For decades, cities relied on performing arts groups to help drive revitalization. Now nearly every company in the country has been shuttered for months, acting as a drag on local business.

What Happens to Cities When the Arts Go Dark?

In the late stages of an epidemic, infections are often sporadic and geographically distributed. Spatially structured stochastic models can capture these important features of disease dynamics, thereby allowing a broader exploration of interventions. Here we develop a stochastic model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission among an interconnected group of population centers representing counties, municipalities, and districts (collectively, “counties”). The model is parameterized with demographic, epidemiological, testing, and travel data from Ontario, Canada. We explore the effects of different control strategies after the epidemic curve has been flattened. We compare a local strategy of reopening (and reclosing, as needed) schools and workplaces county by county, according to triggers for county-specific infection prevalence, to a global strategy of province-wide reopening and reclosing, according to triggers for province-wide infection prevalence. For trigger levels that result in the same number of COVID-19 cases between the two strategies, the local strategy causes significantly fewer person-days of closure, even under high intercounty travel scenarios. However, both cases and person-days lost to closure rise when county triggers are not coordinated and when testing rates vary among counties. Finally, we show that local strategies can also do better in the early epidemic stage, but only if testing rates are high and the trigger prevalence is low. Our results suggest that pandemic planning for the far side of the COVID-19 epidemic curve should consider local strategies for reopening and reclosing.

doi.org/10.1073/pnas.2014385117

Local lockdowns outperform global lockdown on the far side of the COVID-19 epidemic curve

Disinformation campaigns used to require a lot of human effort, but artificial intelligence will take them to a whole new level.

The Supply of Disinformation Will Soon Be Infinite

Clusters of cases have emerged in Brooklyn and New York City’s northern suburbs, in areas with large Orthodox Jewish communities.

Warnings Issued as Virus Cases Rise in New York

present paper presents a SARS-CoV-2 epidemic model to serve as abasis for estimating the incidence of infection, and shows mathemati-cally how modeled transmission dynamics translate into infection indi-cators by incorporating probability distributions for indicator-specifictime lags from infection. Hospital admissions and SARS-CoV-2 RNAin municipal sewage sludge are simultaneously modeled via maximumlikelihood scaling to the underlying transmission model. The resultsdemonstrate that both data series plausibly follow from the transmis-sion model specified and provide a 95% confidence interval estimate ofthe reproductive number0≈24±02. Sensitivity analysis account-ing for alternative lag distributions from infection until hospitalizationand sludge RNA concentration respectively suggests that the detectionof viral RNA in sewage sludge leads hospital admissions by 3 to 5 dayson average. The analysis suggests that stay-at-home restrictions plau-sibly removed 89% of the population from the risk of infection withthe remaining 11% exposed to an unmitigated outbreak that infected9.3% of the total population.

Ascertaining the state of coronavirus outbreaks is crucial for pub-lic health decision-making. Absent repeated representative viral testsamples in the population, public health officials and researchers alikehave relied on lagging indicators ofinfection to make inferences aboutthe direction of the outbreak and attendant policy decisions. Recentlyresearchers have shown that SARS-CoV-2 RNA can be detected in mu-nicipal sewage sludge with measured RNA concentrations rising andfalling suggestively in the shape of an epidemic curve while provid-ing an earlier signal of infection than hospital admissions data. The

Aligning SARS-CoV-2 Indicators via anEpidemic Model: Application to HospitalAdmissions and RNA Detection in SewageSludge

Machine learning methods, combined with large electronic health databases, could enable a personalised approach to medicine through improved diagnosis and prediction of individual responses to therapies. If successful, this strategy would represent a revolution in clinical research and practice. However, although the vision of individually tailored medicine is alluring, there is a need to distinguish genuine potential from hype. We argue that the goal of personalised medical care faces serious challenges, many of which cannot be addressed through algorithmic complexity, and call for collaboration between traditional methodologists and experts in medical machine learning to avoid extensive research waste.

