This knowledge, of course, does not come exclusively from cell phone data; many would argue that this knowledge is common sense, widespread, and not new. When Ebola began to spread in West Africa in early , Buckee and colleagues at Sweden's Karolinska Institute were eager to apply the same technology to a new disease. They were mistaken on both counts. In disease containment, correlations alone are very weak points of departure for education and eradication campaigns. There is a vast anthropological literature on the interventions that lead to improved health outcomes see also Chandler and Beisel ; Kamat ; Kelly and Beisel ; Packard and Brown The approximate ecologies of the Harvard study model could not be translated to Ebola containment for specific reasons.
The contact tracing that brought an end to Ebola depended on knowing the exact person in an exact location with the disease. CDR data could not identify an individual cell phone user or if a cell phone user was sick with Ebola. CDR data only gave the Harvard researchers information about where millions of cell phones had gone, not where a person infected with malaria was. Further, the malaria model used location and prevalence estimates.
It analyzed and CDR data long after the fact , to estimate the locations of the nearly 15 million Kenyan cell phone subscribers. Estimated location and prevalence—rather than specific people and incidence—are at the core of the Harvard study. Control the mosquito, contain the disease. To assume that Ebola could be tracked in the same way malaria was tracked was to misunderstand the weaknesses of the correlative model itself as well as the modes of containment that could halt Ebola's spread. It is fair to say that CDR data had nothing to do with how the — Ebola containment campaigns actually worked.
Network coverage outside of Freetown is spotty, even though cell phone towers have sprung up across Sierra Leone over the last 15 years in remote rural locations. Mobile phone network coverage in Sierra Leone MapAction By the end of , the Harvard study model was bedeviled by an earlier assumption that telecommunications companies would freely turn over CDR cell phone data during the Ebola crisis.
The computational epidemiologists assumed that telecommunications corporations would be philanthropic with their call record data during the epidemic even though it is more commonly for sale. During the West African epidemic, soon after the WHO's August declaration that Ebola as an emergency of international concern, the computational epidemiologists pushed hard for CDR cell phone data access. UN and Sierra Leone government agencies were directly involved in discussions. But talks broke down and CDR data were not made available to the researchers.
Since the s, conditional caps have been placed on salaries and payments for doctors, nurses, and other health care professionals. NGOs have filled health services voids since, but unevenly.
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Rural regions, like the area where Ebola first emerged, have the lowest levels of both health care practitioners and facilities. The models tend to come with a certainty and clarity that feels hopeful amidst the chaos of emergency. Harvard models are given the benefit of the doubt, even when they lack basic implementation logic, as in this case.
They are often uncritically enabled further by other global health leaders. Technologies, though, need local health infrastructure to plug in to in times of crisis and epidemic.
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Importantly, prioritizing experimental technology like big data for Ebola containment was at odds with what many interlocutors concerned with health care in Sierra Leone consistently say they want. They want primary health care infrastructure first. That would have been the best defense against Ebola, they say. Despite being well intentioned and prodigiously hyped, the big data technologies for detecting and containing Ebola had no demonstrable affect on Ebola containment or improved health outcomes. Technology troubles started with thing—self problems and only amplified with dependence on newsfeeds and networks that could not deliver respite in places where too few investments in health infrastructures have been made.
Successful health technologies, at their most basic, are tools that get a job done to improve health outcomes. Big data—Ebola containment tools did not meet this measure. Seared into my mind in a way I cannot unsee is the image a U. We were sharing a meal at an international conference when he said that sick people would be identified and traced by their cell phone up to the point when a drone would take over the job. From his vivid description, my mind's eye saw a running man on the ground, pursued by a dark flying object—a drone—chased toward the open side door of a van, security forces ready to whisk him away.
This is a chilling image in a place where South African mercenaries were hired by the Sierra Leonean government on the advice of expatriate global security advisors to shoot villagers presumed to be rebels from helicopters during a war not so long ago Rubin In , two years after the war's end, MI24 helicopter gunships and MI8 armed observation helicopters were a part of Operation Blue Vigilance, a UN peacekeeping mission Silberfein and Conteh that regularly flew in the airspace over the same West African border region where Ebola first emerged.
