Government imposes mandatory measures after failing to contain new surge in infections

Long a Holdout From Covid-19 Restrictions, Sweden Ends Its Pandemic Experiment

presumably the "disambiguation" between the scenarios has happened now? so, is the model still featuring in Independent Sage recommendations? are there further revisions/fixes coming? does this change perceptions on more 'mainstream' epidem. models?

..even under pessimistic assumptions about loss of humoral immunity endowed by antibodies." 7/n

"Put simply, an effective herd immunity—that works hand-in-hand appropriate public health and local lockdown measures—requires less than 20% seroprevalence. This seroprevalence has already been reached in many countries and is sufficient to preclude a traumatic second wave, 6/n

and .. 5/n

Please see 50 for a more comprehensive analysis. Note that in a month or two, death rates should disambiguate between these scenarios." 4/n

..of contact tracing (FTTIS)—modelled as the probability of self-isolating, given one is infected but asymptomatic. It can be seen that even with a relatively low efficacy of 25%, elimination is possible by November, with convergence to zero fatality rates... 3/n

"These posterior predictions suggest that, under the assumption that immunity endures for a year or two, there may be a mild inflation of fatality rates over the autumn, peaking at about 30 per day. This second wave could be eliminated completely with an increase in the efficacy

I just had cause to revisit the Friston modelling paper from Sept: https://medrxiv.org/content/10.1101/2020.09.01.20185876v1.full.pdf… 1/n

10.1126/sciadv.abe0166

Transmission of highly infectious respiratory diseases, including SARS-CoV-2, is facilitated by the transport of exhaled droplets and aerosols that can remain suspended in air for extended periods of time. A passenger car cabin represents one such situation with an elevated risk of pathogen transmission. Here, we present results from numerical simulations to assess how the in-cabin microclimate of a car can potentially spread pathogenic species between occupants for a variety of open and closed window configurations. We estimate relative concentrations and residence times of a noninteracting, passive scalar—a proxy for infectious particles—being advected and diffused by turbulent airflows inside the cabin. An airflow pattern that travels across the cabin, farthest from the occupants, can potentially reduce the transmission risk. Our findings reveal the complex fluid dynamics during everyday commutes and nonintuitive ways in which open windows can either increase or suppress airborne transmission.

Airflows inside passenger cars and implications for airborne disease transmission

10.1126/science.abe9403

INTRODUCTIONThe emergence of three lethal coronaviruses in <20 years and the urgency of the COVID-19 pandemic have prompted efforts to develop new therapeutic strategies, including by repurposing existing agents. After performing a comparative analysis of the three pathogenic human coronaviruses severe acute respiratory syndrome coronavirus 1 (SARS-CoV-1), SARS-CoV-2, and Middle East respiratory syndrome coronavirus (MERS-CoV), we identified shared biology and host-directed drug targets to prioritize therapeutics with potential for rapid deployment against current and future coronavirus outbreaks.RATIONALEExpanding on our recent SARS-CoV-2 interactome, we mapped the virus-host protein-protein interactions for SARS-CoV-1 and MERS-CoV and assessed the cellular localization of each viral protein across the three strains. We conducted two genetic screens of SARS-CoV-2 interactors to prioritize functionally-relevant host factors and structurally characterized one virus-host interaction. We then tested the clinical relevance of three more host factors by assessing risk in genetic cohorts or observing effectiveness of host factor–targeting drugs in real-world evidence.RESULTSQuantitative comparison of the 389 interactors of SARS-CoV-2, 366 of SARS-CoV-1, and 296 of MERS-CoV highlighted interactions with host processes that are conserved across all three viruses, including where nonorthologous proteins from different virus strains seem to fill similar roles. We also localized each individually-expressed viral protein by microscopy and then raised and validated antisera against 14 SARS-CoV-2 proteins to determine their localization during infection.On the basis of two independent genetic perturbation screens, we identified 73 host factors that, when depleted, caused significant changes in SARS-CoV-2 replication. From this list of potential drug targets, we validated the biological and clinical relevance of Tom70, IL17RA, PGES-2, and SigmaR1.A 3-Å cryo–electron microscopy structure of Tom70, a mitochondrial import receptor, in complex with SARS-CoV-2 ORF9b, provides insight into how ORF9b may modulate the host immune response. Using curated genome-wide association study data, we found that individuals with genotypes corresponding to higher soluble IL17RA levels in plasma are at decreased risk of COVID-19 hospitalization.To demonstrate the value of our data for drug repurposing, we identified SARS-CoV-2 patients who were prescribed drugs against prioritized targets and asked how they fared compared with carefully matched patients treated with clinically similar drugs that do not inhibit SARS-CoV-2. Both indomethacin, an inhibitor of host factor PGES-2, and typical antipsychotics, selected for their interaction with sigma receptors, showed effectiveness against COVID-19 compared with celecoxib and atypical antipsychotics, respectively.CONCLUSIONBy employing an integrative and collaborative approach, we identified conserved mechanisms across three pathogenic coronavirus strains and further investigated potential drug targets. This versatile approach is broadly applicable to other infectious agents and disease areas.