10.1016/S2589-7500(20)30200-4

Time to reality check the promises of machine learning-powered precision medicine

The development and distribution of a COVID-19 vaccine has the potential to greatly change the course of the pandemic; however, ensuring equitable access will require countries, organisations, and corporations to place their trust in global health. The COVID-19 vaccine initiative (COVAX) shows how public–private partnerships can exacerbate existing chasms1Spinney L How the race for a Covid-19 vaccine is getting dirty.https://www.theguardian.com/society/2020/aug/30/how-the-race-for-a-covid-19-vaccine-got-dirtyDate: Aug 30, 2020Date accessed: September 5, 2020Google Scholar or allow organisations, such as WHO, to guide a realistic and adequate approach.Development of vaccines that meet regulatory and licensing requirements involves high costs in terms of facilities, equipment, and human resources and is a lengthy process that often fails. The high cost restricts many countries from developing a vaccine,2Plotkin S Robinson JM Cunningham G Iqbal R Larsen S The complexity and cost of vaccine manufacturing—an overview.Vaccine. 2017; 35: 4064-4071Crossref PubMed Scopus (59) Google Scholar which causes low income and middle-income countries to rely on research and development from more powerful economies. Additionally, research highlights the challenges in reaching population-level effectiveness with a vaccine, regardless of production capacity, because of weak delivery infrastructures and barriers to access that determine uptake.3

10.1016/S2214-109X(20)30415-0

COVID-19 vaccination: returning to WHO's Health For All

10.1038/s41586-020-2798-3

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in late 2019 in China and caused a coronavirus disease 2019 (COVID-19) pandemic. To mitigate the public health, economic and societal impacts of the virus, a vaccine is urgently needed. The development of SARS-CoV-2 vaccines was initiated in early January 2020 when the sequence of the virus became available and moved at record speed with one Phase I trial already starting in March 2020 and currently more than 180 vaccines in various stages of development. Phase I/II trial data is already available for several vaccine candidates and many have moved into Phase III trials. The data available so far suggests that effective and safe vaccines might become available within months rather than years.

SARS-CoV-2 vaccines in development

Christian Drosten helped spare his country from the worst of Covid. Now he’s worried about the second wave.

Germany Has Its Own Dr. Fauci—and Actually Follows His Advice

Sir Simon Stevens rejects idea proposed by scientists to contain Covid second wave

Asking over-65s to shield is 'age-based apartheid', boss of NHS England says

Incorporating nudging and other behavioral insights from psychological science into public policy has become de rigueur for governments around the world. Nudging was originally developed as an attempt to reconcile state intervention and individual liberty. It is based on the assumption that people’s cognitive abilities and self-control are so limited relative to the complexity of the world that structural changes to their environments, or “choice architectures,” are often required for them to act in their own best interest.

Creating Citizen Choice Architects

Doctors, nurses and others are carrying a heavy burden for a nation that isn't doing enough to help them.

The Coronavirus Is Creating A Mental Health Crisis For Health Care Workers

The constant mantra is the virus must be suppressed and contained. But how do you do this when people can be infectious without knowing they have it? Where it can be passed on silently because people do not develop symptoms?

Covid: Is it time we learned to live with the virus?

arXiv:2009.09973

The ability to share social network data at the level of individual connections is beneficial to science: not only for reproducing results, but also for researchers who may wish to use it for purposes not foreseen by the data releaser. Sharing such data, however, can lead to serious privacy issues, because individuals could be re-identified, not only based on possible nodes' attributes, but also from the structure of the network around them. The risk associated with re-identification can be measured and it is more serious in some networks than in others. Various optimization algorithms have been proposed to anonymize the network while keeping the number of changes minimal. However, existing algorithms do not provide guarantees on where the changes will be made, making it difficult to quantify their effect on various measures. Using network models and real data, we show that the average degree of networks is a crucial parameter for the severity of re-identification risk from nodes' neighborhoods. Dense networks are more at risk, and, apart from a small band of average degree values, either almost all nodes are re-identifiable or they are all safe. Our results allow researchers to assess the privacy risk based on a small number of network statistics which are available even before the data is collected. As a rule-of-thumb, the privacy risks are high if the average degree is above 10. Guided by these results we propose a simple method based on edge sampling to mitigate the re-identification risk of nodes. Our method can be implemented already at the data collection phase. Its effect on various network measures can be estimated and corrected using sampling theory. These properties are in contrast with previous methods arbitrarily biasing the data. In this sense, our work could help in sharing network data in a statistically tractable way.

