1 week ago: 60 Swiss economists call for lockdown

A few weeks ago this- second wave arrives:

Quick preamble on "why": I have been monitoring the Swiss response for 2 reasons: 1. The Swiss COVID response has surprised me 2. personal interest (full disclosure: safety concerns about a family member to contextualise potential bias), so:

alarmism vs denial in Switzerland...or some observations on the Swiss COVID response a monster thread...

A study published in the BMJ evaluates the accuracy of the AbC-19TM Rapid Test for detection of previous SARS-CoV-2 infection in key workers.

expert reaction to study evaluating the accuracy of the AbC-19TM Rapid Test for SARS-CoV-2 antibodies

Medical ethicist Ezekiel Emanuel discusses a framework for equitably allocating COVID-19 vaccines based on preventing premature deaths and mitigating long-term economic impacts

How to Decide Who Should Get a COVID-19 Vaccine First

Why is it that Sweden, which was first to introduce mandatory use of seatbelts in cars, seems to be the last country to recommend face masks to prevent  community transmission of COVID-19 spread? Sweden has been a champion for evidence-driven public health. Even when the weight of evidence has been questionable, national authorities have opted for precautionary measures in the name of health and wellbeing of its citizens, such as maintaining a state monopoly on alcohol sales.

An effective national response to COVID-19: what not to learn from Sweden

As the UK enters into its second national lockdown, a possible light at the end of the long COVID tunnel emerged from a small country in Central Europe. Last weekend, Slovakia tested 3.6 million people for coronavirus – 97% of the eligible population of people aged 10-65. 

How Slovakia tested 3.6 million people for COVID-19 in a single weekend

Much remains uncertain for business, the economy, domestic politics and international relations

Ten ways coronavirus crisis will shape world in long term

We should turn to people who know more than we do — particularly for topics that are complex

How the COVID-19 crisis exposes widespread climate change hypocrisy

Treasury rejects researcher’s claim that its scheme was closely linked to rise in cases

'Eat out to help out' may have caused sixth of Covid clusters over summer

Economists warn many unemployed families won’t be able to pay rent and utilities without more stimulus aid from Congress

Millions of Americans are heading into the holidays unemployed and over $5,000 behind on rent

10.1038/s41591-020-01170-z

Nearly a year after the first cases of COVID-19 were reported, it is time to look back and assess what could have been predicted by health experts.

COVID-19: what health experts could and could not predict

The surge of new cases in October is concentrated in colder, less populated, more Republican-leaning counties. Places that suffered springtime or summer surges have more cases in October, adjusted for other factors.

The geography of the COVID19 third wave

2010.16004

The rapid global spread of COVID-19 has led to an unprecedented demand for effective methods to mitigate the spread of the disease, and various digital contact tracing (DCT) methods have emerged as a component of the solution. In order to make informed public health choices, there is a need for tools which allow evaluation and comparison of DCT methods. We introduce an agent-based compartmental simulator we call COVI-AgentSim, integrating detailed consideration of virology, disease progression, social contact networks, and mobility patterns, based on parameters derived from empirical research. We verify by comparing to real data that COVI-AgentSim is able to reproduce realistic COVID-19 spread dynamics, and perform a sensitivity analysis to verify that the relative performance of contact tracing methods are consistent across a range of settings. We use COVI-AgentSim to perform cost-benefit analyses comparing no DCT to: 1) standard binary contact tracing (BCT) that assigns binary recommendations based on binary test results; and 2) a rule-based method for feature-based contact tracing (FCT) that assigns a graded level of recommendation based on diverse individual features. We find all DCT methods consistently reduce the spread of the disease, and that the advantage of FCT over BCT is maintained over a wide range of adoption rates. Feature-based methods of contact tracing avert more disability-adjusted life years (DALYs) per socioeconomic cost (measured by productive hours lost). Our results suggest any DCT method can help save lives, support re-opening of economies, and prevent second-wave outbreaks, and that FCT methods are a promising direction for enriching BCT using self-reported symptoms, yielding earlier warning signals and a significantly reduced spread of the virus per socioeconomic cost.