Comparative host-coronavirus protein interaction networks reveal pan-viral disease mechanisms

Renewed restrictions may be necessary, but they have a heavy toll.

What Has Lockdown Done to Us?

10.1101/2020.10.29.20222174

Background Concerns have been raised that the response to the UK COVID-19 pandemic may have worsened physical and mental health, and reduced use of health services. However, the scale of the problem is unquantified, impeding development of effective mitigations. We asked what has happened to general practice contacts for acute physical and mental health outcomes during the pandemic?Methods Using electronic health records from the Clinical Research Practice Datalink (CPRD) Aurum (2017-2020), we calculated weekly primary care contacts for selected acute physical and mental health conditions (including: anxiety, depression, acute alcohol-related events, asthma and chronic obstructive pulmonary disease [COPD] exacerbations, cardiovascular and diabetic emergencies). We used interrupted time series (ITS) analysis to formally quantify changes in conditions after the introduction of population-wide restrictions (‘lockdown’) compared to the period prior to their introduction in March 2020.Findings The overall population included 9,863,903 individuals on 1st January 2017. Primary care contacts for all conditions dropped dramatically after introduction of population-wide restrictions. By July 2020, except for unstable angina and acute alcohol-related events, contacts for all conditions had not recovered to pre-lockdown levels. The largest reductions were for contacts for: diabetic emergencies (OR: 0.35, 95% CI: 0.25-0.50), depression (OR: 0.53, 95% CI: 0.52-0.53), and self-harm (OR: 0.56, 95% CI: 0.54-0.58).Interpretation There were substantial reductions in primary care contacts for acute physical and mental conditions with restrictions, with limited recovery by July 2020. It is likely that much of the deficit in care represents unmet need, with implications for subsequent morbidity and premature mortality. The conditions we studied are sufficiently severe that any unmet need will have substantial ramifications for the people experiencing the conditions and healthcare provision. Maintaining access must be a key priority in future public health planning (including further restrictions).

COVID-19 collateral: Indirect acute effects of the pandemic on physical and mental health in the UK

10.1103/PhysRevE.102.042314

Motivated by the importance of individual differences in risk perception and behavior change in people's responses to infectious disease outbreaks (particularly the ongoing COVID-19 pandemic), we propose a heterogeneous disease-behavior-information transmission model, in which people's risk of getting infected is influenced by information diffusion, behavior change, and disease transmission. We use both a mean-field approximation and Monte Carlo simulations to analyze the dynamics of the model. Information diffusion influences behavior change by allowing people to be aware of the disease and adopt self-protection and subsequently affects disease transmission by changing the actual infection rate. Results show that (a) awareness plays a central role in epidemic prevention, (b) a reasonable fraction of overreacting nodes are needed in epidemic prevention (c) the basic reproduction number R0<math xmlns="http://www.w3.org/1998/Math/MathML"><msub><mi>R</mi><mn>0</mn></msub></math> has different effects on epidemic outbreak for cases with and without asymptomatic infection, and (d) social influence on behavior change can remarkably decrease the epidemic outbreak size. This research indicates that the media and opinion leaders should not understate the transmissibility and severity of diseases to ensure that people become aware of the disease and adopt self-protection to protect themselves and the whole population.

