Paper by Cass Sunstein: “Many incentives are monetary, and when private or public institutions seek to change behavior, it is natural to change monetary incentives. But many other incentives are a product of social meanings, about which people may not much deliberate, but which can operate as subsidies or as taxes. In some times and places, for example the social meaning of smoking has been positive, increasing the incentive to smoke; in other times and places, it has been negative, and thus served to reduce smoking.
With respect to safety and health, social meanings change radically over time, and they can be dramatically different in one place from what they are in another. Often people live in accordance with meanings that they deplore, or at least wish were otherwise. But it is exceptionally difficult for individuals to alter meanings on their own. Alteration of meanings can come from law, which may, through a mandate, transform the meaning of action into a bland, “I comply with law,” or into a less bland, “I am a good citizen.” Alteration of social meanings can also come from large-scale private action, engineered or promoted by “meaning entrepreneurs,” who can turn the meaning of action from, “I am an oddball,” to, “I do my civic duty,” or, “I protect others from harm.” Sometimes subgroups rebel against new or altered meanings, produced by law or meaning entrepreneurs, but often those meanings stick and produce significant change….(More)”.
Paper by Victoria Perez and Justin M. Ross: “Networks of overlapping local governments are the front line of governmental responses to pandemics. Local governments, both general purpose (municipalities, counties, etc.) and special districts (school, fire, police, hospital, etc.), implement state and federal directives while acting as a producer and as a third-party payer in the healthcare system. They possess local information necessary in determining the best use of finite resources and available assets. Furthermore, a liberal society requires voluntary cooperation of citizens skeptical of opportunistic authoritarianism. Therefore, successful local governance instills a reassuring division of political power.
The COVID-19 pandemic has created two significant challenges for local governments in their efforts to respond effectively to the crisis: public finance and intergovernmental collaboration. This brief recommends practical solutions to meet these challenges….(More)”.
Karen Hao at MIT Technology Review: “When it comes to predicting the spread of an infectious disease, it’s crucial to understand what Ryan Tibshirani, an associate professor at Carnegie Mellon University, calls the “the pyramid of severity.” The bottom of the pyramid is asymptomatic carriers (those who have the infection but feel fine); the next level is symptomatic carriers (those who are feeling ill); then come hospitalizations, critical hospitalizations, and finally deaths.
Every level of the pyramid has a clear relationship to the next: “For example, sadly, it’s pretty predictable how many people will die once you know how many people are under critical care,” says Tibshirani, who is part of CMU’s Delphi research group, one of the best flu-forecasting teams in the US. The goal, therefore, is to have a clear measure of the lower levels of the pyramid, as the foundation for forecasting the higher ones.
But in the US, building such a model is a Herculean task. A lack of testing makes it impossible to assess the number of asymptomatic carriers. The results also don’t accurately reflect how many symptomatic carriers there are. Different counties have different testing requirements—some choosing only to test patients who require hospitalization. Test results also often take upwards of a week to return.
The remaining option is to measure symptomatic carriers through a large-scale, self-reported survey. But such an initiative won’t work unless it covers a big enough cross section of the entire population. Now the Delphi group, which has been working with the Centers for Disease Control and Prevention to help it coordinate the national pandemic response, has turned to the largest platforms in the US: Facebook and Google.
In a new partnership with Delphi, both tech giants have agreed to help gather data from those who voluntarily choose to report whether they’re experiencing covid-like symptoms. Facebook will target a fraction of their US users with a CMU-run survey, while Google has thus far been using its Opinion Rewards app, which lets users respond to questions for app store credit. The hope is this new information will allow the lab to produce county-by-county projections that will help policymakers allocate resources more effectively.
Neither company will ever actually see the survey results; they’re merely pointing users to the questions administered and processed by the lab. The lab will also never share any of the raw data back to either company. Still, the agreements represent a major deviation from typical data-sharing practices, which could raise privacy concerns. “If this wasn’t a pandemic, I don’t know that companies would want to take the risk of being associated with or asking directly for such a personal piece of information as health,” Tibshirani says.
