Tech Firms Are Spying on You. In a Pandemic, Governments Say That’s OK.

Curated on Posted on by Stefaan Verhulst

Sam Schechner, Kirsten Grind and Patience Haggin at the Wall Street Journal: “While an undergraduate at the University of Virginia, Joshua Anton created an app to prevent users from drunk dialing, which he called Drunk Mode. He later began harvesting huge amounts of user data from smartphones to resell to advertisers.

Now Mr. Anton’s company, called X-Mode Social Inc., is one of a number of little-known location-tracking companies that are being deployed in the effort to reopen the country. State and local authorities wielding the power to decide when and how to reopen are leaning on these vendors for the data to underpin those critical judgment calls.

In California, Gov. Gavin Newsom’s office used data from Foursquare Labs Inc. to figure out if beaches were getting too crowded; when the state discovered they were, it tightened its rules. In Denver, the Tri-County Health Department is monitoring counties where the population on average tends to stray more than 330 feet from home, using data from Cuebiq Inc.

Researchers at the University of Texas in San Antonio are using movement data from a variety of companies, including the geolocation firm SafeGraph, to guide city officials there on the best strategies for getting residents back to work.

Many of the location-tracking firms, data brokers and other middlemen are part of the ad-tech industry, which has come under increasing fire in recent years for building what critics call a surveillance economy. Data for targeting ads at individuals, including location information, can also end up in the hands of law-enforcement agencies or political groups, often with limited disclosure to users. Privacy laws are cropping up in states including California, along with calls for federal privacy legislation like that in the European Union.

But some public-health authorities are setting aside those concerns to fight an unprecedented pandemic. Officials are desperate for all types of data to identify people potentially infected with the virus and to understand how they are behaving to predict potential hot spots—whether those people realize it or not…(More)”

Evaluating Data Types

Curated on Posted on by Stefaan Verhulst

A Guide for Decision Makers using Data to Understand the Extent and Spread of COVID-19 by the National Academies: “This rapid expert consultation provides insight into the strengths and weaknesses of the data on the COVID-19 pandemic by applying five criteria to seven types of data available to support decision making. It was produced through the Societal Experts Action Network (SEAN), an activity of the National Academies of Sciences, Engineering, and Medicine that is sponsored by the National Science Foundation. SEAN links researchers in the social, behavioral, and economic sciences with decision makers to respond to policy questions arising from the COVID-19 pandemic….(More)”

Collective intelligence, not market competition, will deliver the best Covid-19 vaccine

Curated on Posted on by Stefaan Verhulst

Els Torreele at StatNews: “…Imagine mobilizing the world’s brightest and most creative minds — from biotech and pharmaceutical industries, universities, government agencies, and more — to work together using all available knowledge, innovation, and infrastructure to develop an effective vaccine against Covid-19. A true “people’s vaccine” that would be made freely available to all people in all countries. That’s what an open letter by more than 140 world leaders and experts calls for.

Unfortunately, that is not how the race for a Covid-19 vaccine is being run. The rules of that game are oblivious to the goal of maximizing global health outcomes and access.

Despite a pipeline of more than 100 vaccine candidates reflecting massive public and private efforts, there exists no public-health-focused way to design or prioritize the development of the most promising candidates. Instead, the world is adopting a laissez-faire approach and letting individual groups and companies compete for marketing authorization, each with their proprietary vaccine candidate, and assume that the winner of that race will be the best vaccine to tackle the pandemic.

Science thrives, and technological progress is made, when knowledge is exchanged and shared freely, generating collective intelligence by building on the successes and failures of others in real time instead of through secretive competition. Regrettably, market logic has come to overtake medicinal product innovation, including the unproven premise that competition is an efficient way to advance science and deliver the best solutions for public health….(More)”.

The Long Shadow Of The Future

Curated on Posted on by Stefaan Verhulst

Steven Weber and Nils Gilman at Noema: “We’re living through a real-time natural experiment on a global scale. The differential performance of countries, cities and regions in the face of the COVID-19 pandemic is a live test of the effectiveness, capacity and legitimacy of governments, leaders and social contracts.

