Paper by Vasileios Lampos et al: “Previous research has demonstrated that various properties of infectious diseases can be inferred from online search behaviour. In this work we use time series of online search query frequencies to gain insights about the prevalence of COVID-19 in multiple countries. We first develop unsupervised modelling techniques based on associated symptom categories identified by the United Kingdom’s National Health Service and Public Health England. We then attempt to minimise an expected bias in these signals caused by public interest—as opposed to infections—using the proportion of news media coverage devoted to COVID-19 as a proxy indicator. Our analysis indicates that models based on online searches precede the reported confirmed cases and deaths by 16.7 (10.2–23.2) and 22.1 (17.4–26.9) days, respectively. We also investigate transfer learning techniques for mapping supervised models from countries where the spread of the disease has progressed extensively to countries that are in earlier phases of their respective epidemic curves. Furthermore, we compare time series of online search activity against confirmed COVID-19 cases or deaths jointly across multiple countries, uncovering interesting querying patterns, including the finding that rarer symptoms are better predictors than common ones. Finally, we show that web searches improve the short-term forecasting accuracy of autoregressive models for COVID-19 deaths. Our work provides evidence that online search data can be used to develop complementary public health surveillance methods to help inform the COVID-19 response in conjunction with more established approaches….(More)”.
Monitoring the R-Citizen in the Time of Coronavirus
Paper by John Flood and Monique Lewis: “The COVID pandemic has overwhelmed many countries in their attempts at tracking and tracing people infected with the disease. Our paper examines how tracking and tracing is done looking at manual and technological means. It raises the issues around efficiency and privacy, etc. The paper investigates more closely the approaches taken by two countries, namely Taiwan and the UK. It shows how tracking and tracing can be handled sensitively and openly compared to the bungled attempts of the UK that have led to the greatest number of dead in Europe. The key messages are that all communications around tracking and tracing need to open, clear, without confusion and delivered by those closest to the communities receiving the messages.This occurred in Taiwan but in the UK the central government chose to close out local government and other local resources. The highly centralised dirigiste approach of the government alienated much of the population who came to distrust government. As local government was later brought into the COVID fold the messaging improved. Taiwan always remained open in its communications, even allowing citizens to participate in improving the technology around COVID. Taiwan learnt from its earlier experiences with SARS, whereas the UK ignored its pandemic planning exercises from earlier years and even experimented with crude ideas of herd immunity by letting the disease rip through the population–an idea soon abandoned.
We also derive a new type of citizen from the pandemic, namely the R citizen. This unfortunate archetype is both a blessing and a curse. If the citizen scores over 1 the disease accelerates and the R citizen is chastised, whereas if the citizen declines to zero it disappears but receives no plaudits for their behaviour. The R citizen can neither exist or die, rather like Schrödinger’s cat. R citizens are of course datafied individuals who are assemblages of data and are treated as distinct from humans. We argue they cannot be so distinguished without rendering them inhuman. This is as much a moral category as it is a scientific one….(More)”.
A Worldwide Assessment of COVID-19 Pandemic-Policy Fatigue
Paper by Anna Petherick et al: “As the COVID-19 pandemic lingers, signs of “pandemic-policy fatigue” have raised worldwide concerns. But the phenomenon itself is yet to be thoroughly defined, documented, and delved into. Based on self-reported behaviours from samples of 238,797 respondents, representative of the populations of 14 countries, as well as global mobility and policy data, we systematically examine the prevalence and shape of people’s alleged gradual reduction in adherence to governments’ protective-behaviour policies against COVID-19. Our results show that from March through December 2020, pandemic-policy fatigue was empirically meaningful and geographically widespread. It emerged for high-cost and sensitising behaviours (physical distancing) but not for low-cost and habituating ones (mask wearing), and was less intense among retired people, people with chronic diseases, and in countries with high interpersonal trust. Particularly due to fatigue reversal patterns in high- and upper-middle-income countries, we observe an arch rather than a monotonic decline in global pandemic-policy fatigue….(More)”.
How Elvis Got Americans to Accept the Polio Vaccine
Hal Hershfield and Ilana Brody at Scientific American: “Campaigns to change behavior thrive on three factors: social influence, social norms and vivid examples…In late 1956, Elvis Presley was on the precipice of global stardom. “Heartbreak Hotel” had reached number one on the charts earlier that year and Love Me Tender, his debut film,would be released in November. In the midst of this trajectory, he was booked as a guest on the most popular TV show at the time, The Ed Sullivan Show. But he wasn’t only there to perform his hits. Before the show started, and in front of the press and Ed Sullivan himself, Presley flashed his swoon-worthy smile, rolled up his sleeves and let a New York state official stick a needle loaded up with the polio vaccine in his arm.
