It has been seemingly impossible to watch the news over the past few weeks without hearing about the emergence of Ebola in the United States. While only three people have come down with symptoms in the U.S., West African countries are battling major outbreaks that doctors and other healthcare workers are struggling to contain. But what if in the future controlling the spread of disease shifted from being a largely reactionary exercise to a proactive one? Science, it turns out, is moving more and more toward that paradigm thanks to the ongoing revolution in data science.
First identified in the mid-1970s in the Sudan and the Democratic Republic of Congo, Ebola has killed thousands of people—nearly all in West Africa—during its current outbreak, which registers as the most significant in the virus’s young history, according to the World Health Organization. For all the fear it incites, the virus is nonetheless difficult to transmit and is not airborne, with patients only contagious when they are symptomatic.
That hasn’t prevented Ebola from spreading outside of West Africa to Western Europe and the U.S., where the first person to develop symptoms inside the country had recently traveled to Liberia and contracted the virus. Two of the nurses who helped treat him eventually came down with Ebola after what officials said was a breach in protocol for handling patients, and they are currently undergoing treatment at the National Institutes of Health and Emory University.
Despite repeated assurances from public health officials at the Centers for Disease Control and Prevention (C.D.C.), it has been difficult to quell concerns about Ebola, prompting a growing number of elected officials to push for improved disease detection and tracking. Researchers in the public and private sectors have already made progress on this front, as they’re increasingly using data to better predict outbreaks of potentially deadly viruses like Ebola.
Though different organizations are leveraging data in different ways, CDC researchers have recently focused their efforts on collecting information from mobile calls made to emergency lines across West Africa. This data, scientists recently reported in the journal PLOS, has given them an in-depth view into the spread of the disease, as scientists are now able to plot the emergence and spread of Ebola across the western part of the continent. This research would not have been possible a decade ago when mobile phones were far less common both in Africa and across the globe. With mobile phone ownership nearing 100% in many parts of Africa, however, scientists now have access to huge amounts of data that can be mined for actionable information.
The private sector is also pushing data-based epidemiological research forward, according to M.I.T.’s Technology Review. Among other organizations that are spearheading this sort of groundbreaking research is Flowminder, a nonprofit that collected and subsequently analyzed vast troves of mobile usage data in Senegal. Visualizing this data revealed common travel patterns in the country, giving public health officials a nuanced understanding of how the virus spreads and where it’ll likely be transmitted next.
Even though our collective national attention is squarely focused on the spread of Ebola, scientists have sought to use data to predict virus outbreaks for the better part of the past decade. In 2009, for example, researchers at the University of Florida reported that they had successfully traced an outbreak of malaria using cell phone data they collected. According to Science Daily, UF scientists were able to do this by analyzing data from more than 21 million calls made from the mobile phones of residents of Zanzibar.
From this information they were then able to identify common travel patterns among the country’s population, giving them insight into where the country was still vulnerable to malaria, a pathogen widely believed to be responsible for an astounding 50% of all deaths in human history. Drawing on their work, government and public health officials started moving to restrict travel to a specific area of Tanzania where malaria remained an endemic health problem. Thanks to research initiatives like this, Zanzibar was able to reduce infections from 40% of its population to less than 1%, bringing it closer to its goal of outright eradication.
Though still in its relative infancy as a research area, this data-based approach to identifying and charting the spread of disease could one day enable researchers to stop an outbreak from spreading across oceans and even local borders. Recent breakthroughs in data science could also spur more entrepreneurs and existing businesses to set their sights on the emergent and potentially revolutionary field, a shift that could have an outsize impact on the future of public health.