Stay alert, infodemic, Black Death: the fascinating origins of pandemic terms

Curated on Posted on by Stefaan Verhulst

Simon Horobin at The Conversation: “Language always tells a story. As COVID-19 shakes the world, many of the words we’re using to describe it originated during earlier calamities – and have colourful tales behind them.

In the Middle Ages, for example, fast-spreading infectious diseases were known as plagues – as in the Bubonic plague, named for the characteristic swellings (or buboes) that appear in the groin or armpit. With its origins in the Latin word plaga meaning “stroke” or “wound”, plague came to refer to a wider scourge through its use to describe the ten plagues suffered by the Egyptians in the biblical book of Exodus.

An alternative term, pestilence, derives from Latin pestis (“plague”), which is also the origin of French peste, the title of the 1947 novel by Albert Camus (La Peste, or The Plague) which has soared up the bestseller charts in recent weeks. Latin pestis also gives us pest, now used to describe animals that destroy crops, or any general nuisance or irritant. Indeed, the bacterium that causes Bubonic plague is called Yersinia pestis….

The later plagues of the 17th century led to the coining of the word epidemic. This came from a Greek word meaning “prevalent”, from epi “upon” and demos “people”. The more severe pandemic is so called because it affects everyone (from Greek pan “all”).

A more recent coinage, infodemic, a blend of info and epidemic, was introduced in 2003 to refer to the deluge of misinformation and fake news that accompanied the outbreak of SARS (an acronym formed from the initial letters of “severe acute respiratory syndrome”).

The 17th-century equivalent of social distancing was “avoiding someone like the plague”. According to Samuel Pepys’s account of the outbreak that ravaged London in 1665, infected houses were marked with a red cross and had the words “Lord have mercy upon us” inscribed on the doors. Best to avoid properties so marked….(More)”.

Open science: after the COVID-19 pandemic there can be no return to closed working

Curated on Posted on by Stefaan Verhulst

Article by Virginia Barbour and Martin Borchert: “In the few months since the first case of COVID-19 was identified, the underlying cause has been isolated, its symptoms agreed on, its genome sequenced, diagnostic tests developed, and potential treatments and vaccines are on the horizon. The astonishingly short time frame of these discoveries has only happened through a global open science effort.

The principles and practices underpinning open science are what underpin good research—research that is reliable, reproducible, and has the broadest impact possible. It specifically requires the application of principles and practices that make research FAIR (Findable, Accessible, Interoperable, Reusable); researchers are making their data and preliminary publications openly accessible, and then publishers are making the peer-reviewed research immediately and freely available to all. The rapid dissemination of research—through preprints in particular as well as journal articles—stands in contrast to what happened in the 2003 SARS outbreak when the majority of research on the disease was published well after the outbreak had ended.

Many outside observers might reasonably assume, given the digital world we all now inhabit, that science usually works like this. Yet this is very far from the norm for most research. Science is not something that just happens in response to emergencies or specific events—it is an ongoing, largely publicly funded, national and international enterprise….

Sharing of the underlying data that journal articles are based on is not yet a universal requirement for publication, nor are researchers usually recognised for data sharing.

There are many benefits associated with an open science model. Image adapted from: Gaelen Pinnock/UCT; CC-BY-SA 4.0 .

Once published, even access to research is not seamless. The majority of academic journals still require a subscription to access. Subscriptions are expensive; Australian universities alone currently spend more than $300 million per year on subscriptions to academic journals. Access to academic journals also varies between universities with varying library budgets. The main markets for subscriptions to the commercial journal literature are higher education and health, with some access to government and commercial….(More)”.

Smart cities during COVID-19: How cities are turning to collective intelligence to enable smarter approaches to COVID-19.

Curated on Posted on by Stefaan Verhulst

Article by Peter Baeck and Sophie Reynolds: One of the most prominent examples of how technology and data is being used to empower citizens is happening in Seoul. Here the city has used its ‘citizens as mayors’ philosophy for smart cities; an approach which aims to equip citizens with the same real-time access to information as the mayor. Seoul has gone further than most cities in making information about the COVID-19 outbreak in the city accessible to citizens. Its dashboard is updated multiple times daily and allows citizens to access the latest anonymised information on confirmed patients’ age, gender and dates of where they visited and when, after developing symptoms. Citizens can access even more detailed information; down to visited restaurants and cinema seat numbers.

