Posts by Collection

articles

Overlapping spatial clusters of sugar-sweetened beverage intake and body mass index in Geneva state, Switzerland

Published in Nutrition and Diabetes, 2019

Obesity and obesity-related diseases represent a major public health concern. Recently, studies have substantiated the role of sugar-sweetened beverages (SSBs) consumption in the development of these diseases. The fine identification of populations and areas in need for public health intervention remains challenging. This study investigates the existence of spatial clustering of SSB intake frequency (SSB-IF) and body mass index (BMI), and their potential spatial overlap in a population of adults of the state of Geneva using a fine-scale geospatial approach. Read more

A reference map of the human binary protein interactome

Published in Nature, 2020

Global insights into cellular organization and genome function require comprehensive understanding of the interactome networks that mediate genotype–phenotype relationships1,2. Here we present a human ‘all-by-all’ reference interactome map of human binary protein interactions, or ‘HuRI’. With approximately 53,000 protein–protein interactions, HuRI has approximately four times as many such interactions as there are high-quality curated interactions from small-scale studies. The integration of HuRI with genome3, transcriptome4 and proteome5 data enables cellular function to be studied within most physiological or pathological cellular contexts. We demonstrate the utility of HuRI in identifying the specific subcellular roles of protein–protein interactions. Inferred tissue-specific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms that might underlie tissue-specific phenotypes of Mendelian diseases. HuRI is a systematic proteome-wide reference that links genomic variation to phenotypic outcomes. Read more

Seroprevalence of anti-SARS-CoV-2 IgG antibodies in Geneva, Switzerland (SEROCoV-POP): a population-based study

Published in The Lancet, 2020

Background Assessing the burden of COVID-19 on the basis of medically attended case numbers is suboptimal given its reliance on testing strategy, changing case definitions, and disease presentation. Population-based serosurveys measuring anti-severe acute respiratory syndrome coronavirus 2 (anti-SARS-CoV-2) antibodies provide one method for estimating infection rates and monitoring the progression of the epidemic. Here, we estimate weekly seroprevalence of anti-SARS-CoV-2 antibodies in the population of Geneva, Switzerland, during the epidemic.
Methods The SEROCoV-POP study is a population-based study of former participants of the Bus Santé study and their household members. We planned a series of 12 consecutive weekly serosurveys among randomly selected participants from a previous population-representative survey, and their household members aged 5 years and older. We tested each participant for anti-SARS-CoV-2-IgG antibodies using a commercially available ELISA. We estimated seroprevalence using a Bayesian logistic regression model taking into account test performance and adjusting for the age and sex of Geneva’s population. Here we present results from the first 5 weeks of the study.
Findings Between April 6 and May 9, 2020, we enrolled 2766 participants from 1339 households, with a demographic distribution similar to that of the canton of Geneva. In the first week, we estimated a seroprevalence of 4·8% (95% CI 2·4–8·0, n=341). The estimate increased to 8·5% (5·9–11·4, n=469) in the second week, to 10·9% (7·9–14·4, n=577) in the third week, 6·6% (4·3–9·4, n=604) in the fourth week, and 10·8% (8·2–13·9, n=775) in the fifth week. Individuals aged 5–9 years (relative risk [RR] 0·32 [95% CI 0·11–0·63]) and those older than 65 years (RR 0·50 [0·28–0·78]) had a significantly lower risk of being seropositive than those aged 20–49 years. After accounting for the time to seroconversion, we estimated that for every reported confirmed case, there were 11·6 infections in the community.
Interpretation These results suggest that most of the population of Geneva remained uninfected during this wave of the pandemic, despite the high prevalence of COVID-19 in the region (5000 reported clinical cases over <2·5 months in the population of half a million people). Assuming that the presence of IgG antibodies is associated with immunity, these results highlight that the epidemic is far from coming to an end by means of fewer susceptible people in the population. Further, a significantly lower seroprevalence was observed for children aged 5–9 years and adults older than 65 years, compared with those aged 10–64 years. These results will inform countries considering the easing of restrictions aimed at curbing transmission.
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Geospatial digital monitoring of COVID-19 cases at high spatiotemporal resolution

