Road traffic accidents (RTAs) are a major public health concern, resulting in an estimated 1.2 million deaths and 50 million injuries worldwide each year. In the developing world, RTAs are among the leading cause of death and injury; Ethiopia in particular experiences the highest rate of such accidents. Thus, methods to reduce accident severity are of great interest to traffic agencies and the public at large. In this work, we applied data mining technologies to link recorded road characteristics to accident severity in Ethiopia, and developed a set of rules that could be used by the Ethiopian Traffic Agency to improve safety.

Data mining and machine learning technologies for business applications have evolved over the past two decades, and are regularly applied in contemporary organizations to everything from manufacturing to online advertising in fields ranging from health care to motor racing. Unfortunately, data mining techniques are not applied as often to problems in the developing world. Despite the fact that some industries, such as banks, airlines, courts, and telecommunications firms, necessitate data storage as part of their business process. We argue that data mining could be used to reduce infrastructure inefficiencies, which is one of the largest problems faced by Africa. We demonstrate that we can potentially reduce the infrastructure inefficiency of the Ethiopian telecommunications industry by ranking customers according to their likelihood of nonpayment using a data mining approach.

Cellular phones can be used as mobile sensors, continuously logging users’ behavior including movement, communication and proximity to others. While it is well understood that data generated from mobile phones includes a record of phone calls, there are also more sophisticated data types, such as Bluetooth or cell tower proximity logging, which reveal movement patterns and day-to-day human interactions. We explore the possibility of using mobile phone data to compare movement and communication patterns across cultures. The goal of this proof-of-concept study is to quantify behavior in order to compare different populations. We compare our ability to predict future calling behavior and movement patterns from the cellular phone data of subjects in two distinct groups: a set of university students at MIT in the United States and the University of Nairobi in Kenya. In addition, we show how Bluetooth data may be used to estimate the diffusion of an airborne pathogen outbreak in the different populations.