There is little question that any future public health surveillance by military drone would be a socially fraught intervention, as was the militarization of the Ebola epidemic more generally see Benton , ; Sandvik The military officer I spoke with imagined the literal capture of people sick with Ebola. Global public health leadership—like the WHO, the World Bank, the Gates Foundation, and the UN—have pushed hard in the last two decades to build databases to bank health data from every country in the world.
Now is a good time to ask broader and harder questions about data technology and its uses. During an epidemic, what counts as essential data? What data will solve an emergency health crisis? What kind of data matter? For the West African Ebola case, containment solutions did not lie in being able to count how many people were infected and had died, though this was important for showing the world the magnitude and spread of the crisis. Even further removed from solving containment challenges was the CDR data that epidemiologists imagined would be collected from cell phones.
Actual solutions, instead, were found in what West Africans and anthropologists already knew and could talk and write about. In September , about the same time that computational epidemiologists were pushing the West African telecommunications companies to give them the CDR data and anthropologists were waving their hands for attention like keeners with an answer in class e. A door opened; anthropologists and their work were about to be taken seriously. Anthropologists entered the conversations by noting that Ebola transmission was as much an attack on the social body as it was a deadly viral pathogen.
They were able to explain that people were primarily getting sick from taking care of sick loved ones, a social obligation few could abandon. And people were getting sick when they prepared the dead for a dignified burial. West Africans and anthropologists were essential for explaining to the world how people were getting sick and what respectful disease containment could look like. We live in a time when reasonable people feel righteous enough during an emergency of Ebola's magnitude to insist that local telecommunications companies devote energies and fixed resources to getting customer cell phone data to computer science labs in Boston rather than setting up and facilitating additional communication networks and satellite servers for medical responders in Ebola hot spots.
Even if the model had worked as the computational epidemiologists imagined, it would only have been capable of identifying vague geographies for additional interventions. During the Ebola outbreak, did West Africans and anthropologists understand the nature of the health crisis better without big data? Yes, there is plenty of evidence that is true e.
Computational epidemiologists may continue to play with what they believe is big data's unrealized potential during epidemics, but the world is better served when what counts as data in global health draws first from what people in affected communities and anthropologists already know. Based on what actually worked to contain the — West African Ebola epidemic, when we are faced with the challenges of epidemic containment in the future, it is sensible to herald anthropological theory, method, data, and knowledge as anticipatory technologies of the first instance.
My expertise is in Sierra Leone where I have worked over several decades. For the explanatory purposes of this article, I use the GPS satellite system as the default satellite system reference. The European Union is currently developing a system called Galileo, and China is developing a global system called Compass Bhatta A great irony for the Sierra Leone case, when we account for the number of cell phones and SIM cards people claim as their own, is that any count disproportionally overcounts owners of one or more cell phones while missing other large groups of people without cell phones, for example, in the rural areas where Ebola emerged.
Their data collection methodologies are not transparent see BuddeComm and there is no means of verification by the public. Prevalence is the proportion of a population that has a disease over a given period, usually a year. Incidence refers to the number of new cases of a disease occurring during a given period.
It was adapted from animal studies that captured, marked, released, and then recaptured animals. National Center for Biotechnology Information , U. Medical Anthropology Quarterly. Med Anthropol Q.
Published online Apr Susan L. Erikson 1. Author information Article notes Copyright and License information Disclaimer. Erikson, Email: ac.
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Abstract Evidence from Sierra Leone reveals the significant limitations of big data in disease detection and containment efforts. Keywords: global health, technology, big data, Ebola, cell phones, digital humanitarianism. Open in a separate window.
Figure 1. Big Data as an Anticipatory Technology Big data is much talked about but often not clearly defined. Figure 2. Figure 3. Figure 4.
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Figure 5. Figure 6. Modeling Ebola Transmission with Malaria Disease Exemplars One of the reasons cell phones were conscripted in the fight to contain Ebola was that they were previously used to track malaria. Figure 7. Pesky Issues of Data Access and Health Systems Yearnings By the end of , the Harvard study model was bedeviled by an earlier assumption that telecommunications companies would freely turn over CDR cell phone data during the Ebola crisis. References Cited Abramowitz S. Somatosphere September Anticipation: Technoscience, Life, Affect, Temporality.
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