Without such cooperation, the researchers would’ve been hard pressed to find the data anywhere else. Several other apps allow users to self-report symptoms, including a popular one in the UK known as the Covid Symptom Tracker that has been downloaded over 1.5 million times. But none of them offer the same systematic and expansive coverage as a Facebook or Google-administered survey, says Tibshirani. He hopes the project will collect millions of responses each week….(More)”.
Nic Fildes and Javier Espinoza at the Financial Times: “When the World Health Organization launched a 2007 initiative to eliminate malaria on Zanzibar, it turned to an unusual source to track the spread of the disease between the island and mainland Africa: mobile phones sold by Tanzania’s telecoms groups including Vodafone, the UK mobile operator.
Working together with researchers at Southampton university, Vodafone began compiling sets of location data from mobile phones in the areas where cases of the disease had been recorded.
Mapping how populations move between locations has proved invaluable in tracking and responding to epidemics. The Zanzibar project has been replicated by academics across the continent to monitor other deadly diseases, including Ebola in west Africa….
With much of Europe at a standstill as a result of the coronavirus pandemic, politicians want the telecoms operators to provide similar data from smartphones. Thierry Breton, the former chief executive of France Telecom who is now the European commissioner for the internal market, has called on operators to hand over aggregated location data to track how the virus is spreading and to identify spots where help is most needed.
Both politicians and the industry insist that the data sets will be “anonymised”, meaning that customers’ individual identities will be scrubbed out. Mr Breton told the Financial Times: “In no way are we going to track individuals. That’s absolutely not the case. We are talking about fully anonymised, aggregated data to anticipate the development of the pandemic.”
But the use of such data to track the virus has triggered fears of growing surveillance, including questions about how the data might be used once the crisis is over and whether such data sets are ever truly anonymous….(More)”.
Thirteen modules have been created by leading global experts in major disasters such as the post-election violence in Kenya in 2008, the Fukushima nuclear plant disaster in 2011, the Ebola crisis in 2014, the Zika outbreak in 2016, and the current coronavirus. The course is designed to help those responding to coronavirus make use of volunteerism.
As the COVID-19 pandemic reaches unprecedented proportions and spreads to more than 150 countries on six continents, policymakers are struggling to answer questions such as “How do we predict how the virus will spread?,” “How do we help the elderly and the homebound?,” “How do we provide economic assistance to those affected by business closures?,” and more.
In each mini-lecture, those who have learned how to mobilize groups of people online to manage in a crisis present the basic concepts and tools to learn, analyze, and implement a crowdsourced public response. Lectures include
Introduction: Why Collective Intelligence Matters in a Crisis
Chatbots and Social Media Strategies for Crisis (led by Nashin Mahtani, PetaBencana.id, Indonesia)
Conclusion: Lessons Learned
The course explores such innovative uses of crowdsourcing as Safecast’s implementation of citizen science to gather information about environmental conditions after the meltdown of the Fukushima nuclear plant; Ushahidi, an online platform in Kenya for crowdsourcing data for crisis relief, human rights advocacy, transparency, and accountability campaigns; and “Ask a Scientist,” an interactive tool developed by The GovLab with the Federation of American Scientists and the New Jersey Office of Innovation, in which a network of scientists answer citizens’ questions about COVID-19.
Announcement: “To address the COVID-19 pandemic and other dynamic threats, The GovLab has called for the development of a new data infrastructure and ecosystem. Establishing data collaboratives in a responsible manner often necessitates the creation of data sharing agreements and other legal documentation — a strain on time and capacity both for data holders and those who could use data in the public interest.
Today, to support the development of data collaboratives in a responsible and agile way, we are sharing a new tool that addresses the complexity in preparing a Data Sharing Agreement from Contracts for Data Collaboration (a joint initiative of SDSN-TReNDS, the World Economic Forum, The GovLab, and the University of Washington’s Information Risk Research Initiative). Providing a checklist to support organizations with reviewing, negotiating and preparing Data Sharing Arrangements, the intent is to strengthen stakeholder trust and help accelerate responsible data sharing arrangements given the urgency of the global pandemic.
(Please note that the check list is a tool for formulating and understanding legal issues, but we are not offering it as legal advice.)
