How data science can ease the COVID-19 pandemic

Curated on Posted on by Stefaan Verhulst

Nigam Shah and Jacob Steinhardt at Brookings: “Social distancing and stay-at-home orders in the United States have slowed the infection rate of SARS-CoV-2, the pathogen that causes COVID-19. This has halted the immediate threat to the U.S. healthcare system, but consensus on a long-term plan or solution to the crisis remains unclear.  As the reality settles in that there are no quick fixes and that therapies and vaccines will take several months if not years to invent, validate, and mass produce, this is a good time to consider another question: How can data science and technology help us endure the pandemic while we develop therapies and vaccines?

Before policymakers reopen their economies, they must be sure that the resulting new COVID-19 cases will not force local healthcare systems to resort to crisis standards of care. Doing so requires not just prevention and suppression of the virus, but ongoing measurement of virus activity, assessment of the efficacy of suppression measures, and forecasting of near-term demand on local health systems. This demand is highly variable given community demographics, the prevalence of pre-existing conditions, and population density and socioeconomics.

Data science can already provide ongoing, accurate estimates of health system demand, which is a requirement in almost all reopening plans. We need to go beyond that to a dynamic approach of data collection, analysis, and forecasting to inform policy decisions in real time and iteratively optimize public health recommendations for re-opening. While most reopening plans propose extensive testing, contact tracing, and monitoring of population mobility, almost none consider setting up such a dynamic feedback loop. Having such feedback could determine what level of virus activity can be tolerated in an area, given regional health system capacity, and adjust population distancing accordingly.

We propose that by using existing technology and some nifty data science, it is possible to set up that feedback loop, which would maintain healthcare demand under the threshold of what is available in a region. Just as the maker community stepped up to cover for the failures of the government to provide adequate protective gear to health workers, this is an opportunity for the data and tech community to partner with healthcare experts and provide a measure of public health planning that governments are unable to do. Therefore, the question we invite the data science community to focus on is: How can data science help forecast regional health system resource needs given measurements of virus activity and suppression measures such as population distancing?…

Concretely, then, the crucial “data science” task is to learn the counterfactual function linking last week’s population mobility and today’s transmission rates to project hospital demand two weeks later. Imagine taking past measurements of mobility around April 10 in a region (such as the Santa Clara County’s report from COVID-19 Community Mobility Reports), the April 20 virus transmission rate estimate for the region (such as from http://rt.live), and the April 25 burden on the health system (such as from the Santa Clara County Hospitalization dashboard), to learn a function that uses today’s mobility and transmission rates to anticipate needed hospital resources two weeks later. It is unclear how many days of data of each proxy measurement we need to reliably learn such a function, what mathematical form this function might take, and how we do this correctly with the observational data on hand and avoid the trap of mere function-fitting. However, this is the data science problem that needs to be tackled as a priority. 

Adopting such technology and data science to keep anticipated healthcare needs under the threshold of availability in a region requires multiple privacy trade-offs, which will require thoughtful legislation so that the solutions invented for enduring the current pandemic do not lead to loss of privacy in perpetuity. However, given the immense economic as well as hidden medical toll of the shutdown, we urgently need to construct an early warning system that tells us to enhance suppression measures if the next COVID-19 outbreak peak might overwhelm our regional healthcare system. It is imperative that we focus our attention on using data science to anticipate, and manage, regional health system resource needs based on local measurements of virus activity and effects of population distancing….(More)”.

Digital tools against COVID-19: Framing the ethical challenges and how to address them

Curated on Posted on by Stefaan Verhulst

Paper by Urs Gasser et al: “Data collection and processing via digital public health technologies are being promoted worldwide by governments and private companies as strategic remedies for mitigating the COVID-19 pandemic and loosening lockdown measures. However, the ethical and legal boundaries of deploying digital tools for disease surveillance and control purposes are unclear, and a rapidly evolving debate has emerged globally around the promises and risks of mobilizing digital tools for public health. To help scientists and policymakers navigate technological and ethical uncertainty, we present a typology of the primary digital public health applications currently in use. Namely: proximity and contact tracing, symptom monitoring, quarantine control, and flow modeling. For each, we discuss context-specific risks, cross-sectional issues, and ethical concerns. Finally, in recognition of the need for practical guidance, we propose a navigation aid for policymakers made up of ten steps for the ethical use of digital public health tools….(More)”.