Published in The Lancet Digital Health, 2020

The novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has impacted our societies on an unprecedented scale. Worldwide, lockdowns and quarantines have been implemented to contain the spread of the virus, and are currently in place for more than 50% of the global population. These restrictive physical distancing measures raise many concerns regarding their adverse impact on our societies, economies, and health-care systems. Read more

Socioeconomically Disadvantaged Neighborhoods Face Increased Persistence of SARS-CoV-2 Clusters

Published in Frontiers in Public Health, 2021

Objective: To investigate the association between socioeconomic deprivation and the persistence of SARS-CoV-2 clusters.
Methods: We analyzed 3,355 SARS-CoV-2 positive test results in the state of Geneva (Switzerland) from February 26 to April 30, 2020. We used a spatiotemporal cluster detection algorithm to monitor SARS-CoV-2 transmission dynamics and defined spatial cluster persistence as the time in days from emergence to disappearance. Using spatial cluster persistence measured outcome and a deprivation index based on neighborhood-level census socioeconomic data, stratified survival functions were estimated using the Kaplan-Meier estimator. Population density adjusted Cox proportional hazards (PH) regression models were then used to examine the association between neighborhood socioeconomic deprivation and persistence of SARS-CoV-2 clusters.
Results: SARS-CoV-2 clusters persisted significantly longer in socioeconomically disadvantaged neighborhoods. In the Cox PH model, the standardized deprivation index was associated with an increased spatial cluster persistence (hazard ratio [HR], 1.43 [95% CI, 1.28–1.59]). The adjusted tercile-specific deprivation index HR was 1.82 [95% CI, 1.56–2.17].
Conclusions: The increased risk of infection of disadvantaged individuals may also be due to the persistence of community transmission. These findings further highlight the need for interventions mitigating inequalities in the risk of SARS-CoV-2 infection and thus, of serious illness and mortality. Read more

Geospatial Analysis of Sodium and Potassium Intake: A Swiss Population-Based Study

Published in Nutrients, 2021

Inadequate sodium and potassium dietary intakes are associated with major, yet preventable, health consequences. Local public health interventions can be facilitated and informed by fine-scale geospatial analyses. In this study, we assess the existence of spatial clustering (i.e., an unusual concentration of individuals with a specific outcome in space) of estimated sodium (Na), potassium (K) intakes, and Na:K ratio in the Bus Santé 1992–2018 annual population-based surveys, including 22,495 participants aged 20–74 years, residing in the canton of Geneva, using the local Moran’s I spatial statistics. We also investigate whether socio-demographic and food environment characteristics are associated with identified spatial clustering, using both global ordinary least squares (OLS) and local geographically weighted regression (GWR) modeling. We identified clear spatial clustering of Na:K ratio, Na, and K intakes. The GWR outperformed the OLS models and revealed spatial variations in the associations between explanatory and outcome variables. Older age, being a woman, higher education, and having a lower access to supermarkets were associated with higher Na:K ratio, while the opposite was seen for having the Swiss nationality. Socio-demographic characteristics explained a major part of the identified clusters. Socio-demographic and food environment characteristics significantly differed between individuals in spatial clusters of high and low Na:K ratio, Na, and K intakes. These findings could guide prioritized place-based interventions tailored to the characteristics of the identified populations. Read more

Detection of Spatiotemporal Clusters of COVID-19–Associated Symptoms and Prevention Using a Participatory Surveillance App: Protocol for the @choum Study

