COVID-19 response and data protection law in the EU and US

Curated on Posted on by Stefaan Verhulst

Article by Cathy Cosgrove: “Managing the COVID-19 outbreak and stopping its spread is now a global challenge. In addition to the significant health and medical responses underway around the world, governments and public health officials are focused on how to monitor, understand and prevent the spread of the virus. Data protection and privacy laws, including the EU General Data Protection Regulation and various U.S. laws, are informing these responses.

One major response to limiting the spread of infection is contact tracing, which is the practice of identifying and monitoring anyone who may have come into contact with an infected person. Employers and educational institutions are also imposing travel restrictions, instituting self-quarantine policies, limiting visitors, and considering whether to require medical examinations. These responses necessarily involve obtaining and potentially sharing personal information, including data about an individual’s health, travel, personal contacts, and employment. For example, in the U.S., the Centers for Disease Control and Prevention has asked airlines for the name, date of birth, address, phone number and email address for passengers on certain flights. 

As IAPP Editorial Director Jedidiah Bracy, CIPP, explored in his piece on balancing personal privacy with public interest last week, this collection and processing of personal data is creating substantial discussion about what data protection limitations may be required or appropriate. Even China — which is using AI and big data to manage the outbreak — has issued guidance recognizing the need to limit the collection of data and its use during this public health crisis….(More)”.

How Taiwan Used Big Data, Transparency and a Central Command to Protect Its People from Coronavirus

Curated on Posted on by Stefaan Verhulst

Article by Beth Duff-Brown: “…So what steps did Taiwan take to protect its people? And could those steps be replicated here at home?

Stanford Health Policy’s Jason Wang, MD, PhD, an associate professor of pediatrics at Stanford Medicine who also has a PhD in policy analysis, credits his native Taiwan with using new technology and a robust pandemic prevention plan put into place at the 2003 SARS outbreak.

“The Taiwan government established the National Health Command Center (NHCC) after SARS and it’s become part of a disaster management center that focuses on large-outbreak responses and acts as the operational command point for direct communications,” said Wang, a pediatrician and the director of the Center for Policy, Outcomes, and Prevention at Stanford. The NHCC also established the Central Epidemic Command Center, which was activated in early January.

“And Taiwan rapidly produced and implemented a list of at least 124 action items in the past five weeks to protect public health,” Wang said. “The policies and actions go beyond border control because they recognized that that wasn’t enough.”

Wang outlines the measures Taiwan took in the last six weeks in an article published Tuesday in the Journal of the American Medical Association.

“Given the continual spread of COVID-19 around the world, understanding the action items that were implemented quickly in Taiwan, and the effectiveness of these actions in preventing a large-scale epidemic, may be instructive for other countries,” Wang and his co-authors wrote.

Within the last five weeks, Wang said, the Taiwan epidemic command center rapidly implemented those 124 action items, including border control from the air and sea, case identification using new data and technology, quarantine of suspicious cases, educating the public while fighting misinformation, negotiating with other countries — and formulating policies for schools and businesses to follow.

Big Data Analytics

The authors note that Taiwan integrated its national health insurance database with its immigration and customs database to begin the creation of big data for analytics. That allowed them case identification by generating real-time alerts during a clinical visit based on travel history and clinical symptoms.

Taipei also used Quick Response (QR) code scanning and online reporting of travel history and health symptoms to classify travelers’ infectious risks based on flight origin and travel history in the last 14 days. People who had not traveled to high-risk areas were sent a health declaration border pass via SMS for faster immigration clearance; those who had traveled to high-risk areas were quarantined at home and tracked through their mobile phones to ensure that they stayed home during the incubation period.

The country also instituted a toll-free hotline for citizens to report suspicious symptoms in themselves or others. As the disease progressed, the government called on major cities to establish their own hotlines so that the main hotline would not become jammed….(More)”.

How Singapore sends daily Whatsapp updates on coronavirus

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Medha Basu at GovInsider: “How do you communicate with citizens as a pandemic stirs fear and spreads false news? Singapore has trialled WhatsApp to give daily updates on the Covid-19 virus.

