WHO Uses Mobile Data to Track Ebola Spread in Democratic Republic of Congo

Health officials and the World Health Organization (WHO) are utilizing anonymized mobile phone data to map population movements during an Ebola outbreak in the eastern Democratic Republic of Congo. This data-driven approach allows responders to identify high-risk areas based on travel patterns rather than simple geography.

The current outbreak is caused by the Bundibugyo species of Ebola, a strain for which there are currently no approved vaccines or treatments. Since the outbreak began in mid-May, more than 6,250 cases have been confirmed across six provinces, resulting in 3,039 deaths. To combat the spread, the Swedish non-profit Flowminder is analyzing aggregated, anonymized records provided free of charge by Vodacom, a telecom operator that holds nearly 35% of the DRC market with over 26 million active subscriptions.

Shifting the Risk Model

Traditionally, health risk assessments assume a virus spreads to the immediate surrounding areas of an infection. However, the WHO has described this as the fastest-growing outbreak on record, driven by high population mobility linked to trade and mining in provinces such as Ituri. This mobility means the virus can leapfrog across distances to reach major transport hubs.

According to Olivier Le Polain, head of epidemiology and analytics for response at the WHO Health Emergencies Programme, population movements have always been a factor in outbreaks but are particularly critical in the current epidemic. By analyzing these patterns, officials were able to strengthen operations in hubs like Kisangani before cases were even detected. The effectiveness of this method was evidenced by initial tracking between April 3 and April 23; movements from Bunia, Mongbwalu, and Rwampara were analyzed, and all ten of the top destination areas subsequently reported confirmed cases.

Implications for Public Health

This represents a novel application of big data in an Ebola response, shifting the strategy from proximity-based models to mobility-based models. Linus Bengtsson, founder of Flowminder, stated that the goal is to provide estimates of how people move, which serves as a predictor for how infectious people move. By anticipating the virus's path, health officials can allocate limited surveillance and response resources more efficiently, potentially preventing the virus from reaching densely populated urban centers or crossing international borders.

Remaining Challenges

Despite the success of the initiative, the analysis faces significant technical limitations. Because the data relies solely on Vodacom, the model lacks a complete picture of the entire population's movements. Furthermore, the current system is unable to fully track international border crossings, leaving a critical gap in the ability to monitor the virus as it moves toward neighboring countries.