Privacy and Uniqueness of Neighborhoods in Social Networks

arXiv:2009.08940

We investigate how the initial number of infected individuals affects the behavior of the critical susceptible-infected-recovered process. We analyze the outbreak size distribution, duration of the outbreaks, and the role of fluctuations

An infection process near criticality: Influence of the initial condition

Immunization has always been a proxy for wider fears about social control, a history reminds us

Vaccines — lessons from three centuries of protest

doi.org/10.1371

Compartmental epidemic models have been used extensively to study the historical spread of infectious diseases and to inform strategies for future control. A critical parameter of any such model is the transmission rate. Temporal variation in the transmission rate has a profound influence on disease spread. For this reason, estimation of time-varying transmission rates is an important step in identifying mechanisms that underlie patterns in observed disease incidence and mortality. Here, we present and test fast methods for reconstructing transmission rates from time series of reported incidence. Using simulated data, we quantify the sensitivity of these methods to parameters of the data-generating process and to mis-specification of input parameters by the user. We show that sensitivity to the user’s estimate of the initial number of susceptible individuals—considered to be a major limitation of similar methods—can be eliminated by an efficient, “peak-to-peak” iterative technique, which we propose. The method of transmission rate estimation that we advocate is extremely fast, for even the longest infectious disease time series that exist. It can be used independently or as a fast way to obtain better starting conditions for computationally expensive methods, such as iterated filtering and generalized profiling.

Fast estimation of time-varying infectious disease transmission rates

10.1038/s41562-020-00954-0

Local news outlets have struggled to stay open in the more competitive market of digital media. Some have noted that this decline may be due to the ways in which digital platforms direct attention to some news outlets and not others. To test this theory, we collected 12.29 million responses to Google News searches within all US counties for a set of keywords. We compared the number of local outlets reported in the results against the number of national outlets. We find that, unless consumers are searching specifically for topics of local interest, national outlets dominate search results. Features correlated with local supply and demand, such as the number of local outlets and demographics associated with local news consumption, are not related to the likelihood of finding a local news outlet. Our findings imply that platforms may be diverting web traffic and desperately needed advertising dollars away from local news.

Auditing local news presence on Google News

Poll shows 74% favour publicly-run system as concerns over contractors like Serco grow.

Exclusive: Public Want Test And Trace Taken Off Private Firms

10.31234/osf.io/uvh7s

The recognition and management of the socio-emotional pain facing the COVID-19 pandemic refer to different, but interdependent, clues regarding cognitive and emotional aspects of the pandemic threat, considering the need of social distancing as a prophylactic procedure to avoid spreading the pathogen. The socio-emotional condition at the time of outbreak subsidizes the (re)modulation of interactive neural circuits underlying the risk assessment behaviour at physical, emotional, and social levels. Experiences of social isolation, exclusion or affective loss are generally considered to be some of the most “painful” things that people face. The threats of social disconnection are processed by some of the same neural structures that process basic threats to survival. The lack of social connection can be "painful" due to an overlap in the neural circuitry responsible for both physical and emotional pain related to feelings of social rejection. Indeed, many of us go to great lengths to avoid situations that may engender these experiences. Because of this, this work focusses on times of pandemic, the somatization mentioned above seeks the interconnection and/or interdependence between neural systems related to emotional and cognitive processes, so that the person involved in that aversive social environment becomes aware of himself, the others, and the threatening situation experienced to avoid daily psychological and neuropsychiatric effects. Social distancing during the isolation evokes the formation of social distress, raising the intensity of learned fear that people acquire, consequently enhancing the emotional and social pain.