The goal is to provide citizens with the information needed to take precautionary measures, self-monitor and report if they start showing symptoms after visiting one of the “infection points.” To help allay people’s fears and reduce the stigma associated with businesses that have been identified as “infection points”, the city government also provides citizens with information about the nearest testing clinics and makes “clean zones” (places that have been disinfected after visits by confirmed patients) searchable for users.

In addition to national and institutional responses there are (at least) five ways collective intelligence approaches are helping city governments, companies and urban communities in the fight against COVID-19:

1. Open sharing with citizens about the spread and management of COVID-19:

Based on open data provided by public agencies, private sector companies are using the city as a platform to develop their own real-time dashboards and mobile apps to further increase public awareness and effectively disseminate disease information. This has been the case with Corona NOW, Corona Map, Corona 100m in Seoul, Korea – which allow people to visualise data on confirmed coronavirus patients, along with patients’ nationality, gender, age, which places the patient has visited, and how close citizens are to these coronavirus patients. Developer Lee Jun-young who created the Corona Map app, said he built it because he found that the official government data was too difficult to understand.

Meanwhile in city state Singapore, the dashboard developed by UpCode scrapes data provided by the Singapore Ministry of Health’s own dashboard (which is exceptionally transparent about coronavirus case data) to make it cleaner and easier to navigate, and vastly more insightful. For instance, it allows you to learn about the average recovery time for those infected.

UpCode is making its platform available for others to re-use in other contexts.

2. Mobilising community-led responses to tackle COVID-19

Crowdfunding is being used in a variety of ways to get short-term targeted funding to a range of worthy causes opened up by the COVID-19 crisis. Examples include helping to fundraise for community activities for those directly affected by the crisis, backing tools and products that can address the crisis (such as buying PPE) and pre-purchasing products and services from local shops and artists. A significant proportion of the UK’s 1,000 plus mutual aid initiatives are now turning to crowdfunding as a way to rapidly respond to the new and emerging needs occurring at the city-wide and hyperlocal (i.e. streets and neighbourhood) levels.

Aberdeen City Mutual Aid group set up a crowdfunded community fund to cover the costs of creating a network of volunteers across the city, as well as any expenses incurred at food shops, fuel costs for deliveries and purchasing other necessary supplies. Similarly, the Feed the Heroes campaign was launched with an initial goal of raising €250 to pay for food deliveries for frontline staff who are putting in extra hours at the Mater Hospital, Dublin during the coronavirus outbreak….(More)”.

Covid-19 means systems thinking is no longer optional

Curated on Posted on by Stefaan Verhulst

Seth Reynolds at NPC: “Never has the interdependence of our world been experienced by so many, so directly, so rapidly and so simultaneously. Our response to one threat, Covid-19, has unleashed a deluge of secondary and tertiary consequences that have swept across the globe faster than the virus itself. The butterfly effect has taken on new dimensions, as the reality of system interdependence at multiple levels has been brought directly into our homes and news feeds:

  • Individually, an innocuous bus journey sends a stranger to intensive care in a fortnight
  • Societally, health charities are warning that actions taken in response to one health crisis – Covid-19 – could lead to up to 11,000 deaths of women in childbirth around the world because of another – namely, 9.5m women not getting access to family planning intervention.
  • Governmentally, some systemic consequences of decision-making are there for all to see, while others are less immediately apparent – for example, Trump’s false proclamation of testing availability “for anyone that wants one”  ended up actually reducing the availability of tests by immediately increasing demand.  It even reduced the already scarce supply of protective masks, which must be disposed of after testing.

Students will be studying coronavirus for years. A systems lens can help us learn essential lessons. Covid-19 has provided many clear examples of effective systemic action, and stark lessons in the consequences of non-systemic thinking. Leaders and decision-makers everywhere are being compelled to think broader and deeper about causation and consequence. Decisions taken, even words spoken, without systemic awareness can have – indeed have had – profoundly damaging effects.

Systemic thinking, planning, action and leadership must now be mainstreamed – individually, organisationally, societally, across public, private and charity sectors. As one American diplomat recently reflected: “from climate change to the coronavirus, complex adaptive systems thinking is key to handling crises”. In fact, some epidemiologists, suddenly the world’s most valuable profession, have been calling for more systemic ways of working for years. However, we currently do not think and act in accordance with how our complex systems function and this has been part of the Covid-19 problem…(More)”.

