Machine Learning Shows Social Media Greatly Affects COVID-19 Beliefs

Curated on Posted on by Stefaan Verhulst

Jessica Kent at HealthITAnalytics: “Using machine learning, researchers found that people’s biases about COVID-19 and its treatments are exacerbated when they read tweets from other users, a study published in JMIR showed.

The analysis also revealed that scientific events, like scientific publications, and non-scientific events, like speeches from politicians, equally influence health belief trends on social media.

The rapid spread of COVID-19 has resulted in an explosion of accurate and inaccurate information related to the pandemic – mainly across social media platforms, researchers noted.

“In the pandemic, social media has contributed to much of the information and misinformation and bias of the public’s attitude toward the disease, treatment and policy,” said corresponding study author Yuan Luo, chief Artificial Intelligence officer at the Institute for Augmented Intelligence in Medicine at Northwestern University Feinberg School of Medicine.

“Our study helps people to realize and re-think the personal decisions that they make when facing the pandemic. The study sends an ‘alert’ to the audience that the information they encounter daily might be right or wrong, and guide them to pick the information endorsed by solid scientific evidence. We also wanted to provide useful insight for scientists or healthcare providers, so that they can more effectively broadcast their voice to targeted audiences.”…(More)”.

Selected Readings on Data, Gender, and Mobility

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By Michelle Winowatan, Uma Kalkar, Andrew Young, and Stefaan Verhulst

The Living Library’s Selected Readings series seeks to build a knowledge base on innovative approaches for improving the effectiveness and legitimacy of governance. This curated and annotated collection of recommended works on the topic of data, gender, and mobility was originally published in 2017, and updated in 2021.

This edition of the Selected Readings was  developed as part of an ongoing project at the GovLab, supported by Data2X, in collaboration with UNICEF, DigitalGlobe, IDS (UDD/Telefonica R&D), and the ISI Foundation, to establish a data collaborative to analyze unequal access to urban transportation for women and girls in Chile. We thank all our partners for their suggestions to the below curation – in particular Leo Ferres at IDS who got us started with this collection; Ciro Cattuto and Michele Tizzoni from the ISI Foundation; and Bapu Vaitla at Data2X for their pointers to the growing data and mobility literature. 

Introduction

Daily mobility is key for gender equity. Access to transportation contributes to women’s agency and independence. The ability to move from place to place safely and efficiently can allow women to access education, work, and the public domain more generally. Yet, mobility is not just a means to access various opportunities. It is also a means to enter the public domain.

Women’s mobility is a multi-layered challenge

Women’s daily mobility, however, is often hampered by social, cultural, infrastructural, and technical barriers. Cultural bias, for instance, limits women’s mobility in a way that women are confined to an area with close proximity to their house due to society’s double standard on women to be homemakers. From an infrastructural perspective, public transportation mostly only accommodates home-to-work trips, when in reality women often make more complex trips with multiple stops, for example, at the market, school, healthcare provider – sometimes called “trip chaining.” From a safety perspective, women tend to avoid making trips in certain areas and/or at certain times due to a constant risk of being sexually harassed n public places. Women are also pushed toward more expensive transportation – such as taking a cab instead of a bus or train – based on safety concerns.

The growing importance of (new sources of) data

Researchers are increasingly experimenting with ways to address these interdependent problems through the analysis of diverse datasets, often collected by private sector businesses and other non-governmental entities. Gender-disaggregated mobile phone records, geospatial data, satellite imagery, and social media data, to name a few, are providing evidence-based insight into gender and mobility concerns. Such data collaboratives – the exchange of data across sectors to create public value – can help governments, international organizations, and other public sector entities in the move toward more inclusive urban and transportation planning, and the promotion of gender equity.

The below curated set of readings seek to focus on the following areas:

  1. Insights on how data can inform gender empowerment initiatives,
  2. Emergent research into the capacity of new data sources – like call detail records (CDRs) and satellite imagery – to increase our understanding of human mobility patterns, and,
  3. Publications exploring data-driven policy for gender equity in mobility.

Readings are listed in alphabetical order.

We selected the readings based upon their focus (gender and/or mobility related); scope and representativeness (going beyond one project or context); type of data used (such as CDRs and satellite imagery); and date of publication.

Annotated Reading List

Data and Gender

Blumenstock, Joshua, and Nathan Eagle. Mobile Divides: Gender, Socioeconomic Status, and Mobile Phone Use in Rwanda. ACM Press, 2010.

  • Using traditional survey and mobile phone operator data, this study analyzes gender and socioeconomic divides in mobile phone use in Rwanda, where it is found that the use of mobile phones is significantly more prevalent in men and the higher class.
  • The study also shows the differences in the way men and women use phones, for example: women are more likely to use a shared phone than men.
  • The authors frame their findings around gender and economic inequality in the country to the end of providing pointers for government action.