Press Release: “As part of its ongoing response to the COVID-19 crisis, PARIS21 released today a policy brief at the intersection of statistics and policy making to help inform the measures taken to address the pandemic.
The COVID-19 pandemic has brought data to the centre of policy making and public attention. A diverse ecosystem of data producers, both private and public, report rates of infection, fatality and recovery on a daily basis. However, a proliferation of data, which is at times contradictory, can also lead to confusion and mistrust among data users.
Meanwhile, policymakers, development partners and citizens need to take quick, informed actions to design interventions that reach the most vulnerable and leave no one behind. As countries comply with lockdowns and other containment measures, national statistical systems (NSSs) face a dual effect of growing data demand and constrained supply. This in turn may squeeze NSSs beyond their institutional capacity.
At the same time, alternative data sources such as mobile phone or satellite data are in abundance. These data could potentially complement traditional sources such as censuses, surveys and administrative systems. However, with scant governance frameworks to scale and sustain their use, policy action is not yet based on a convergence of evidence.
This policy brief introduces a conceptual framework that describes the adverse effects of the crisis on NSSs in developing countries. Moreover, it suggests short and medium-term actions to mitigate the negative effects by:
1. Focusing data production on priority economic, social and demographic data. 2. Communicating proactively with citizens, academia, private sector and policy makers. 3. Positioning the NSO as advisor and knowledge bank for national governments.
NSSs contribute significantly to robust policy responses in a crisis. The brief thus calls on national statistical offices to assume a central role as coordinators of the NSSs and chart the way toward improved data ecosystem governance for informing policies during and after COVID-19….(More)”.
Essay by stefania milan and Emiliano Treré at Data & Policy: “If numbers are the conditions of existence of the COVID-19 problem, we ought to pay attention to the actual (in)ability of many countries in the South to test their population for the virus, and to produce reliable population statistics more in general — let alone to adequately care for them. It is a matter of a “data gap” as well as of data quality, which even in “normal” times hinders the need for “evidence-based policy making, tracking progress and development, and increasing government accountability” (Chen et al., 2013). And while the World Health Organization issues warning about the “dramatic situation” concerning the spread of COVID-19 in the African continent, to name just one of the blind spots of our datasets of the global pandemic, the World Economic Forum calls for “flattening the curve” in developing countries. Progress has been made following the revision of the United Nations’ Millennium Development Goals in 2005, with countries in the Global South have been invited (and supported) to devise National Strategies for the Development of Statistics. Yet, a cursory look at the NYU GovLab’s valuable repository of data collaboratives” addressing the COVID-19 pandemic reveals the virtual absence of data collection and monitoring projects in the South of the hemisphere. The next obvious step is the dangerous equation “no data=no problem”.
Disease and “whiteness”
Epidemiology and pharmacogenetics (i.e. the study of the genetic basis of how people respond to pharmaceuticals), to name but a few amongst the number of concerned life sciences, are largely based on the “inclusion of white/Caucasians in studies and the exclusion of other ethnic groups” (Tutton, 2007). In other words, modeling of disease evolution and the related solutions are based on datasets that take into account primarily — and in fact almost exclusively — the caucasian population. This is a known problem in the field, which derives from the “assumption that a Black person could be thought of as being White”, dismissing specificities and differences. This problem has been linked to the “lack of social theory development, due mainly to the reluctance of epidemiologists to think about social mechanisms (e.g., racial exploitation)” (Muntaner, 1999, p. 121). While COVID-19 represents a slight variation on this trend, having been first identified in China, the problem on the large scale remains. And in times of a health emergency as global as this one, risks to be reinforced and perpetuated.
A succulent market for the industry
In the lack of national testing capacity, the developing world might fall prey to the blooming industry of genetic and disease testing, on the one hand, and of telecom-enabled population monitoring on the other. Private companies might be able to fill the gap left by the state, mapping populations at risk — while however monetizing their data. The case of 23andme is symptomatic of this rise of industry-led testing, which constitutes a double-edge sword. On the one hand, private actors might supply key services that resource-poor or failing states are unable to provide. On the other hand, however, the distorted and often hidden agendas of profit-led players reveals its shortcomings and dangers. If we look at the telecom industry, we note how it has contributed to track disease propagation in a number of health emergencies such as Ebola. And if the global open data community has called for smoother data exchange between the private and the public sector to collectively address the spread of the virus,in the absence of adequate regulatory frameworks in the Global South, for example in the field of privacy and data retention, local authorities might fall prey to outside interventions of dubious nature….(More)”.
