Many of you are writing about race, racism and white supremacy right now. Please use this chart to correctly identify Black Americans. Accuracy in how we identify folks is paramount always but especially right now.

Study’s findings highlight potential long-term damage of UK’s early testing strategy

People who think they have had Covid-19 ‘less likely to download contact-tracing app’

Our World in Data presents the data and research to make progress against the world’s largest problems.This blog post draws on data and research discussed in our entry on the Coronavirus Pandemic. We update this chart – but not the text – daily

Is the world making progress against the pandemic? We built the chart to answer this question

2006.05410

Forecasting the evolution of contagion dynamics is still an open problem to which mechanistic models only offer a partial answer. To remain mathematically and/or computationally tractable, these models must rely on simplifying assumptions, thereby limiting the quantitative accuracy of their predictions and the complexity of the dynamics they can model. Here, we propose a complementary approach based on deep learning where the effective local mechanisms governing a dynamic are learned automatically from time series data. Our graph neural network architecture makes very few assumptions about the dynamics, and we demonstrate its accuracy using stochastic contagion dynamics of increasing complexity on static and temporal networks. By allowing simulations on arbitrary network structures, our approach makes it possible to explore the properties of the learned dynamics beyond the training data. Our results demonstrate how deep learning offers a new and complementary perspective to build effective models of contagion dynamics on networks.

Deep learning of stochastic contagion dynamics on complex networks

LONDON (Reuters) - Population-wide face mask use could push COVID-19 transmission down to controllable levels for national epidemics, and could prevent further waves of the pandemic disease when combined with lockdowns, according to a British study on Wednesday.

Widespread mask-wearing could prevent COVID-19 second waves: study

From lethal pandemic to common cold: what we can learn from the events of 1889-90

Could the key to Covid be found in the Russian pandemic?

As the world begins to unlock, many of us will be seeing friends and family again - albeit with guidelines on how close you can get to one another. But why is it more difficult to stay physically apart from friends and family than a stranger in a supermarket queue? Nicola Davis speaks to Prof John Drury about the psychology of physical distancing and why we like to be near those we feel emotionally close with

Covid-19: the psychology of physical distancing - podcast

Following on from its successful conception during the Curious 2019 summer events programme, the RSE has launched their ‘Tea & Talk‘ series as a podcast to provide access to experts talking on a wide range of subjects and provide the opportunity for listeners to learn something new, expand their horizons and hear from national and world experts in their respective disciplines. Hosted by Dr Rebekah Widdowfield, listen to a new episode every Friday.

Tea & Talk Podcast

A second wave of coronavirus cases would be disastrous – but there are ways to prevent this happening

Is the worst of the pandemic behind us? Here's what scientists know

Existing compartmental mathematical modelling methods for epidemics, such as SEIR models, cannot accurately represent effects of contact tracing. This makes them inappropriate for evaluating testing and contact tracing strategies to contain an outbreak. An alternative used in practice is the application of agent- or individual-based models (ABM). However ABMs are complex, less well-understood and much more computationally expensive. This paper presents a new method for accurately including the effects of Testing, contact-Tracing and Isolation (TTI) strategies in standard compartmental models. We derive our method using a careful probabilistic argument to show how contact tracing at the individual level is reflected in aggregate on the population level. We show that the resultant SEIR-TTI model accurately approximates the behaviour of a mechanistic agent-based model at far less computational cost. The computationally efficiency is such that it be easily and cheaply used for exploratory modelling to quantify the required levels of testing and tracing, alone and with other interventions, to assist adaptive planning for managing disease outbreaks.

10.1101/2020.05.14.20101808

Testing, tracing and isolation in compartmental models

Background There is limited evidence of genuine equal partnership where power is shared with young people with mental health difficulties throughout all research stages, particularly in data collection and analysis. Objective To describe how our qualitative study, exploring young peoples’ perceptions on the feasibility of using technology to detect mental health deterioration, was co‐produced using principles of co‐production, whilst reflecting on impact, challenges and recommendations. Methods Young people with experience of mental health difficulties were appointed and then worked with researchers throughout all research stages. The study was evaluated against the five principles of co‐production. Reflections from researchers and young people were collected throughout. Results Seven young people formed an initial Young People's Advisory Group (YPAG); three became co‐researchers. Reflection was key throughout the process. Sharing power became easier and more evident as trust, confidence and mutual respect grew over time, particularly after a safe space was established. The safe space was crucial for open discussions, and our WhatsApp group enabled continual communication, support and shared decision‐making. The resulting co‐produced topic guide, coding framework, thematic map, papers and presentations demonstrated significant impact. Conclusions To our knowledge, this is the first qualitative mental health study to be co‐produced using the principles of co‐production. Our rigorous assessment can be utilized as an informative document to help others to produce meaningful co‐produced future research. Although co‐production takes time, it makes significant impact to the research, researchers and co‐researchers. Flexible funding for spontaneous suggestions from co‐researchers and more time for interview training is recommended.