Effect of heterogeneous risk perception on information diffusion, behavior change, and disease transmission

10.1038/s41562-020-00994-6

During COVID-19, governments and the public are fighting not only a pandemic but also a co-evolving infodemic—the rapid and far-reaching spread of information of questionable quality. We analysed more than 100 million Twitter messages posted worldwide during the early stages of epidemic spread across countries (from 22 January to 10 March 2020) and classified the reliability of the news being circulated. We developed an Infodemic Risk Index to capture the magnitude of exposure to unreliable news across countries. We found that measurable waves of potentially unreliable information preceded the rise of COVID-19 infections, exposing entire countries to falsehoods that pose a serious threat to public health. As infections started to rise, reliable information quickly became more dominant, and Twitter content shifted towards more credible informational sources. Infodemic early-warning signals provide important cues for misinformation mitigation by means of adequate communication strategies.

Assessing the risks of ‘infodemics’ in response to COVID-19 epidemics

10.1103/PhysRevE.102.040302

Infectious disease outbreaks are expected to grow exponentially in time when left unchecked. Containment measures such as lockdown and social distancing can drastically alter the growth dynamics of the outbreak. This is the case for the 2019–2020 COVID-19 outbreak, which is characterized by a power-law growth. Strikingly however, the power-law exponent is different across countries. Here I illustrate the relationship between these two extreme scenarios, exponential and power-law growth, based on the impact of superspreaders and lockdown strategies to contain the outbreak. The theory predicts a relationship between the power- law exponent and the time interval between the first case and lockdown that is validated by the observed COVID-19 data across different countries.

Superspreaders and lockdown timing explain the power-law dynamics of COVID-19

10.1038/s41562-020-01000-9

Large events and gatherings, particularly those taking place indoors, have been linked to multitransmission events that have accelerated the pandemic spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To provide real-time, geolocalized risk information, we developed an interactive online dashboard that estimates the risk that at least one individual with SARS-CoV-2 is present in gatherings of different sizes in the United States. The website combines documented case reports at the county level with ascertainment bias information obtained via population-wide serological surveys to estimate real-time circulating, per-capita infection rates. These rates are updated daily as a means to visualize the risk associated with gatherings, including county maps and state-level plots. The website provides data-driven information to help individuals and policy makers make prudent decisions (for example, increasing mask-wearing compliance and avoiding larger gatherings) that could help control the spread of SARS-CoV-2, particularly in hard-hit regions.

Real-time, interactive website for US-county-level COVID-19 event risk assessment

10.1103/PhysRevE.102.052304

The constituents of a complex system exchange information to function properly. Their signaling dynamics often leads to the appearance of emergent phenomena, such as phase transitions and collective behaviors. While information exchange has been widely modeled by means of distinct spreading processes—such as continuous-time diffusion, random walks, synchronization and consensus—on top of complex networks, a unified and physically grounded framework to study information dynamics and gain insights about the macroscopic effects of microscopic interactions is still eluding us. In this paper, we present this framework in terms of a statistical field theory of information dynamics, unifying a range of dynamical processes governing the evolution of information on top of static or time-varying structures. We show that information operators form a meaningful statistical ensemble and their superposition defines a density matrix that can be used for the analysis of complex dynamics. As a direct application, we show that the von Neumann entropy of the ensemble can be a measure of the functional diversity of complex systems, defined in terms of the functional differentiation of higher-order interactions among their components. Our results suggest that modularity and hierarchy, two key features of empirical complex systems—from the human brain to social and urban networks—play a key role to guarantee functional diversity and, consequently, are favored.

Statistical physics of complex information dynamics

10.1038/s41562-020-01012-5

Affective polarization has become a defining feature of twenty-first-century US politics, but we do not know how it relates to citizens’ policy opinions. Answering this question has fundamental implications not only for understanding the political consequences of polarization, but also for understanding how citizens form preferences. Under most political circumstances, this is a difficult question to answer, but the novel coronavirus pandemic allows us to understand how partisan animus contributes to opinion formation. Using a two-wave panel that spans the outbreak of COVID-19, we find a strong association between citizens’ levels of partisan animosity and their attitudes about the pandemic, as well as the actions they take in response to it. This relationship, however, is more muted in areas with severe outbreaks of the disease. Our results make clear that narrowing of issue divides requires not only policy discourse but also addressing affective partisan hostility.

