The Causal Structure of Food Shortage in Uganda


How soon in advance can we predict a food shortage? Variables such as market prices, drought, migrations, previous regional production, and seasonal variations all play a role in this classification and causal structure learning model to predict whether a rural inhabitant is likely to encounter difficulty in obtaining food. - G. Okori

Catastrophe Modeling for Rwandan Disease Surveillance

Can mobile phones be used as an early warning system for disease outbreaks? Bayesian anomaly detection algorithms may be able to quantify behavioral signatures associated with cholera outbreaks in Rwanda. If successful, these alogithms could lead to the deployment of next generation of disease surveillance systems in some of the world's regions that need it the most. - A. Kapoor, N. Eagle, E. Horvitz

Spatiotemporal Diffusion of Contraceptive Norms in the D.R.

How do contraceptive norms spread through rural areas of the developing world? Spatiotemporal diffusion models have the potential to better evaluate the efficacy of HIV prevention techniques and inform policy decisions related to public health. - H. Yoshioka, N. Eagle

A Causal Model for Quality of Schooling

A key challenge for policymakers in many developing countries is to decide which intervention or collection of interventions works best to improve learning outcomes in their schools. Our aim is to develop a causal model that explains student learning outcomes in terms of observable characteristics as well as conditions and processes difficult to observe directly. - N. McGinn, M. Moussavi

Mobility Models of Malaria in East Africa

How do human mobility patterns affect the spread of malaria? Aggregating longitudinal movement data from 15M mobile phones in East Africa, it may be possible to gain a better understanding of the implications of human movement on the spread of disease. - N. Ferguson, D. Hollingsworth, N. Eagle

Generative Models of the Nairobi Slums

Over one billion people - or nearly one in every three urban residents - live in informal settlements and slums. Coupling mobile phone data with mathematical models and statistical inference, we hope to better understand the dynamics of these establishments and ultimately develop predictive models to better serve this underrepresented population. - A. Wesolowski, N. Eagle

Computational Transport Planning and Modeling in Kigali

Kigali's cities planners are inundated with data about how urban infrastructure in Rwanda's capital is being utilized. Generative models are needed to better inform decisions ranging from broad transport planning questions to the minutia such as the optimal placement of the next public latrines. - A. Vaccari, N. Eagle

Modeling the Dynamics of Urbanization on Social Support Networks

What is attracting migrants to urban areas within the developing world? Using 4 years of movement and communication data, it is possible to model the reinforcing social mechanisms that could explain their recent rapid growth. - L. Bettencourt, Y. de Montjoye, N. Eagle

Expectation-Maximization for Mobile Crowdsourcing

There are over one billion mobile phone subscribers who live on less than 5 dollars a day. Using techniques such as Expectation-Maximization, we are developing a system that enables people to earn small amounts of money by completing simple tasks on their phones. - N. Eagle, B. Olding

Is Crime a Contagion?

Can we quantify a crime wave? Is crime contagious? Given the time, place, and nature of a crime, we are attempting to infer casual relationships between crimes and locations across a city. - J. Toole, J. Plotkin, N. Eagle

Quantifying the Stability of Society

Is there such a thing as a 'poverty trap'? Logistic classifiers applied on communication and census data point to a new mechanism for poverty that relates to the persistence of relationships. This analysis shows that economic exchanges flow primarily through these persistent edges and the inability to maintain these ties can prevent upward economic mobility. - Y. de Montjoye, A. Clauset, N. Eagle

Economic Shocks in Rwanda

Do people react to economic shocks in a similar manner? Time-series analysis of anonymized mobile phone records coupled with random surveys, will hopefully lead to better insight about the dynamics of rural economies. - J. Blumenstock, N. Eagle

Communication as a Lens into Poverty

How do communication patterns reflect poverty? We find the principal components of a wide range of diversity metrics, including Shannon entropy, explain over two-thirds the variance of regional socioeconomic status. - N. Eagle, M. Macy, R. Claxton

Identifying Need and Risk

Can mobile phones identify high-risk behavior? A group of 10 male sex-workers in coastal Kenya where provided with mobile phones that logged communication, proximity and movement behavior. When coupled with self-report surveys, we are attempting to develop a system that can infer the onset of high-risk behavior and deliver salient information in real-time. - E. Sanders, N. Eagle