Bosco, Claudio, et al. Mapping Indicators of Female Welfare at High Spatial Resolution. WorldPop and Flowminder, 2015.

  • This report focuses on early adolescence in girls, which often comes with higher risk of violence, fewer economic opportunity, and restrictions on mobility. Significant data gaps, methodological and ethical issues surrounding data collection for girls also create barriers for policymakers to create evidence-based policy to address those issues.
  • The authors analyze geolocated household survey data, using statistical models and validation techniques, and creates high-resolution maps of various sex-disaggregated indicators, such as nutrition level, access to contraception, and literacy, to better inform local policy making processes.
  • Further, it identifies the gender data gap and issues surrounding gender data collection, and provides arguments for why having  comprehensive data can help create better policy and contribute to the achievements of the Sustainable Development Goals (SDGs).

Buvinic, Mayra, Rebecca Furst-Nichols, and Gayatri Koolwal. Mapping Gender Data Gaps. Data2X, 2014.

  • This study identifies gaps in gender data in developing countries on health, education, economic opportunities, political participation, and human security issues.
  • It recommends ways to close the gender data gap through censuses and micro-level surveys, service and administrative records, and emphasizes how “big data” in particular can fill the missing data that will be able to measure the progress of women and girls well being. The authors argue that identifying these gaps is key to achieving SDG 5: advancing gender equality and women’s empowerment.

Catalyzing Inclusive Financial Systems: Chile’s Commitment to Women’s Data. Data2X, 2014.

  • This article analyzes global and national data in the banking sector to fill the gap of sex-disaggregated data in Chile. The purpose of the study is to describe the difference in spending behavior and priorities between women and men, identify the challenges for women in accessing financial services, and create policies that promote women inclusion in Chile.

Ready to Measure: Twenty Indicators for Monitoring SDG Gender Targets. Open Data Watch and Data2X, 2016.

  • Using readily available data, this study identifies 20 SDG indicators related to gender issues that can serve as a baseline measurement for advancing gender equality, such as percentage of women aged 20-24 who were married or in a union before age 18 (child marriage), proportion of seats held by women in national parliament, and share of women among mobile telephone owners, among others.

Ready to Measure Phase II: Indicators Available to Monitor SDG Gender Targets. Open Data Watch and Data2X, 2017.

  • The Phase II paper is an extension of the Ready to Measure Phase I above. Where Phase I identifies the readily available data to measure women and girls well-being, Phase II provides information on how to access this data and summarizes insights extracted from it.
  • Phase II elaborates the insights about data gathered from ready to measure indicators and finds that although underlying data to measure indicators of women and girls’ wellbeing is readily available in most cases, it is typically not sex-disaggregated.
  • Over one in five – 53 out of 232 – SDG indicators specifically refer to women and girls. However, further analysis from this study reveals that at least 34 more indicators should be disaggregated by sex. For instance, there should be 15 more sex-disaggregated indicators for SDG number 3: “Ensure healthy lives and promote well-being for all at all ages.”
  • The report recommends national statistical agencies to take the lead and assert additional effort to fill the data gap by utilizing tools such as the statistical model to fill the current gender data gap for each of the SDGs.

Reed, Philip J., Muhammad Raza Khan, and Joshua Blumenstock. Observing gender dynamics and disparities with mobile phone metadata. International Conference on Information and Communication Technologies and Development (ICTD), 2016.

  • The study analyzes mobile phone logs of millions of Pakistani residents to explore whether there is a difference in mobile phone usage behavior between male and female and determine the extent to which gender inequality is reflected in mobile phone usage.
  • It utilizes mobile phone data to analyze the pattern of usage behavior between genders, and socioeconomic and demographic data obtained from census and advocacy groups to assess the state of gender equality in each region in Pakistan.
  • One of its findings is a strong positive correlation between the proportion of female mobile phone users and education score.

Stehlé, Juliette, et al. Gender homophily from spatial behavior in a primary school: A sociometric study. 2013.

  • This paper seeks to understand homophily, a human behavior that characterizes interactions with peers who have similarities in “physical attributes to tastes or political opinions”. Further, it seeks to identify the magnitude of influence, a type of homophily applied to social structures.
  • Focusing on gender interaction among primary school aged children in France, this paper collects data from wearable devices from 200 children in the period of 2 days and measures the physical proximity and duration of the interaction among those children in the playground.
  • It finds that interaction patterns are significantly determined by grade and class structure of the school. This means that children belonging to the same class have most interactions, and that lower grades usually do not interact with higher grades.
  • From a gender lens, this study finds that mixed-gender interaction lasts shorter relative to same-gender interaction. In addition, interaction among girls is also longer compared to interaction among boys. These indicate that the children in this school tend to have stronger relationships within their own gender, or what the study calls gender homophily. It further finds that gender homophily is apparent in all classes.