Can We Track COVID-19 and Protect Privacy at the Same Time?

Curated on Posted on by Stefaan Verhulst

Sue Halpern at the New Yorker: “…Location data are the bread and butter of “ad tech.” They let marketers know you recently shopped for running shoes, are trying to lose weight, and have an abiding affection for kettle corn. Apps on cell phones emit a constant trail of longitude and latitude readings, making it possible to follow consumers through time and space. Location data are often triangulated with other, seemingly innocuous slivers of personal information—so many, in fact, that a number of data brokers claim to have around five thousand data points on almost every American. It’s a lucrative business—by at least one estimate, the data-brokerage industry is worth two hundred billion dollars. Though the data are often anonymized, a number of studies have shown that they can be easily unmasked to reveal identities—names, addresses, phone numbers, and any number of intimacies.

As Buckee knew, public-health surveillance, which serves the community at large, has always bumped up against privacy, which protects the individual. But, in the past, public-health surveillance was typically conducted by contract tracing, with health-care workers privately interviewing individuals to determine their health status and trace their movements. It was labor-intensive, painstaking, memory-dependent work, and, because of that, it was inherently limited in scope and often incomplete or inefficient. (At the start of the pandemic, there were only twenty-two hundred contact tracers in the country.)

Digital technologies, which work at scale, instantly provide detailed information culled from security cameras, license-plate readers, biometric scans, drones, G.P.S. devices, cell-phone towers, Internet searches, and commercial transactions. They can be useful for public-health surveillance in the same way that they facilitate all kinds of spying by governments, businesses, and malign actors. South Korea, which reported its first covid-19 case a month after the United States, has achieved dramatically lower rates of infection and mortality by tracking citizens with the virus via their phones, car G.P.S. systems, credit-card transactions, and public cameras, in addition to a robust disease-testing program. Israel enlisted Shin Bet, its secret police, to repurpose its terrorist-tracking protocols.  China programmed government-installed cameras to point at infected people’s doorways to monitor their movements….(More)”.

COVID-19 Outbreak Prediction with Machine Learning

Curated on Posted on by Stefaan Verhulst

Paper by Sina F. Ardabili et al: “Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved.

This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models….(More)”.

Doctors are using AI to triage covid-19 patients. The tools may be here to stay

Curated on Posted on by Stefaan Verhulst

Karen Hao at MIT Technology Review: “The pandemic, in other words, has turned into a gateway for AI adoption in health care—bringing both opportunity and risk. On the one hand, it is pushing doctors and hospitals to fast-track promising new technologies. On the other, this accelerated process could allow unvetted tools to bypass regulatory processes, putting patients in harm’s way.

“At a high level, artificial intelligence in health care is very exciting,” says Chris Longhurst, the chief information officer at UC San Diego Health. “But health care is one of those industries where there are a lot of factors that come into play. A change in the system can have potentially fatal unintended consequences.”

Before the pandemic, health-care AI was already a booming area of research. Deep learning, in particular, has demonstrated impressive results for analyzing medical images to identify diseases like breast and lung cancer or glaucoma at least as accurately as human specialists. Studies have also shown the potential of using computer vision to monitor elderly people in their homes and patients in intensive care units.

But there have been significant obstacles to translating that research into real-world applications. Privacy concerns make it challenging to collect enough data for training algorithms; issues related to bias and generalizability make regulators cautious to grant approvals. Even for applications that do get certified, hospitals rightly have their own intensive vetting procedures and established protocols. “Physicians, like everybody else—we’re all creatures of habit,” says Albert Hsiao, a radiologist at UCSD Health who is now trialing his own covid detection algorithm based on chest x-rays. “We don’t change unless we’re forced to change.”

As a result, AI has been slow to gain a foothold. “It feels like there’s something there; there are a lot of papers that show a lot of promise,” said Andrew Ng, a leading AI practitioner, in a recent webinar on its applications in medicine. But “it’s not yet as widely deployed as we wish.”…

In addition to the speed of evaluation, Durand identifies something else that may have encouraged hospitals to adopt AI during the pandemic: they are thinking about how to prepare for the inevitable staff shortages that will arise after the crisis. Traumatic events like a pandemic are often followed by an exodus of doctors and nurses. “Some doctors may want to change their way of life,” he says. “What’s coming, we don’t know.”…(More)”

Lack of design input in healthcare is putting both patients and doctors at risk, says physician

Curated on Posted on by Stefaan Verhulst

Marcus Fairs at DeZeen: “Hospitals “desperately need designers” to improve everything from the way they tackle coronavirus to the layout of operating theatres and the design of medical charts, according to a senior US doctor.