How Statistics Can Help — Going Beyond COVID-19

Curated on Posted on by Stefaan Verhulst

Blog by Walter J. Radermacher at Data & Policy: “It is rightly pointed out that in the midst of a crisis of enormous dimensions we needed high quality statistics with utmost urgency, but that instead we are in danger of drowning in an ocean of data and information. The pandemic is accompanied and exacerbated by an infodemic. At this moment, and in this confusion and search for solutions, it seems appropriate to take advice from previous initiatives and draw lessons for the current situation. More than 20 years ago in the United Kingdom, the report “Statistics — A Matter of Trust” laid the foundations for overcoming the previously spreading crisis of confidence through a solidly structured statistical system. This report does not stand alone in international comparison. Rather, it is one of a series of global, European and national measures and agreements which, since the fall of the Berlin Wall in 1989, have strengthened official statistics as the backbone of policy in democratic societies, with the UN Fundamental Statistical Principles and the EU Statistics Code of Practice being prominent representatives. So, if we want to deal with our current difficulties, we should address precisely those points that have emerged as determining factors for the quality of statistics, with the following three questions: What (statistical products, quality profile)? How (methods)? Who (institutions)? The aim must be to ensure that statistical information is suitable for facilitating the resolution of conflicts by eliminating the need to argue about the facts and only about the conclusions to be drawn from them.

In the past, this task would have led relatively quickly to a situation where the need for information would have been directed to official statistics as the preferred provider; this has changed recently for many reasons. On the one hand, there is the danger that the much-cited data revolution and learning algorithms (so-called AI) are presented as an alternative to official statistics (which are perceived as too slow, too inflexible and too expensive), instead of emphasizing possible commonalities and cross-fertilization possibilities. On the other hand, after decades of austerity policies, official statistics are in a similarly defensive situation to that of the public health system in many respects and in many countries: There is a lack of financial reserves, personnel and know-how for the new and innovative work now so urgently needed.

It is therefore required, as in the 1990s, to ask the fundamental question again, namely, do we (still and again) really deserve official statistics as the backbone of democratic decision-making, and if so, what should their tasks be, how should they be financed and anchored in the political system?…(More)”.

The public debate around COVID-19 demonstrates our ongoing and misplaced trust in numbers

Curated on Posted on by Stefaan Verhulst

Ville Aula at LSE Blogs: “Read the front page of any major newspaper and I guarantee that the latest number of patients who have tested positive for COVID-19 and the number of mortalities will feature heavily. Open your social media accounts and you will quickly encounter graphs that show the mounting numbers of cases in different countries, complemented by modelling projections. These numbers and graphs feed the popular imagination of how well countries are “flattening the curve”, a concept that has brought epidemiological modelling inspired language to everyone’s lips. 

Numbers, graphs, and data are thus playing an essential part in how we experience the pandemic. The endless flows of numbers from different countries are meticulously compared with those from others. These comparisons then form the basis to how individual countries are portrayed and ranked in the global pandemic drama. 

But, there is also doubt in the air. We distrust the existing numbers and call for ever-more accurate information. For example, there has been a lively debate on how widespread the pandemic has been in China, an issue that connects directly to how tests are administered and cases reported. Equally, numbers from Europe do not provide indisputable or uniform information on the pandemic either, because their collection is subject to vastly different policies, practices, and contexts that make comparisons difficult. We also lack the scientific consensus that would allow us to link the number of mortalities to the prevalence of the virus, yet mortalities are still often taken as the most solid form of information on the pandemic.  These doubts have fuelled demands to do systematic population level testing of the virus prevalence, which is just a different way of saying that we need more numbers. 

Numbers are thus both the problem and the solution, and we want more of them. However, what makes numbers useful for developing better treatments and policies, does not necessarily lead to the same outcomes when applied to public debate. In the broader sphere of public debate, such tendencies reveal a longing for the veracity of data during times of uncertainty. Even when such calls are founded on demands for transparency in the name of democracy or healthy skepticism of existing data, they are entangled in a faulty logic of data itself eventually providing a solid standing for public debate….(More)”.