Pledge: “Immediate action is required to halt the COVID-19 Pandemic and treat those it has affected. It is a practical and moral imperative that every tool we have at our disposal be applied to develop and deploy technologies on a massive scale without impediment.
We therefore pledge to make our intellectual property available free of charge for use in ending the COVID-19 pandemic and minimizing the impact of the disease.
We will implement this pledge through a license that details the terms and conditions under which our intellectual property is made available.
How to make the Pledge
The first step for organizations wishing to make the Pledge is to publicly commit to making intellectual property relevant to COVID-19 freely available, by:
Posting a public statement that the organization is making the Pledge, on their website.
Issuing an official press release.
And then sending us a link to this statement, a point of contact in the organization, and, at the organization’s discretion, a copy of their logo to display on this site.
How to implement the Pledge
The next step for organizations who have made the Pledge is to implement it via a license detailing the terms and conditions under which their intellectual property is made available. There are three options for doing so:
Adopt the Open COVID License, created by our legal team for organizations that wish to implement the Pledge simply and immediately on terms shared by many other organizations.
Create a custom license that accomplished the intent of the Pledge.
Identify existing license(s) that accomplish the goals of the Pledge.
As with making the Pledge, send us links to the license or licenses, a point of contact in the organization, and, at the organization’s discretion, a copy of their logo to display on this site….(More)”.
Norman Fenton, Magda Osman, Martin Neil, and Scott McLachlan at The Conversation: “Suppose we wanted to estimate how many car owners there are in the UK and how many of those own a Ford Fiesta, but we only have data on those people who visited Ford car showrooms in the last year. If 10% of the showroom visitors owned a Fiesta, then, because of the bias in the sample, this would certainly overestimate the proportion of Ford Fiesta owners in the country.
Estimating death rates for people with COVID-19 is currently undertaken largely along the same lines. In the UK, for example, almost all testing of COVID-19 is performed on people already hospitalised with COVID-19 symptoms. At the time of writing, there are 29,474 confirmed COVID-19 cases (analogous to car owners visiting a showroom) of whom 2,352 have died (Ford Fiesta owners who visited a showroom). But it misses out all the people with mild or no symptoms.
Concluding that the death rate from COVID-19 is on average 8% (2,352 out of 29,474) ignores the many people with COVID-19 who are not hospitalised and have not died (analogous to car owners who did not visit a Ford showroom and who do not own a Ford Fiesta). It is therefore equivalent to making the mistake of concluding that 10% of all car owners own a Fiesta.
There are many prominent examples of this sort of conclusion. The Oxford COVID-19 Evidence Service have undertaken a thorough statistical analysis. They acknowledge potential selection bias, and add confidence intervals showing how big the error may be for the (potentially highly misleading) proportion of deaths among confirmed COVID-19 patients.
They note various factors that can result in wide national differences – for example the UK’s 8% (mean) “death rate” is very high compared to Germany’s 0.74%. These factors include different demographics, for example the number of elderly in a population, as well as how deaths are reported. For example, in some countries everybody who dies after having been diagnosed with COVID-19 is recorded as a COVID-19 death, even if the disease was not the actual cause, while other people may die from the virus without actually having been diagnosed with COVID-19.
However, the models fail to incorporate explicit causal explanations in their modelling that might enable us to make more meaningful inferences from the available data, including data on virus testing.
What a causal model would look like. Author provided
We have developed an initial prototype “causal model” whose structure is shown in the figure above. The links between the named variables in a model like this show how they are dependent on each other. These links, along with other unknown variables, are captured as probabilities. As data are entered for specific, known variables, all of the unknown variable probabilities are updated using a method called Bayesian inference. The model shows that the COVID-19 death rate is as much a function of sampling methods, testing and reporting, as it is determined by the underlying rate of infection in a vulnerable population….(More)”