Strengthening Gender Measures and Data in the COVID-19 Era: An Urgent Need for Change. Paris 21, 2021.

  • COVID-19 has exacerbated gender disparities, especially with regard to women’s livelihoods, unpaid labor, mental health, and risk of gender-based violence. Gaps in gender data impedes robust, data-driven, and effective policies to quantify, analyse, and respond to these issues. 
  • Without this information, the full effects of the COVID-19 pandemic cannot be understood. This report calls on National Statistical Systems, survey managers, funders, multilateral agencies, researchers, and policymakers to collect gender-intentional and disaggregated data that is standardized and comparable to address key areas of concern for women and girls. Additionally, it seeks to link non-traditional data sources, such as social media and news media, with existing frameworks to fill in knowledge gaps. Moreover, this information must be rendered accessible for all stakeholders to maximize the potential of the information. Post-pandemic, conscious collection and collation of gendered data is vital to preempt policy problems.

The Sex, Gender and COVID-19 Project: The COVID-19 Sex-Disaggregated Data Tracker. 2021.

  • This data tracker, produced by Global Health 50/50, the African Population and Health Research Center, and the International Center for Research on Women, tracks which countries and datasets have reported sex-disaggregated data on COVID-19 testing, confirmed cases, hospitalizations, and deaths.

Data and Mobility

Bengtsson, Linus, et al. Using Mobile Phone Data to Predict the Spatial Spread of Cholera. Flowminder, 2015.

  • This study seeks to predict the 2010 cholera epidemic in Haiti using 2.9 million anonymous mobile phone SIM cards and reported cases of Cholera from the Haitian Directorate of Health, where 78 study areas were analyzed in the period of October 16 – December 16, 2010.
  • From this dataset, the study creates a mobility matrix that indicates mobile phone movement from one study area to another and combines that with the number of reported cases of cholera in the study areas to calculate the infectious pressure level of those areas.
  • The main finding of its analysis shows that the outbreak risk of a study area correlates positively with the infectious pressure level, where an infectious pressure of over 22 results in an outbreak within 7 days. Further, it finds that the infectious pressure level can inform the sensitivity and specificity of the outbreak prediction.
  • It hopes to improve infectious disease containment by identifying areas with highest risks of outbreaks.

Calabrese, Francesco, et al. Understanding Individual Mobility Patterns from Urban Sensing Data: A Mobile Phone Trace Example. SENSEable City Lab, MIT, 2012.

  • This study compares mobile phone data and odometer readings from annual safety inspections to characterize individual mobility and vehicular mobility in the Boston Metropolitan Area, measured by the average daily total trip length of mobile phone users and average daily Vehicular Kilometers Traveled (VKT).
  • The study found that, “accessibility to work and non-work destinations are the two most important factors in explaining the regional variations in individual and vehicular mobility, while the impacts of populations density and land use mix on both mobility measures are insignificant.” Further, “a well-connected street network is negatively associated with daily vehicular total trip length.”
  • This study demonstrates the potential for mobile phone data to provide useful and updatable information on individual mobility patterns to inform transportation and mobility research.

Campos-Cordobés, Sergio, et al. “Chapter 5 – Big Data in Road Transport and Mobility Research.” Intelligent Vehicles. Edited by Felipe Jiménez. Butterworth-Heinemann, 2018.

  • This study outlines a number of techniques and data sources – such as geolocation information, mobile phone data, and social network observation – that could be leveraged to predict human mobility.
  • The authors also provide a number of examples of real-world applications of big data to address transportation and mobility problems, such as transport demand modeling, short-term traffic prediction, and route planning.

Gauvin, Laetitia et al. Gender gaps in urban mobility. Humanities and Information Science. Humanities & Social Sciences Communications vol. 7, issue 11, 2020.

  • This article discusses how urbanization affects mobility of women in realizing their rights. It points out the historic lack of gender disaggregated data for urban planning, leading to transportation designs that do not best accommodate the needs of women.
  • Examining the case study of urban mobility through a gendered lens in the large and growing metropolitan area of Santiago, Chile, the article examines the mobility traces from Call Detail Records (CDRs) of an anonymized cohort of mobile phone users, sorted by gender, over 3 months. It then mapped differences between men and women with regard to socio-demographic indicators and mobility differences across the city and through the Santiago transportation network structure and identified points of interests frequented by either sex to inform gendered mobility needs in urban areas.