At that point, the polio virus had been ravaging the American landscape for years, and approximately 60,000 children were infected annually. By 1955, hope famously arrived in the form of Jonas Salk’s vaccine. But despite the literally crippling effects of the virus and the promising results of the vaccination, many Americans simply weren’t getting vaccinated. In fact, when Presley appeared on the Sullivan show, immunization levels among American teens were at an abysmal 0.6 percent.
You might think that threats to children’s health and life expectancy would be enough to motivate people to get vaccinated. Yet, convincing people to get a vaccine is a challenging endeavor. Intuitively, it seems like it would be wise to have doctors and other health officials communicate the need to receive the vaccine. Or, failing that, we might just need to give people more information about the effectiveness of the vaccine itself…(More)”.
A recommendation and risk classification system for connecting rough sleepers to essential outreach services
Paper by Harrison Wilde et al: “Rough sleeping is a chronic experience faced by some of the most disadvantaged people in modern society. This paper describes work carried out in partnership with Homeless Link (HL), a UK-based charity, in developing a data-driven approach to better connect people sleeping rough on the streets with outreach service providers. HL’s platform has grown exponentially in recent years, leading to thousands of alerts per day during extreme weather events; this overwhelms the volunteer-based system they currently rely upon for the processing of alerts. In order to solve this problem, we propose a human-centered machine learning system to augment the volunteers’ efforts by prioritizing alerts based on the likelihood of making a successful connection with a rough sleeper. This addresses capacity and resource limitations whilst allowing HL to quickly, effectively, and equitably process all of the alerts that they receive. Initial evaluation using historical data shows that our approach increases the rate at which rough sleepers are found following a referral by at least 15% based on labeled data, implying a greater overall increase when the alerts with unknown outcomes are considered, and suggesting the benefit in a trial taking place over a longer period to assess the models in practice. The discussion and modeling process is done with careful considerations of ethics, transparency, and explainability due to the sensitive nature of the data involved and the vulnerability of the people that are affected….(More)”.
Re-use of smart city data: The need to acquire a social license through data assemblies
Written Testimony by Stefaan G. Verhulst before the New York City Council Committee on Technology: “…In crises such as these, calls for the city to harness technology and data to help policy-makers find solutions grow louder and stronger. Many have spoken about accelerating already ongoing work to turn New York into “a smart city” — using digital technology to connect, protect, and improve the lives of its residents. Some of this proposed work could involve the use of sensors to collect data on how people live and work across New York City. Other work could involve expanding the city’s relationships with private organizations through data collaboratives. Data collaboratives, which are central to our work at the GovLab, are a new form of collaboration that extends beyond the conventional public-private partnership model, in which participants from different sectors exchange their data to create public value. The city already operates one such data collaborative in the form of the NYC Recovery Data Partnership, a partnership that allows New York-based private and civic organizations to provide their data to analysts at city agencies to inform the COVID-19 pandemic response. I have the privilege of serving as an advisor to that initiative.
Data collaboration takes place widely through a variety of institutional, contractual and technical structures and instruments. Borrowing in language and inspiration from the open data movement, the emerging data collaborative movement has proven its value and possible positive impact. Data reuse has the potential to improve disease treatment, identify better ways to source supplies, monitor adherence to non-pharmaceutical restrictions, and provide a range of other public benefits. Whether it is informing decision-making or shaping the development of new tools and techniques, it is clear that data has tremendous potential to mitigate the worst effects of this pandemic.
However, as promising and attractive as reusing data might seem, it is important to keep in mind that there also exist widespread concerns and challenges….
My colleagues and I at The GovLab believe the Data Assembly methodology offers the city a new way forward on the issues under discussion today, as they relate to smart cities. In our view, oversight cannot just be a reactive process of responding to complaints but a proactive one, inviting city residents, data holders, and advocacy groups to the table to determine what is and is not acceptable. Amid rapidly changing circumstances, the city needs ways to collect and synthesize actionable and diverse public input to identify concerns, expectations, and opportunities. We encourage the city to explore assembling mini-publics of its own or, failing that, commission legitimate partners to lead such efforts.
New York faces many challenges in 2021 but I do not doubt the capacity of its people to overcome these struggles. Through people-led innovation and processes, the city can ensure that data re-use conducted as part of the smart city is deemed legitimate and more effective and targeted. It can also support the city in ensuring work across the city is more open, collaborative, and legitimate…(More)”.
