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)”.

Protecting Data Privacy and Rights During a Crisis are Key to Helping the Most Vulnerable in Our Community

Curated on Posted on by Stefaan Verhulst

Blog by Amen Ra Mashariki: “Governments should protect the data and privacy rights of their communities even during emergencies. It is a false trade-off to require more data without protection. We can and should do both — collect the appropriate data and protect it. Establishing and protecting the data rights and privacy of our communities’ underserved, underrepresented, disabled, and vulnerable residents is the only way we can combat the negative impact of COVID-19 or any other crisis.

Building trust is critical. Governments can strengthen data privacy protocols, beef up transparency mechanisms, and protect the public’s data rights in the name of building trust — especially with the most vulnerable populations. Otherwise, residents will opt out of engaging with government, and without their information, leaders like first responders will be blind to their existence when making decisions and responding to emergencies, as we are seeing with COVID-19.

As Chief Analytics Officer of New York City, I often remembered the words of Defense Secretary Donald Rumsfeld, especially with regards to using data during emergencies, that there are “known knowns, known unknowns, and unknown unknowns, and we will always get hurt by the unknown unknowns.” Meaning the things we didn’t know — the data that we didn’t have — was always going to be what hurt us during times of emergencies….

There are three key steps that governments can do right now to use data most effectively to respond to emergencies — both for COVID-19 and in the future.

Seek Open Data First

In times of crisis and emergencies, many believe that government and private entities, either purposefully or inadvertently, are willing to trample on the data rights of the public in the name of appropriate crisis response. This should not be a trade-off. We can respond to crises while keeping data privacy and data rights in the forefront of our minds. Rather than dismissing data rights, governments can start using data that is already openly available. This seems like a simple step, but it does two very important things. First, it forces you to understand the data that is already available in your jurisdiction. Second, it grows your ability to fill the gaps with respect to what you know about the city by looking outside of city government. …(More)”.

Continuity in Legislatures Amid COVID-19

Curated on Posted on by Stefaan Verhulst

Blog by Sam DeJohn, Anirudh Dinesh, and Dane Gambrell: “As COVID-19 changes how we work, governments everywhere are experimenting with new ways to adapt and continue legislative operations under current physical restrictions. From city councils to state legislatures and national parliaments, more public servants are embracing and advocating for the use of new technologies to convene, deliberate, and vote.

On April 20th, GovLab published an initial overview of such efforts in the latest edition of the CrowdLaw Communique. As the United States Congress wrestles with the question of whether to allow remote voting, the GovLab has compiled an update on those international and state legislatures that are the furthest ahead with the use of new technology to continue operations.

NORTH AMERICA

In the US, On April 16, over 60 former members of Congress participated in a “Mock Remote Hearing” exercise to test the viability of online proceedings during the COVID-19 pandemic.

In Kentucky, when they last met on April 1, that State’s House of Representatives adopted new rules allowing lawmakers to vote remotely by sending in photos of a ballot to designated managers on the House Floor.” (WFPL). Lawmakers have also altered voting procedures to limit the number of lawmakers on the House floor. Members will vote in groups of 25 and may vote by paper ballot (NCSL).

New Jersey lawmakers made history on March 25 when members of the General Assembly called into a conference line to cast their votes remotely on several bills related to the coronavirus pandemic. NJ lawmakers moved 12 bills that day via remote voting.

On the west coast of the United States, the city council of Kirkland, Washington, recently held its first virtual city council meeting. Many cities and counties in California have also begun holding their meetings via Zoom.

As compiled by the National Council of State Legislatures, states that have changed rules — many just in the past few weeks — to allow full committee action and/or remote voting include: Iowa, Kentucky, Minnesota, New Jersey, North Carolina, Utah, and Vermont. Other states have specifically said they are seriously considering allowing remote action, including New Hampshire, New Mexico, New York, and Wyoming.

EUROPE

In the European Union, Parliament is temporarily allowing remote participation to avoid spreading COVID-19 (Library of Congress). With regard to voting, all members, even those participating in person, will receive a ballot sent by email to their official email address. The ballot, which must contain the name and vote of the MP in a readable form and the MP’s signature, must be returned from their official email address to the committee or plenary services in order to be counted. The ballot must be received in the dedicated official European Parliament mailbox by the time the vote is closed.

In Spain, MPs have been casting votes using the Congress’s intranet system, which has been in place since 2012. Rather than voting in real time, voting is typically open for a two-hour period before the session to vote for the alternative or amendment proposals and for a two-hour period following the session in which the proposals are debated to vote the final text….(More)”.

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)”.

The digital tools that can keep democracy going during lockdown

Curated on Posted on by Stefaan Verhulst

Rosalyn Old at Nesta: “In the midst of the COVID-19 global pandemic, governments at all levels are having to make decisions to postpone elections and parliamentary sessions, all while working remotely and being under pressure to deliver fast-paced and effective decision-making.

In times of crisis, there can be a tension between the instinct to centralise decision-making for efficiency, sacrificing consultation in the process, and the need to get citizens on board with plans for large-scale changes to everyday life. While such initial reactions are understandable, in the current and next phases we need a different approach – democracy must go on.

Effective use of digital tools can provide a way to keep parliamentary and government processes going in a way that enhances rather than threatens democracy. This is a unique opportunity to experiment with digital methods to address a number of business-as-usual pain points in order to support institutions and citizen engagement in the long term.

Digital tools can help with the spectrum of decision-making

While digital tools can’t give the answers, they can support the practicalities of remote decision-making. Our typology of digital democracy shows how digital tools can be used to harness the wisdom of the crowd in different stages of a process:A typology of digital democracy

A typology of digital democracy

Digital tools can collect information from different sources to provide an overview of the options. To weigh up pros and cons, platforms such as Your Priorities and Consul enable people to contribute arguments. If you need a sense of what is important and to try to find consensus, Pol.is and Loomio may help. To quickly gauge support for different options from stakeholders, platforms such as All Our Ideas enable ranking of a live bank of ideas. If you need to gather questions and needs of citizens, head to platforms like Sli.do or online forms or task management tools like Trello or Asana….(More)”.