COVI-AgentSim: an Agent-based Model for Evaluating Methods of Digital Contact Tracing

2010.16019

Over the last two decades, alongside the increased availability of large network datasets, we have witnessed the rapid rise of network science. For many systems, however, the data we have access to is not a direct description of the underlying network. More and more, we see the drive to study networks that have been inferred or reconstructed from non-network data---in particular, using time series data from the nodes in a system to infer likely connections between them. Selecting the most appropriate technique for this task is a challenging problem in network science. Different reconstruction techniques usually have different assumptions, and their performance varies from system to system in the real world. One way around this problem could be to use several different reconstruction techniques and compare the resulting networks. However, network comparison is also not an easy problem, as it is not obvious how best to quantify the differences between two networks, in part because of the diversity of tools for doing so. The netrd Python package seeks to address these two parallel problems in network science by providing, to our knowledge, the most extensive collection of both network reconstruction techniques and network comparison techniques (often referred to as graph distances) in a single library (this https URL). In this article, we detail the two main functionalities of the netrd package. Along the way, we describe some of its other useful features. This package builds on commonly used Python packages and is already a widely used resource for network scientists and other multidisciplinary researchers. With ongoing open-source development, we see this as a tool that will continue to be used by all sorts of researchers to come.

netrd: A library for network reconstruction and graph distances

10.1016/j.eclinm.2020.100599

I'm a global health researcher working to address health and gender inequalities in the Global South. During my work in areas where Malaria or Dengue Fever are endemic, I always took extra precautions to avoid getting infected. I never anticipated that while living in a large, urban city from Canada I would be at higher risk… Until the COVID-19 pandemic.

My journey with COVID-19

As the COVID-19 pandemic continues, the effects could be potentially devastating to global democracy and the upcoming U.S. election. On the World Class podcast, Larry Diamond and Nathaniel Persily discuss what needs to be done to ensure a healthy election in November with host Michael McFaul.

Our Democracy Depends on a Safe Election in November

Covid-19 upended our jobs. We've tried to adapt, but what about the long term? BBC Worklife asks dozens of experts to flag the biggest questions we should be asking in 2020 and beyond.

Coronavirus: How the world of work may change forever

2010.14630

Countries and cities around the world have resorted to unprecedented mobility restrictions to combat COVID 19-transmission. Here we exploit a natural experiment whereby Colombian cities implemented varied lockdown policies based on ID number and gender to analyse the impact of these policies on urban mobility. Using mobile phone data, we find that the severity of local lockdown rules, measured in the number of days citizens are allowed to go out, does not correlate with mobility reduction. Instead, we find that larger, wealthier cities with a more formalized and complex industrial structure experienced greater reductions in mobility. Commuters are more likely to stay home when their work is located in wealthy or commercially/industrially formalized neighbourhoods. Hence, our results indicate that cities' employment characteristics and workfrom home capabilities are the primary determinants of mobility reduction.

Controlling COVID-19: Labor structure is more important than lockdown policy

10.1016/S0140-6736(20)32262-5

COVID-19 is about the politics of the body. In a series of lectures and essays in the 1970s and early 1980s, Michel Foucault (who died in 1984) argued that the discipline of public health emerged with the birth of capitalism in the 18th century. The body came to be understood as an instrument of economic production, of labour power, and so became a subject of significant political interest. Medicine and public health were endorsed as tools to enhance these productive forces, to ensure that people were fit for work. The priority given to the body as an important determinant of mercantilist prosperity ran parallel with a further historical turn—the meaning of government. The idea of government began with the narrow objective of retaining jurisdiction over a defined territory. But in the 18th century, European governments incorporated the idea of economy into their practice. Economy then referred to the family. Advances in statistical measurement brought attention to an entirely new concept for governments to consider—that of population. Governments switched their focus from families to populations as the units on which their political economies depended. Population became, according to Foucault, “the ultimate end of government”.