The World Health Organisation’s chief praised Singapore’s reaction to the outbreak. “We are very impressed with the efforts they are making to find every case, follow up with contacts, and stop transmission,” Tedros Adhanom Ghebreyesus said.

Since late January, the government has been providing two to three daily updates on cases via the messaging app. “Fake news is typically propagated through Whatsapp, so messaging with the same interface can help stem this flow,” Sarah Espaldon, Operations Marketing Manager from Singapore’s Open Government Products unit told GovInsider….

The niche system became newly vital as Covid-19 arrived, with fake news and fear following quickly in a nation that still remembers the fatal SARS outbreak of 2003. The tech had to be upgraded to ensure it could cope with new demand, and get information out rapidly before misinformation could sow discord.

The Open Government Products team used three tools to adapt Whatsapp and create a rapid information sharing system.

1. AI Translation

Singapore has four official languages – Chinese, English, Malay and Tamil. Government used an AI tool to rapidly translate the material from English, so that every community receives the information as quickly as possible.

An algorithm produces the initial draft of the translation, which is then vetted by civil servants before being sent out on WhatsApp. The AI was trained using text from local government communications so is able to translate references and names of Singapore government schemes. This project was built by the Ministry of Communication and Information and Agency for Science, Technology and Research in collaboration with GovTech.

2. Make it easy to sign up

People specify their desired language through an easy sign up form. Singapore used Form.Sg, a tool that allows officials to launch a new mailing list in 30 minutes and connect to other government systems. A government-built form ensures that data is end-to-end encrypted and connected to the government cloud.

3. Fast updates

The updates were initially too slow in reaching people. It took four hours to add new subscribers to the recipient list and the system could send only 10 messages per second. “With 500,000 subscribers, it would take almost 14 hours for the last person to get the message,” Espaldon says….(More)”.

Open peer-review platform for COVID-19 preprints

Curated on Posted on by Stefaan Verhulst

Michael A. Johansson & Daniela Saderi in Nature: “The public call for rapid sharing of research data relevant to the COVID-19 outbreak (see go.nature.com/2t1lyp6) is driving an unprecedented surge in (unrefereed) preprints. To help pinpoint the most important research, we have launched Outbreak Science Rapid PREreview, with support from the London-based charity Wellcome. This is an open-source platform for rapid review of preprints related to emerging outbreaks (see https://outbreaksci.prereview.org).

These reviews comprise responses to short, yes-or-no questions, with optional commenting. The questions are designed to capture structured, high-level input on the importance and quality of the research, which can be aggregated across several reviews. Scientists who have ORCID IDs can submit their reviews as they read the preprints (currently limited to the medRxiv, bioRxiv and arXiv repositories). The reviews are open and can be submitted anonymously.

Outbreaks of pathogens such as the SARS-CoV-2 coronavirus that is responsible for COVID-19 move fast and can affect anyone. Research to support outbreak response needs to be fast and open, too, as do mechanisms to review outbreak-related research. Help other scientists, as well as the media, journals and public-health officials, to find the most important COVID-19 preprints now….(More)”.

How big data is dividing the public in China’s coronavirus fight – green, yellow, red

Curated on Posted on by Stefaan Verhulst

Article by Viola Zhou: “On Valentine’s Day, a 36-year-old lawyer Matt Ma in the eastern Chinese province of Zhejiang discovered he had been coded “red”.The colour, displayed in a payment app on his smartphone, indicated that he needed to be quarantined at home even though he had no symptoms of the dangerous coronavirus.

Without a green light from the system, Ma could not travel from his ancestral hometown of Lishui to his new home city of Hangzhou, which is now surrounded by checkpoints set up to contain the epidemic.

Ma is one of the millions of people whose movements are being choreographed by the government through software that feeds on troves of data and issues orders that effectively dictate whether they must stay in or can go to work.Their experience represents a slice of China’s desperate attempt to stop the coronavirus by using a mixed bag of cutting-edge technologies and old-fashioned surveillance. It was also a rare real-world test of the use of technology on a large scale to halt the spread of communicable diseases.

