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When new pathogens emerge, numerous questions arise about their future spread, some of which can be addressed with probabilistic forecasts. The many uncertainties about the epidemiology of emerging pathogens can make it difficult to choose among model structures and assumptions, however. To assess the potential for uncertainties about emerging pathogens to affect forecasts of their spread, we evaluated the performance of a suite of 16 forecasting models in the context of the 2015-2016 Zika epidemic in Colombia. Each model featured a different combination of assumptions about the role of human mobility in driving transmission, spatiotemporal variation in transmission potential, and the number of times the virus was introduced. All models used the same core transmission model and the same iterative data assimilation algorithm to generate forecasts. By assessing forecast performance through time using logarithmic scoring with ensemble weighting, we found that which model assumptions had the most ensemble weight changed through time. In particular, spatially coupled models had higher ensemble weights in the early and late phases of the epidemic, whereas non-spatial models had higher ensemble weights at the peak of the epidemic. We compared forecast performance of the equally-weighted ensemble model to each individual model and identified a trade-off whereby certain individual models outperformed the ensemble model early in the epidemic but the ensemble model outperformed all individual models on average. On balance, our results suggest that suites of models that span uncertainty across alternative assumptions are necessary to obtain robust forecasts in the context of emerging infectious diseases.

Crop type mapping represents one of the most challenging problems in remote sensing. Spatial, spectral, and temporal information are required in order to obtain a unambiguous distinction among the types of crop. This paper presents a multi-sensor approach, where labelled high-resolution images from drones, limited to small areas, are used to enhance the classification ability of machine learning models based on Sentinel 2 time series. The project described in this paper is organized into three major activities. The first part focused on the exploitation of RGB drone images by using transfer learning and convolutional networks, and it has already been described in a previous work by the team. The second part deals with preliminary analysis of multi-spectral Sentinel 2 time-series using the labelled data from the drones campaign and trees-based machine learning algorithms. Finally, the third ongoing part deals with the combination of drones and satellite data in order to show how drones data can help the Sentinel 2 classification by reducing the effort needed to collect reference crop type information.

Information and Communication Technologies offer new possibilities for teachers to enhance their teaching methods. The increasing use of personal computers, tablets, interactive whiteboards, or even multitouch tabletops in the classrooms seems to attract the interest of the students. However, there are not many tools that allow teachers to create multimedia activities for all these technologies in an effortless way. Most of current authoring tools either focus on creating content for only one device or they do not fully exploit the benefits of rich content for designing engaging educational activities. In this paper, we present an authoring toolkit composed by two applications: DEDOS-Editor, which allow teachers to design their own learning activities, and DEDOS-Web, which allows the students to perform those activities adapting them to multiple devices. To test both tools, we have performed two evaluations. One with teachers to test the authoring tools and a second one with primary school students to test if the activities designed with this tool enhance their learning process. Results show that DEDOS-Editor is an easy to learn authoring tool which helps teachers to complement their learning methods while DEDOS-Web is flexible enough to create several learning scenarios from just one set of activities, factors which lead to achieving positive learning outcomes.

Mapping the distribution of poverty in developing countries is essential for humanitarian organizations and policymakers to formulate targeted programs and aid. However, traditional methods for obtaining socioeconomic data can be time-consuming, expensive, and labor-intensive. Recent studies have demonstrated the effectiveness of combining machine learning and satellite images to estimate wealth in sub-Saharan African countries (Xie et al., 2016, Jean et al., 2016). In this study, we investigate the extent to which this method can be applied in the context of the Philippine archipelago to predict four different socioeconomic indicators: wealth level, years of education, access to electricity, and access to water. We also propose an alternative, cost-effective approach that leverages a combination of volunteered geographic information from OpenStreetMap and nighttime lights satellite imagery for estimating socioeconomic indicators. The best models, which incorporate regional indicators as predictors, explain approximately 63% of the variation in asset-based wealth. Our findings also indicate that models trained on publicly available, volunteer-curated geographic data achieve the same predictive performance as that of models trained using proprietary satellite images.

Today, 95% of the global population has 2G mobile phone coverage and the number of individuals who own a mobile phone is at an all time high. Mobile phones generate rich data on billions of people across different societal contexts and have in the last decade helped redefine how we do research and build tools to understand society. As such, mobile phone data has the potential to revolutionize how we tackle humanitarian problems, such as the many suffered by refugees all over the world. While promising, mobile phone data and the new computational approaches bring both opportunities and challenges. Mobile phone traces contain detailed information regarding people's whereabouts, social life, and even financial standing. Therefore, developing and adopting strategies that open data up to the wider humanitarian and international development community for analysis and research while simultaneously protecting the privacy of individuals is of paramount importance. Here we outline the challenging situation of children on the move and actions UNICEF is pushing in helping displaced children and youth globally, and discuss opportunities where mobile phone data can be used. We identify three key challenges: data access, data and algorithmic bias, and operationalization of research, which need to be addressed if mobile phone data is to be successfully applied in humanitarian contexts.