The Shape of Epidemics

Curated on Posted on by Stefaan Verhulst

Essay by David S. Jones and Stefan Helmreich: “…Is the most recent rise in new cases—the sharp increase in case counts and hospitalizations reported this week in several states—a second wave, or rather a second peak of a first wave? Will the world see a devastating second wave in the fall?

Such imagery of waves has pervaded talk about the propagation of the infection from the beginning. On January 29, just under a month after the first instances of COVID-19 were reported in Wuhan, Chinese health officials published a clinical report about their first 425 cases, describing them as “the first wave of the epidemic.” On March 4 the French epidemiologist Antoine Flahault asked, “Has China just experienced a herald wave, to use terminology borrowed from those who study tsunamis, and is the big wave still to come?” The Asia Times warned shortly thereafter that “a second deadly wave of COVID-19 could crash over China like a tsunami.” A tsunami, however, struck elsewhere, with the epidemic surging in Iran, Italy, France, and then the United States. By the end of April, with the United States having passed one million cases, the wave forecasts had become bleaker. Prominent epidemiologists predicted three possible future “wave scenarios”—described by one Boston reporter as “seascapes,” characterized either by oscillating outbreaks, the arrival of a “monster wave,” or a persistent and rolling crisis.


From Kristine Moore et al., “The Future of the COVID-19 Pandemic” (April 30, 2020). Used with permission from the Center for Infectious Disease Research and Policy, University of Minnesota.

While this language may be new to much of the public, the figure of the wave has long been employed to describe, analyze, and predict the behavior of epidemics. Understanding this history can help us better appreciate the conceptual inheritances of a scientific discipline suddenly at the center of public discussion. It can also help us judge the utility as well as limitations of those representations of epidemiological waves now in play in thinking about the science and policy of COVID-19. As the statistician Edward Tufte writes in his classic work The Visual Display of Quantitative Information (1983), “At their best, graphics are instruments for reasoning about quantitative information.” The wave, operating as a hybrid of the diagrammatic, mathematical, and pictorial, certainly does help to visualize and think about COVID-19 data, but it also does much more. The wave image has become an instrument for public health management and prediction—even prophecy—offering a synoptic, schematic view of the dynamics it describes.

This essay sketches this backstory of epidemic waves, which falls roughly into three eras: waves emerge first as a device of data visualization, then evolve into an object of mathematical modeling and causal investigation and finally morph into a tool of persuasion, intervention, and governance. Accounts of the wave-like rise and fall of rates of illness and death in populations first appeared in the mid-nineteenth century, with England a key player in developments that saw government officials collect data permitting the graphical tabulation of disease trends over time. During this period the wave image was primarily metaphorical, a heuristic way of talking about patterns in data. Using curving numerical plots, epidemiologists offered analogies between the spread of infection and the travel of waves, sometimes transposing the temporal tracing of epidemic data onto maps of geographical space. Exactly what mix of forces—natural or social—generated these “epidemic waves” remained a source of speculation….(More)”.

Five ways to ensure that models serve society: a manifesto

Curated on Posted on by Stefaan Verhulst

Andrea Saltelli et al at Nature: “The COVID-19 pandemic illustrates perfectly how the operation of science changes when questions of urgency, stakes, values and uncertainty collide — in the ‘post-normal’ regime.

Well before the coronavirus pandemic, statisticians were debating how to prevent malpractice such as p-hacking, particularly when it could influence policy1. Now, computer modelling is in the limelight, with politicians presenting their policies as dictated by ‘science’2. Yet there is no substantial aspect of this pandemic for which any researcher can currently provide precise, reliable numbers. Known unknowns include the prevalence and fatality and reproduction rates of the virus in populations. There are few estimates of the number of asymptomatic infections, and they are highly variable. We know even less about the seasonality of infections and how immunity works, not to mention the impact of social-distancing interventions in diverse, complex societies.