10.1111/hex.13088

Reflections, impact and recommendations of a co‐produced qualitative study with young people who have experience of mental health difficulties

Calls for the adoption of complex systems approaches, including agent-based modeling, in the field of epidemiology have largely centered on the potential for such methods to examine complex disease etiologies, which are characterized by feedback behavior, interference, threshold dynamics, and multiple interacting causal effects. However, considerable theoretical and practical issues impede the capacity of agent-based methods to examine and evaluate causal effects and thus illuminate new areas for intervention. We build on this work by describing how agent-based models can be used to simulate counterfactual outcomes in the presence of complexity. We show that these models are of particular utility when the hypothesized causal mechanisms exhibit a high degree of interdependence between multiple causal effects and when interference (i.e., one person's exposure affects the outcome of others) is present and of intrinsic scientific interest. Although not without challenges, agent-based modeling (and complex systems methods broadly) represent a promising novel approach to identify and evaluate complex causal effects, and they are thus well suited to complement other modern epidemiologic methods of etiologic inquiry.

10.1093/aje/kwu274

Formalizing the Role of Agent-Based Modeling in Causal Inference and Epidemiology

2001.02436

We provide a description of the Epidemics on Networks (EoN) python package designed for studying disease spread in static networks. The package consists of over 100100 methods available for users to perform stochastic simulation of a range of different processes including SIS and SIR disease, and generic simple or comlex contagions.

EoN (Epidemics on Networks): a fast, flexible Python package for simulation, analytic approximation, and analysis of epidemics on networks

10.1007/978-3-319-50806-1

This textbook provides an exciting new addition to the area of network science featuring a stronger and more methodical link of models to their mathematical origin and explains how these relate to each other with special focus on epidemic spread on networks. The content of the book is at the interface of graph theory, stochastic processes and dynamical systems. The authors set out to make a significant contribution to closing the gap between model development and the supporting mathematics. This is done by:Summarising and presenting the state-of-the-art in modeling epidemics on networks with results and readily usable models signposted throughout the book;Presenting different mathematical approaches to formulate exact and solvable models;Identifying the concrete links between approximate models and their rigorous mathematical representation;Presenting a model hierarchy and clearly highlighting the links between model assumptions and model complexity;Providing a reference source for advanced undergraduate students, as well as doctoral students, postdoctoral researchers and academic experts who are engaged in modeling stochastic processes on networks;Providing software that can solve differential equation models or directly simulate epidemics on networks.Replete with numerous diagrams, examples, instructive exercises, and online access to simulation algorithms and readily usable code, this book will appeal to a wide spectrum of readers from different backgrounds and academic levels. Appropriate for students with or without a strong background in mathematics, this textbook can form the basis of an advanced undergraduate or graduate course in both mathematics and other departments alike. 

Mathematics of Epidemics on Networks

Understanding and Exploring Network Epidemiology in the Time of Coronavirus (Net-COVID) was a special online workshop series presented by the University of Maryland’s COMBINE program in Network Biology in partnership with the University of Vermont’s Complex Systems Center.Videos of our Tutorials & Seminars and our Discussion/ Working Group Series are available for those who couldn't join our live sessions in April 2020. Content is aimed at the level of STEM graduate students.

