The Ebola outbreak began in December of 2013 in a small village near the border between Guinea and Liberia. From that remote epicenter emerged a pattern of infection that blazed across parts of six countries in West Africa, sparking a number of isolated cases in Europe and the United States. Now, as the tide turns against the virus, with reported cases slowing dramatically, it’s worth reflecting on what made a difference in the response and what will most impact the long-term recovery and rebuilding of West Africa’s health systems.
Among many factors, Direct Relief’s inclusion among Fast Company’s 10 most innovative not-for-profit companies suggests that part of the answer may well be “better maps.”
Direct Relief began mapping the spread of Ebola cases in April, 2014 as it became apparent that the case rates were increasing faster than in past outbreaks. Using open data compiled from World Health Organization and Centers for Disease Control and Prevention (CDC) sources, as well as reports from partners like HealthMap.org, we started to see the virus moving into areas where long-standing local partners were operating health programs. This information helped inform where early supply shipments could be targeted. Then, as the case counts mounted over the summer and dramatically larger response efforts were assembled in the early fall, those same maps became living records of where epidemiology met humanitarian response. Questions like, “Where are the hotspots?” and “Where is the virus moving?” could be paired with questions like, “Where are the supply shortages the worst?” “Where are health facilities still functioning?” and “Where has aid that we’ve sent been received?”
What Difference Does a Map Make?
Mapping, in other words, connects different pieces of a complex landscape in such a way that each piece informs and deepens the understanding of the others. Without knowing where case counts are likely to increase, and where those increasing case counts might be the result of things like the absence of sufficient medical supplies, it’s difficult to fill the most significant gaps in supply. Without the ability to demonstrate where aid has gone and what good it did, it’s difficult to assure donors and other key stakeholders of the efficacy of their donations. And without close attention to changing dynamics and needs at the community level, it’s difficult to strategize how to improve health systems moving forward. Fortunately, Direct Relief and its partner organizations are well placed to use geography and data analysis to improve the quality of response activities.
An illustrative example of such a partnership involves Direct Relief’s collaboration with the Liberian community health worker (CHW) organization Last Mile Health. The model which makes Last Mile Health effective involves tracking a range of health conditions, including possible Ebola exposure, at the household level. This allows teams of community health workers to target specific interventions and analyze the distribution of conditions and needs.
Direct Relief delivered a number of GPS units to Last Mile Health. The units helped facilitate real-time messaging from remote areas, which allowed some of the first maps of Last Mile’s catchment areas to be built. As Last Mile’s coverage area expanded with the Ebola epidemic, Direct Relief and Last Mile connected with teams from Humanitarian OpenStreetMap. Together, the organizations worked to establish the basis for a dynamic local-area epidemiology by mapping every individual household structure tied to CHW outreach efforts. As a result, these maps will provide crucial information infrastructure for any future health system in Liberia.
The Ebola outbreak reveals, once again, that better maps provide better insights into how to provide better care and more resilient systems for the people who need it most. That’s an insight that needs to carry over more frequently into the everyday work of building better health systems everywhere around the world so that events like what has transpired over the past year in West Africa is less likely to happen again.