PHYSICAL, EMOTIONAL, AND SOCIAL PAIN DURING COVID-19 PANDEMIC-RELATED SOCIAL ISOLATION

The COVID-19 pandemic poses many real-world moral dilemmas, which can pit the needs and rights of the many against the needs and rights of the few. We investigated the influence of this contemporary global crisis on moral judgments in older adults, who are at greatest personal risk from the pandemic. We hypothesized that during this pandemic, individuals would give fewer utilitarian responses to hypothetical dilemmas, accompanied by higher levels of confidence and emotion elicitation. Our pre-registered analysis (https://osf.io/g2wtp) involved two waves of data collection, before (2014) and during (2020) the COVID-19 pandemic, regarding three categories of moral dilemmas (personal rights, agent-centered permissions, and special obligations). While utilitarian responses considered across all categories of dilemma did not differ, participants during the 2020 wave gave fewer utilitarian responses to dilemmas involving personal rights; that is, they were less willing to violate the personal rights of others to produce the best overall outcomes.

10.31234/osf.io/yjn3u

No Utilitarians in a Pandemic? Shifts in Moral Reasoning during the COVID-19 Global Health Crisis

10.31234/osf.io/nvm2j

In the present research, we approached utopian thinking from an individual differences perspective and developed the utopian impulse as a psychological construct, defined as the propensity to have thoughts and engage in actions whose purpose is to transform the current society into a better one in the future by addressing existing global issues.

Utopian Impulse: An Individual-differences Approach to Transformative Social Change

10.31234/osf.io/dr42a

Choices under conditions of risk often have consequences not just for ourselves, but for others. Yet, it is unclear how the other’s identity (stranger, close friend, etc.) influences risky choices made on their behalf. Here, two groups of undergraduates made a series of risky economic decisions for themselves, for another person, or for both themselves and another person (i.e., shared outcomes); one group of participants made choices involving a same-sex stranger (n = 29), the other made choices involving a same-sex close friend (n = 28). Hierarchical Bayesian Estimation of computations underlying risky decision-making revealed that relative to choosing for themselves, people were more risk averse, more loss averse, and more consistent when choices involved another person. Interestingly, partner identity differentially modulated decision computations. People became risk neutral and more consistent when choosing for friends relative to strangers. In sum, these findings suggest that the complexity of the social world is mirrored in its nuanced consequences for our choices.

Choosing for another: Social context changes dissociable computational mechanisms of risky decision-making

10.31234/osf.io/q8kjv

Amidst the global COVID-19 pandemic, there is an urgent need for establishing knowledge about risk factors for adverse health outcomes associated with loneliness and social isolation. In this study, we show that self-perceived loneliness coincides with objective measures of social isolation as well as the personality trait neuroticism, and that these comorbidities contribute to differential associations with risk factors including depression, social deprivation, unhealthy lifestyle behaviors, cardiovascular risk, and aging of the brain. The findings contribute to identifying groups of individuals who may be vulnerable to loneliness and associated health problems, and emphasize the need for public-health initiatives addressing socioeconomic conditions as well as social, mental, and physical health to reduce the risk of loneliness and adverse health outcomes in the population.

Risk factors associated with loneliness, social isolation, and neuroticism in the UK Biobank cohort

Even before classes began, the Harvard Law School LL.M. Class of 2021 created a variety of virtual social events that have bonded them closely

Making Lemonade from Lemons

In the newly robust digital world, HLS is transforming what it means to get a legal education

COVID adaptation

10.31234/osf.io/cwktm

Many aspects of handling the COVID-19 pandemic in Western countries bear resemblance to game-design patterns like point displays and leader boards, the visible assumption of roles, classic archetypes, collection and hoarding of resources, and spatial awareness. We argue that these patterns emerge as people lack cultural and individual norms and cognitive scripts to handle a pandemic, in contrast to other catastrophic events like wars and major economic crises. Understanding this spontaneous ludification of a serious and complex situation in terms of Johan Huizinga's homo ludens can raise awareness for possible failings in dealing with COVID-19. It also has the potential to strengthen people's motivation for cooperative effort.

A Game of COVID. Ludification as a Way to Make Sense of a Pandemic

Claim The return to universities could cause 50,000 deaths from Covid-19 without “strong controls”. Conclusion This comes from a research paper that has not been peer-reviewed. It is based on several assumptions, including that every student gets infected, and nothing is done to stop it.