Net-COVIDUnderstanding and Exploring Network Epidemiology in the Time of Coronavirus

In the United States and around the world, the conversation around COVID-19 is shifting toward reopening. But how do we know when it’s safe to reopen schools, businesses, communities, and countries? How do we make and follow a careful plan? And what will our new normal be when we get there? Some of the best tools we have to make these decisions are epidemiological models, which predict how the disease will spread. Alessandro Vespignani, director of the Network Science Institute and Sternberg Family distinguished university professor of physics, computer sciences, and health science, is leading one of the major modeling efforts and his work is informing the decisions being made at Northeastern, and around the world.  The university is planning a phased reopening, bringing faculty and staff back first, with the intention of opening all campuses to students in the fall. Following guidance from public health officials regarding COVID-19, the university is considering a number of safety measures, such as large-scale testing of students, faculty, and staff, as well as contact tracing for those who test positive for the virus. The priority, says Joseph E. Aoun, president of Northeastern, is “maintaining the health and wellbeing of the Northeastern University community—and the world beyond our campuses.”

‘A network is crucial to describe how infectious disease spreads, and this is what we do’

10.25561/77482

The global impact of COVID-19 has been profound, and the public health threat it represents is the most serious seen in a respiratory virus since the 1918 H1N1 influenza pandemic. Here we present the results of epidemiological modelling which has informed policymaking in the UK and other countries in recent weeks. In the absence of a COVID-19 vaccine, we assess the potential role of a number of public health measures – so-called non-pharmaceutical interventions (NPIs) – aimed at reducing contact rates in the population and thereby reducing transmission of the virus. In the results presented here, we apply a previously published microsimulation model to two countries: the UK (Great Britain specifically) and the US. We conclude that the effectiveness of any one intervention in isolation is likely to be limited, requiring multiple interventions to be combined to have a substantial impact on transmission. Two fundamental strategies are possible: (a) mitigation, which focuses on slowing but not necessarily stopping epidemic spread – reducing peak healthcare demand while protecting those most at risk of severe disease from infection, and (b) suppression, which aims to reverse epidemic growth, reducing case numbers to low levels and maintaining that situation indefinitely. Each policy has major challenges. We find that that optimal mitigation policies (combining home isolation of suspect cases, home quarantine of those living in the same household as suspect cases, and social distancing of the elderly and others at most risk of severe disease) might reduce peak healthcare demand by 2/3 and deaths by half. However, the resulting mitigated epidemic would still likely result in hundreds of thousands of deaths and health systems (most notably intensive care units) being overwhelmed many times over. For countries able to achieve it, this leaves suppression as the preferred policy option. We show that in the UK and US context, suppression will minimally require a combination of social distancing of the entire population, home isolation of cases and household quarantine of their family members. This may need to be supplemented by school and university closures, though it should be recognised that such closures may have negative impacts on health systems due to increased absenteeism. The major challenge of suppression is that this type of intensive intervention package – or something equivalently effective at reducing transmission – will need to be maintained until a vaccine becomes available (potentially 18 months or more) – given that we predict that transmission will quickly rebound if interventions are relaxed. We show that intermittent social distancing – triggered by trends in disease surveillance – may allow interventions to be relaxed temporarily in relative short time windows, but measures will need to be reintroduced if or when case numbers rebound. Last, while experience in China and now South Korea show that suppression is possible in the short term, it remains to be seen whether it is possible long-term, and whether the social and economic costs of the interventions adopted thus far can be reduced.

Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand

In the absence of effective treatments or a vaccine, social distancing has been the only public health measure available to combat the COVID-19 pandemic to date. In the US, implementing this response has been left to state, county, and city officials, and many localities have issued some form of a stay-at-home order. Without existing tools and with limited resources, localities struggled to understand how their orders changed behavior. In response, several technology companies opened access to their users' location data. As part of the COVID-19 Data Mobility Data Network, we obtained access to Facebook User data and developed four key metrics and visualizations to monitor various aspects of adherence to stay at home orders. These metrics were carefully incorporated into static and interactive visualizations for dissemination to local officials. All code is open source and freely available at https://github.com/ryanlayer/COvid19

10.1101/2020.06.04.20122093

Case Study: Using Facebook Data to Monitor Adherence to Stay-at-home Orders in Colorado and Utah

social media play a central role in scientific responding to the pandemic (and increasingly in science communication more generally), but scientist's Twitter feeds include lots of information that we would never include in our scientific articles and...

