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


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


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)”.

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


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


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)”.

Smart Village Technology


Book by Srikanta Patnaik, Siddhartha Sen and Magdi S. Mahmoud: “This book offers a transdisciplinary perspective on the concept of “smart villages” Written by an authoritative group of scholars, it discusses various aspects that are essential to fostering the development of successful smart villages. Presenting cutting-edge technologies, such as big data and the Internet-of-Things, and showing how they have been successfully applied to promote rural development, it also addresses important policy and sustainability issues. As such, this book offers a timely snapshot of the state-of-the-art in smart village research and practice….(More)”.

The many perks of using critical consumer user data for social benefit


Sushant Kumar at LiveMint: “Business models that thrive on user data have created profitable global technology companies. For comparison, market capitalization of just three tech companies, Google (Alphabet), Facebook and Amazon, combined is higher than the total market capitalization of all listed firms in India. Almost 98% of Facebook’s revenue and 84% of Alphabet’s come from serving targeted advertising powered by data collected from the users. No doubt, these tech companies provide valuable services to consumers. It is also true that profits are concentrated with private corporations and societal value for contributors of data, that is, the user, can be much more significant….

In the existing economic construct, private firms are able to deploy top scientists and sophisticated analytical tools to collect data, derive value and monetize the insights.

Imagine if personalization at this scale was available for more meaningful outcomes, such as for administering personalized treatment for diabetes, recommending crop patterns, optimizing water management and providing access to credit to the unbanked. These socially beneficial applications of data can generate undisputedly massive value.

However, handling critical data with accountability to prevent misuse is a complex and expensive task. What’s more, private sector players do not have any incentives to share the data they collect. These challenges can be resolved by setting up specialized entities that can manage data—collect, analyse, provide insights, manage consent and access rights. These entities would function as a trusted intermediary with public purpose, and may be named “data stewards”….(More)”.

See also: http://datastewards.net/ and https://datacollaboratives.org/

If China valued free speech, there would be no coronavirus crisis


Verna Yu in The Guardian: “…Despite the flourishing of social media, information is more tightly controlled in China than ever. In 2013, an internal Communist party edict known as Document No 9 ordered cadres to tackle seven supposedly subversive influences on society. These included western-inspired notions of press freedom, “universal values” of human rights, civil rights and civic participation. Even within the Communist party, cadres are threatened with disciplinary action for expressing opinions that differ from the leadership.

Compared with 17 years ago, Chinese citizens enjoy even fewer rights of speech and expression. A few days after 34-year-old Li posted a note in his medical school alumni social media group on 30 December, stating that seven workers from a local live-animal market had been diagnosed with an illness similar to Sars and were quarantined in his hospital, he was summoned by police. He was made to sign a humiliating statement saying he understood if he “stayed stubborn and failed to repent and continue illegal activities, (he) will be disciplined by the law”….

Unless Chinese citizens’ freedom of speech and other basic rights are respected, such crises will only happen again. With a more globalised world, the magnitude may become even greater – the death toll from the coronavirus outbreak is already comparable to the total Sars death toll.

Human rights in China may appear to have little to do with the rest of the world but as we have seen in this crisis, disaster could occur when China thwarts the freedoms of its citizens. Surely it is time the international community takes this issue more seriously….(More)”.

Urban Poverty Alleviation Endeavor Through E-Warong Program: Smart City (Smart People) Concept Initiative in Yogyakarta


Paper by Djaka Marwasta and Farid Suprianto: “In the era of Industrial Revolution 4.0, technology became a factor that could contribute significantly to improving the quality of life and welfare of the people of a nation. Information and Communication Technology (ICT) penetration through Internet of Things (IoT), Big Data, and Artificial Intelligence (AI) which are disruptively, has led to fundamental advances in civilization. The expansion of Industrial Revolution 4.0 has also changed the pattern of government and citizen relations which has implications for the needs of policy governance and internal government transformation. One of them is a change in social welfare development policies, where government officials are required to be responsive to social dynamics that have consequences for increasing demands for public accountability and transparency.

This paper aims to elaborate on the e-Warong program as one of the breakthroughs to reduce poverty by utilizing digital technology. E-Warong (electronic mutual cooperation shop) is an Indonesian government program based on the empowerment of the poor Grass Root Innovation (GRI) with an approach to building group awareness in encouraging the independence of the poor to develop joint ventures through mutual cooperation with utilizing ICT advantages. This program is an implementation of the Smart City concept, especially Smart Economy, within the Sustainable Development Goals framework….(More)”.