Mathematical models produce highly uncertain numbers that predict future infections, hospitalizations and deaths under various scenarios. Rather than using models to inform their understanding, political rivals often brandish them to support predetermined agendas. To make sure predictions do not become adjuncts to a political cause, modellers, decision makers and citizens need to establish new social norms. Modellers must not be permitted to project more certainty than their models deserve; and politicians must not be allowed to offload accountability to models of their choosing2,3.

This is important because, when used appropriately, models serve society extremely well: perhaps the best known are those used in weather forecasting. These models have been honed by testing millions of forecasts against reality. So, too, have ways to communicate results to diverse users, from the Digital Marine Weather Dissemination System for ocean-going vessels to the hourly forecasts accumulated by weather.com. Picnickers, airline executives and fishers alike understand both that the modelling outputs are fundamentally uncertain, and how to factor the predictions into decisions.

Here we present a manifesto for best practices for responsible mathematical modelling. Many groups before us have described the best ways to apply modelling insights to policies, including for diseases4 (see also Supplementary information). We distil five simple principles to help society demand the quality it needs from modelling….(More)”.

UN Data Strategy

Curated on Posted on by Stefaan Verhulst

United Nations: “As structural UN reforms consolidate, we are focused on building the data, digital, technology and innovation capabilities that the UN needs to succeed in the 21st century. The Secretary General’s “Data Strategy for Action by Everyone, Everywhere” is our agenda for the data-driven transformation.

Data permeates all aspects of our work, and its power—harnessed responsibly—is critical to the global agendas we serve. The UN family’s footprint, expertise and connectedness create unique opportunities to advance global “data action” with insight, impact and integrity. To help unlock more potential, 50 UN entities jointly designed this Strategy as a comprehensive playbook for data-driven change based on global best practice…

Our strategy pursues a simple idea: we focus not on process, but on learning, iteratively, to deliver data use cases that add value for stakeholders based on our vision, outcomes and principles. Use cases – purposes for which data is used – already permeate our organization. We will systematically identify and deliver them through dedicated data action portfolios. While new capabilities will in part emerge through “learning by doing”, we will also strengthen organizational enablers to deliver on our vision, including shifts in people and culture, partnerships, data governance and technology….(More)”.

United Nations Data Strategy

Politicians ignore far-out risks: they need to up their game

Curated on Posted on by Stefaan Verhulst

The Economist: “In 1993 this newspaper told the world to watch the skies. At the time, humanity’s knowledge of asteroids that might hit the Earth was woefully inadequate. Like nuclear wars and large volcanic eruptions, the impacts of large asteroids can knock seven bells out of the climate; if one thereby devastated a few years’ worth of harvests around the globe it would kill an appreciable fraction of the population. Such an eventuality was admittedly highly unlikely. But given the consequences, it made actuarial sense to see if any impact was on the cards, and at the time no one was troubling themselves to look.

Asteroid strikes were an extreme example of the world’s wilful ignorance, perhaps—but not an atypical one. Low-probability, high-impact events are a fact of life. Individual humans look for protection from them to governments and, if they can afford it, insurers. Humanity, at least as represented by the world’s governments, reveals instead a preference to ignore them until forced to react—even when foresight’s price-tag is small. It is an abdication of responsibility and a betrayal of the future.

Covid-19 offers a tragic example. Virologists, epidemiologists and ecologists have warned for decades of the dangers of a flu-like disease spilling over from wild animals. But when sars–cov-2 began to spread very few countries had the winning combination of practical plans, the kit those plans required in place and the bureaucratic capacity to enact them. Those that did benefited greatly. Taiwan has, to date, seen just seven covid-19 deaths; its economy has suffered correspondingly less.

Pandemics are disasters that governments have experience of. What therefore of truly novel threats? The blazing hot corona which envelops the Sun—seen to spectacular effect during solar eclipses—intermittently throws vast sheets of charged particles out into space. These cause the Northern and Southern Lights and can mess up electric grids and communications. But over the century or so in which electricity has become crucial to much of human life, the Earth has never been hit by the largest of these solar eructations. If a coronal mass ejection (cme) were to hit, all sorts of satellite systems needed for navigation, communications and warnings of missile attacks would be at risk. Large parts of the planet could face months or even years without reliable grid electricity (see Briefing). The chances of such a disaster this century are put by some at better than 50:50. Even if they are not that high, they are still higher than the chances of a national leader knowing who in their government is charged with thinking about such things.