“This kind of massive use of technology is unprecedented,” said Christos Lynteris, a medical anthropologist at the University of St Andrews who has studied epidemics in China.

But Hangzhou’s experiment has also revealed the pitfalls of applying opaque formulas to a large population.

In the city’s case, there are reports of people being marked incorrectly, falling victim to an algorithm that is, by the government’s own admission, not perfect….(More)”.

Crowdsourcing data to mitigate epidemics

Curated on Posted on by Stefaan Verhulst

Gabriel M Leung and Kathy Leung at The Lancet: “Coronavirus disease 2019 (COVID-19) has spread with unprecedented speed and scale since the first zoonotic event that introduced the causative virus—severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)—into humans, probably during November, 2019, according to phylogenetic analyses suggesting the most recent common ancestor of the sequenced genomes emerged between Oct 23, and Dec 16, 2019. The reported cumulative number of confirmed patients worldwide already exceeds 70 000 in almost 30 countries and territories as of Feb 19, 2020, although that the actual number of infections is likely to far outnumber this case count.

During any novel emerging epidemic, let alone one with such magnitude and speed of global spread, a first task is to put together a line list of suspected, probable, and confirmed individuals on the basis of working criteria of the respective case definitions. This line list would allow for quick preliminary assessment of epidemic growth and potential for spread, evidence-based determination of the period of quarantine and isolation, and monitoring of efficiency of detection of potential cases. Frequent refreshing of the line list would further enable real-time updates as more clinical, epidemiological, and virological (including genetic) knowledge become available as the outbreak progresses….

We surveyed different and varied sources of possible line lists for COVID-19 (appendix pp 1–4). A bottleneck remains in carefully collating as much relevant data as possible, sifting through and verifying these data, extracting intelligence to forecast and inform outbreak strategies, and thereafter repeating this process in iterative cycles to monitor and evaluate progress. A possible methodological breakthrough would be to develop and validate algorithms for automated bots to search through cyberspaces of all sorts, by text mining and natural language processing (in languages not limited to English) to expedite these processes.In this era of smartphone and their accompanying applications, the authorities are required to combat not only the epidemic per se, but perhaps an even more sinister outbreak of fake news and false rumours, a so-called infodemic…(More)”.

This emoji could mean your suicide risk is high, according to AI

Curated on Posted on by Stefaan Verhulst

Rebecca Ruiz at Mashable: “Since its founding in 2013, the free mental health support service Crisis Text Line has focused on using data and technology to better aid those who reach out for help. 

Unlike helplines that offer assistance based on the order in which users dialed, texted, or messaged, Crisis Text Line has an algorithm that determines who is in most urgent need of counseling. The nonprofit is particularly interested in learning which emoji and words texters use when their suicide risk is high, so as to quickly connect them with a counselor. Crisis Text Line just released new insights about those patterns. 

Based on its analysis of 129 million messages processed between 2013 and the end of 2019, the nonprofit found that the pill emoji, or 💊, was 4.4 times more likely to end in a life-threatening situation than the word suicide. 

Other words that indicate imminent danger include 800mg, acetaminophen, excedrin, and antifreeze; those are two to three times more likely than the word suicide to involve an active rescue of the texter. The loudly crying emoji face, or 😭, is similarly high-risk. In general, the words that trigger the greatest alarm suggest the texter has a method or plan to attempt suicide or may be in the process of taking their own life. …(More)”.

Our personal health history is too valuable to be harvested by the tech giants

Curated on Posted on by Stefaan Verhulst

Eerke Boiten at The Guardian: “…It is clear that the black box society does not only feed on internet surveillance information. Databases collected by public bodies are becoming more and more part of the dark data economy. Last month, it emerged that a data broker in receipt of the UK’s national pupil database had shared its access with gambling companies. This is likely to be the tip of the iceberg; even where initial recipients of shared data might be checked and vetted, it is much harder to oversee who the data is passed on to from there.