The progression of the initial outbreak in different countries followed three main patterns. Countries like Singapore and Taiwan represented Pattern A, where (despite many connections to the original source of the outbreak in China) vigilant government action effectively cut off community transmission, keeping total cases and deaths low. China and South Korea represented Pattern B: an initial uncontrolled outbreak followed by draconian government interventions that succeeded in getting at least the first wave of the outbreak under control.

Pattern C is represented by countries like Italy and Iran, where waiting too long to lock down populations led to a short-term exponential growth of new cases that overwhelmed the healthcare system and resulted in a large number of deaths. In the United States, the lack of effective and universally applied social isolation mechanisms, as well as a fragmented healthcare system and a significant delay in rolling out mass virus testing, led to a replication of Pattern C, at least in densely populated places like New York City and Chicago.“Regime type isn’t correlated with outcomes.”

Despite the Chinese and Americans blaming each other and crediting their own political system for successful responses, the course of the virus didn’t score easy political points on either side of the new Cold War. Regime type isn’t correlated with outcomes. Authoritarian and democratic countries are included in each of the three patterns of responses: authoritarian China and democratic South Korea had effective responses to a dramatic breakout; authoritarian Singapore and democratic Taiwan both managed to quarantine and contain the virus; authoritarian Iran and democratic Italy both experienced catastrophe.

It’s generally a mistake to make long-term forecasts in the midst of a hurricane, but some outlines of lasting shifts are emerging. First, a government or society’s capacity for technical competence in executing plans matters more than ideology or structure. The most effective arrangements for dealing with the pandemic have been found in countries that combine a participatory public culture of information sharing with operational experts competently executing decisions. Second, hyper-individualist views of privacy and other forms of risk are likely to be submerged as countries move to restrict personal freedoms and use personal data to manage public and aggregated social risks. Third, countries that are able to successfully take a longer view of planning and risk management will be at a significant advantage….(More)”.

Individualism During Crises: Big Data Analytics of Collective Actions amid COVID-19

Curated on Posted on by Stefaan Verhulst

Paper by Bo Bian et al: “Collective actions, such as charitable crowdfunding and social distancing, are useful for alleviating the negative impact of the COVID-19 pandemic. However, engagements in these actions across the U.S. are “consistently inconsistent” and are frequently linked to individualism in the press. We present the first evidence on how individualism shapes online and offline collective actions during a crisis through big data analytics. Following economic historical studies, we leverage GIS techniques to construct a U.S. county-level individualism measure that traces the time each county spent on the American frontier between 1790 and 1890. We then use high-dimensional fixed-effect models, text mining, geo-distributed big data computing and a novel identification strategy based on migrations to analyze GoFundMe fundraising activities as well as county- and individual-level social distancing compliance.

Our analysis uncovers several insights. First, higher individualism reduces both online donations and social distancing during the COVID-19 pandemic. An interquartile increase in individualism reduces COVID-related charitable campaigns and funding by 48% and offsets the effect of state lockdown orders on social distancing by 41%. Second, government interventions, such as stimulus checks, can potentially mitigate the negative effect of individualism on charitable crowdfunding. Third, the individualism effect may be partly driven by a failure to internalize the externality of collective actions: we find stronger results in counties where social distancing generates higher externalities (those with higher population densities or more seniors). Our research is the first to uncover the potential downsides of individualism during crises. It also highlights the importance of big data-driven, culture-aware policymaking….(More)”.

Democracies contain epidemics most effectively

Curated on Posted on by Stefaan Verhulst

The Economist: “Many people would look at the covid-19 pandemic and conclude that democracies are bad at tackling infectious diseases. America and the eu had months to prepare after China sounded the alarm in January. Both have subsequently suffered more than 300 confirmed deaths per 1m people. China’s Communist Party reports an official death rate that is 99% lower, and has trumpeted its apparent success in containing the outbreak domestically.

Yet most data suggest that political freedom can be a tonic against disease. The Economist has analysed epidemics from 1960 to 2019. Though these outbreaks varied in contagiousness and lethality, a clear correlation emerged. Among countries with similar wealth, the lowest death rates tend to be in places where most people can vote in free and fair elections. Other definitions of democracy give similar results.