But at the same time, the inevitability of value-ladenness or politicization doesn't mean that there are no meaningful degrees here: just like bias might be impossible to eliminate, we can all identify more and less egregious cases

and worldviews influences their science and has argued that a strict separation is not possible (a claim you might or might not buy). Likewise, scientific facts and opinions may have political consequences and end up feeding into increasingly polarised debate

this is an important discussion! What needs clarifying is what "objective" and "disinterested" can and cannot (and should and should not) mean in a science context A wealth of research in the philosophy of science and history of science has shown that scientists values and

A multidisciplinary team of scientists, policymakers, government officials, and academics present a framework for classifying evidence used in policy

Standards for evidence in policy decision-making

Addendum: I just remembered, I mentioned the critiques of this literature to one of the authors of the preprint on twitter on April 2. Latest version of the preprint was posted April 9, but to be fair maybe the final draft was already finalized by Apr 2.

I'm less qualified to judge the rest, but it worries me that, for the lit I know best, they don't mention the very serious critiques, and they make very strong claims. That they do this while promising to highlight uncertainty/quality issues is maybe even more worrisome. /end

(I'd also argue that the implications of the claims in this paragraph are a bit outlandish and harmful as applied to COVID19 - it's not the type or amount of stress that matters, it's how you think about it? Really? Maybe for taking a math test, but for COVID19?) 7/n

"help reorganize our priorities, and can help lead to deeper relationships and a greater appreciation for life." At least some of this research is problematic, and the criticisms are known & published in visible outlets, e.g., https://journals.sagepub.com/doi/full/10.1177/0963721419827017… https://sciencedirect.com/science/article/pii/S0272735817302842… 6/n

I also found some problems. The literature I know best among the ones they review is the post traumatic growth literature. The authors write "the past twenty years of research on coping and stress suggest that it's not the type or amount of stress that determines its impact. 4/n

In this blog post, @StatModeling digs into a couple of their claims and finds that they are lacking in terms of portraying the uncertainty and the quality of the evidence. (Note: the post has attitude, but the points are good) https://statmodeling.stat.columbia.edu/2020/05/12/2-perspectives-on-the-relevance-of-social-science-to-our-current-predicament-1-social-scientists-should-back-off-or-2-social-science-has-a-lot-to-offer/… 3/n

Abstract: "we note the nature and quality of previous research, including uncertainty and unsettled issues" Intro: "we try to describe the quality of evidence to facilitate careful, critical engagement" Great goal. This encourages us to trust the authors. Do they deliver? 2/n

At the risk of piling on (tho the paper's been dowloaded > 8k times, so continued critical examination is called for, right?), here's one of the reasons I'm worried about "Using social & behavioural science to support COVID-19 pandemic response" (https://psyarxiv.com/y38m9) 1/n

10.1016/j.cpr.2018.07.003

The majority of multi-wave studies examining resilience in adulthood have involved growth mixture modeling (GMM). We critically evaluate the central conclusion from this body of work that “resilience is commonplace”. Our emphasis is on two questionable methodological assumptions underlying this conclusion: (1) the variances are the same across trajectories (i.e., homogeneity of variance) and (2) the amount of change does not differ across individuals (i.e., slope variances are zero). Seventy-seven empirical studies were included that used GMM to examine resilience to diverse adversities in adulthood. Of these 77 relevant studies, 66 (86%) assumed homogeneity of variances across trajectories and 52 (68%) set slope variances to zero; in the minority of studies where these assumptions were not applied (particularly the homogeneity of variance assumption), the resilient trajectory was among the smallest. Furthermore, 63 (82%) of the 77 studies conferred labels of resilience based on a single outcome, which is problematic as resilience is never an “across-the-board” phenomenon. Based on our conclusions, we discuss three important directions for future research: (1) replication across samples and measures, (2) illumination of processes leading to resilience, and (3) incorporation of a multidimensional approach. We conclude by outlining a resilience framework for research, practice, and policy.

Re-evaluating the notion that resilience is commonplace: A review and distillation of directions for future research, practice, and policy

10.1177/0963721419827017

The literature on resilience and posttraumatic growth has been instrumental in highlighting the human capacity to overcome adversity by illuminating that there are different pathways individuals may follow. Although the theme of strength from adversity is attractive and central to many disciplines and certain cultural narratives, this claim lacks robust empirical evidence. Specific issues include methodological approaches of using growth-mixture modeling in resilience research and retrospective assessments of growth. Conceptually, limitations exist in the examination of which outcomes are most appropriate for studying resilience and growth. We discuss new research intended to overcome these limitations, with a focus on prospective longitudinal designs and the value of integrating these disciplines for furthering our understanding of the human capacity to overcome adversity.