The fact that no governments have ever seen a really big cme, or a volcanic eruption large enough to affect harvests around the world—the most recent was Tambora, in 1815—may explain their lack of forethought. It does not excuse it. Keeping an eye on the future is part of what governments are for. Scientists have provided them with the tools for such efforts, but few academics will undertake the work unbidden, unfunded and unsung. Private business may take some steps when it perceives specific risks, but it will not put together plans for society at large….(More)”.

Opportunities of Artificial Intelligence

Curated on Posted on by Stefaan Verhulst

Report for the European Parliament: “A vast range of AI applications are being implemented by European industry, which can be broadly grouped into two categories: i) applications that enhance the performance and efficiency of processes through mechanisms such as intelligent monitoring, optimisation and control; and ii) applications that enhance human-machine collaboration.

At present, such applications are being implemented across a broad range of European industrial sectors. However, some sectors (e.g. automotive, telecommunications, healthcare) are more advanced in AI deployment than others (e.g. paper and pulp, pumps, chemicals). The types of AI applications
implemented also differ across industries. In less digitally mature sectors, clear barriers to adoption have been identified, including both internal (e.g. cultural resistance, lack of skills, financial considerations) and external (e.g. lack of venture capital) barriers. For the most part, and especially for SMEs, barriers to the adoption of AI are similar to those hindering digitalisation. The adoption of such AI applications is anticipated to deliver a wide range of positive impacts, for individual firms, across value chains, as well as at the societal and macroeconomic levels. AI applications can bring efficiency, environmental and economic benefits related to increased production output and quality, reduced maintenance costs, improved energy efficiency, better use of raw materials and reduced waste. In addition, AI applications can add value through product personalisation, improve customer service and contribute to the development of new product classes, business models and even sectors. Workforce benefits (e.g. improved workplace safety) are also being delivered by AI applications.

Alongside these firm-level benefits and opportunities, significant positive societal and economy-wide impacts are envisaged. More specifically, substantial increases in productivity, innovation, growth and job creation have been forecasted. For example, one estimate anticipates labour productivity increases of 11-37% by 2035. In addition, AI is expected to positively contribute to the UN Sustainable Development Goals and the capabilities of AI and machine learning to address major health challenges, such as the current COVID-19 health pandemic, are also noteworthy. For instance, AI systems have the potential to accelerate the lead times for the development of vaccines and drugs.

However, AI adoption brings a range of challenges…(More)”.

The Decline and Rise of Democracy: A Global History from Antiquity to Today

Curated on Posted on by Stefaan Verhulst

Book by David Stasavage: “Historical accounts of democracy’s rise tend to focus on ancient Greece and pre-Renaissance Europe. The Decline and Rise of Democracy draws from global evidence to show that the story is much richer—democratic practices were present in many places, at many other times, from the Americas before European conquest, to ancient Mesopotamia, to precolonial Africa. Delving into the prevalence of early democracy throughout the world, David Stasavage makes the case that understanding how and where these democracies flourished—and when and why they declined—can provide crucial information not just about the history of governance, but also about the ways modern democracies work and where they could manifest in the future.

Drawing from examples spanning several millennia, Stasavage first considers why states developed either democratic or autocratic styles of governance and argues that early democracy tended to develop in small places with a weak state and, counterintuitively, simple technologies. When central state institutions (such as a tax bureaucracy) were absent—as in medieval Europe—rulers needed consent from their populace to govern. When central institutions were strong—as in China or the Middle East—consent was less necessary and autocracy more likely. He then explores the transition from early to modern democracy, which first took shape in England and then the United States, illustrating that modern democracy arose as an effort to combine popular control with a strong state over a large territory. Democracy has been an experiment that has unfolded over time and across the world—and its transformation is ongoing.