“We desperately need designers to help organize the environment and products to help keep the correct focus on a patient, and reduce distraction,” said Dr Sam Smith, a clinical physician at Massachusetts General Hospital in Boston.

“We need designers at every turn, but they are so infrequently consulted,” he added. “In the end, most physicians burn out early because, in part, we are lacking well designed cognitive and physical spaces to help process the information smoothly.”…

“Visual hierarchy is a huge problem in medicine,” Smith said, giving an example. “This is very evident in online medical charts. Very poor visual hierarchy exists because designers were not consulted in the platform or details of the patient information organization or presentation.”

“This inability to incorporate good visual hierarchy, for example organizing a complex medical history in a visual way to emphasize what really needs attention for the patient, has led to ineffective care, and even patient harm on occasions over the years,” he explained.

“I have seen it in my 20 years of practice time and time again. Doctors are humans too, and the demands on them processing huge amounts of information are high.”…(More)”.

Covid-19: the rise of a global collective intelligence?

Curated on Posted on by Stefaan Verhulst

Marc Santolini at the Conversation: “All around the world, scientists and practitioners are relentlessly harnessing data on the pandemic to model its progression, predict the impact of possible interventions and develop solutions to medical equipment shortages, generating open-source data and codes to be reused by others.

Research and innovation is now in a collaborative frenzy just as contagious as the coronavirus. Is this the rise of the famous “collective intelligence” supposed to solve our major global problems?

The rise of a global collective intelligence

The beginning of the epidemic saw “traditional” research considerably accelerate and open its means of production, with journals such as Science, Nature and The Lancet immediately granting public access to publications on the coronavirus and Covid-19.

The academic world is in ebullition. Every day, John Hopkins University updates an open and collaborative stream of data on the epidemic, which have already been reused more than 11,000 times. Research results are published immediately on pre-print servers or laboratory websites. Algorithms and interactive visualizations are flourishing on GitHub; outreach videos on YouTube. The figures are staggering, with nearly 9,000 academic articles published on the subject to date.

More recently, popular initiatives bringing together a variety of actors have emerged outside institutional frameworks, using online platforms. For example, a community of biologists, engineers and developers has emerged on the Just One Giant Lab (JOGL) collaborative platform to develop low-cost, open-source solutions against the virus. This platform, which we developed with Leo Blondel (Harvard University) and Thomas Landrain (La Paillasse, PILI) over the past three years, is designed as a virtual, open and distributed research institute aimed at developing solutions to the Sustainable Development Goals (SDGs) defined by the United Nations. Communities use it to self-organize and provide innovative solutions to urgent problems requiring fundamentally interdisciplinary skills and knowledge. The platform facilitates coordination by linking needs and resources within the community, animating research programs, and organising challenges….(More)”.

Crowdsourcing a crisis response for COVID-19 in oncology

Curated on Posted on by Stefaan Verhulst

Aakash Desai et al in Nature Medicine: “Crowdsourcing efforts are currently underway to collect and analyze data from patients with cancer who are affected by the COVID-19 pandemic. These community-led initiatives will fill key knowledge gaps to tackle crucial clinical questions on the complexities of infection with the causative coronavirus SARS-Cov-2 in the large, heterogeneous group of vulnerable patients with cancer…(More)”

Digital solutions to revolutionise community empowerment

Curated on Posted on by Stefaan Verhulst

Article by Alan Marcus: “…The best responses to Covid-19 have harmonised top-down policies and grassroots organisation. In the UK, more than 700,000 volunteers for the National Health Service are being organised through GoodSAM—an app that, like many gig economy platforms, allows individuals to switch on availability for delivering supplies to vulnerable people.

Perhaps the best example is Taiwan, where officials have kept the rate of infection to a fraction of even highly-rated Singapore. Coordinating public and private groups, the country has deployed a range of online services, including a system for mapping and allocating rationed face masks developed by Digital Minister Audrey Tang and members of an online hacktivist chatroom. …

Effective responses to the crisis show the value of inclusive government and hint at more resilient models for managing our communities. So far, governments, businesses and individuals have pooled resources to deliver country-wide responses. However, this model should be pushed further. Digital tools should be provided to communities to organise themselves, develop locally tailored solutions and get involved in the governance of their town or neighbourhood.