Published in Journal of Medical Internet Research (JMIR), 2021

Background: The early detection of clusters of infectious diseases such as the SARS-CoV-2–related COVID-19 disease can promote timely testing recommendation compliance and help to prevent disease outbreaks. Prior research revealed the potential of COVID-19 participatory syndromic surveillance systems to complement traditional surveillance systems. However, most existing systems did not integrate geographic information at a local scale, which could improve the management of the SARS-CoV-2 pandemic.
Objective: The aim of this study is to detect active and emerging spatiotemporal clusters of COVID-19–associated symptoms, and to examine (a posteriori) the association between the clusters’ characteristics and sociodemographic and environmental determinants.
Methods: This report presents the methodology and development of the @choum (English: “achoo”) study, evaluating an epidemiological digital surveillance tool to detect and prevent clusters of individuals (target sample size, N=5000), aged 18 years or above, with COVID-19–associated symptoms living and/or working in the canton of Geneva, Switzerland. The tool is a 5-minute survey integrated into a free and secure mobile app (CoronApp-HUG). Participants are enrolled through a comprehensive communication campaign conducted throughout the 12-month data collection phase. Participants register to the tool by providing electronic informed consent and nonsensitive information (gender, age, geographically masked addresses). Symptomatic participants can then report COVID-19–associated symptoms at their onset (eg, symptoms type, test date) by tapping on the @choum button. Those who have not yet been tested are offered the possibility to be informed on their cluster status (information returned by daily automated clustering analysis). At each participation step, participants are redirected to the official COVID-19 recommendations websites. Geospatial clustering analyses are performed using the modified space-time density-based spatial clustering of applications with noise (MST-DBSCAN) algorithm.
Results: The study began on September 1, 2020, and will be completed on February 28, 2022. Multiple tests performed at various time points throughout the 5-month preparation phase have helped to improve the tool’s user experience and the accuracy of the clustering analyses. A 1-month pilot study performed among 38 pharmacists working in 7 Geneva-based pharmacies confirmed the proper functioning of the tool. Since the tool’s launch to the entire population of Geneva on February 11, 2021, data are being collected and clusters are being carefully monitored. The primary study outcomes are expected to be published in mid-2022.
Conclusions: The @choum study evaluates an innovative participatory epidemiological digital surveillance tool to detect and prevent clusters of COVID-19–associated symptoms. @choum collects precise geographic information while protecting the user’s privacy by using geomasking methods. By providing an evidence base to inform citizens and local authorities on areas potentially facing a high COVID-19 burden, the tool supports the targeted allocation of public health resources and promotes testing. Read more

expressions

IPoXP: Internet Protocol over Xylophone Players

We introduce IP over Xylophone Players (IPoXP), a novel Internet protocol between two computers using xylophone-based Arduino interfaces. In our implementation, human operators are situated within the lowest layer of the network, transmitting data between computers by striking designated keys. We discuss how IPoXP inverts the traditional mode of human-computer interaction, with a computer using the human as an interface to communicate with another computer Read more

0 (the game)

One of the many forks of the popular game 1024 by Veewo Studio (which is conceptually similar to Threes by Asher Vollmer). Try to combine all the 0 tiles until they add up to 1. Read more

robots.txt.php

An algorithmically-generated robots.txt, which disallows all bots with one exception: the bot requesting the file is allowed full access. Read more

dystopedia

A Markov chain Twitter bot trained on titles of Wikipedia articles that have been deleted. Read more

AcademicPages

AcademicPages is a ready-to-fork GitHub Pages template for academic personal websites, based on structured data in markdown files. I created it for this website, then released it so others can make their own, which are hosted for free by GitHub. Over 500 people have! Read more

talks

Actor-Network Theory

Published in Social Aspects of Information Systems course, 2013

An introduction to Actor Network Theory for students in the Masters of Information Management and Systems (MIMS) course Read more

Governing the Commons

Published in History of Information, 2014

A lecture on the history of Wikipedia, in the broader context of the history of reference works. Read more

Moderating Online Conversation Spaces

Published in Social Aspects of Information Systems course, 2015

An overview of how various online platforms moderate content, discussing issues that link up to the theories discussed in the Social Aspects of Information Systems class. Read more

Peer Production and Wikipedia

Published in Social Aspects of Information Systems course, 2015

An overview of Wikipedia and other peer production platforms, discussing issues that link up to the theories discussed in the Social Aspects of Information Systems class. Read more

The Bot Multiple: Unpacking the Materialities of Automated Software Agents

Published in Annual Meeting of the Society for the Social Study of Science (4S), 2015

I examine the roles that automated software agents (or bots) play in the governance and moderation of Wikipedia, Twitter, and reddit – three online platforms that differently uphold a related set of commitments to ‘open’ and ‘public’ online participation. Read more