Lin, Miao, and Wen-Jing Hsu. Mining GPS Data for Mobility Patterns: A Survey. Pervasive and Mobile Computing vol. 12, 2014.

  • This study surveys the current field of research using high resolution positioning data (GPS) to capture mobility patterns.
  • The survey focuses on analyses related to frequently visited locations, modes of transportation, trajectory patterns, and placed-based activities. The authors find “high regularity” in human mobility patterns despite high levels of variation among the mobility areas covered by individuals.

Phithakkitnukoon, Santi, Zbigniew Smoreda, and Patrick Olivier. Socio-Geography of Human Mobility: A Study Using Longitudinal Mobile Phone Data. PLoS ONE, 2012.

  • This study used a year’s call logs and location data of approximately one million mobile phone users in Portugal to analyze the association between individuals’ mobility and their social networks.
  • It measures and analyze travel scope (locations visited) and geo-social radius (distance from friends, family, and acquaintances) to determine the association.
  • It finds that 80% of places visited are within 20 km of an individual’s nearest social ties’ location and it rises to 90% at 45 km radius. Further, as population density increases, distance between individuals and their social networks decreases.
  • The findings in this study demonstrates how mobile phone data can provide insights to “the socio-geography of human mobility”.

Semanjski, Ivana, and Sidharta Gautama. Crowdsourcing Mobility Insights – Reflection of Attitude Based Segments on High Resolution Mobility Behaviour Data. vol. 71, Transportation Research, 2016.

  • Using cellphone data, this study maps attitudinal segments that explain how age, gender, occupation, household size, income, and car ownership influence an individual’s mobility patterns. This type of segment analysis is seen as particularly useful for targeted messaging.
  • The authors argue that these time- and space-specific insights could also provide value for government officials and policymakers, by, for example, allowing for evidence-based transportation pricing options and public sector advertising campaign placement.

Silveira, Lucas M., et al. MobHet: Predicting Human Mobility using Heterogeneous Data Sources. vol. 95, Computer Communications , 2016.

  • This study explores the potential of using data from multiple sources (e.g., Twitter and Foursquare), in addition to GPS data, to provide a more accurate prediction of human mobility. This heterogenous data captures popularity of different locations, frequency of visits to those locations, and the relationships among people who are moving around the target area. The authors’ initial experimentation finds that the combination of these sources of data are demonstrated to be more accurate in identifying human mobility patterns.

Wilson, Robin, et al. Rapid and Near Real-Time Assessments of Population Displacement Using Mobile Phone Data Following Disasters: The 2015 Nepal Earthquake. PLOS Current Disasters, 2016.

  • Utilizing call detail records of 12 million mobile phone users in Nepal, this study seeks spatio-temporal details of the population after the earthquake on April 25, 2015.
  • It seeks to answer the problem of slow and ineffective disaster response, by capturing near real-time displacement patterns provided by mobile phone call detail records, in order to inform humanitarian agencies on where to distribute their assistance. The preliminary results of this study were available nine days after the earthquake.
  • This project relies on the foundational cooperation with mobile phone operators, who supplied the de-identified data from 12 million users before the earthquake.
  • The study finds that shortly after the earthquake there was an anomalous population movement out of the Kathmandu Valley, the most impacted area, to surrounding areas. The study estimates 390,000 more people  than normal had left the valley.

Data, Gender and Mobility

Althoff, Tim, et al. “Large-Scale Physical Activity Data Reveal Worldwide Activity Inequality.” Nature, 2017.

  • This study’s analysis of worldwide physical activity is built on a dataset containing 68 million days of physical activity of 717,527 people collected through their smartphone accelerometers.
  • The authors find a significant reduction in female activity levels in cities with high active inequality, where high active inequality is associated with low city walkability – walkability indicators include pedestrian facilities (city block length, intersection density, etc.) and amenities (shops, parks, etc.).
  • Further, they find that high active inequality is associated with high levels of inactivity-related health problems, like obesity.

Borker, Girija. Safety First: Street Harassment and Women’s Educational Choices in India.Stop Street Harassment, 2017.

  • Using data collected from SafetiPin, an application that allows users to mark an area on a map as safe or not, and Safecity, another application that lets users share their experience of harassment in public places, Borker analyzes the safety of travel routes surrounding different colleges in India and their effect on women’s college choices.
  • The study finds that women are willing to go to a lower ranked college in order to avoid higher risk of street harassment. Women who choose the best college from their set of options, spend an average of $250 more each year to access safer modes of transportation.