Offline: COVID-19—a crisis of power

2011.00991

The multilayer network framework has served to describe and uncover a number of novel and unforeseen physical behaviors and regimes in interacting complex systems. However, the majority of existing studies are built on undirected multilayer networks while most complex systems in nature exhibit directed interactions. Here, we propose a framework to analyze diffusive dynamics on multilayer networks consisting of at least one directed layer. We rigorously demonstrate that directionality in multilayer networks can fundamentally change the behavior of diffusive dynamics: from monotonic (in undirected systems) to non-monotonic diffusion with respect to the interlayer coupling strength. Moreover, for certain multilayer network configurations, the directionality can induce a unique superdiffusion regime for intermediate values of the interlayer coupling, wherein the diffusion is even faster than that corresponding to the theoretical limit for undirected systems, i.e., the diffusion in the integrated network obtained from the aggregation of each layer. We theoretically and numerically show that the existence of superdiffusion is fully determined by the directionality of each layer and the topological overlap between layers. We further provide a formulation of multilayer networks displaying superdiffusion. Our results highlight the significance of incorporating the interacting directionality in multilevel networked systems and provide a framework to analyze dynamical processes on interconnected complex systems with directionality.

Unique superdiffusion induced by directionality in multiplex networks

2011.00445

We investigate the evolution of epidemics over dynamical networks when nodes choose to interact with others in a selfish and decentralized manner. Specifically, we analyze the susceptible-asymptomatic-infected-recovered (SAIR) epidemic in the framework of activity-driven networks with heterogeneous node degrees and time-varying activation rates, and derive both individual and degree-based mean-field approximations of the exact state evolution. We then present a game-theoretic model where nodes choose their activation probabilities in a strategic manner using current state information as feedback, and characterize the quantal response equilibrium (QRE) of the proposed setting. We then consider the activity-driven susceptible-infected-susceptible (SIS) epidemic model, characterize equilibrium activation probabilities and analyze epidemic evolution in closed-loop. Our numerical results provide compelling insights into epidemic evolution under game-theoretic activation. Specifically, for the SAIR epidemic, we show that under suitable conditions, the epidemic can persist, as any decrease in infected proportion is counteracted by an increase in activity rates by the nodes. For the SIS epidemic, we show that in regimes where there is an endemic state, the infected proportion could be significantly smaller under game-theoretic activation if the loss upon infection is sufficiently high.

Impacts of Game-Theoretic Activation on Epidemic Spread over Dynamical Networks

Just let the virus run free and protect only the weakest, some urge. Why that won’t work.

‘Herd Immunity’ Is an Inviting Idea but Terrible Policy

Renée DiResta is leading the fight against online disinformation. On the World Class Podcast, she describes what it’s like to expose malign actors in the emerging world of ceaseless propaganda and conspiracy theories.

Society Needs to Adapt to a World of Widespread Disinformation

Summary.    To fight Covid-19 the U.S. must be open to ideas from everywhere, including developing countries. Sometimes, less-wealthy countries can offer simple, low-tech solutions that are highly effective at containing infectious diseases.  A team at Northeastern University spent two months scouring the Internet for other ideas that less wealthy countries have used to address the pandemic in areas including prevention, testing, isolation, quarantining, treatment, and reopening and have organized more than 50 ideas across these categories on a website, Reverse Innovation to Fight Covid-19,

Global Crowdsourcing Can Help the U.S. Beat the Pandemic

An emphasis on self-determination contributed to dramatically lower case numbers than among non-Indigenous groups

How Indigenous Communities in Canada Organized an Exemplary Public Health Response to COVID

New research found ‘autoantibodies’ similar to those in lupus and rheumatoid arthritis patients. But patients may also benefit from treatments for those autoimmune diseases.

Some Covid Survivors Have Antibodies That Attack the Body, not Virus

The theory underlying network science predates the internet. But in 2020, it became essential to understanding our interconnected world.

The Science That Spans #MeToo, Memes, and Covid-19

10.1016/S0140-6736(20)32221-2

“How can we follow the science when scientists haven't the foggiest.” “‘Big mouth’ scientists losing trust of Ministers.” Headlines such as these are increasingly common in UK newspapers. Scientists are no longer seen as providing impartial, independent advice to government. They are seen as being responsible for crashing economies, driving up unemployment, and ruining livelihoods. Chris Whitty, England's Chief Medical Officer, and Patrick Vallance, Chief Scientific Adviser, have received the sharpest criticism. But attacks are now spreading to members of the scientific committees that advise government. They stand accused of leaking documents to journalists, promoting their own personal views, and collaborating with opposition political parties. The argument is that if a scientist is a member of a group advising ministers, they have chosen to be part of the government team and must abide by a rule of collective responsibility. The breakdown of trust between scientists and politicians is so great that senior figures in public health are being blocked from speaking to reporters, even on subjects unrelated to COVID-19.

