios at birth in 1920 (the short-run). We find robust evidence of persistent effects on health, educational attainment and productivity more than twenty years later. Our study highlights the importance of documenting the legacy of historical shocks in understanding the development trajectories of countries over time.

The Brazilian Bombshell? The Long-Term Impact of the 1918 Influenza Pandemic the South American Way

10.3386/w27430

We analyze the effects of an epidemic in three standard macroeconomic models. We find that the neoclassical model does not rationalize the positive comovement of consumption and investment observed in recessions associated with an epidemic. Introducing monopolistic competition into the neoclassical model remedies this shortcoming even when prices are completely flexible. Finally, sticky prices lead to a larger recession but do not fundamentally alter the predictions of the monopolistic competition model.

Epidemics in the Neoclassical and New Keynesian Models

10.3386/w27272

We analyze how investor expectations about economic growth and stock returns changed during the February-March 2020 stock market crash induced by the COVID-19 pandemic, as well as during the subsequent partial stock market recovery. We surveyed retail investors who are clients of Vanguard at three points in time: (i) on February 11-12, around the all-time stock market high, (ii) on March 11-12, after the stock market had collapsed by over 20%, and (iii) on April 16-17, after the market had rallied 25% from its lowest point. Following the crash, the average investor turned more pessimistic about the short-run performance of both the stock market and the real economy. Investors also perceived higher probabilities of both further extreme stock market declines and large declines in short-run real economic activity. In contrast, investor expectations about long-run (10-year) economic and stock market outcomes remained largely unchanged, and, if anything, improved. Disagreement among investors about economic and stock market outcomes also increased substantially following the stock market crash, with the disagreement persisting through the partial market recovery. Those respondents who were the most optimistic in February saw the largest decline in expectations, and sold the most equity. Those respondents who were the most pessimistic in February largely left their portfolios unchanged during and after the crash.

Inside the Mind of a Stock Market Crash

10.3386/w27049

A key issue for the ongoing COVID-19 pandemic is whether non-pharmaceutical public-health interventions (NPIs) retard death rates. The best information about these effects likely comes from flu-related excess deaths in large U.S. cities during the second wave of the Great Influenza Pandemic, September 1918-February 1919. NPIs, as measured by Markel, et al. (2007), are in three categories: school closings, prohibitions on public gatherings, and quarantine/isolation. Although an increase in NPIs clearly flattened the curve in the sense of sharply reducing the ratio of peak to average death rates, the estimated effect on overall deaths is small and statistically insignificant. One possibility is that the NPIs were not more successful in curtailing overall mortality because the average duration of NPIs was only around one month. Another possibility is that NPIs mainly delay deaths rather than eliminating them.

Non-Pharmaceutical Interventions and Mortality in U.S. Cities during the Great Influenza Pandemic, 1918-1919

10.3386/w27387

We use data from the aggregate stock market and dividend futures to quantify how investors’ expectations about economic growth evolve across horizons in response to the coronavirus outbreak and subsequent policy responses until June 2020. Dividend futures, which are claims to dividends on the aggregate stock market in a particular year, can be used to directly compute a lower bound on growth expectations across maturities or to estimate expected growth using a forecasting model. We show how the actual forecast and the bound evolve over time. As of June 8, our forecast of annual growth in dividends is down 9% in the US and 14% in the EU compared to January 1, and our forecast of GDP growth is down by 2.0% in the US and 3.1% in the EU. The lower bound on the change in expected dividends is -18% in the US and -25% in the EU at the 2-year horizon. News about fiscal stimulus around March 24 boosts the stock market and long-term growth but did little to increase short-term growth expectations. Expected dividend growth has improved since April 1 in both the US and the EU. We conclude by developing and estimating a simple model of the crisis to understand the joint dynamics of short-term dividend futures, stock markets, and bond markets.