This model requires open communication between local people and the organisations responsible for administrating neighbourhoods—be they governments or businesses. … 

The platform provides significant opportunities for optimising crisis response and elevating quality of life. For example, a popular solution for market vendors forced to close by Covid-19 has been offering delivery services. As well as the businesses, this benefits local people, who can bypass overcrowded superstores or overcapacity online grocery deliveries. While grassroots movements are largely left to organise themselves, this is a missed opportunity for collaboration with local administrators.

By communicating with vendors, the administrator can not only establish an online platform to coordinate their services, but also connect them with local people to help deliver the service, such as van owners who can loan their vehicles. Moreover, the administrator can collect feedback on local infrastructure needed to improve services, such as communal cold lockers for receiving groceries when no-one is home.

By integrating this model into the day-to-day governance of our communities, we can unite community action with top-down resources, empowering local people to co-own the evolution of their neighbourhoods and helping administrators prioritise projects that maximise quality of life.

As Solnit wrote: “A disaster is a lot like a revolution when it comes to disruption and improvisation.” Pushed to their limits, countries are pioneering ways of coordinating local and national action. From this wave of innovation, we can empower communities to become more resilient in crises, more inclusive in their governance and more engaged in the determination of their future….(More)”.

To recover faster from Covid-19, open up: Managerial implications from an open innovation perspective

Curated on Posted on by Stefaan Verhulst

Paper by Henry Chesbrough: “Covid-19 has severely tested our public health systems. Recovering from Covid-19 will soon test our economic systems. Innovation will have an important role to play in recovering from the aftermath of the coronavirus. This article discusses both how to manage innovation as part of that recovery, and also derives some lessons from how we have responded to the virus so far, and what those lessons imply for managing innovation during the recovery.

Covid-19’s assault has prompted a number of encouraging developments. One development has been the rapid mobilization of scientists, pharmaceutical companies and government officials to launch a variety of scientific initiatives to find an effective response to the virus. As of the time of this writing, there are tests underway of more than 50 different compounds as possible vaccines against the virus.1 Most of these will ultimately fail, but the severity of the crisis demands that we investigate every plausible candidate. We need rapid, parallel experimentation, and it must be the test data that select our vaccine, not internal political or bureaucratic processes.

A second development has been the release of copious amounts of information about the virus, its spread, and human responses to various public health measures. The Gates Foundation, working with the Chan-Zuckerberg Foundation and the White House Office of Science and Technology Policy have joined forces to publish all of the known medical literature on the coronavirus, in machine-readable form. This was done with the intent to accelerate the analysis of the existing research to identify possible new avenues of attack against Covid-19. The coronavirus itself was synthesized early on in the outbreak by scientists in China, providing the genetic sequence of the virus, and showing where it differed from earlier viruses such as SARS and MERS. This data was immediately shared widely with scientists and researchers around the world. At the same time, GITHUB and the Humanitarian Data Exchange each have an accumulating series of datasets on the geography of the spread of the disease (including positive test cases, hospitalizations, and deaths).

What these developments have in common is openness. In fighting a pandemic, speed is crucial, and the sooner we know more and are able to take action, the better for all of us. Opening up mobilizes knowledge from many different places, causing our learning to advance and our progress against the disease to accelerate. Openness unleashes a volunteer army of researchers, working in their own facilities, across different time zones, and different countries. Openness leverages the human capital available in the world to tackle the disease, and also accesses the physical capital (such as plant and equipment) already in place to launch rapid testing of possible solutions. This openness corresponds well to an academic body of work called open innovation (Chesbrough, 2003; Chesbrough, 2019).

Innovation is often analyzed in terms of costs, and the question of whether to “make or buy” often rests on which approach costs less. But in a pandemic, time is so valuable and essential, that the question of costs is far less important than the ability to get to a solution sooner. The Covid-19 disease appears to be doubling every 3–5 days, so a delay of just a few weeks in the search for a new vaccine (they normally take 1–2 years to develop, or more) might witness multiple doublings of size of the population infected with the disease. It is for this reason that Bill Gates is providing funds to construct facilities in advance for producing the leading vaccine candidates. Though the facilities for the losing candidates will not be used, it will save precious time to make the winning vaccine in high volume, once it is found.

Open innovation can help speed things up….(More)”.