Scraping Wikipedia Data

Published in The Hacker Within, BIDS, 2016

A tutorial (with Jupyter notebooks) about how to use APIs to query structured data from Wikipedia articles and the Wikidata project. Read more

Community Sustainability in Wikipedia: A Review of Research and Initiatives

Published in PyData SF, 2016

Wikipedia relies on one of the world’s largest open collaboration communities. Since 2001, the community has grown substantially and faced many challenges. This presentation reviews research and initiatives around community sustainability in Wikipedia that are relevant for many open source projects, including issues of newcomer retention, governance, automated moderation, and marginalized groups. Read more

“The Wisdom of Bots:” An ethnographic study of the delegation of governance work to information infrastructures in Wikipedia

Published in Annual Meeting of the Society for the Social Study of Science (4S), 2016

Wikipedians rely on software agents to govern the ‘anyone can edit’ encyclopedia project, in the absence of more formal and traditional organizational structures. Lessons from Wikipedia’s bots speak to debates about how algorithms are being delegated governance work in sites of cultural production. Read more

Jupyter and the Changing Rituals around Computation

Published in JupyterCon, 2017

We (Stuart Geiger, Brittany Fiore-Gartland, and Charlotte Cabasse-Mazel) share ethnographic findings made observing and working with Jupyter notebooks, focusing on how people use Jupyter to create and deliver computational narratives in particular local contexts, like classrooms, hackathons, research collaborations, and more. Read more

Computational Ethnography and the Ethnography of Computation

Published in Berkeley Institute for Data Science, 2017

Ethnography is traditionally a qualitative and inductive methodology – with its origins in cultural anthropology – that is now widely used to holistically investigate people’s lived experiences in and across cultures. In this talk, I define and discuss two ways of thinking about the role of ethnographic methods around computation, then discuss how my research relates to both. Read more

Are the bots really fighting? Behind the scenes of a reproducible replication

Published in UC-Berkeley Department of Statistics: Reproducible and Collaborative Data Science, 2017

A guest lecture for Fernando Perez’s STAT 159/259 course on Reproducible and Collaborative Data Science, in which I discuss issues of open science and reproducibility around our recent paper Operationalizing conflict and cooperation between automated software agents in Wikipedia: A replication and expansion of ‘Even Good Bots Fight’ Read more

“But it wouldn’t be an encyclopedia; it would be a wiki”: The changing imagined affordances of wikis, 1995-2002

Published in 2017 Annual Meeting of the Association of Internet Researchers, 2017

This paper examines the early history of “anyone can edit” wiki software – originally developed in 1995, six years before Wikipedia’s origin. While today, the idea of a wiki is associated with large-scale, massively-distributed encyclopedic knowledge production, this was not always the case. Articles on pre-Wikipedia wikis were often closer to a Joycean stream of consciousness than Wikipedia’s Britannica-inspired texts that speak in single voice, and the underlying wiki platform lacked many of the affordances that are now taken for granted in wiki platforms. In fact, the creator of the first wiki advised Wikipedia’s co-founders that the goals of creating a general-purpose encyclopedia and a wiki were inherently contradictory. Read more

The Humanity of Artificial Intelligence

Published in Bay Area Science Festival, 2017

Today, “artificial intelligence” seems to be everywhere – in our phones, vacuums, hospitals, and inboxes – but it can be hard to separate science fiction from science fact. Many discussions about AI imagine a fully autonomous superintelligence that designs itself with little to no human intervention, making decisions in ways that humans cannot possibly understand. Yet the work of designing, developing, engineering, training, and testing such systems requires a massive amount of human labor, which is typically erased when such systems are released as products. In this talk, I give a human-centered, behind-the-scenes introduction to machine learning, illustrating the creative, interpretive, and often messy work humans do to make autonomous agents work. Understanding the humanity behind artificial intelligence is important if we want to think constructively about issues of bias, fairness, accountability, and transparency in AI. Read more

Computational Ethnography and the Ethnography of Computation: The Case for Context

Published in School of Information and Library Science, University of North Carolina at Chapel Hill, 2018

Ethnography is traditionally a qualitative and inductive methodology that is now widely used to holistically investigate people’s lived experiences in and across cultures. In this talk, I define and discuss two ways of thinking about the role of ethnographic methods around computation, then discuss how my research relates to both. Read more