Affective polarization, local contexts and public opinion in America

Superspreaders, infected individuals who result in an outsized number of secondary cases, are believed to underlie a significant fraction of total SARS-CoV-2 transmission. Here, we combine empirical observations of SARS-CoV and SARS-CoV-2 transmission and extreme value statistics to show that the distribution of secondary cases is consistent with being fat-tailed, implying that large superspreading events are extremal, yet probable, occurrences. We integrate these results with interaction-based network models of disease transmission and show that superspreading, when it is fat-tailed, leads to pronounced transmission by increasing dispersion. Our findings indicate that large superspreading events should be the targets of interventions that minimize tail exposure.

Evidence that coronavirus superspreading is fat-tailed

10.1371/journal.pcbi.1008274

Extensive non-pharmaceutical and physical distancing measures are currently the primary interventions against coronavirus disease 2019 (COVID-19) worldwide. It is therefore urgent to estimate the impact such measures are having. We introduce a Bayesian epidemiological model in which a proportion of individuals are willing and able to participate in distancing, with the timing of distancing measures informed by survey data on attitudes to distancing and COVID-19. We fit our model to reported COVID-19 cases in British Columbia (BC), Canada, and five other jurisdictions, using an observation model that accounts for both underestimation and the delay between symptom onset and reporting. We estimated the impact that physical distancing (social distancing) has had on the contact rate and examined the projected impact of relaxing distancing measures. We found that, as of April 11 2020, distancing had a strong impact in BC, consistent with declines in reported cases and in hospitalization and intensive care unit numbers; individuals practising physical distancing experienced approximately 0.22 (0.11–0.34 90% CI [credible interval]) of their normal contact rate. The threshold above which prevalence was expected to grow was 0.55. We define the “contact ratio” to be the ratio of the estimated contact rate to the threshold rate at which cases are expected to grow; we estimated this contact ratio to be 0.40 (0.19–0.60) in BC. We developed an R package ‘covidseir’ to make our model available, and used it to quantify the impact of distancing in five additional jurisdictions. As of May 7, 2020, we estimated that New Zealand was well below its threshold value (contact ratio of 0.22 [0.11–0.34]), New York (0.60 [0.43–0.74]), Washington (0.84 [0.79–0.90]) and Florida (0.86 [0.76–0.96]) were progressively closer to theirs yet still below, but California (1.15 [1.07–1.23]) was above its threshold overall, with cases still rising. Accordingly, we found that BC, New Zealand, and New York may have had more room to relax distancing measures than the other jurisdictions, though this would need to be done cautiously and with total case volumes in mind. Our projections indicate that intermittent distancing measures—if sufficiently strong and robustly followed—could control COVID-19 transmission. This approach provides a useful tool for jurisdictions to monitor and assess current levels of distancing relative to their threshold, which will continue to be essential through subsequent waves of this pandemic.

Quantifying the impact of COVID-19 control measures using a Bayesian model of physical distancing

Components in many real-world complex systems depend on each other for the resources required for survival and may die of a shortage. These patterns of dependencies often take the form of a complex network whose structure potentially affects how the resources produced in the system are efficiently shared among its components, which in turn decides a network's survivability. Here we present a simple threshold model that provides insight into this relationship between the network structure and survivability. We show that, as a combined effect of local sharing and finite lifetime of resources, many components in a complex system may die of lack of resources even when a sufficient amount is available in the system. We also obtain a surprising result that although the scale-free networks exhibit a significantly higher survivability compared to their homogeneous counterparts, a vertex in the latter survives longer on average. Finally, we demonstrate that the system's survivability can be substantially improved by changing the way vertices distribute resources among the neighbors. Our work is a step towards understanding the relationship between intricate resource dependencies present in many real-world complex systems and their survivability.