We cannot replicate this analysis for covid-19 yet, as it is still spreading at different rates around the world. Western democracies were hit early, in big cities with large flows of people from abroad. Daily deaths are now declining in these places but rising in developing countries, which tend to be less connected and more autocratic….

One consistent measure that is available in most countries, but not China, is Google’s index of mobility via smartphone apps. Researchers at Oxford University reckon that, after adjusting for a country’s wealth and other characteristics, democracies saw a 35% larger reduction in movement in response to lockdown policies. The drop in New Zealand, for example, was twice that in autocratic Bahrain.

People who praise China for its handling of covid-19 would do better to look at Taiwan, a neighbouring democracy. China wasted valuable time in December by intimidating doctors who warned of a lethal virus. Taiwan swiftly launched tracing measures in January—and has suffered only seven deaths…(More)”.

Sharing Health Data and Biospecimens with Industry — A Principle-Driven, Practical Approach

Curated on Posted on by Stefaan Verhulst

Kayte Spector-Bagdady et al at the New England Journal of Medicine: “The advent of standardized electronic health records, sustainable biobanks, consumer-wellness applications, and advanced diagnostics has resulted in new health information repositories. As highlighted by the Covid-19 pandemic, these repositories create an opportunity for advancing health research by means of secondary use of data and biospecimens. Current regulations in this space give substantial discretion to individual organizations when it comes to sharing deidentified data and specimens. But some recent examples of health care institutions sharing individual-level data and specimens with companies have generated controversy. Academic medical centers are therefore both practically and ethically compelled to establish best practices for governing the sharing of such contributions with outside entities.1 We believe that the approach we have taken at Michigan Medicine could help inform the national conversation on this issue.

The Federal Policy for the Protection of Human Subjects offers some safeguards for research participants from whom data and specimens have been collected. For example, researchers must notify participants if commercial use of their specimens is a possibility. These regulations generally cover only federally funded work, however, and they don’t apply to deidentified data or specimens. Because participants value transparency regarding industry access to their data and biospecimens, our institution set out to create standards that would better reflect participants’ expectations and honor their trust. Using a principlist approach that balances beneficence and nonmaleficence, respect for persons, and justice, buttressed by recent analyses and findings regarding contributors’ preferences, Michigan Medicine established a formal process to guide our approach….(More)”.

How data analysis helped Mozambique stem a cholera outbreak

Curated on Posted on by Stefaan Verhulst

Andrew Jack at the Financial Times: “When Mozambique was hit by two cyclones in rapid succession last year — causing death and destruction from a natural disaster on a scale not seen in Africa for a generation — government officials added an unusual recruit to their relief efforts. Apart from the usual humanitarian and health agencies, the National Health Institute also turned to Zenysis, a Silicon Valley start-up.

As the UN and non-governmental organisations helped to rebuild lives and tackle outbreaks of disease including cholera, Zenysis began gathering and analysing large volumes of disparate data. “When we arrived, there were 400 new cases of cholera a day and they were doubling every 24 hours,” says Jonathan Stambolis, the company’s chief executive. “None of the data was shared [between agencies]. Our software harmonised and integrated fragmented sources to produce a coherent picture of the outbreak, the health system’s ability to respond and the resources available.

“Three and a half weeks later, they were able to get infections down to zero in most affected provinces,” he adds. The government attributed that achievement to the availability of high-quality data to brief the public and international partners.

“They co-ordinated the response in a way that drove infections down,” he says. Zenysis formed part of a “virtual control room”, integrating information to help decision makers understand what was happening in the worst hit areas, identify sources of water contamination and where to prioritise cholera vaccinations.

It supported an “mAlert system”, which integrated health surveillance data into a single platform for analysis. The output was daily reports distilled from data issued by health facilities and accommodation centres in affected areas, disease monitoring and surveillance from laboratory testing….(More)”.

