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The scientific and ethical feasibility of immunity passports

There is much debate about the use of immunity passports in the response to the COVID-19 pandemic. Some have argued that immunity passports are unethical and impractical, pointing to uncertainties relating to COVID-19 immunity, issues with testing, perverse incentives, doubtful economic benefits, privacy concerns, and the risk of discriminatory effects. We first review the scientific feasibility of immunity passports. Considerable hurdles remain, but increasing understanding of the neutralising antibody response to COVID-19 might make identifying members of the community at low risk of contracting and transmitting COVID-19 possible. We respond to the ethical arguments against immunity passports and give the positive ethical arguments. First, a strong presumption should be in favour of preserving people's free movement if at all feasible. Second, failing to recognise the reduced infection threat immune individuals pose risks punishing people for low-risk behaviour. Finally, further individual and social benefits are likely to accrue from allowing people to engage in free movement. Challenges relating to the implementation of immunity passports ought to be met with targeted solutions so as to maximise their benefit.
immunity passport
low risk
vaccine, trial, approve, drug, healthy
tackle, european, fund, database, indonesia
Adherence to COVID‐19 Precautionary Measures: Applying the Health Belief Model and Generalised Social Beliefs to a Probability Community Sample

Background In the face of the global pandemic of coronavirus disease‐2019 (COVID‐19), people’s adherence to precautionary behavioral measures (e.g. social distancing) largely influences the effectiveness of those measures in containing the spread of the coronavirus. The present study aims at testing the applicability of the health belief model (HBM) and generalised social beliefs (i.e. social axioms) to explore strategies for promoting adherence to COVID‐19 precautionary measures. Methods We conducted a telephone survey with a two‐step stratified random sampling method and obtained a probability sample of 616 adults in Macao, China (18–87 years old; 60.9% women) in April 2020. Results Our participants showed stronger adherence to some COVID‐19 precautionary measures (e.g. face mask wearing; 96.4%) but not others (e.g. social distancing; 42.3%). Their adherence to those measures was found to be significantly associated with four HBM factors and two social axioms, after controlling for gender, age, and years of education. Conclusions The HBM and the generalised social beliefs of social cynicism and reward for application can be applied to understanding adherence to precautionary measures against COVID‐19. Strategies based on beliefs were proposed to facilitate the promotion of precautionary measures.
social distancing
face mask
safety measure
vaccination, adherence, hesitancy, uptake, sectional
intention, behavior, message, guideline, preventive
Performance of Prediction Models for Covid-19: The Caudine Forks of the External Validation

Development and implementation of risk prediction models to aid risk stratification and resource allocation could improve the current scenario. Clinical prediction models (CPMs) aim to predict an individual's expected outcome value, or an individual's risk of an outcome being present (diagnostic) or happening in the future (prognostic), based on sets of identified predictor variables [1, 2]. A plethora of such models have been described during the first wave of the Covid-19 epidemic: a recent “living” systematic review identified (at the time of writing) 145 CPMs focused on Covid-19 patients [3].Unfortunately, many of the existing Covid-19 CPMs have been identified to be at high risk of bias, due to poor reporting, over-estimation of predictive performance, and lack of external validation [3]. External validation, which is an important aspect during the development process of any CPM, can independently evaluate the model focusing on data independent to those data used to derive the model [1, 2]. Crucially, this step assesses the generalisability/transportability of the CPM into new populations before they are recommended for widespread clinical implementation.
empirical evidence
machine, twitter, learn, technology, application
bayesian, causal, measurement, replication, statistical
Trusted Decision-Making: Data Governance for Creating Trust in Data Science Decision Outcomes

Organizations are increasingly introducing data science initiatives to support decision-making. However, the decision outcomes of data science initiatives are not always used or adopted by decision-makers, often due to uncertainty about the quality of data input. It is, therefore, not surprising that organizations are increasingly turning to data governance as a means to improve the acceptance of data science decision outcomes. In this paper, propositions will be developed to understand the role of data governance in creating trust in data science decision outcomes. Two explanatory case studies in the asset management domain are analyzed to derive boundary conditions. The first case study is a data science project designed to improve the efficiency of road management through predictive maintenance, and the second case study is a data science project designed to detect fraudulent usage of electricity in medium and low voltage electrical grids without infringing privacy regulations. The duality of technology is used as our theoretical lens to understand the interactions between the organization, decision-makers, and technology. The results show that data science decision outcomes are more likely to be accepted if the organization has an established data governance capability. Data governance is also needed to ensure that organizational conditions of data science are met, and that incurred organizational changes are managed efficiently. These results imply that a mature data governance capability is required before sufficient trust can be placed in data science decision outcomes for decision-making.
big data
data science
opinion, science, society, insight, economist
peer, publish, publication, review, preregistration
How to Avoid COVID while Voting
Oct. 14, 2020 · · Original resource · article

Epidemiologists offer tips for U.S. voters and poll workers to limit their chances of getting infected
infection prevention
school, bar, nhs, urge, reopen
like, ease, stop, critic, away
The Geography of COVID-19 in Sweden
Oct. 13, 2020 · · Original resource · article