Frias-Martinez, Vanessa, Enrique Frias-Martinez, and Nuria Oliver. A Gender-Centric Analysis of Calling Behavior in a Developing Economy Using Call Detail Records. Association for the Advancement of Artificial Intelligence, 2010.

  • Using encrypted Call Detail Records (CDRs) of 10,000 participants in a developing economy, this study analyzes the behavioral, social, and mobility variables to determine the gender of a mobile phone user, and finds that there is a difference in behavioral and social variables in mobile phone use between female and male.
  • It finds that women have higher usage of phone in terms of number of calls made, call duration, and call expenses compared to men. Women also have bigger social network, meaning that the number of unique phone numbers that contact or get contacted is larger. It finds no statistically significant difference in terms of distance made between calls in men and women.
  • Frias-Martinez et al recommends to take these findings into consideration when designing a cellphone based service.

Psylla, Ioanna, Piotr Sapiezynski, Enys Mones, Sune Lehmann. The role of gender in social network organization. PLoS ONE 12, December 20, 2017.

  • Using a large dataset of high resolution data collected through mobile phones, as well as detailed questionnaires, this report studies gender differences in a large cohort. The researchers consider mobility behavior and individual personality traits among a group of more than 800 university students.
  • Analyzing mobility data, they find both that women visit more unique locations over time, and that they have more homogeneous time distribution over their visited locations than men, indicating the time commitment of women is more widely spread across places.

The Landscape of Big Data and Gender. Data2X, February, 2021.

  • Under the backdrop of COVID-19, this report reaffirms that big data initiatives to study mobility, health, and social norms through gendered lenses have greatly progressed. More private companies and think tanks have launched data collection and sharing efforts to spur innovative projects to address COVID-19 complications.
  • However, economic opportunity, security, and civic action have been lagging behind. Big data collection among these topics is complicated by the lack of sex-disaggregated datasets, gender disparities in technology access, and the lack of gender-tags among big data.
  • Large technology firms, especially social networks like Facebook, LinkedIn, Uber, and more, create a large amount of gender-organized data. The report found that users and data-holding companies are willing to share this information for public policy reasons so long as it provides value and is protected. To this end, Data2X, alongside its partners, champion the use of data collaboratives to use gender sorted information for social good.

Vaitla, Bapu. Big Data and the Well Being of Women and Girls: Applications on the Social Scientific Frontier. Data2X, Apr. 2017.

  • In this study, the researchers use geospatial data, credit card and cell phone information, and social media posts to identify problems–such as malnutrition, education, access to healthcare, mental health–facing women and girls in developing countries.
  • From the credit card and cell phone data in particular, the report finds that analyzing patterns of women’s spending and mobility can provide useful insight into Latin American women’s “economic lifestyles.”
  • Based on this analysis, Vaitla recommends that various untraditional big data be used to fill gaps in conventional data sources to address the common issues of invisibility of women and girls’ data in institutional databases.

COVID vaccination studies: plan now to pool data, or be bogged down in confusion

Curated on Posted on by Stefaan Verhulst

Natalie Dean at Nature: “More and more COVID-19 vaccines are rolling out safely around the world; just last month, the United States authorized one produced by Johnson & Johnson. But there is still much to be learnt. How long does protection last? How much does it vary by age? How well do vaccines work against various circulating variants, and how well will they work against future ones? Do vaccinated people transmit less of the virus?

Answers to these questions will help regulators to set the best policies. Now is the time to make sure that those answers are as reliable as possible, and I worry that we are not laying the essential groundwork. Our current trajectory has us on course for confusion: we must plan ahead to pool data.

Many questions remain after vaccines are approved. Randomized trials generate the best evidence to answer targeted questions, such as how effective booster doses are. But for others, randomized trials will become too difficult as more and more people are vaccinated. To fill in our knowledge gaps, observational studies of the millions of vaccinated people worldwide will be essential….

Perhaps most importantly, we must coordinate now on plans to combine data. We must take measures to counter the long-standing siloed approach to research. Investigators should be discouraged from setting up single-site studies and encouraged to contribute to a larger effort. Funding agencies should favour studies with plans for collaborating or for sharing de-identified individual-level data.

Even when studies do not officially pool data, they should make their designs compatible with others. That means up-front discussions about standardization and data-quality thresholds. Ideally, this will lead to a minimum common set of variables to be collected, which the WHO has already hammered out for COVID-19 clinical outcomes. Categories include clinical severity (such as all infections, symptomatic disease or critical/fatal disease) and patient characteristics, such as comorbidities. This will help researchers to conduct meta-analyses of even narrow subgroups. Efforts are under way to develop reporting guidelines for test-negative studies, but these will be most successful when there is broad engagement.