An AI Epidemiologist Sent the First Warnings of the Wuhan Virus


Eric Niiler at Wired: “On January 9, the World Health Organization notified the public of a flu-like outbreak in China: a cluster of pneumonia cases had been reported in Wuhan, possibly from vendors’ exposure to live animals at the Huanan Seafood Market. The US Centers for Disease Control and Prevention had gotten the word out a few days earlier, on January 6. But a Canadian health monitoring platform had beaten them both to the punch, sending word of the outbreak to its customers on December 31.

BlueDot uses an AI-driven algorithm that scours foreign-language news reports, animal and plant disease networks, and official proclamations to give its clients advance warning to avoid danger zones like Wuhan.

Speed matters during an outbreak, and tight-lipped Chinese officials do not have a good track record of sharing information about diseases, air pollution, or natural disasters. But public health officials at WHO and the CDC have to rely on these very same health officials for their own disease monitoring. So maybe an AI can get there faster. “We know that governments may not be relied upon to provide information in a timely fashion,” says Kamran Khan, BlueDot’s founder and CEO. “We can pick up news of possible outbreaks, little murmurs or forums or blogs of indications of some kind of unusual events going on.”…

The firm isn’t the first to look for an end-run around public health officials, but they are hoping to do better than Google Flu Trends, which was euthanized after underestimating the severity of the 2013 flu season by 140 percent. BlueDot successfully predicted the location of the Zika outbreak in South Florida in a publication in the British medical journal The Lancet….(More)”.

How randomised trials became big in development economics


Seán Mfundza Muller, Grieve Chelwa, and Nimi Hoffmann at the Conversation: “…One view of the challenge of development is that it is fundamentally about answering causal questions. If a country adopts a particular policy, will that cause an increase in economic growth, a reduction in poverty or some other improvement in the well-being of citizens?

In recent decades economists have been concerned about the reliability of previously used methods for identifying causal relationships. In addition to those methodological concerns, some have argued that “grand theories of development” are either incorrect or at least have failed to yield meaningful improvements in many developing countries.

Two notable examples are the idea that developing countries may be caught in a poverty trap that requires a “big push” to escape and the view that institutions are key for growth and development.

These concerns about methods and policies provided a fertile ground for randomised experiments in development economics. The surge of interest in experimental approaches in economics began in the early 1990s. Researchers began to use “natural experiments”, where for example random variation was part of a policy rather than decided by a researcher, to look at causation.

But it really gathered momentum in the 2000s, with researchers such as the Nobel awardees designing and implementing experiments to study a wide range of microeconomic questions.

Randomised trials

Proponents of these methods argued that a focus on “small” problems was more likely to succeed. They also argued that randomised experiments would bring credibility to economic analysis by providing a simple solution to causal questions.

These experiments randomly allocate a treatment to some members of a group and compare the outcomes against the other members who did not receive treatment. For example, to test whether providing credit helps to grow small firms or increase their likelihood of success, a researcher might partner with a financial institution and randomly allocate credit to applicants that meet certain basic requirements. Then a year later the researcher would compare changes in sales or employment in small firms that received the credit to those that did not.

Randomised trials are not a new research method. They are best known for their use in testing new medicines. The first medical experiment to use controlled randomisation occurred in the aftermath of the second world war. The British government used it to assess the effectiveness of a drug for tuberculosis treatment.

In the early 20th century and mid-20th century American researchers had used experiments like this to examine the effects of various social policies. Examples included income protection and social housing.

The introduction of these methods into development economics also followed an increase in their use in other areas of economics. One example was the study of labour markets.

Randomised control trials in economics are now mostly used to evaluate the impact of social policy interventions in poor and middle-income countries. Work by the 2019 Nobel awardees – Michael Kremer, Abhijit Banerjee and Esther Duflo – includes experiments in Kenya and India on teacher attendancetextbook provisionmonitoring of nurse attendance and the provision of microcredit.

The popularity, among academics and policymakers, of the approach is not only due to its seeming ability to solve methodological and policy concerns. It is also due to very deliberate, well-funded advocacy by its proponents….(More)”.