Amidst rising democratic anxieties, The Decline and Rise of Democracy widens the historical lens on the growth of political institutions and offers surprising lessons for all who care about governance….(More)”.

Peer-Reviewed Scientific Journals Don’t Really Do Their Job

Curated on Posted on by Stefaan Verhulst

Article by Simine Vazire: “THE RUSH FOR scientific cures and treatments for Covid-19 has opened the floodgates of direct communication between scientists and the public. Instead of waiting for their work to go through the slow process of peer review at scientific journals, scientists are now often going straight to print themselves, posting write-ups of their work to public servers as soon as they’re complete. This disregard for the traditional gatekeepers has led to grave concerns among both scientists and commentators: Might not shoddy science—and dangerous scientific errors—make its way into the media, and spread before an author’s fellow experts can correct it? As two journalism professors suggested in an op-ed last month for The New York Times, it’s possible the recent spread of so-called preprints has only “sown confusion and discord with a general public not accustomed to the high level of uncertainty inherent in science.”

There’s another way to think about this development, however. Instead of showing (once again) that formal peer review is vital for good science, the last few months could just as well suggest the opposite. To me, at least—someone who’s served as an editor at seven different journals, and editor in chief at two—the recent spate of decisions to bypass traditional peer review gives the lie to a pair of myths that researchers have encouraged the public to believe for years: First, that peer-reviewed journals publish only trustworthy science; and second, that trustworthy science is published only in peer-reviewed journals.

Scientists allowed these myths to spread because it was convenient for us. Peer-reviewed journals came into existence largely to keep government regulators off our backs. Scientists believe that we are the best judges of the validity of each other’s work. That’s very likely true, but it’s a huge leap from that to “peer-reviewed journals publish only good science.” The most selective journals still allow flawed studies—even really terribly flawed ones—to be published all the time. Earlier this month, for instance, the journal Proceedings of the National Academy of Sciences put out a paper claiming that mandated face coverings are “the determinant in shaping the trends of the pandemic.” PNAS is a very prestigious journal, and their website claims that they are an “authoritative source” that works “to publish only the highest quality scientific research.” However, this paper was quickly and thoroughly criticized on social media; by last Thursday, 45 researchers had signed a letter formally calling for its retraction.

Now the jig is up. Scientists are writing papers that they want to share as quickly as possible, without waiting the months or sometimes years it takes to go through journal peer review. So they’re ditching the pretense that journals are a sure-fire quality control filter, and sharing their papers as self-published PDFs. This might be just the shakeup that peer review needs….(More)”.

A Council of Citizens Should Regulate Algorithms

Curated on Posted on by Stefaan Verhulst

Federica Carugati at Wired: “…A new report by OpenAI suggests we should create external auditing bodies to evaluate the societal impact of algorithm-based decisions. But the report does not specify what such bodies should look like.

We don’t know how to regulate algorithms, because their application to societal problems involves a fundamental incongruity. Algorithms follow logical rules in order to optimize for a given outcome. Public policy is all a matter of trade-offs: optimizing for some groups in society necessarily makes others worse off.

Resolving social trade-offs requires that many different voices be heard. This may sound radical, but it is in fact the original lesson of democracy: Citizens should have a say. We don’t know how to regulate algorithms, because we have become shockingly bad at citizen governance.

Is citizen governance feasible today? Sure, it is. We know from social scientists that a diverse group of people can make very good decisions. We also know from a number of recent experiments that citizens can be called upon to make decisions on very tough policy issues, including climate change, and even to shape constitutions. Finally, we can draw from the past for inspiration on how to actually build citizen-run institutions.

The ancient Athenians—the citizens of the world’s first large-scale experiment in democracy—built an entire society on the principle of citizen governance. One institution stands out for our purposes: the Council of Five Hundred, a deliberative body in charge of all decisionmaking, from war to state finance to entertainment. Every year, 50 citizens from each of the 10 tribes were selected by lot to serve. Selection occurred among those that had not served the year before and had not already served twice.