Offline: Science and politics in the era of COVID-19

10.7249/RRA704-3

Given the size and scope of the Russian propaganda campaign that targeted the U.S. electorate in 2016, it is critical to understand both the impact of that campaign and the mechanisms that can reduce the impact of future campaigns. This report, the third in a four-part series, describes a study conducted by RAND researchers to assess how people react to and engage with Russia's online propaganda and to determine whether the negative effects of that engagement can be mitigated by brief media literacy advisories or by labeling the source of the propaganda. Russia targets the extremes on both sides of the political divide, and a short media literacy video and labeling intervention were both shown to reduce willingness among particular categories of participants (defined by news consumption habits) to "like" the propaganda.

Russian Propaganda Hits Its Mark Experimentally Testing the Impact of Russian Propaganda and Counter-Interventions

Krupenkin, Masha and Zhu, Kai and Walker, Dylan and Rothschild, David M., If a Tree Falls in the Forest: COVID-19, Media Choices, and Presidential Agenda Setting (September 22, 2020). Available at SSRN: https://ssrn.com/abstract=3697069 or http://dx.doi.org/10.2139/ssrn.3697069

During a time of crisis Americans turn their attention to the news media for critical information about what to expect, who is affected, and how to behave. Throughout the COVID-19 crisis, public safety experts warned that the consequences of a misinformed population would be particularly dire due to the serious nature of the threat and necessity of severe individual collective action to keep the population safe. Thus, those elites who possess the power to set the agenda of the conversation bear a huge responsibility for the general welfare. Among the various agenda-setting mechanisms available to the president is daily press conferences which provide a unique opportunity to leverage public exposure, accelerated by the state of crisis. Yet, mainstream media's daily viewership is many times larger than the president's press conference and we explore their narratives surrounding the COVID-19 pandemic through automated text analysis of complete transcripts of national cable, network, and local news. Of particular importance, we characterize the differences in which topics were covered and how they were covered by various cable media sources. Our analysis reveals polarized narratives around blame, racial and economic disparities, and scientific conclusions about COVID-19. The media is influenced by the president's agenda, even for cable news channels that are consumed by audiences that typically do not support him, but we found strong evidence that the media's choices mediate, and ultimately dominate, the agenda-setting abilities of the president's daily press conferences.

If a Tree Falls in the Forest: COVID-19, Media Choices, and Presidential Agenda Setting

Thinking critically is not a superpower. But the University of Alberta is offering a boost to critical thinking about science with a free online course — how to tell the difference between sound scientific studies and pseudoscience.

Battling fake science: University of Alberta launches free online science literacy course

As the pandemic drags on, following COVID-19 prevention guidelines can feel like more and more of a challenge.

Sick of COVID-19? Here’s why you might have pandemic fatigue

Academic union seeks legal battle over government’s decision to ignore expert advice to move all non-essential teaching online

UK academics: opening of universities was illegal

10.1016/S1473-3099(20)30785-4

Summary of findings from the publication, Li Y et al. The temporal association of introducing and lifting non-pharmaceutical interventions with the time-varying reproduction number (R) of SARS-CoV-2: a modelling study across 131 countries. Lancet Infectious Diseases 2020.

Public health measures and R

10.1016/S1473-3099(20)30785-4

Non-pharmaceutical interventions (NPIs) were implemented by many countries to reduce the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causal agent of COVID-19. A resurgence in COVID-19 cases has been reported in some countries that lifted some of these NPIs. We aimed to understand the association of introducing and lifting NPIs with the level of transmission of SARS-CoV-2, as measured by the time-varying reproduction number (R), from a broad perspective across 131 countries.

The temporal association of introducing and lifting non-pharmaceutical interventions with the time-varying reproduction number (R) of SARS-CoV-2: a modelling study across 131 countries

Of all the changes brought by a pandemic and the responses to it this year, many seem as if they were waiting to happen, given a hard nudge.

