Ride-sharing on the daily home-work-home commute can help individuals save on gasoline and other car-related costs, while at the same time it can reduce traffic and pollution. This paper assesses the potential of ride-sharing for reducing traffic in a city, based on mobility data extracted from 3G Call Description Records (CDRs, for the cities of Barcelona and Madrid) and from Online Social Networks (Twitter, collected for the cities of New York and Los Angeles). We first analyze these data sets to understand mobility patterns, home and work locations, and social ties between users. We then develop an efficient algorithm for matching users with similar mobility patterns, considering a range of constraints. The solution provides an upper bound to the potential reduction of cars in a city that can be achieved by ride-sharing. We use our framework to understand the different constraints and city characteristics on this potential benefit. For example, our study shows that traffic in the city of Madrid can be reduced by 59% if users are willing to share a ride with people who live and work within 1 km; if they can only accept a pick-up and drop-off delay up to 10 minutes, this potential benefit drops to 24%; if drivers also pick up passengers along the way, this number increases to 53%. If users are willing to ride only with people they know ("friends" in the CDR and OSN data sets), the potential of ride-sharing becomes negligible; if they are willing to ride with friends of friends, the potential reduction is up to 31%.

Probabilistic forecasts play an indispensable role in answering questions about the spread of newly emerged pathogens. However, uncertainties about the epidemiology of emerging pathogens can make it difficult to choose among alternative model structures and assumptions. To assess the potential for uncertainties about emerging pathogens to affect forecasts of their spread, we evaluated the performance 16 forecasting models in the context of the 2015-2016 Zika epidemic in Colombia. Each model featured a different combination of assumptions about human mobility, spatiotemporal variation in transmission potential, and the number of virus introductions. We found that which model assumptions had the most ensemble weight changed through time. We additionally identified a trade-off whereby some individual models outperformed ensemble models early in the epidemic, but on average the ensembles outperformed all individual models. Our results suggest that multiple models spanning uncertainty across alternative assumptions are necessary to obtain robust forecasts for emerging infectious diseases.