Dynamic Networks Improve Remote Decision-Making

Curated on Posted on by Stefaan Verhulst

Article by Abdullah Almaatouq and Alex “Sandy” Pentland: “The idea of collective intelligence is not new. Research has long shown that in a wide range of settings, groups of people working together outperform individuals toiling alone. But how do drastic shifts in circumstances, such as people working mostly at a distance during the COVID-19 pandemic, affect the quality of collective decision-making? After all, public health decisions can be a matter of life and death, and business decisions in crisis periods can have lasting effects on the economy.

During a crisis, it’s crucial to manage the flow of ideas deliberatively and strategically so that communication pathways and decision-making are optimized. Our recently published research shows that optimal communication networks can emerge from within an organization when decision makers interact dynamically and receive frequent performance feedback. The results have practical implications for effective decision-making in times of dramatic change….

Our experiments illustrate the importance of dynamically configuring network structures and enabling decision makers to obtain useful, recurring feedback. But how do you apply such findings to real-world decision-making, whether remote or face to face, when constrained by a worldwide pandemic? In such an environment, connections among individuals, teams, and networks of teams must be continually reorganized in response to shifting circumstances and challenges. No single network structure is optimal for every decision, a fact that is clear in a variety of organizational contexts.

Public sector. Consider the teams of advisers working with governments in creating guidelines to flatten the curve and help restart national economies. The teams are frequently reconfigured to leverage pertinent expertise and integrate data from many domains. They get timely feedback on how decisions affect daily realities (rates of infection, hospitalization, death) — and then adjust recommended public health protocols accordingly. Some team members move between levels, perhaps being part of a state-level team for a while, then federal, and then back to state. This flexibility ensures that people making big-picture decisions have input from those closer to the front lines.

Witness how Germany considered putting a brake on some of its reopening measures in response to a substantial, unexpected uptick in COVID-19 infections. Such time-sensitive decisions are not made effectively without a dynamic exchange of ideas and data. Decision makers must quickly adapt to facts reported by subject-area experts and regional officials who have the relevant information and analyses at a given moment….(More)“.

Using Data for COVID-19 Requires New and Innovative Governance Approaches

Curated on Posted on by Stefaan Verhulst

Stefaan G. Verhulst and Andrew Zahuranec at Data & Policy blog: “There has been a rapid increase in the number of data-driven projects and tools released to contain the spread of COVID-19. Over the last three months, governments, tech companies, civic groups, and international agencies have launched hundreds of initiatives. These efforts range from simple visualizations of public health data to complex analyses of travel patterns.

When designed responsibly, data-driven initiatives could provide the public and their leaders the ability to be more effective in addressing the virus. The Atlantic andNew York Times have both published work that relies on innovative data use. These and other examples, detailed in our #Data4COVID19 repository, can fill vital gaps in our understanding and allow us to better respond and recover to the crisis.

But data is not without risk. Collecting, processing, analyzing and using any type of data, no matter how good intention of its users, can lead to harmful ends. Vulnerable groups can be excluded. Analysis can be biased. Data use can reveal sensitive information about people and locations. In addressing all these hazards, organizations need to be intentional in how they work throughout the data lifecycle.

Decision Provenance: Documenting decisions and decision makers across the Data Life Cycle

Unfortunately the individuals and teams responsible for making these design decisions at each critical point of the data lifecycle are rarely identified or recognized by all those interacting with these data systems.

The lack of visibility into the origins of these decisions can impact professional accountability negatively as well as limit the ability of actors to identify the optimal intervention points for mitigating data risks and to avoid missed use of potentially impactful data. Tracking decision provenance is essential.

As Jatinder Singh, Jennifer Cobbe, and Chris Norval of the University of Cambridge explain, decision provenance refers to tracking and recording decisions about the collection, processing, sharing, analyzing, and use of data. It involves instituting mechanisms to force individuals to explain how and why they acted. It is about using documentation to provide transparency and oversight in the decision-making process for everyone inside and outside an organization.

Toward that end, The GovLab at NYU Tandon developed the Decision Provenance Mapping. We designed this tool for designated data stewards tasked with coordinating the responsible use of data across organizational priorities and departments….(More)”