Rational policymaking during a pandemic
Perspective by Loïc Berger et al: “Policymaking during a pandemic can be extremely challenging. As COVID-19 is a new disease and its global impacts are unprecedented, decisions are taken in a highly uncertain, complex, and rapidly changing environment. In such a context, in which human lives and the economy are at stake, we argue that using ideas and constructs from modern decision theory, even informally, will make policymaking a more responsible and transparent process….
The COVID-19 pandemic exposes decision problems faced by governments and international organizations. Policymakers are charged with taking actions to protect their population from the disease while lacking reliable information on the virus and its transmission mechanisms and on the effectiveness of possible measures and their (direct and indirect) health and socioeconomic consequences. The rational policy decision would combine the best available scientific evidence—typically provided by expert opinions and modeling studies. However, in an uncertain and rapidly changing environment, the pertinent evidence is highly fluid, making it challenging to produce scientifically grounded predictions of the outcomes of alternative courses of action….(More)”.
Switzerland to Hold Referendum on Covid-19 Lockdown
James Hookway at the Wall Street Journal: “Switzerland’s system of direct democracy will be put to the test again later this year, this time with a referendum on whether to roll back the government’s powers to impose lockdowns and other measures to slow the Covid-19 pandemic.
The landlocked Alpine nation of 8.5 million people is unusual in providing its people a say on important policy moves by offering referendums if enough people sign a petition for a vote. Last year, Swiss voted on increasing the stock of low-cost housing, tax allowances for children and hunting wolves.
The idea is to provide citizens a check on the power of the federal government, and it is a throwback to the fiercely independent patchwork of cantons, or districts, that were meshed in the medieval period.
Now, the country is set for a referendum on whether to remove the government’s legal authority to order lockdowns and other pandemic restrictions after campaigners submitted a petition of some 86,000 signatures this week—higher than the 50,000 required—triggering a nationwide vote to repeal last year’s Covid-19 Act….(More)”.
How COVID-19 Is Accelerating the Shift Toward a Quantified Society
Essay by Jesse Hirsh: “The COVID-19 pandemic is accelerating global digital transformation and the adoption of digital technologies. It is also enacting a political and cultural shift toward a quantified society: a society in which measurement and predictive modelling dominate (political) decision making, and where surveillance is expansive and pervasive.
While viruses and disease have always been with us, what’s changing is our ability to measure and understand them. This ability comes at a time when globalization (and, by extension, climate change) has transformed the kinds of viruses and diseases we will face.
The knowledge of what can kill us — or is killing us — compels governments and health authorities to both take action in response and gather more data to understand the threat. Like many disasters or other globally impactful events, the COVID-19 pandemic is accelerating the development and implementation of quantification technologies.
Health researchers are now measuring the spread of a virus across the population in ways not previously possible, through the use of a set of data that is ever-growing, especially in countries such as China that have less regard for personal privacy. Canada and the United States are not yet conducting tracking and tracing of infections at a level that would enable containment. This level, however, is due to inadequate staffing rather than insufficient data. Still, the desire for more information remains.
As a result, our ability to measure human health and disease transmission is set to reach new records and capabilities. Through sources ranging from individuals’ use of digital health tools to contact tracing records, health-related data is amassing at a prodigious rate.
What are the impacts or consequences of this dramatic increase in both health data and the perceived value or urgency of that data?…(More)”.
Survey Data and Human Computation for Improved Flu Tracking
Paper by Stefan Wojcik et al: “While digital trace data from sources like search engines hold enormous potential for tracking and understanding human behavior, these streams of data lack information about the actual experiences of those individuals generating the data. Moreover, most current methods ignore or under-utilize human processing capabilities that allow humans to solve problems not yet solvable by computers (human computation). We demonstrate how behavioral research, linking digital and real-world behavior, along with human computation, can be utilized to improve the performance of studies using digital data streams. This study looks at the use of search data to track prevalence of Influenza-Like Illness (ILI). We build a behavioral model of flu search based on survey data linked to users’ online browsing data. We then utilize human computation for classifying search strings. Leveraging these resources, we construct a tracking model of ILI prevalence that outperforms strong historical benchmarks using only a limited stream of search data and lends itself to tracking ILI in smaller geographic units. While this paper only addresses searches related to ILI, the method we describe has potential for tracking a broad set of phenomena in near real-time….(More)”