Viruses Cross Borders. To Fight Them, Countries Must Let Medical Data Flow, Too

Curated on Posted on by Stefaan Verhulst

Nigel Cory at ITIF: “If nations could regulate viruses the way many regulate data, there would be no global pandemics. But the sad reality is that, in the midst of the worst global pandemic in living memory, many nations make it unnecessarily complicated and costly, if not illegal, for health data to cross their borders. In so doing, they are hindering critically needed medical progress.

In the COVID-19 crisis, data analytics powered by artificial intelligence (AI) is critical to identifying the exact nature of the pandemic and developing effective treatments. The technology can produce powerful insights and innovations, but only if researchers can aggregate and analyze data from populations around the globe. And that requires data to move across borders as part of international research efforts by private firms, universities, and other research institutions. Yet, some countries, most notably China, are stopping health and genomic data at their borders.

Indeed, despite the significant benefits to companies, citizens, and economies that arise from the ability to easily share data across borders, dozens of countries—across every stage of development—have erected barriers to cross-border data flows. These data-residency requirements strictly confine data within a country’s borders, a concept known as “data localization,” and many countries have especially strict requirements for health data.

China is a noteworthy offender, having created a new digital iron curtain that requires data localization for a range of data types, including health data, as part of its so-called “cyber sovereignty” strategy. A May 2019 State Council regulation required genomic data to be stored and processed locally by Chinese firms—and foreign organizations are prohibited. This is in service of China’s mercantilist strategy to advance its domestic life sciences industry. While there has been collaboration between U.S. and Chinese medical researchers on COVID-19, including on clinical trials for potential treatments, these restrictions mean that it won’t involve the transfer, aggregation, and analysis of Chinese personal data, which otherwise might help find a treatment or vaccine. If China truly wanted to make amends for blocking critical information during the early stages of the outbreak in Wuhan, then it should abolish this restriction and allow genomic and other health data to cross its borders.

But China is not alone in limiting data flows. Russia requires all personal data, health-related or not, to be stored locally. India’s draft data protection bill permits the government to classify any sensitive personal data as critical personal data and mandate that it be stored and processed only within the country. This would be consistent with recent debates and decisions to require localization for payments data and other types of data. And despite its leading role in pushing for the free flow of data as part of new digital trade agreements, Australia requires genomic and other data attached to personal electronic health records to be only stored and processed within its borders.

Countries also enact de facto barriers to health and genomic data transfers by making it harder and more expensive, if not impractical, for firms to transfer it overseas than to store it locally. For example, South Korea and Turkey require firms to get explicit consent from people to transfer sensitive data like genomic data overseas. Doing this for hundreds or thousands of people adds considerable costs and complexity.

And the European Union’s General Data Protection Regulation encourages data localization as firms feel pressured to store and process personal data within the EU given the restrictions it places on data transfers to many countries. This is in addition to the renewed push for local data storage and processing under the EU’s new data strategy.

Countries rationalize these steps on the basis that health data, particularly genomic data, is sensitive. But requiring health data to be stored locally does little to increase privacy or data security. The confidentiality of data does not depend on which country the information is stored in, only on the measures used to store it securely, such as via encryption, and the policies and procedures the firms follow in storing or analyzing the data. For example, if a nation has limits on the use of genomics data, then domestic organizations using that data face the same restrictions, whether they store the data in the country or outside of it. And if they share the data with other organizations, they must require those organizations, regardless of where they are located, to abide by the home government’s rules.

As such, policymakers need to stop treating health data differently when it comes to cross-border movement, and instead build technical, legal, and ethical protections into both domestic and international data-governance mechanisms, which together allow the responsible sharing and transfer of health and genomic data.

This is clearly possible—and needed. In February 2020, leading health researchers called for an international code of conduct for genomic data following the end of their first-of-its-kind international data-driven research project. The project used a purpose-built cloud service that stored 800 terabytes of genomic data on 2,658 cancer genomes across 13 data centers on three continents. The collaboration and use of cloud computing were transformational in enabling large-scale genomic analysis….(More)”.

Assessing the feasibility of real-world data

Curated on Posted on by Stefaan Verhulst

Blog Post by Manuela Di Fusco: “Real-world data (RWD) and real-world evidence (RWE) are playing an increasing role in healthcare decision making.