The Great Reset is here, like it or not

10.1038/d41586-020-02965-3

Injections of antibodies might prevent mild COVID-19 from becoming severe, but the treatments are expensive and difficult to make.

The race to make COVID antibody therapies cheaper and more potent

The COVID-19 pandemic has provided a unique opportunity to think about the kind of future we want. TIME partnered with the World Economic Forum to ask leading thinkers to share ideas for how to transform the way we live and work.

The Great Reset

In an interview with Christian Drosten, the German virologist looks back on the mistakes he has made in the coronavirus pandemic – and ahead to the dangers that the pandemic still has in store for us.

"I Am Quite Apprehensive about What Might Otherwise Happen in Spring and Summer"

‘They were freaking out’: meet the people treating NHS workers for trauma

10.1126/science.abe9728

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread rapidly in Manaus, the capital of Amazonas state in northern Brazil. The attack rate there is an estimate of the final size of the largely unmitigated epidemic that occurred in Manaus. We use a convenience sample of blood donors to show that by June 2020, 1 month after the epidemic peak in Manaus, 44% of the population had detectable immunoglobulin G (IgG) antibodies. Correcting for cases without a detectable antibody response and for antibody waning, we estimate a 66% attack rate in June, rising to 76% in October. This is higher than in São Paulo, in southeastern Brazil, where the estimated attack rate in October was 29%. These results confirm that when poorly controlled, COVID-19 can infect a large proportion of the population, causing high mortality.

Three-quarters attack rate of SARS-CoV-2 in the Brazilian Amazon during a largely unmitigated epidemic

10.1016/j.jpubeco.2020.104311

Fear, lockdown, and diversion: Comparing drivers of pandemic economic decline 2020

The collapse of economic activity in 2020 from COVID-19 has been immense. An important question is how much of that collapse resulted from government-imposed restrictions versus people voluntarily choosing to stay home to avoid infection. This paper examines the drivers of the economic slowdown using cellular phone records data on customer visits to more than 2.25 million individual businesses across 110 different industries. Comparing consumer behavior over the crisis within the same commuting zones but across state and county boundaries with different policy regimes suggests that legal shutdown orders account for only a modest share of the massive changes to consumer behavior (and that tracking county-level policy conditions is significantly more accurate than using state-level policies alone). While overall consumer traffic fell by 60 percentage points, legal restrictions explain only 7 percentage points of this. Individual choices were far more important and seem tied to fears of infection. Traffic started dropping before the legal orders were in place; was highly influenced by the number of COVID deaths reported in the county; and showed a clear shift by consumers away from busier, more crowded stores toward smaller, less busy stores in the same industry. States that repealed their shutdown orders saw symmetric, modest recoveries in consumer visits, further supporting the small estimated effect of policy. Although the shutdown orders had little aggregate impact, they did have a significant effect in reallocating consumer visits away from “nonessential” to “essential” businesses and from restaurants and bars toward groceries and other food sellers.

10.1093/ije/dyz132

Causal inference requires theory and prior knowledge to structure analyses, and is not usually thought of as an arena for the application of prediction modelling. However, contemporary causal inference methods, premised on counterfactual or potential outcomes approaches, often include processing steps before the final estimation step. The purposes of this paper are: (i) to overview the recent emergence of prediction underpinning steps in contemporary causal inference methods as a useful perspective on contemporary causal inference methods, and (ii) explore the role of machine learning (as one approach to ‘best prediction’) in causal inference. Causal inference methods covered include propensity scores, inverse probability of treatment weights (IPTWs), G computation and targeted maximum likelihood estimation (TMLE). Machine learning has been used more for propensity scores and TMLE, and there is potential for increased use in G computation and estimation of IPTWs.

Reflection on modern methods: when worlds collide—prediction, machine learning and causal inference

The U.S. Census Bureau’s Small Business Pulse Survey (SBPS) started asking businesses in April of last year when they expect to return to normal levels of operations, a crucial indication of how they are adapting to the pandemic, social distancing, and other public health strategies.

16.5% of Businesses Experienced Little or No Effect from Pandemic, 55.3% Expect Long-Term Challenges