Resource dependency and survivability in complex networks

A flood of coronavirus research swept websites and journals this year. It changed how and what scientists study, a Nature analysis shows.

How a torrent of COVID science changed research publishing — in seven charts

10.1073/pnas.2023895118

The world is getting closer, enabling far-ranging human movements as well as disease diffusions (1). This greater interconnectedness has drawn our attention to a core feature of the real world—the “small-world” characteristic (2). Thinking from a network perspective, the world consists of closely connected communities which are bridged by random, long-distance connections. However, this network structure has made the world more vulnerable to infectious disease.During the early stage of the COVID-19 pandemic, mobility restrictions such as lockdown measures have proven their worth in mitigating disease spread (3⇓–5). The current challenge is averting disease burden while promoting socioeconomic recovery. In order to craft solutions, we really need to detail and translate the effect of mobility restrictions.Schlosser et al. (6) bring us answers to two key questions surrounding the effect of COVID-19 lockdown: How does the structural mobility network change? What are the impacts on epidemic spreading? The authors use mobile phone data to uncover structural changes in mobility in Germany during the pandemic. They show a profound restructuring of the mobility network—a more local, clustered network by reducing long-distance travels. They relate this structure to epidemic transmission, pointing to the prominent effectiveness of this structural change to suppress epidemic curves and slow down the spatial spread. This study underscores the complex consequences of mobility restrictions, for policymakers, and provides general implications for similar scenarios in the future.

Mobility restrictions are more than transient reduction of travel activities

With the festive season ahead, Nature examines what is known about the risks of COVID spread, and how researchers will spend their time off.

Coronavirus and public holidays: what the data say

10.1073/pnas.2019324117

COVID-19 is a global pandemic with over 25 million cases worldwide. Currently, treatments are limited, and there is no approved vaccine. Interventions such as handwashing, masks, social distancing, and “social bubbles” are used to limit community transmission, but it is challenging to choose the best interventions for a given activity. Here, we provide a quantitative framework to determine which interventions are likely to have the most impact in which settings. We introduce the concept of “event R,” the expected number of new infections due to the presence of a single infectious individual at an event. We obtain a fundamental relationship between event R and four parameters: transmission intensity, duration of exposure, the proximity of individuals, and the degree of mixing. We use reports of small outbreaks to establish event R and transmission intensity in a range of settings. We identify principles that guide whether physical distancing, masks and other barriers to transmission, or social bubbles will be most effective. We outline how this information can be obtained and used to reopen economies with principled measures to reduce COVID-19 transmission.

Event-specific interventions to minimize COVID-19 transmission

10.1103/PhysRevE.102.062306

Mathematical modeling of epidemics is fundamental to understand the mechanism of the disease outbreak and provides helpful indications for effectiveness of interventions for policy makers. The metapopulation network model has been used in the analysis of epidemic dynamics by taking individual migration between patches into account. However, so far, most of the previous studies unrealistically assume that transmission rates within patches are the same, neglecting the nonuniformity of intervention measures in hindering epidemics. Here, based on the assumption that interventions deployed in a patch depend on its population size or economic level, which have shown a positive correlation with the patch's degree in networks, we propose a metapopulation network model to explore a network structure-based intervention strategy, aiming at understanding the interplay between intervention strategy and other factors including mobility patterns, initial population, as well as the network structure. Our results demonstrate that interventions to patches with different intensity are able to suppress the epidemic spreading in terms of both the epidemic threshold and the final epidemic size. Specifically, the intervention strategy targeting the patches with high degree is able to efficiently suppress epidemics. In addition, a detrimental effect is also observed depending on the interplay between the intervention measures and the initial population distribution. Our study opens a path for understanding epidemic dynamics and provides helpful insights into the implementation of countermeasures for the control of epidemics in reality.

Network structure-based interventions on spatial spread of epidemics in metapopulation networks

n the final Coronapod of 2020, we dive into the scientific literature to reflect on the COVID-19 pandemic. Researchers have discovered so much about SARS-CoV-2 – information that has been vital for public health responses and the rapid development of effective vaccines. But we also look forward to 2021, and the critical questions that remain to be answered about the pandemic.