The conduct of RWD studies involves many interconnected stages, ranging from the definition of research questions of high scientific interest, to the design of a study protocol and statistical plan, and the conduct of the analyses, quality reviews, publication and presentation to the scientific community. Every stage requires extensive knowledge, expertise and efforts from the multidisciplinary research team.

There are a number of well-accepted guidelines for good procedural practices in RWD . Despite their stress on the importance of data reliability, relevance and studies being fit for purpose, their recommendations generally focus on methods/analyses and transparent reporting of results. There often is little focus on feasibility concerns at the early stages of a study; ongoing RWD initiatives, too, focus on improving standards and practices for data collection and analyses.

RWD and RWE are playing an increasing role in healthcare decision making.”

The availability and use of new data sources, which have the ability to store health-related data, have been growing globally, and include mobile technologies, electronic patient-reported outcome tools and wearables [1]. 

As data sources exist in various formats, and are often created for non-research purposes, they have inherent associated limitations – such as missing data. Determining the best approach for collecting complete and quality data is of critical importance. At study conception, it is not always clear if it is reasonable to expect that the research question of interest could be fully answered and all analyses carried out. Numerous methodological and data collection challenges can emerge during study execution. However, some of these downstream study challenges could be proactively addressed through an early feasibility study, concurrent to protocol development. For example, during this exploratory study, datasets may be explored carefully to ensure data points deemed relevant for the study are routinely ascertained and captured sufficiently, despite potential missing data and/or other data source limitations.

Determining the best approach for collecting complete and quality data is of critical importance.”

This feasibility assessment serves primarily as a first step to gain knowledge of the data and ensure realistic assumptions are included in the protocol; relevant sensitivity analyses can test those assumptions, hence setting the basis for successful study development.  

Below is a list of key feasibility questions which may guide the technical exploration and conceptualization of a retrospective RWD study. The list is based on experience supporting observational studies on a global scale and is not intended to be exhaustive and representative of all preparatory activities. This technical feasibility analysis should be carried out while considering other relevant aspects, including the novelty and strategic value of the study versus the existing evidence – in the form of randomized controlled trial data and other RWE –, the intended audience, data access/protection, reporting requirements and external validity aspects.

This feasibility assessment serves primarily as a first step to gain knowledge of the data and ensure realistic assumptions are included in the protocol…”

The list may support early discussions among study team members during the preparation and determination of a RWD study.

  • Can the population be accurately identified in the data source?

Diagnosis and procedures can be identified through International Classification of Diseases codes; published code validation studies on the population of interest can be a useful guide.

  • How generalizable is the population of the data source?

Generalizability issues should be recognized upfront. For example, the patient population for which data is available in the data source might be restricted to a specific geographic region, health insurance plan (e.g. Medicare or commercial), system (hospital/inpatient and ambulatory) or group (e.g. age, gender)…(More)”.

10 Tips for Making Sense of COVID-19 Models for Decision-Making

Curated on Posted on by Stefaan Verhulst

Elizabeth Stuart et al at John Hopkins School of Public Health: “Models can be enormously useful in the context of an epidemic if they synthesize evidence and scientific knowledge. The COVID-19 pandemic is a complex phenomenon and in such a dynamic setting it is nearly impossible to make informed decisions without the assistance models can provide. However, models don’t perfectly capture reality: They simplify reality to help answer specific questions.

Below are 10 tips for making sense of COVID-19 models for decision-making such as directing health care resources to certain areas or identifying how long social distancing policies may need to be in effect.

Flattening the Curve for COVIX-19
  1. Make sure the model fits the question you are trying to answer.
    There are many different types of models and a wide variety of questions that models can be used to address. There are three that can be helpful for COVID-19:
    1. Models that simplify how complex systems work, such as disease transmission. This is often done by putting people into compartments related to how a disease spreads, like “susceptible,” “infected,” and “recovered.” While these can be overly simplistic with few data inputs and don’t allow for the uncertainty that exists in a pandemic, they can be useful in the short term to understand basic structures. But these models generally cannot be implemented in ways that account for complex systems or when there is ongoing system or individual behavioral change.
    2. Forecasting models try to predict what will actually happen. They work by using existing data to project out conclusions over a relatively short time horizon. But these models are challenging to use for mid-term assessment—like a few months out—because of the evolving nature of pandemics.
    3. Strategic models show multiple scenarios to consider the potential implications of different interventions and contexts. These models try to capture some of the uncertainty about the underlying disease processes and behaviors. They might take a few values of such as the case fatality ratio or the effectiveness of social distancing measures, and play out different scenarios for disease spread over time. These kinds of models can be particularly useful for decision-making.
  2. Be mindful that forecast models are often built with the goal of change, which affects their shelf life.
    The irony of many COVID-19 modeling purposes is that in some cases, especially for forecasting, a key purpose in building and disseminating the model is to invoke behavior change at individual or system levels—e.g., to reinforce the need for physical distancing.