These simple organizational rules facilitated broad participation, knowledge aggregation, and citizen learning. First, because the term was limited and could not be iterated more than twice, over time a broad section of the population—rich and poor, educated and not—participated in decisionmaking. Second, because the council represented the whole population (each tribe integrated three different geographic constituencies), it could draw upon the diverse knowledge of its members. Third, at the end of their mandate, councillors returned home with a body of knowledge about the affairs of their city that they could share with their families, friends, and coworkers, some of whom already served and some who soon would. Certainly, the Athenians did not follow through on their commitment to inclusion. As a result, many people’s voices went unheard, including those of women, foreigners, and slaves. But we don’t need to follow the Athenian example on this front.

A citizen council for algorithms modeled on the Athenian example would represent the entire American citizen population. We already do this with juries (although it is possible that, when decisions affect a specific constituency, a better fit with the actual polity might be required). Citizens’ deliberations would be informed by agency self-assessments and algorithmic impact statements for decision systems used by government agencies, and internal auditing reports for industry, as well as reports from investigative journalists and civil society activists, whenever available. Ideally, the council would act as an authoritative body or as an advisory board to an existing regulatory agency….(More)”.

COVID Response Alliance for Social Entrepreneurs

Curated on Posted on by Stefaan Verhulst

Article by François Bonnici: “…Social innovators and social entrepreneurs have been working to solve market failures and demonstrate more sustainable models to build inclusive economies for years. The Schwab Foundation 2020 Impact Report “Two Decades of Impact” demonstrated how the network of 400 leading social innovators and entrepreneurs it supports have improved the lives of more than 622 million people, protecting livelihoods, driving movements for social inclusion and environmental sustainability, and providing improved access to health, sanitation, education and energy.

From providing reliable information, services and care for the most vulnerable, to developing community tracing initiatives or mental health support through mobile phones, the work of social entrepreneurs is even more critical during the COVID-19 pandemic, as they reach those who the market and governments are unable to account for.

But right now, these front-line organizations face severe constraints or even bankruptcy. Decades of work in the impact sector are at stake.

Over the past four decades, a sophisticated impact ecosystem has emerged to support the work of social innovators and impact enterprises. This includes funding provided by capital sources ranging from philanthropy and impact investing, intermediaries providing certification and standards, peer networks of learning and policy and regulation of this new “social economy” seeking to embed inclusive and sustainable organizational approaches imbued with principles of equality, justice and respect for our planet.

From this ecosystem, 40 leading global organizations collectively supporting more than 15,000 social entrepreneurs have united to launch the COVID Response Alliance for Social Entrepreneurs. The aim is to share knowledge, experience and resources to coordinate and amplify social entrepreneurs’ response to COVID-19….(More)”.

Best Practices to Cover Ad Information Used for Research, Public Health, Law Enforcement & Other Uses

Curated on Posted on by Stefaan Verhulst

Press Release: “The Network Advertising Initiative (NAI) released privacy Best Practices for its members to follow if they use data collected for Tailored Advertising or Ad Delivery and Reporting for non-marketing purposes, such as sharing with research institutions, public health agencies, or law enforcement entities.

“Ad tech companies have data that can be a powerful resource for the public good if they follow this set of best practices for consumer privacy,” said Leigh Freund, NAI President and CEO. “During the COVID-19 pandemic, we’ve seen the opportunity for substantial public health benefits from sharing aggregate and de-identified location data.”

The NAI Code of Conduct – the industry’s premier self-regulatory framework for privacy, transparency, and consumer choice – covers data collected and used for Tailored Advertising or Ad Delivery and Reporting. The NAI Code has long addressed certain non-marketing uses of data collected for Tailored Advertising and Ad Delivery and Reporting by prohibiting any
eligibility uses of such data, including uses for credit, insurance, healthcare, and employment decisions.

The NAI has always firmly believed that data collected for advertising purposes should not have a negative effect on consumers in their daily lives. However, over the past year, novel data uses have been introduced, especially during the recent health crisis. In the case of opted-in data such as Precise Location Information, a company may determine a user would benefit from more detailed disclosure in a just-in-time notice about non-marketing uses of the data being collected….(More)”.