This paper examines the geographic factors that are associated with the spread of COVID-19 in Sweden. The country is a useful case study to examine because it did not impose mandatory lockdowns, and thus we would expect the virus to spread in a more unimpeded way across communities. A growing body of research has examined the role of factors like density, household size, air connectivity, income, race and ethnicity, age, political affiliation, temperature and climate, and policy measure like lockdowns and physical distancing among others. The research examines the effects of some of these factors on the geographic variation of COVID-19 cases and on deaths, across both municipalities and neighborhoods. Our findings show that the geographic variation in COVID-19 is significantly but modestly associated with variables like density, population size, and the socio-economic characteristics of places, and somewhat more associated with variables for household size. What matters more is the presence of high-risk nursing homes and the onset of infections with places that were hit earlier by COVID-19 cases experiencing more severe outbreaks. Still, all these variables explain little of the geographic variation in COVID-19 across Sweden. There appears to be a high degree of randomness in the geographic variation of COVID-19 across Sweden and the degree to which some places were hit harder than others.
mobility, crime, gdp, employment, restriction
hotspot, u.s, rise, record, come
Excess Deaths From COVID-19 and Other Causes, March-July 2020

Previous studies of excess deaths (the gap between observed and expected deaths) during the coronavirus disease 2019 (COVID-19) pandemic found that publicly reported COVID-19 deaths underestimated the full death toll, which includes documented and undocumented deaths from the virus and non–COVID-19 deaths caused by disruptions from the pandemic.1,2 A previous analysis found that COVID-19 was cited in only 65% of excess deaths in the first weeks of the pandemic (March-April 2020); deaths from non–COVID-19 causes (eg, Alzheimer disease, diabetes, heart disease) increased sharply in 5 states with the most COVID-19 deaths.1 This study updates through August 1, 2020, the estimate of excess deaths and explores temporal relationships with state reopenings (lifting of coronavirus restrictions).
official data
longitudinal change
excess mortality
cause of death
loosening restrictions
death, england, estimate, excess, wale
mobility, crime, gdp, employment, restriction
Patient self-reported awareness of COVID: Overconfidence in knowledge, underestimation of risk.

Background: Oncology patients have had to adapt to minimize the risks of contracting COVID-19. We assessed patient knowledge of COVID, and the impact of the pandemic on their behaviours, concerns and healthcare experience, to identify any further education/quality improvement needs. Methods: Following ethical approval, a 16 page survey was distributed to 120 oncology patients attending the day unit of a tertiary Irish cancer center for systemic anti-cancer therapy (May/June 2020). The Irish COVID rate during this period was 33.8 new cases/day (pop. 4.9 million). Results: 101 responses were received. Cancer types included breast (19%), gastrointestinal (29%), head and neck (11%), and lung (13%). 31% had been tested for COVID; just 1 patient was positive. 100% were aware of advice to “cocoon” and reported good understanding of this. 75% reported complete compliance, but of those, 73% were not social-distancing within their homes, 22% received visitors, and 36% continued to shop in-store; of these, 42% shopped as/more often than pre-COVID. Of the 51 patients regularly shopping, many were not using risk-reduction strategies e.g. social distancing (22%), mask-wearing (20%), using “priority shopping’ hours (31%), avoiding public transport (26%). 94% felt confident/very confident in recognizing COVID symptoms, but 66% did not recognize two or more key symptoms from a list of 10, most frequently aches/pains (58%), fatigue (55%), altered smell/taste (33%) and dyspnea (14%). The number recognized did not correlate with confidence (p = 0.9) or desire for more information about COVID (p = 0.9). 40% did not feel they were at higher risk of contracting COVID, while 15% thought they were no more likely to be very sick than an average person if infected. Many did not know that chemotherapy, steroids, radiation, and immunotherapy can impact morbidity/mortality in COVID (31%, 70%, 44% and 49% respectively). 46% were somewhat/very fearful of COVID, but this did not strongly predict for either protective (e.g. mask-wearing: OR 1.1, 95% CI 0.3-4.8 p = 0.9), or risk behaviors (e.g. continuing to shop frequently: OR 0.5, 95% CI 0.1-1.4 p = 0.2). 66% would like more cancer specific information, particularly about prevention (45%) and symptoms (33%), with a preference for written information (74%). Conclusions: Despite self-reported confidence in knowledge, patient’s self-assessments of their risk category and the preventative strategies they should use may be inaccurate. Increased education about risk, cocooning and symptom recognition is necessary.
risk perception
safety behavior
vaccination, adherence, hesitancy, uptake, sectional
intention, behavior, message, guideline, preventive
2·5 million more child marriages due to COVID-19 pandemic

The COVID-19 pandemic's damage to education and the economy could reverse decades of progress on child marriage and pregnancy. Sophie Cousins reports.
minority, racial, violence, woman, capital
country, surge, city, economy, rich
Efficient Scientific Self-Correction in Times of Crisis

Science has been invaluable in combating the COVID-19 pandemic and its consequences. However, science is not flawless: especially research that is performed and written up under high time pressure may be susceptible to errors. Luckily, one of the core principles of science is its ability to self-correct. Traditionally, scientific self-correction is achieved through replication, but this takes time and resources; both of which are scarce. In this chapter, I argue for an additional, more efficient self-correction mechanism: analytical reproducibility checks.
opinion, science, society, insight, economist
peer, publish, publication, review, preregistration