Reopening universities will almost certainly not cause 50,000 deaths

Experts have questioned shortcomings and lack of clarity in vital registration, testing practices, and classification of COVID-19 deaths. Patralekha Chatterjee reports from New Delhi.

10.1016/S0140-6736(20)31857-2

Is India missing COVID-19 deaths?

‘Every health worker has the right to be safe at work, and it is a scandal that so many are paying the ultimate price,’ Amnesty official says

Coronavirus: 7,000 health workers have died around world, Amnesty reports

This report describes the aggregation and anonymization process applied to the initial version of COVID-19 Search Trends symptoms dataset

2009.01265

Google COVID-19 Search Trends Symptoms Dataset: Anonymization Process Description

10.31234/osf.io/njwmp

The nature of the COVID-19 pandemic may require governments to use privacy-encroaching technologies to help contain its spread. One technology involves co-location tracking through mobile Wi-Fi, GPS, and Bluetooth to permit health agencies to monitor people's contact with each other, thereby triggering targeted social-distancing when a person turns out to be infected. The effectiveness of tracking relies on the willingness of the population to support such privacy encroaching measures. We report the results of two large surveys in the United Kingdom, conducted during the peak of the pandemic, that probe people's attitudes towards various tracking technologies. The results show that by and large there is widespread acceptance for co-location tracking. Acceptance increases when the measures are explicitly time-limited and come with opt-out clauses or other assurances of privacy. Another possible future technology to control the pandemic involves "immunity passports", which could be issued to people who carry antibodies for the COVID-19 virus, potentially implying that they are immune and therefore unable to spread the virus to other people. Immunity passports have been considered as a potential future step to manage the pandemic. We probe people's attitudes towards immunity passports and find considerable support overall, although around 20% of the public strongly oppose passports.

Public acceptance of Privacy-Encroaching Policies to Address the COVID-19 Pandemic in the United Kingdom

10.31234/osf.io/9sj4a

The COVID-19 pandemic is having an impact on physical and mental health. Most studies report the impact on mental health and mental distress during the pandemic. As a result of various stressors (such as lockdown, quarantines, and misinformation) there is heightened fear of a pandemic. The sufferer may experience a variety of symptoms of anxiety, depression, and even psychosis. In predisposed vulnerable individuals, fear of COVID-19 is perpetuating pain and dysfunction. This study discussed the ABC framework of fear and influencer to better understand the different levels of symptoms and interventions. There is an urgent need to integrate mental health into primary health-care centers. The attending physician should be aware of the stress disorders associated with the pandemic. This article introduces a handy and practical portrayal of the ABC framework that can be taught to individuals in distress during clinical visits to primary care centers providing awareness of the relationship between thinking, emotional and behavioral responses.

ABC framework of fear of COVID-19 for psychotherapeutic intervention in Nepal: a review

Proposals to help area hard-hit by Covid include offering empty shops to chefs and artists

Revive London's West End with culture vouchers, urges thinktank

10.31234/osf.io/jqa83

The enormous scale of the available information and products on the Internet has necessitated the development of algorithms that intermediate between options and human users. These AI/machine learning algorithms attempt to provide the user with relevant information. In doing so, the algorithms may incur potential negative consequences stemming from the need to select items about which it is uncertain to increase predictive accuracy versus the need to select items about which it is certain to increase recommendation accuracy. This tension between predicting relevant recommendations to the users and learning about the user's interests can be considered an instantiation of the well-known exploration-exploitation tradeoff in the context of information filtering and recommender systems. Building from existing machine learning algorithms, we introduce a parameterized model that unifies and interpolates between recommending relevant information and active learning. We present three experiments investigating the unified model. Specifically, we illustrate the tradeoffs of optimizing prediction and recommendation within a tightly controlled concept-learning paradigm, show the conditions under which a broad parameter range can optimize for both, and identify the effects of human variability on algorithm performance. Thus, combining methods and models from cognitive science and computer science, we quantify implications of tradeoffs between recommendation accuracy and learning about preferences of human users, demonstrating the value of experimental approaches to understanding real world human-machine feedback loops.

Unifying recommendation and active learning for information filtering and recommender systems