There are many important questions that will be addressed only by observational studies, and data that can be combined are much more powerful than lone results. We need to plan these studies with as much care and intentionality as we would for randomized trials….(More)”.

Covid-19 Data Cards: Building a Data Taxonomy for Pandemic Preparedness

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Open Data Charter: “…We want to initiate the repair of the public’s trust through the building of a Pandemic Data Taxonomy with you — a network of data users and practitioners.

Building on feedback we got from our call to identify high value Open COVID-19 Data, we have structured a set of data cards, including key data types related to health issues, legal and socioeconomic impacts and fiscal transparency, within which are well-defined data models and dictionaries. Our target audience for this data taxonomy are governments. We are hoping this framework is a starting point towards building greater consistency around pandemic data release, and flag areas for better cooperation and standardisation within and between our governments and communities around the world.

We hope that together, with the input and feedback from a diverse group of data users and practitioners, we can have at the end of this public consultation and open-call, a document by a global collective, one that we can present to governments and public servants for their buy-in to reform our data infrastructures to be better prepared for future outbreaks.

In order to analyze the variables necessary to manage and investigate the different aspects of a pandemic, as exemplified by COVID-19, and based on a review of the type of data being released by 25 countries — we categorised the data in 4 major categories:

  • General — Contains the general concepts that all the files have in common and are defined, such as the METADATA, global sections of RISKS and their MITIGATION and the general STANDARDS required for the use, management and publication of the data. Then, a link to a spreadsheet, where more details of the precision, update frequency, publication methods and specific standards of each data set are defined.
  • Health Data — Describes how to manage and potentially publish the follow-up information on COVID-19 cases, considering data with temporal, geographical and demographic distribution along with the details for the study of the evolution of the disease.
  • Legal and Socioeconomic Impact Data — Contains the regulations, actions, measures, restrictions, protocols, documents and all the information regarding quarantine and the socio-economic impact as well as medical, labor or economic regulations for each data publisher.
  • Fiscal Data — Contains all budget allocations in accordance with the overall approved Pandemic budget, as well as the implemented adjustments. It also identifies specific allocations for facing prevention, detection, control, treatment and containment of the virus, as well as possible budget reallocations from other sectors or items derived from the actions mentioned above or by the derived economic constraints. It’s based on the recommendations made by GIFT and Open Contracting….(More)”

E-mail Is Making Us Miserable

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Cal Newport at The New Yorker: “In early 2017, a French labor law went into effect that attempted to preserve the so-called right to disconnect. Companies with fifty or more employees were required to negotiate specific policies about the use of e-mail after work hours, with the goal of reducing the time that workers spent in their in-boxes during the evening or over the weekend. Myriam El Khomri, the minister of labor at the time, justified the new law, in part, as a necessary step to reduce burnout. The law is unwieldy, but it points toward a universal problem, one that’s become harder to avoid during the recent shift toward a more frenetic and improvisational approach to work: e-mail is making us miserable.

To study the effects of e-mail, a team led by researchers from the University of California, Irvine, hooked up forty office workers to wireless heart-rate monitors for around twelve days. They recorded the subjects’ heart-rate variability, a common technique for measuring mental stress. They also monitored the employees’ computer use, which allowed them to correlate e-mail checks with stress levels. What they found would not surprise the French. “The longer one spends on email in [a given] hour the higher is one’s stress for that hour,” the authors noted. In another study, researchers placed thermal cameras below each subject’s computer monitor, allowing them to measure the tell-tale “heat blooms” on a person’s face that indicate psychological distress. They discovered that batching in-box checks—a commonly suggested “solution” to improving one’s experience with e-mail—is not necessarily a panacea. For those people who scored highly in the trait of neuroticism, batching e-mails actually made them more stressed, perhaps because of worry about all of the urgent messages they were ignoring. The researchers also found that people answered e-mails more quickly when under stress but with less care—a text-analysis program called Linguistic Inquiry and Word Count revealed that these anxious e-mails were more likely to contain words that expressed anger. “While email use certainly saves people time effort in communicating, it also comes at a cost, the authors of the two studies concluded. Their recommendation? To “suggest that organizations make a concerted effort to cut down on email traffic.”