    This makes it difficult to assess the performance of forecasting models since the results of the model itself (and reactions to it) become part of the system. In these cases, a forecasting model may look like it was inaccurate, but it may have been accurate for an unmitigated scenario with no behavior change. In fact, a public health success may be when the forecasts do not come to be!
  3. Look for models (and underlying collaborations) that include diverse aspects and expertise.
    One of the challenges in modeling COVID-19 is the multitude of factors involved: infectious disease dynamics, social and behavioral factors such as how frequently individuals interact, economic factors such as employment and safety net policies, and more.

    One benefit is that we do know that COVID-19 is an infectious disease and we have a good understanding about how related diseases spread. Likewise, health economists and public health experts have years of experience understanding complex social systems. Look for models, and their underlying collaborations, that take advantage of that breadth of existing knowledge….(More)”.

A call for a new generation of COVID-19 models

Curated on Posted on by Stefaan Verhulst

Blog post by Alex Engler: “Existing models have been valuable, but they were not designed to support these types of critical decisions. A new generation of models that estimate the risk of COVID-19 spread for precise geographies—at the county or even more localized level—would be much more informative for these questions. Rather than produce long-term predictions of deaths or hospital utilization, these models could estimate near-term relative risk to inform local policymaking. Going forward, governors and mayors need local, current, and actionable numbers.

Broadly speaking, better models would substantially aid in the “adaptive response” approach to re-opening the economy. In this strategy, policymakers cyclically loosen and re-tighten restrictions, attempting to work back towards a healthy economy without moving so fast as to allow infections to take off again. In an ideal process, restrictions would be eased at such a pace that balances a swift return to normalcy with reducing total COVID-19 infections. Of course, this is impossible in practice, and thus some continued adjustments—the flipping of various controls off and on again—will be necessary. More precise models can help improve this process, providing another lens into when it will be safe to relax restrictions, thus making it easier to do without a disruptive back-and-forth. A more-or-less continuous easing of restrictions is especially valuable, since it is unlikely that second or third rounds of interventions (such as social distancing) would achieve the same high rates of compliance as the first round.

The proliferation of Covid19 Data

These models can incorporate cases, test-positive rates, hospitalization information, deaths, excess deaths, and other known COVID-19 data. While all these data sources are incomplete, an expanding body of research on COVID-19 is making the data more interpretable. This research will become progressively more valuable with more data on the spread of COVID-19 in the U.S. rather than data from other countries or past pandemics.

Further, a broad range of non-COVID-19 data can also inform risk estimates: Population density, age distributions, poverty and uninsured rates, the number of essential frontline workers, and co-morbidity factors can also be included. Community mobility reports from Google and Unacast’s social distancing scorecard can identify how easing restrictions are changing behavior. Small area estimates also allow the models to account for the risk of spread from other nearby geographies. Geospatial statistics cannot account for infectious spread between two large neighboring states, but they would add value for adjacent zip codes. Lastly, many more data sources are in the works, like open patient data registries, the National Institutes of Health’s (NIH) study of asymptomatic persons, self-reported symptoms data from Facebook, and (potentially) new randomized surveys. In fact, there are so many diverse and relevant data streams, that models can add value simply be consolidating daily information into just a few top-line numbers that are comparable across the nation.

FiveThirtyEight has effectively explained that making these models is tremendously difficult due to incomplete data, especially since the U.S. is not testing enough or in statistically valuable ways. These challenges are real, but decision-makers are currently using this same highly flawed data to make inferences and policy choices. Despite the many known problems, elected officials and public health services have no choice. Frequently, they are evaluating the data without the time and expertise to make reasoned statistical interpretations based on epidemiological research, leaving significant opportunity for modeling to help….(More)”.