Computational Ethnography and the Ethnography of Computation: The Case for Context

Published in School of Information Sciences, University of Illinois at Urbana-Champaign, 2018

Ethnography is traditionally a qualitative and inductive methodology that is now widely used to holistically investigate people’s lived experiences in and across cultures. In this talk, I define and discuss two ways of thinking about the role of ethnographic methods around computation, then discuss how my research relates to both. Read more

Computational Ethnography and the Ethnography of Computation: The Case for Context

Published in College of Information Studies, University of Maryland at College Park, 2018

Ethnography is traditionally a qualitative and inductive methodology that is now widely used to holistically investigate people’s lived experiences in and across cultures. In this talk, I define and discuss two ways of thinking about the role of ethnographic methods around computation, then discuss how my research relates to both. Read more

Publics: Witnessing and Measuring

Published in UC-Berkeley: Human Contexts and Ethics of Data course, 2018

A guest lecture for Cathryn Carson and Margo Boenig-Liptsin’s course on Human Contexts and Ethics of Data (HIST 182C, STS 100C), focusing on how various publics generate, analyze, and interpret data. Read more

The Human Contexts of Data: Infrastructures, Institutions, and Interpretations

Published in University of Manchester, Data Science Institute, 2018

In this talk, I discuss the role of qualitative and ethnographic methods in relation to computer, information, and data science. These holistic, reflexive, and meta-level approaches to studying data and computation in context help us better understand how to both support and practice data analytics at various scales. Read more

Computational Ethnography and the Ethnography of Computation: The Case for Context

Published in IT University of Copenhagen, ETHOSlab, 2018

Ethnography is traditionally a qualitative and inductive methodology that is now widely used to holistically investigate people’s lived experiences in and across cultures. In this talk, I define and discuss two ways of thinking about the role of ethnographic methods around computation, then discuss how my research relates to both. Read more

Key Values: What We Talk About When We Talk About ‘Open Science’

Published in Open Science Symposium, Department of Second Language Studies, University of Hawaiʻi at Mānoa, 2018

Openness in science is hard to disagree with as an abstract principle, but what exactly do we mean when we call for science to be made open – or more open than before? In this talk, I introduce and unpack the many different goals, strategies, products, values, and assumptions of the broad open science movement. Read more

Knowing User Populations at Scale: From the Science of the State to Platform Governmentality

Published in 2018 Annual Conference of the International Communication Association, 2018

How can institutions that own and operate large-scale social media platforms come to know “their users” at scale? In this talk, I discuss ways of knowing user populations at scale, drawing on Foucault’s account of governmentality, particularly the role of statistics in the formation of the modern nation state. Read more

The Types, Roles, and Practices of Documentation in Data Analytics Open Source Software Libraries: A Collaborative Ethnography of Documentation Work

Published in 2018 European Conference on Computer-Supported Cooperative Work, 2018

Data analytics increasingly relies on open source software (OSS) libraries that extend scripted languages like python and R. Software documentation for these libraries is crucial for people across all experience levels, but documentation work raises many challenges, particularly in open source communities. In this collaboration between ethnographers and data scientists, we discuss the types, roles, practices, and motivations around documentation in data analytics OSS libraries. Read more

Designing and Using Data Science Ethically

Published in Machine Learning and User Experience San Francisco (MLUXSF), 2018

With the rise of Machine Learning and AI to solve human-focused needs, how do we design and use data science ethically to help empower and support people? Read more

Garbage In, Garbage Out? Do Machine Learning Application Papers in Social Computing Report Where Human-Labeled Training Data Comes From?

Published in ACM FAT* 2020, 2020

Many machine learning projects for new application areas involve teams of humans who label data for a particular purpose, from hiring crowdworkers to the paper’s authors labeling the data themselves. Such a task is quite similar to (or a form of) structured content analysis, which is a longstanding methodology in the social sciences and humanities, with many established best practices. In this paper, we investigate to what extent a sample of machine learning application papers in social computing — specifically papers from ArXiv and traditional publications performing an ML classification task on Twitter data — give specific details about whether such best practices were followed. Read more

teaching