Other researchers have found similar connections between e-mail and unhappiness. A study, published in 2019, looked at long-term trends in the health of a group of nearly five thousand Swedish workers. They found that repeated exposure to “high information and communication technology demands” (translation: a need to be constantly connected) were associated with “suboptimal” health outcomes. This trend persisted even after they adjusted the statistics for potential complicating factors such as age, sex, socioeconomic status, health behavior, body-mass index, job strain, and social support. Of course, we don’t really need data to capture something that so many of us feel intuitively. I recently surveyed the readers of my blog about e-mail. “It’s slow and very frustrating. . . . I often feel like email is impersonal and a waste of time,” one respondent said. “I’m frazzled—just keeping up,” another admitted. Some went further. “I feel an almost uncontrollable need to stop what I’m doing to check email,” one person reported. “It makes me very depressed, anxious and frustrated.”…(More)”

Dialogues about Data: Building trust and unlocking the value of citizens’ health and care data

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Nesta Report by Sinead Mac Manus and Alice Clay: “The last decade has seen exponential growth in the amount of data generated, collected and analysed to provide insights across all aspects of industry. Healthcare is no exception. We are increasingly seeing the value of using health and care data to prevent ill health, improve health outcomes for people and provide new insights into disease and treatments.

Bringing together common themes across the existing research, this report sets out two interlinked challenges to building a data-driven health and care system. This is interspersed with best practice examples of the potential of data to improve health and care, as well as cautionary tales of what can happen when this is done badly.

The first challenge we explore is how to increase citizens’ trust and transparency in data sharing. The second challenge is how to unlock the value of health and care data.

We are excited about the role for participatory futures – a set of techniques that systematically engage people to imagine and create more sustainable, inclusive futures – in helping governments and other organisations work with citizens to engage them in debate about their health and care data to build a data-driven health and care system for the benefit of all….(More)”.

How can stakeholder engagement and mini-publics better inform the use of data for pandemic response?

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Andrew Zahuranec, Andrew Young and Stefaan G. Verhulst at the OECD Participo Blog Series:

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“What does the public expect from data-driven responses to the COVID-19 pandemic? And under what conditions?” These are the motivating questions behind The Data Assembly, a recent initiative by The GovLab at New York University Tandon School of Engineering — an action research center that aims to help institutions work more openly, collaboratively, effectively, and legitimately.

Launched with support from The Henry Luce Foundation, The Data Assembly solicited diverse, actionable public input on data re-use for crisis response in the United States. In particular, we sought to engage the public on how to facilitate, if deemed acceptable, the use of data that was collected for a particular purpose for informing COVID-19. One additional objective was to inform the broader emergence of data collaboration— through formal and ad hoc arrangements between the public sector, civil society, and those in the private sector — by evaluating public expectation and concern with current institutional, contractual, and technical structures and instruments that may underpin these partnerships.

The Data Assembly used a new methodology that re-imagines how organisations can engage with society to better understand local expectations regarding data re-use and related issues. This work goes beyond soliciting input from just the “usual suspects”. Instead, data assemblies provide a forum for a much more diverse set of participants to share their insights and voice their concerns.

This article is informed by our experience piloting The Data Assembly in New York City in summer 2020. It provides an overview of The Data Assembly’s methodology and outcomes and describes major elements of the effort to support organisations working on similar issues in other cities, regions, and countries….(More)”.

Surveillance and the ‘New Normal’ of Covid-19: Public Health, Data, and Justice

Curated on Posted on by Stefaan Verhulst

Report by the Social Science Research Council: “The Covid-19 pandemic has dramatically altered the way nations around the world use technology in public health. As the virus spread globally, some nations responded by closing businesses, shuttering schools, limiting gatherings, and banning travel. Many also deployed varied technological tools and systems to track virus exposure, monitor outbreaks, and aggregate hospital data.

Some regions are still grappling with crisis-level conditions, and others are struggling to navigate the complexities of vaccine rollouts. Amid the upheavals, communities are adjusting to a new normal, in which mask-wearing has become as commonplace as seatbelt use and digital temperature checks are a routine part of entering public buildings.

Even as the frenzy of emergency responses begins to subside, the emergent forms of surveillance that have accompanied this new normal persist. As a consequence, societies face new questions about how to manage the monitoring systems created in response to the virus, what processes are required in order to immunize populations, and what new norms the systems have generated. How they answer these questions will have long-term impacts on civil liberties, governance, and the role of technology in society. The systems implemented amid the public health emergency could jeopardize individual freedoms and exacerbate harms to already vulnerable groups, particularly if they are adapted to operate as permanent social management tools. At the same time, growing public awareness about the impact of public health technologies could also provide a catalyst for strengthening democratic engagement and demonstrating the urgency of improving governance systems. As the world transitions in and out of pandemic crisis modes, there is an opportunity to think broadly about strengthening public health systems, policymaking, and the underlying structure of our social compacts.