Coronavirus: Impact on Stock Prices and Growth Expectations

10.1016/S2468-2667(20)30146-8

BackgroundThe COVID-19 pandemic has placed unprecedented strain on health-care systems. Frailty is being used in clinical decision making for patients with COVID-19, yet the prevalence and effect of frailty in people with COVID-19 is not known. In the COVID-19 in Older PEople (COPE) study we aimed to establish the prevalence of frailty in patients with COVID-19 who were admitted to hospital and investigate its association with mortality and duration of hospital stay.MethodsThis was an observational cohort study conducted at ten hospitals in the UK and one in Italy. All adults (≥18 years) admitted to participating hospitals with COVID-19 were included. Patients with incomplete hospital records were excluded. The study analysed routinely generated hospital data for patients with COVID-19. Frailty was assessed by specialist COVID-19 teams using the clinical frailty scale (CFS) and patients were grouped according to their score (1–2=fit; 3–4=vulnerable, but not frail; 5–6=initial signs of frailty but with some degree of independence; and 7–9=severe or very severe frailty). The primary outcome was in-hospital mortality (time from hospital admission to mortality and day-7 mortality).FindingsBetween Feb 27, and April 28, 2020, we enrolled 1564 patients with COVID-19. The median age was 74 years (IQR 61–83); 903 (57·7%) were men and 661 (42·3%) were women; 425 (27·2%) had died at data cutoff (April 28, 2020). 772 (49·4%) were classed as frail (CFS 5–8) and 27 (1·7%) were classed as terminally ill (CFS 9). Compared with CFS 1–2, the adjusted hazard ratios for time from hospital admission to death were 1·55 (95% CI 1·00–2·41) for CFS 3–4, 1·83 (1·15–2·91) for CFS 5–6, and 2·39 (1·50–3·81) for CFS 7–9, and adjusted odds ratios for day-7 mortality were 1·22 (95% CI 0·63–2·38) for CFS 3–4, 1·62 (0·81–3·26) for CFS 5–6, and 3·12 (1·56–6·24) for CFS 7–9.InterpretationIn a large population of patients admitted to hospital with COVID-19, disease outcomes were better predicted by frailty than either age or comorbidity. Our results support the use of CFS to inform decision making about medical care in adult patients admitted to hospital with COVID-19.

The effect of frailty on survival in patients with COVID-19 (COPE): a multicentre, European, observational cohort study

This work seeks to compare the number of confirmed cases and deaths caused by COVID-19 among the BRICS member countries using data from Johns Hopkins University. The situation experienced by the BRICS is worrying. Brazil, Russia, India, and South Africa are among the five countries with the highest number of confirmed cases. Special attention should be given to Brazil, which ranks in second place regarding to the number of confirmed cases in the world. In addition, India will have the highest number of infections in March 2021, according to projections of Massachusetts Institute of Technology (MIT).

10.31219/osf.io/dmtfy

Confirmed cases and deaths by COVID-19: A comparison among the BRICS countries

COVID-19: These countries are most at risk from falling tourism

Global travel restrictions, put in place to curtail the coronavirus pandemic, have had a devastating effect on the tourism industry. Data from the World Travel & Tourism Council, shows which countries stand the most to lose from a downturn in tourism. Mexico is perhaps the most vulnerable, with 15.5% of its GDP relying on the travel and tourism industry. Spain and Italy are also highly vulnerable, owing 14.3% and 13.0% of its GDP to tourism respectively. In the U.S., despite just 8.6% of GDP, the U.S., travel and tourism still jeopardizes 16.8 million jobs.

Trusting the experts takes more than belief

Radical public health responses to the pandemic around the world have asked us to make unprecedented changes to our daily lives. Social distancing measures require compliance with recommendations, instructions, and legal orders that come with undeniable sacrifices for almost all of us (though these sacrifices are far from equally distributed). These extreme public measures depend for their success on public trust.

Preprint servers have existed for decades, but the fight against the coronavirus has seen their use soar. They’re changing how science is done—but need important guardrails.

How the COVID-19 crisis has prompted a revolution in scientific publishing

Harvard Law faculty and alumni weigh in on what it will take to run safe and reliable elections in the age of COVID-19 (function (window, $, undefined) { WebFont.load({ google: { families: ['Playfair Display'] } }); $(document).ready(function () { $('#feature-title').css({ 'font-family': 'Playfair Display', 'font-size': '2.625em', 'font-weight': 900, 'font-style': 'normal', 'text-transform': 'none' }); $('#feature-subtitle').css({ 'font-family': '"franklin-gothic-urw", sans-serif', 'font-size': '1.3125em', 'clear': 'both' }) }); })(window, jQuery);

When Voting Is a Risky Choice