COVID-19 shows the need for a global alliance of experts who can fast-track the capacity building of developing countries in the business of counting.
The COVID-19 pandemic is sweeping the world. First identified in mainland China in December 2019, it has rapidly reached the four corners of the globe, to the point that the only “corona-free” land is reportedly Antarctica. News reports globally are filled with numbers and figures of various kinds. We count the number of tests, we follow the rise of the total individuals who tested positive to the virus, we mourn the dead looking at the daily death toll. These numbers are deeply ingrained in their socio-economic and political geography, as the virus follows distinct diffusion curves, but also because distinct countries and institutions count differently (and often these distinct ways of counting are not even made apparent). What is clear is that what gets counted exists, in both state policies and people’s imaginaries. Numbers affect our ability to care, share empathy, and donate to relief efforts and emergency services. Numbers are the condition of existence of the problem, and of a country or given social reality on the global map of concerns. Yet most countries from the so-called Global South are virtually absent from this number-based narration of the pandemic. Why, and with what consequences?
Data availability and statistical capacity in developing countries
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 emisphere. 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.
The populism and racism factors
Lack of reliable numbers to accurately portray the COVID-19 pandemic as it spreads to the Southern hemisphere also offers fertile ground to distorted and malicious narratives mobilized for political reasons. To name just one, it allows populist leaders like Brazil’s Jair Bolsonaro to announce the “return to normality” in the country, dismissing the harsh reality as a collective “hysteria”. In Italy, the ‘fake news’ that migrant populations of African origin would be “immune” to the disease sweeped social media, unleashing racist comments and anti-migrant calls for action. While the same rumor that has reportedly been circulating in the African continent as well and populism has been hitting hard in Western democracies as well, it might be have more dramatic consequences in the more populous countries of the South. In Mexico, left-wing populist president Andrés Manuel López Obrador responded to the coronavirus emergency insisting that Mexicans should “keep living life as usual”. He did not stop his tour in the south of the country and frequently contradicted the advice of public health officials, systematically ignoring social distancing by touching, hugging and kissing his supporters and going as far as considering the pandemic as a plot to derail his presidency. These dangerous comments, assumptions and attitudes are a byproduct of the lack of reliable data and testing that we signal in this article.
The risk of universalising the problem
Luckily, the long experience and harsh familiarity in coping with disasters, catastrophes and emergencies has also prompted various countries from the Global South to deploy effective measures of containment more quickly than many countries in the Global North.
In the lack of reliable data from the South, however, modeling the diffusion of the disease might be difficult. The temptation will likely be to ”import” models and “appropriate” predictions from other countries and socio-economic realities, and then base domestic measures and policies on them. “Universalizing” the problem as well as the solutions, as we warned in a 2019 article, is tempting, especially in these times of global uncertainty. Universalizing entails erroneously thinking that the problem manifests itself in exactly the same manner everywhere, disregarding local features to “other” approaches. Coupled with the “whiteness” observed earlier, this gives rise to an explosive cocktail that is likely to create more problems than it solves.
Beyond the blind spot?
While many have enough to worry about “at home”, the largest portion of the world population today resides in the so-called Global South, with all the very concrete challenges of the situation. For instance, for a good portion of the 1,3 billion Indian citizens now on lockdown, staying at home might mean starving. How can the global community–open data experts, researchers, life science scholars, digital rights activists, to name but a few–contribute to “fix” the widening data divide that risks severely weakening any local effort to curb the expansion of COVID-19 to populations that are often already at the margins? We argue that the issue at stake here is not simply whether we pump in the much-needed resources or how we collaborate, but it is also a matter of where do we turn the eye–in other words, where we decide to look. COVID-19 will likely make apparents the need of a global alliance of experts of various kinds who, jointly with civil society organizations, can fast-track the capacity building of developing countries in the business of counting.
This article has been published simultaneously on the the Big Data from the South blog and on Open Movements / Open Democracy.