Health data, the rich population-wide information held within the NHS, is another such example. Pharmaceutical companies and internet giants have been eyeing the NHS’s extensive databases for commercial exploitation for many years. Google infamously claimed it could save 100,000 lives if only it had free rein with all our health data. If there really is such value hidden in NHS data, do we really want Google to extract it to sell it to us? Google still holds health data that its subsidiary DeepMind Health obtained illegally from the NHS in 2016.

Although many health data-sharing schemes, such as in the NHS’s register of approved data releases], are said to be “anonymised”, this offers a limited guarantee against abuse.

There is just too much information included in health data that points to other aspects of patients’ lives and existence. If recipients of anonymised health data want to use it to re-identify individuals, they will often be able to do so by combining it, for example, with publicly available information. That this would be illegal under UK data protection law is a small consolation as it would be extremely hard to detect.

It is clear that providing access to public organisations’ data for research purposes can serve the greater good and it is unrealistic to expect bodies such as the NHS to keep this all in-house.

However, there are other methods by which to do this, beyond the sharing of anonymised databases. CeLSIUS, for example, a physical facility where researchers can interrogate data under tightly controlled conditions for specific registered purposes, holds UK census information over many years.

These arrangements prevent abuse, such as through deanonymisation, do not have the problem of shared data being passed on to third parties and ensure complete transparency of the use of the data. Online analogues of such set-ups do not yet exist, but that is where the future of safe and transparent access to sensitive data lies….(More)”.

Self-interest and data protection drive the adoption and moral acceptability of big data technologies: A conjoint analysis approach

Curated on Posted on by Stefaan Verhulst

Paper by Rabia I.Kodapanakka, lMark J.Brandt, Christoph Kogler, and Iljavan Beest: “Big data technologies have both benefits and costs which can influence their adoption and moral acceptability. Prior studies look at people’s evaluations in isolation without pitting costs and benefits against each other. We address this limitation with a conjoint experiment (N = 979), using six domains (criminal investigations, crime prevention, citizen scores, healthcare, banking, and employment), where we simultaneously test the relative influence of four factors: the status quo, outcome favorability, data sharing, and data protection on decisions to adopt and perceptions of moral acceptability of the technologies.

We present two key findings. (1) People adopt technologies more often when data is protected and when outcomes are favorable. They place equal or more importance on data protection in all domains except healthcare where outcome favorability has the strongest influence. (2) Data protection is the strongest driver of moral acceptability in all domains except healthcare, where the strongest driver is outcome favorability. Additionally, sharing data lowers preference for all technologies, but has a relatively smaller influence. People do not show a status quo bias in the adoption of technologies. When evaluating moral acceptability, people show a status quo bias but this is driven by the citizen scores domain. Differences across domains arise from differences in magnitude of the effects but the effects are in the same direction. Taken together, these results highlight that people are not always primarily driven by self-interest and do place importance on potential privacy violations. They also challenge the assumption that people generally prefer the status quo….(More)”.

The Story of Goldilocks and Three Twitter’s APIs: A Pilot Study on Twitter Data Sources and Disclosure

Curated on Posted on by Stefaan Verhulst

Paper by Yoonsang Kim, Rachel Nordgren and Sherry Emery: “Public health and social science increasingly use Twitter for behavioral and marketing surveillance. However, few studies provide sufficient detail about Twitter data collection to allow either direct comparisons between studies or to support replication.

The three primary application programming interfaces (API) of Twitter data sources are Streaming, Search, and Firehose. To date, no clear guidance exists about the advantages and limitations of each API, or about the comparability of the amount, content, and user accounts of retrieved tweets from each API. Such information is crucial to the validity, interpretation, and replicability of research findings.

This study examines whether tweets collected using the same search filters over the same time period, but calling different APIs, would retrieve comparable datasets. We collected tweets about anti-smoking, e-cigarettes, and tobacco using the aforementioned APIs. The retrieved tweets largely overlapped between three APIs, but each also retrieved unique tweets, and the extent of overlap varied over time and by topic, resulting in different trends and potentially supporting diverging inferences. Researchers need to understand how different data sources can influence both the amount, content, and user accounts of data they retrieve from social media, in order to assess the implications of their choice of data source…(More)”.