Coronapod: The big COVID research papers of 2020

10.1038/s41586-020-03095-6

As countries in Europe gradually relaxed lockdown restrictions after the first wave, test–trace–isolate strategies became critical to maintain the incidence of coronavirus disease 2019 (COVID-19) at low levels1,2. Reviewing their shortcomings can provide elements to consider in light of the second wave that is currently underway in Europe. Here we estimate the rate of detection of symptomatic cases of COVID-19 in France after lockdown through the use of virological3 and participatory syndromic4 surveillance data coupled with mathematical transmission models calibrated to regional hospitalizations2. Our findings indicate that around 90,000 symptomatic infections, corresponding to 9 out 10 cases, were not ascertained by the surveillance system in the first 7 weeks after lockdown from 11 May to 28 June 2020, although the test positivity rate did not exceed the 5% recommendation of the World Health Organization (WHO)5. The median detection rate increased from 7% (95% confidence interval, 6–8%) to 38% (35–44%) over time, with large regional variations, owing to a strengthening of the system as well as a decrease in epidemic activity. According to participatory surveillance data, only 31% of individuals with COVID-19-like symptoms consulted a doctor in the study period. This suggests that large numbers of symptomatic cases of COVID-19 did not seek medical advice despite recommendations, as confirmed by serological studies6,7. Encouraging awareness and same-day healthcare-seeking behaviour of suspected cases of COVID-19 is critical to improve detection. However, the capacity of the system remained insufficient even at the low epidemic activity achieved after lockdown, and was predicted to deteriorate rapidly with increasing incidence of COVID-19 cases. Substantially more aggressive, targeted and efficient testing with easier access is required to act as a tool to control the COVID-19 pandemic. The testing strategy will be critical to enable partial lifting of the current restrictive measures in Europe and to avoid a third wave.

Underdetection of cases of COVID-19 in France threatens epidemic control

10.1073/pnas.2009787117

Humans and viruses have been coevolving for millennia. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, the virus that causes COVID-19) has been particularly successful in evading our evolved defenses. The outcome has been tragic—across the globe, millions have been sickened and hundreds of thousands have died. Moreover, the quarantine has radically changed the structure of our lives, with devastating social and economic consequences that are likely to unfold for years. An evolutionary perspective can help us understand the progression and consequences of the pandemic. Here, a diverse group of scientists, with expertise from evolutionary medicine to cultural evolution, provide insights about the pandemic and its aftermath. At the most granular level, we consider how viruses might affect social behavior, and how quarantine, ironically, could make us susceptible to other maladies, due to a lack of microbial exposure. At the psychological level, we describe the ways in which the pandemic can affect mating behavior, cooperation (or the lack thereof), and gender norms, and how we can use disgust to better activate native “behavioral immunity” to combat disease spread. At the cultural level, we describe shifting cultural norms and how we might harness them to better combat disease and the negative social consequences of the pandemic. These insights can be used to craft solutions to problems produced by the pandemic and to lay the groundwork for a scientific agenda to capture and understand what has become, in effect, a worldwide social experiment.

The pandemic exposes human nature: 10 evolutionary insights

The COVID-19 pandemic, combined with widespread financial and political instability, has served as a sustained source of stress for virtually everyone throughout 2020. Older adults are particularly vulnerable to contracting a severe or deadly case of the disease, but a study published in Psychological Science confirms previous research suggesting that age is associated with greater emotional well-being despite the risk posed by coronavirus.

Older Adults Maintain Emotional Advantage Amid COVID-19

In the past fortnight, two vaccine stories made headlines around the world. Novavax announced spectacular results for its phase 3 trial, while preliminary data suggest the AstraZeneca vaccine is ineffective against the South African variant. These two vaccines comprise the bulk of Australia’s vaccine portfolio, and the results should prompt an urgent rethink of our vaccination strategy. Australia won’t reach herd immunity with the current plan.

Herd immunity is the end game for the pandemic, but the AstraZeneca vaccine won’t get us there

I'm quite happy to engage in debate over the interpretation of data, but I'm disappointed with the tactics some people use. If you pay attention, you'll see people who don't (and can't) identify errors of fact, but rather resort to ad hominem attacks. Keep an eye out for that.