The stakes are high: an enduring lesson from history is that moments of crisis often recast the roles of governments and the rights of individuals. Moments of crisis often recast the roles of governments and the rights of individuals.In this moment of flux, the Social Science Research Council calls on policymakers, technologists, data scientists, health experts, academics, activists, and communities around the world to assess the implications of this transformation and seize opportunities for positive social change. The Council seeks to facilitate a shift from reactive modes of crisis response to more strategic forms of deliberation among varied stakeholders. As such, it has convened discussions and directed research in order to better understand the intersection of governance and technologically enabled surveillance in conditions of public health emergencies. Through these activities, the Council aims to provide analysis that can help foster societies that are more resilient, democratic, and inclusive and can, therefore, better withstand future crises.

With these goals in mind, the Council convened a cross-disciplinary, multinational group of experts in the summer of 2020 to survey the landscape of human rights and social justice with regard to technologically driven public health practices. The resulting group—the Public Health, Surveillance, and Human Rights (PHSHR) Network—raised a broad range of questions about governance, social inequalities, data protection, medical systems, and community norms: What rules should govern the sharing of personal health data? How should the efficacy of public health interventions be weighed against the emergence and expansion of new forms of surveillance? How much control should multinational corporations have in designing and implementing nations’ public health technology systems? These are among the questions that pushed members to think beyond traditional professional, geographic, and intellectual boundaries….(More)”.

My Data, My Choice? – German Patient Organizations’ Attitudes towards Big Data-Driven Approaches in Personalized Medicine. An Empirical-Ethical Study

Curated on Posted on by Stefaan Verhulst

Paper by Carolin Martina Rauter, Sabine Wöhlke & Silke Schicktanz: “Personalized medicine (PM) operates with biological data to optimize therapy or prevention and to achieve cost reduction. Associated data may consist of large variations of informational subtypes e.g. genetic characteristics and their epigenetic modifications, biomarkers or even individual lifestyle factors. Present innovations in the field of information technology have already enabled the procession of increasingly large amounts of such data (‘volume’) from various sources (‘variety’) and varying quality in terms of data accuracy (‘veracity’) to facilitate the generation and analyzation of messy data sets within a short and highly efficient time period (‘velocity’) to provide insights into previously unknown connections and correlations between different items (‘value’). As such developments are characteristics of Big Data approaches, Big Data itself has become an important catchphrase that is closely linked to the emerging foundations and approaches of PM. However, as ethical concerns have been pointed out by experts in the debate already, moral concerns by stakeholders such as patient organizations (POs) need to be reflected in this context as well. We used an empirical-ethical approach including a website-analysis and 27 telephone-interviews for gaining in-depth insight into German POs’ perspectives on PM and Big Data. Our results show that not all POs are stakeholders in the same way. Comparing the perspectives and political engagement of the minority of POs that is currently actively involved in research around PM and Big Data-driven research led to four stakeholder sub-classifications: ‘mediators’ support research projects through facilitating researcher’s access to the patient community while simultaneously selecting projects they preferably support while ‘cooperators’ tend to contribute more directly to research projects by providing and implemeting patient perspectives. ‘Financers’ provide financial resources. ‘Independents’ keep control over their collected samples and associated patient-related information with a strong interest in making autonomous decisions about its scientific use. A more detailed terminology for the involvement of POs as stakeholders facilitates the adressing of their aims and goals. Based on our results, the ‘independents’ subgroup is a promising candidate for future collaborations in scientific research. Additionally, we identified gaps in PO’s knowledge about PM and Big Data. Based on these findings, approaches can be developed to increase data and statistical literacy. This way, the full potential of stakeholder involvement of POs can be made accessible in discourses around PM and Big Data….(More)”.

Public-Private Partnerships: Compound and Data Sharing in Drug Discovery and Development

Curated on Posted on by Stefaan Verhulst

Paper by Andrew M. Davis et al: “Collaborative efforts between public and private entities such as academic institutions, governments, and pharmaceutical companies form an integral part of scientific research, and notable instances of such initiatives have been created within the life science community. Several examples of alliances exist with the broad goal of collaborating toward scientific advancement and improved public welfare. Such collaborations can be essential in catalyzing breaking areas of science within high-risk or global public health strategies that may have otherwise not progressed. A common term used to describe these alliances is public-private partnership (PPP). This review discusses different aspects of such partnerships in drug discovery/development and provides example applications as well as successful case studies. Specific areas that are covered include PPPs for sharing compounds at various phases of the drug discovery process—from compound collections for hit identification to sharing clinical candidates. Instances of PPPs to support better data integration and build better machine learning models are also discussed. The review also provides examples of PPPs that address the gap in knowledge or resources among involved parties and advance drug discovery, especially in disease areas with unfulfilled and/or social needs, like neurological disorders, cancer, and neglected and rare diseases….(More)”.