Why the pandemic experts failed: We’re still thinking about pandemic data in the wrong ways

‘Not until early May, when the CDC published its own deeply inadequate data dashboard, did we realize the depth of its ignorance. And when the White House reproduced one of our charts, it confirmed our fears: The government was using our data. For months, the American government had no idea how many people were sick with COVID-19, how many were lying in hospitals, or how many had died. And the COVID Tracking Project at The Atlantic, started as a temporary volunteer effort, had become a de facto source of pandemic data for the United States.

‘After spending a year building one of the only U.S. pandemic-data sources, we have come to see the government’s initial failure here as the fault on which the entire catastrophe pivots. The government has made progress since May; it is finally able to track pandemic data. Yet some underlying failures remain unfixed. The same calamity could happen again.

‘Data might seem like an overly technical obsession, an oddly nerdy scapegoat on which to hang the deaths of half a million Americans. But data are how our leaders apprehend reality. In a sense, data are the federal government’s reality. As a gap opened between the data that leaders imagined should exist and the data that actually did exist, it swallowed the country’s pandemic planning and response.’

Read here (The Atlantic, Mar 16, 2021)

Stanford study suggests indoor dining presents huge Covid-19 infection risk

‘According to the New York Times, the study followed the movement of 98 million people to and from indoor public spaces, then calculated traffic to each spot visited as well as how long people stay and each venue’s square footage. Using the area’s infection rate, they then used “standard infectious disease assumptions” to determine how the illness spread across cities.

‘Stanford computer scientist Jure Leskovec, the senior author of the report, tells the Times that “restaurants were by far the riskiest places” for new infections, “about four times riskier than gyms and coffee shops, followed by hotels,” he says. It’s news that jibes with another recent study from the Centers for Disease Control and Prevention (CDC), which said in September that a study of adults across 11 U.S. cities who tested positive for the novel coronavirus were twice as likely to have dined out within the last two weeks than those who tested negative.’

Read here (Eater San Francisco, Nov 11, 2020)

Why the United States is having a coronavirus data crisis

 ‘Political meddling, disorganization and years of neglect of public-health data management mean the country is flying blind...

‘Almost every day for the past seven months, the Korea Centers for Disease Control and Prevention has updated its website with near-real-time information on local outbreaks. The site also reports several COVID-19 statistics for every region of the country. Data dashboards in Singapore and New Zealand offer similar windows into how the coronavirus is spreading within their borders. This helps policymakers and citizens determine how to go about daily life, while reducing risks—and provides researchers with a wealth of data. 

‘By contrast, the United States offers vanishingly few details on how the disease is spreading, even as people increasingly socialize and travel, and authorities reopen schools and businesses. This state of affairs is frustrating data researchers, who want to help authorities make decisions that can save lives...

‘Although information isn’t the only tool that can be used against a pandemic, South Korea’s attention to data correlates with its overall success at controlling the outbreak: the country has had about 3.5 cases per 10,000 people overall, and there have been around 2 COVID-19 deaths per week over the past month. By contrast, the United States has had 175 cases per 10,000 people overall, and about 7,000 people have died of the disease every week for the past month.’

Read here (Scientific American, August 26, 2020)

Meet the philosopher who is trying to explain the pandemic: Giorgio Agamben criticises the “techno-medical despotism” of quarantines and closing

‘In a society that respects science, expertise confers power. That has good results, but it brings a terrible problem: Illegitimate political power can be disguised as expertise. This was an idea of the French philosopher Michel Foucault, who used it to explain how experts had expanded definitions of criminality and sexual deviancy. One of Italy’s most celebrated thinkers, Giorgio Agamben, has recently applied similar insights to the coronavirus, at the risk of turning himself into a national pariah...

‘Mr. Agamben’s name may ring a bell for some Americans. He was the professor who in 2004, at the height of the “war on terror,” was so alarmed by the new U.S. fingerprinting requirements for foreign visitors that he gave up a post at New York University rather than submit to them. He warned that such data collection was only passing itself off as an emergency measure; it would inevitably become a normal part of peacetime life.

‘His argument about the coronavirus runs along similar lines: The emergency declared by public-health experts replaces the discredited narrative of “national security experts” as a pretext for withdrawing rights and privacy from citizens. “Biosecurity” now serves as a reason for governments to rule in terms of “worst-case scenarios.” This means there is no level of cases or deaths below which locking down an entire nation of 60 million becomes unreasonable. Many European governments, including Italy’s, have developed national contact tracing apps that allow them to track their citizens using cellphones.’