Fixing the Growth Illusion: New Directions for Research in Resilience and Posttraumatic Growth

2 perspectives on the relevance of social science to our current predicament: (1) social scientists should back off, or (2) social science has a lot to offer

Perspective 1: Social scientists should back off This is what the political scientist Anthony Fowler wrote the other day: The public appetite for more information about Covid-19 is understandably insatiable. Social scientists have been quick to respond. . . . While I understand the impulse, the rush to publish findings quickly in the midst of the crisis does little for the public and harms the discipline of social science. Even in normal times, social science suffers from a host of pathologies. Results reported in our leading scientific journals are often unreliable because researchers can be careless, they might selectively report their results, and career incentives could lead them to publish as many exciting results as possible, regardless of validity. A global crisis only exacerbates these problems. . . . and the promise of favorable news coverage in a time of crisis further distorts incentives. . . . Perspective 2: Social science has a lot to offer 42 people published an article that begins: The COVID-19 pandemic represents a massive global health crisis. Because the crisis requires large-scale behaviour change and places significant psychological burdens on individuals, insights from the social and behavioural sciences can be used to help align human behaviour with the recommendations of epidemiologists and public health experts. Here we discuss evidence from a selection of research topics relevant to pandemics, including work on navigating threats, social and cultural influences on behaviour, science communication, moral decision-making, leadership, and stress and coping.

10.1038/s41586-020-2404-8

Governments around the world are responding to the novel coronavirus (COVID-19) pandemic1 with unprecedented policies designed to slow the growth rate of infections. Many actions, such as closing schools and restricting populations to their homes, impose large and visible costs on society, but their benefits cannot be directly observed and are currently understood only through process-based simulations2–4. Here, we compile new data on 1,717 local, regional, and national non-pharmaceutical interventions deployed in the ongoing pandemic across localities in China, South Korea, Italy, Iran, France, and the United States (US). We then apply reduced-form econometric methods, commonly used to measure the effect of policies on economic growth5,6, to empirically evaluate the effect that these anti-contagion policies have had on the growth rate of infections. In the absence of policy actions, we estimate that early infections of COVID-19 exhibit exponential growth rates of roughly 38% per day. We find that anti-contagion policies have significantly and substantially slowed this growth. Some policies have different impacts on different populations, but we obtain consistent evidence that the policy packages now deployed are achieving large, beneficial, and measurable health outcomes. We estimate that across these six countries, interventions prevented or delayed on the order of 62 million confirmed cases, corresponding to averting roughly 530 million total infections. These findings may help inform whether or when these policies should be deployed, intensified, or lifted, and they can support decision-making in the other 180+ countries where COVID-19 has been reported

The effect of large-scale anti-contagion policies on the COVID-19 pandemic

2006.03774

We introduce the problem of explaining graph generation, formulated as controlling the generative process to produce graphs of explainable desired structures. By directing this generative process, we can measure and explain the observed outcomes. We propose SHADOWCAST, a controllable generative model capable of mimicking real-world networks and directing the generation, as a novel approach to this problem. The proposed model is based on a conditional generative adversarial network for graph data. We design it with the capability to maintain generation control by managing the conditions. Comprehensive experiments on three real-world network datasets demonstrate our model's competitive performance in the graph generation task. Furthermore, we direct SHADOWCAST to generate explainable graphs of different structures to show its effective controllability. As the first work to pose the problem of explaining generated graphs by controlling the generation, SHADOWCAST paves the way for future research in this exciting area.

SHADOWCAST: Controlling Network Properties to Explain Graph Generation

2005.13512

One can point to a variety of historical milestones for gender equality in STEM (science, technology, engineering, and mathematics), however, practical effects are incremental and ongoing. It is important to quantify gender differences in subdomains of scientific work in order to detect potential biases and monitor progress. In this work, we study the relevance of gender in scientific collaboration patterns in the Institute for Operations Research and the Management Sciences (INFORMS), a professional society with sixteen peer-reviewed journals. Using their publication data from 1952 to 2016, we constructed a large temporal bipartite network between authors and publications, and augmented the author nodes with gender labels. We characterized differences in several basic statistics of this network over time, highlighting how they have changed with respect to relevant historical events. We find a steady increase in participation by women (e.g., fraction of authorships by women and of new women authors) starting around 1980. However, women still comprise less than 25% of the INFORMS society and an even smaller fraction of authors with many publications. Moreover, we describe a methodology for quantifying the structural role of an authorship with respect to the overall connectivity of the network, using it to measure subtle differences between authorships by women and by men. Specifically, as measures of structural importance of an authorship, we use effective resistance and contraction importance, two measures related to diffusion throughout a network. As a null model, we propose a degree-preserving temporal and geometric network model with emergent communities. Our results suggest the presence of systematic differences between the collaboration patterns of men and women that cannot be explained by only local statistics.