Finally, since I'm in the mood to point out unprofessional behaviour, I'd like to make some comments of a general nature (not directed to Dr Cevik).

Now, the point of this thread is not to seek sympathy or to arrange a pile-on. I don't want to see anyone behaving inappropriately toward Dr Cevik as a result. But I do want people to know what happened. Primarily, I hope calling out such behaviour will prevent a repeat of it.

Disagreements are to be expected from time to time, and I've had my fair share of those. Debate is integral to science, and I welcome it. But attempting to silence others' opinions is unacceptable. It is unbecoming of a scientist, and utterly incompatible with free inquiry.

I've been an academic for a long time - longer than my fresh-faced profile picture might suggest. Not once in my career have I ever encountered such unprofessional behaviour from a colleague, and I'm not going to put up with it.

She listed an astonishing 12 complaints (yes, 12!), said the article was grossly inaccurate, and asked for the article to be retracted. However, no errors of fact were identified, so the article has not been retracted.

I thought that was pretty strange, but laughed the matter off. After all, the complaint didn't go anywhere and I was supported by my university. But last week, she made a complaint to a publisher about an article I recently wrote. It was this article:

I don't like to dwell on negatives, but something important happened recently that I'd like to make public. Shortly before Christmas, @mugecevik made a complaint to my university about me. When asked for details, she didn't provide any. My employer took a dim view of the matter.

2.3 million people were vaccinated within 13 days in Bangladesh

13/ Apparently this type of tactic has a name, "the moat and the bailey", kind of like an inverse straw man. Thanks @raj_a_mehta

12/ Some replies say "just block those scientists." Unfortunately, not that easy. Some of these folks are well-known, are routinely interviewed by major news media. And they attack many different ppl this way. If we block, can't counter the misinformation they spread at times.

11/ This latter tactic is particularly harmful, because it starts to create a perception on Field X that "Field Y is attacking the younger members of Field X, thus the scientists from Field Y are a******s" Which creates barriers to collab. btw fields, precisely when most needed!

10/ One other feature of the "group attack", is that often they seem to call in to colleagues who may be unaware of the tactics. So new scientists barge in, who have been told "so and so are personally attacking me" and they join the fray. Having been manipulated into doing so.

9/ I look forward to continuing to talk with the large majority of scientists in Twitter who do so honestly and candidly. And I hope this may help a few people in identifying the more negative tactics.

8/ I ask all scientists to focus and engage on the arguments. What is the evidence for and against a given hypothesis? That's where Twitter convos can be extraordinary, enabling sorely-needed connections between fields , and that are far slower through journals and conferences.

7/ And you see this over and over. The same groups of scientists using the same sneaking tactics to avoid having to debate arguments. It took me quite a while to understand this pattern. I write this so that others perhaps can learn to recognize it faster.

6/ And they often work in groups. Once this dynamic is established, quickly other scientists show up in the conversation quickly, and join in denouncing the (non-existent) personal attack, trying to discredit me ("you don't know anything about X") instead of engaging argument.

5/ In some cases they play the victim. After they denounce the (non-existent) personal attack from me, they continue with "you are a senior researcher attacking poor me junior / female / etc. researcher" In a public forum like Twitter this can be very hard to counter.

4/ The trickier one are groups of scientists that when confronted with an argument they don't have a real answer to, when I press for an answer for the argument, they say that it is a personal attack from me.

3/ But there are some more nefarious behaviors out there. The simpler one is the scientist that resorts to personal "ad hominem" attacks (https://en.wikipedia.org/wiki/Ad_hominem), as in politics: to combat a scientific argument, discredit the person. I just ignore and block those folks.

2/ First of all say that 95% of the scientists in Twitter are great. This is one of the 2 main reasons I use twitter, that I can e.g. ask questions from experts in other fields and they'll reply etc. (The other reason is the ability to provide info to the population directly)

1/ Some reflections on scientific Twitter sociology I hadn't used Twitter much before the pandemic. I was used to the politics of peer-review, grants, large studies etc. But scientific Twitter can be the Wild West by comparison.