Read here (New York Times, August 21, 2020)

Naomi Klein: How big tech plans to profit from the pandemic

‘The issue is not whether schools must change in the face of a highly contagious virus for which we have neither cure nor inoculation. Like every institution where humans gather in groups, they will change. The trouble, as always in these moments of collective shock, is the absence of public debate about what those changes should look like, and who they should benefit – private tech companies or students?

‘The same questions need to be asked about health. Avoiding doctor’s offices and hospitals during a pandemic makes good sense. But telehealth misses a huge amount. So we need to have an evidence-based debate about the pros and cons of spending scarce public resources on telehealth – rather than on more trained nurses, equipped with all the necessary protective equipment, who are able to make house calls to diagnose and treat patients in their homes.’

Read here (The Guardian, May 13, 2020)

The mechanics of mobile contact tracing: Information collected can be quite extensive

‘A mobile phone App can make contact tracing and notification instantaneous upon case confirmation. By keeping a temporary record of proximity events between individuals, it can immediately alert recent close contacts of diagnosed cases and prompt them to self-isolate.

‘Apps with similar aims have been deployed in China. Public health policy was implemented using an App which was not compulsory but was required to move between quarters and into public spaces and public transport. The App allows a central database to collect data on user movement and coronavirus diagnosis and displays a green, amber or red code to relax or enforce restrictions on movement. The database is reported to be analysed by an artificial intelligence algorithm that issues the colour codes. The App is a plug-in for the WeChat and Alipay Apps and has been generally adopted...’

Read here (Science, March 30, 2020)

Improving epidemic surveillance and response: Big data is dead, long live big data. The Lancet

Urgent investment in surveillance systems and global partnerships are needed to prepare for the pandemics that will continue to emerge in the coming decades. The following are three key challenges that pertain to creating useful epidemic forecasts during an outbreak.

The first challenge: Misaligned incentives. Academics are largely incentivised to write scientific articles and to fund their work through individually led grants... Companies are incentivised by profit, and are rightly beholden to national regulatory frameworks and the public with respect to the data they collect.

The second challenge: Gap between (1) technological or methodological innovation, which often occurs in academic settings in high-income countries, and (2) implementation in field settings, frequently done by NGOs or governments in low-income and middle-income countries.

The third challenge: Epidemic forecasting is inherently uncertain... [With] emerging outbreaks—with COVID-19 highlighting this point—we often lack accurate data about case counts and biological processes driving an epidemic, let alone the behavioural responses of people affected, making it challenging to swiftly adapt or interpret very complex models on the spatiotemporal scales relevant for decision making.

‘These innovations will remain dislocated and impractical until the challenges above are addressed. Encouragingly, all three issues could be improved by moving much of the focus of funding and expertise to the populations most vulnerable to epidemics.’

Read here (The Lancet, March 17, 2020)

Response to COVID-19 in Taiwan: Big data analytics, new technology, and proactive testing

This paper published in the JAMA (Journal of the American Medical Association) network, covers how Taiwan (1) recognised the crisis (2) managed it (3) communicated to the public about it. It concludes:

‘Taiwan’s government learned from its 2003 SARS experience and established a public health response mechanism for enabling rapid actions for the next crisis. Well-trained and experienced teams of officials were quick to recognize the crisis and activated emergency management structures to address the emerging outbreak.

‘In a crisis, governments often make difficult decisions under uncertainty and time constraints. These decisions must be both culturally appropriate and sensitive to the population. Through early recognition of the crisis, daily briefings to the public, and simple health messaging, the government was able to reassure the public by delivering timely, accurate, and transparent information regarding the evolving epidemic. Taiwan is an example of how a society can respond quickly to a crisis and protect the interests of its citizens.’

Read here (JamaNetwork, March 3, 2020)

Read related article in Stanford.edu here

Google health-data scandal spooks researchers. Something to think about as we use big data to help in healthcare

Last year, before the Covid-19 outbreak, ‘Google and one of the largest health-care networks in the United States are embroiled in a data-privacy controversy that researchers fear could jeopardise public trust in data-sharing practices and, potentially, academic studies.

’At issue is a project dubbed Nightingale that gives Google access to the health-care information, including names and other identifiable data, of tens of millions of people without their knowledge. The people were treated at facilities run by the health network Ascension, which is based in St Louis, Missouri.’

Read here (Nature, November 19, 2019)