Gender and collaboration patterns in a temporal scientific authorship network

10.1088/1367-2630/ab74a7

Due to the interconnectedness of financial entities, estimating certain key properties of a complex financial system, including the implied level of systemic risk, requires detailed information about the structure of the underlying network of dependencies. However, since data about financial linkages are typically subject to confidentiality, network reconstruction techniques become necessary to infer both the presence of connections and their intensity. Recently, several 'horse races' have been conducted to compare the performance of the available financial network reconstruction methods. These comparisons were based on arbitrarily chosen metrics of similarity between the real network and its reconstructed versions. Here we establish a generalized maximum-likelihood approach to rigorously define and compare weighted reconstruction methods. Our generalization uses the maximization of a certain conditional entropy to solve the problem represented by the fact that the density-dependent constraints required to reliably reconstruct the network are typically unobserved and, therefore, cannot enter directly, as sufficient statistics, in the likelihood function. The resulting approach admits as input any reconstruction method for the purely binary topology and, conditionally on the latter, exploits the available partial information to infer link weights. We find that the most reliable method is obtained by 'dressing' the best-performing binary method with an exponential distribution of link weights having a properly density-corrected and link-specific mean value and propose two safe (i.e. unbiased in the sense of maximum conditional entropy) variants of it. While the one named CReM A is perfectly general (as a particular case, it can place optimal weights on a network if the bare topology is known), the one named CReM B is recommended both in case of full uncertainty about the network topology and if the existence of some links is certain. In these cases, the CReM B is faster and reproduces empirical networks with highest generalized likelihood among the considered competing models.

A faster horse on a safer trail: generalized inference for the efficient reconstruction of weighted networks

A diary by Royal Free Hospital chief executive Kate Slemeck, reflecting on Britain’s first coronavirus cases, hosting camera crews and the prospect of a second wave.

“We’re preparing for another spike”: A London NHS hospital chief reflects on the peak of Covid-19

The Guardian’s 29 May article (‘Soas to slash budgets and staff as debt crisis worsens in a pandemic’) has brought attention to a worrying development, which risks seeing losses of livelihoods and expertise at a unique and world-renowned institution. The danger is that framing SOAS’s financial difficulties in isolation obscures the fact that this is a sector-wide crisis that will only be resolved by a turnaround in government policy. In a highly marketized education sector, speculation about a university’s future can impact student numbers, the institution’s lifeline. This is the Catch-22 that university sector workers are now trapped in: to name a crisis is to make it worse.

Institutional Vandalism: The University & Covid-19

@PsyArXivBot just got a sibling I just created a twitter bot for @metaarxiv for all the people interested in following meta-research preprints.

Concerns about the next pandemic should spark a push for good city planning and policy rather than a backlash against density and transit upgrades.

As a scientist, it’s clear to me that the collective action vital to success relies on leaders being honest about their strategy

Without public faith in government actions, the UK will never beat Covid-19

An epidemiologist points to new stresses in the U.S. mental health system that may persist from the novel coronavirus pandemic

Zoom Psychiatrists Prep for COVID-19’s Endless Ride

The scientific needs and computational limitations of the twentieth century fashioned classical statistical methodology. Both the needs and limitations have changed in the twenty-first, and so has the methodology. Large-scale prediction algorithms—neural nets, deep learning, boosting, support vector machines, random forests—have achieved star status in the popular press. They are recognizable as heirs to the regression tradition, but ones carried out at enormous scale and on titanic datasets. How do these algorithms compare with standard regression techniques such as ordinary least squares or logistic regression? Several key discrepancies will be examined, centering on the differences between prediction and estimation or prediction and attribution (significance testing). Most of the discussion is carried out through small numerical examples.

10.1080/01621459.2020.1762613

Prediction, Estimation, and Attribution

Scientists are racing to work out why some populations have fared better than others during the pandemic

Are we underestimating how many people are resistant to Covid-19?