2/2 But to make people feel ok about publicly being wrong we need mechanisms for downplaying influence of ego. Twitter, however, in large parts, feels like a big ego enhancement machine. It's not a natural fit.

indeed -reminds me of the Koudenberg talk at SciBeh workshop on politeness online. More generally, to have debate focussed entirely on argument requires making people feel *more comfortable* about publicly being proven wrong. 1/2

both your points on "ending" and "pressing" as derailment tool, seem right to me. The better social media tool for science discourse I dream of has tools for both of these: we need tools for mining and linking arguments such that overall arguments/exchanges are cumulative!

And if you've been on twitter a bit, it's impossible not to notice that "pressing" people on twitter a tactic that people use all the time to derail convos, to troll, etc; so of course people are going to sometimes be repulsed by it, even when used by well-intentioned peers.

To be clear, I'm not condoning twitter pile-ons, etc. But getting on twitter and acting like people (literally anyone) owe you a response is just not going to go well. It's ok to just let the "argument" end and move on.

One thought is that we generally don't "press" strangers or even colleagues in face to face conversations, and when we do, it's usually perceived as pretty aggressive. Not sure why anyone would expect it to work better on twitter.

more on science Twitter - we need better tools, and better understanding of how the medium interacts with content! Join our efforts at http://Scibeh.org

10.1038/d41586-021-00097-w

As more lineages emerge, researchers are struggling with a patchwork of nomenclature.

‘A bloody mess’: Confusion reigns over naming of new COVID variants

Factors influencing COVID-19 vaccineuptakeamong minority ethnic groups

This winter the novel coronavirus may kill more people than heart disease, cancer, stroke, Alzheimer’s or diabetes

COVID Is on Track to Become the U.S.’s Leading Cause of Death—Yet Again

Pandemic's toll on mental health: 'We're suffering some real stuff'

Are you OK? I'm not.

(the fridge metaphor adapted from @ESYudkowsky's old post on how even movements dedicated to rationality can slide into becoming cults) https://lesswrong.com/posts/yEjaj7PWacno5EvWa/every-cause-wants-to-be-a-cult

“What have they got against democracy,” sighs the Democratic People’s Republic of North Korea

Writing the word “sceptical” on your movement doesn’t make you sceptics, any more than writing the word “cold” on a box makes it a fridge. Scepticism involves work, real work, assessing and revising your own beliefs, not just reflexively rejecting what you see as orthodoxy

People's opinions evolve over time as they interact with their friends, family, colleagues, and others. In the study of opinion dynamics on networks, one often encodes interactions between people in the form of dyadic relationships, but many social interactions in real life are polyadic (i.e., they involve three or more people). In this paper, we extend an asynchronous bounded-confidence model (BCM) on graphs, in which nodes are connected pairwise by edges, to hypergraphs. We show that our hypergraph BCM converges to consensus under a wide range of initial conditions for the opinions of the nodes. We show that, under suitable conditions, echo chambers can form on hypergraphs with community structure. We also observe that the opinions of individuals can sometimes jump from one opinion cluster to another in a single time step, a phenomenon (which we call "opinion jumping") that is not possible in standard dyadic BCMs. We also show that there is a phase transition in the convergence time on the complete hypergraph when the variance σ2\sigma^2 of the initial opinion distribution equals the confidence bound cc. Therefore, to determine the convergence properties of our hypergraph BCM when the variance and the number of hyperedges are both large, it is necessary to use analytical methods instead of relying only on Monte Carlo simulations.

A Bounded-Confidence Model of Opinion Dynamics on Hypergraphs

Many counties, particularly small counties, do not have enough paid health care workers to staff vaccination clinics for hours every day, so they rely on volunteers to squeeze in time on days off, and before and after work, to help inoculate people against the deadly virus. The people stepping up are often doctors, nurses and students who give up their free time after long hours in classrooms, medical practices, ICUs, and ERs where they have battled the virus for the past year.

Health care workers, students spending free time as volunteers giving COVID-19 vaccination shots. ‘This is one of the most therapeutic things’