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2020
Conference article
Embargo
Estimating countries' peace index through the lens of the world news as monitored by GDELT
Voukelatou V., Pappalardo L., Miliou I., Gabrielli L., Giannotti F.
Peacefulness is a principal dimension of well-being, and its measurement has lately drawn the attention of researchers and policy-makers. During the last years, novel digital data streams have drastically changed research in this field. In the current study, we exploit information extracted from Global Data on Events, Location, and Tone (GDELT) digital news database, to capture peacefulness through the Global Peace Index (GPI). Applying machine learning techniques, we demonstrate that news media attention, sentiment, and social stability from GDELT can be used as proxies for measuring GPI at a monthly level. Additionally, through the variable importance analysis, we show that each country's socio-economic, political, and military profile emerges. This could bring added value to researchers interested in "Data Science for Social Good", to policy-makers, and peacekeeping organizations since they could monitor peacefulness almost real-time, and therefore facilitate timely and more efficient policy-making.Source: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), pp. 216–225, 06/10/2020, 09/10/2020
DOI: 10.1109/dsaa49011.2020.00034
Project(s): SoBigData-PlusPlus
Metrics:
Voukelatou V., Pappalardo L., Miliou I., Gabrielli L., Giannotti F.
Peacefulness is a principal dimension of well-being, and its measurement has lately drawn the attention of researchers and policy-makers. During the last years, novel digital data streams have drastically changed research in this field. In the current study, we exploit information extracted from Global Data on Events, Location, and Tone (GDELT) digital news database, to capture peacefulness through the Global Peace Index (GPI). Applying machine learning techniques, we demonstrate that news media attention, sentiment, and social stability from GDELT can be used as proxies for measuring GPI at a monthly level. Additionally, through the variable importance analysis, we show that each country's socio-economic, political, and military profile emerges. This could bring added value to researchers interested in "Data Science for Social Good", to policy-makers, and peacekeeping organizations since they could monitor peacefulness almost real-time, and therefore facilitate timely and more efficient policy-making.Source: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), pp. 216–225, 06/10/2020, 09/10/2020
DOI: 10.1109/dsaa49011.2020.00034
Project(s): SoBigData-PlusPlus
Metrics:
See at:
doi.org 
| ieeexplore.ieee.org | CNR ExploRA
2020
Report
Open Access
Predicting seasonal influenza using supermarket retail records
Miliou I., Xiong X., Rinzivillo S., Zhang Q., Rossetti G., Giannotti F., Pedreschi D., Vespignani A.
Increased availability of epidemiological data, novel digital data streams, and the rise of powerful machine learning approaches have generated a surge of research activity on real-time epidemic forecast systems. In this paper, we propose the use of a novel data source, namely retail market data to improve seasonal influenza forecasting. Specifically, we consider supermarket retail data as a proxy signal for influenza, through the identification of sentinel baskets, i.e., products bought together by a population of selected customers. We develop a nowcasting and forecasting framework that provides estimates for influenza incidence in Italy up to 4 weeks ahead. We make use of the Support Vector Regression (SVR) model to produce the predictions of seasonal flu incidence. Our predictions outperform both a baseline autoregressive model and a second baseline based on product purchases. The results show quantitatively the value of incorporating retail market data in forecasting models, acting as a proxy that can be used for the real-time analysis of epidemics.Source: ISTI Technical Reports 2020/009, 2020
DOI: 10.32079/isti-tr-2020/009
Project(s): SoBigData-PlusPlus
Metrics:
Miliou I., Xiong X., Rinzivillo S., Zhang Q., Rossetti G., Giannotti F., Pedreschi D., Vespignani A.
Increased availability of epidemiological data, novel digital data streams, and the rise of powerful machine learning approaches have generated a surge of research activity on real-time epidemic forecast systems. In this paper, we propose the use of a novel data source, namely retail market data to improve seasonal influenza forecasting. Specifically, we consider supermarket retail data as a proxy signal for influenza, through the identification of sentinel baskets, i.e., products bought together by a population of selected customers. We develop a nowcasting and forecasting framework that provides estimates for influenza incidence in Italy up to 4 weeks ahead. We make use of the Support Vector Regression (SVR) model to produce the predictions of seasonal flu incidence. Our predictions outperform both a baseline autoregressive model and a second baseline based on product purchases. The results show quantitatively the value of incorporating retail market data in forecasting models, acting as a proxy that can be used for the real-time analysis of epidemics.Source: ISTI Technical Reports 2020/009, 2020
DOI: 10.32079/isti-tr-2020/009
Project(s): SoBigData-PlusPlus
Metrics:
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ISTI Repository 
| CNR ExploRA
2021
Journal article
Open Access
Predicting seasonal influenza using supermarket retail records
Miliou I., Xiong X., Rinzivillo S., Zhang Q., Rossetti G., Giannotti F., Pedreschi D., Vespignani A.
Increased availability of epidemiological data, novel digital data streams, and the rise of powerful machine learning approaches have generated a surge of research activity on realtime epidemic forecast systems. In this paper, we propose the use of a novel data source, namely retail market data to improve seasonal influenza forecasting. Specifically, we consider supermarket retail data as a proxy signal for influenza, through the identification of sentinel baskets, i.e., products bought together by a population of selected customers. We develop a nowcasting and forecasting framework that provides estimates for influenza incidence in Italy up to 4 weeks ahead. We make use of the Support Vector Regression (SVR) model to produce the predictions of seasonal flu incidence. Our predictions outperform both a baseline autoregressive model and a second baseline based on product purchases. The results show quantitatively the value of incorporating retail market data in forecasting models, acting as a proxy that can be used for the real-time analysis of epidemics.Source: PLoS computational biology 17 (2021). doi:10.1371/journal.pcbi.1009087
DOI: 10.1371/journal.pcbi.1009087
Metrics:
Miliou I., Xiong X., Rinzivillo S., Zhang Q., Rossetti G., Giannotti F., Pedreschi D., Vespignani A.
Increased availability of epidemiological data, novel digital data streams, and the rise of powerful machine learning approaches have generated a surge of research activity on realtime epidemic forecast systems. In this paper, we propose the use of a novel data source, namely retail market data to improve seasonal influenza forecasting. Specifically, we consider supermarket retail data as a proxy signal for influenza, through the identification of sentinel baskets, i.e., products bought together by a population of selected customers. We develop a nowcasting and forecasting framework that provides estimates for influenza incidence in Italy up to 4 weeks ahead. We make use of the Support Vector Regression (SVR) model to produce the predictions of seasonal flu incidence. Our predictions outperform both a baseline autoregressive model and a second baseline based on product purchases. The results show quantitatively the value of incorporating retail market data in forecasting models, acting as a proxy that can be used for the real-time analysis of epidemics.Source: PLoS computational biology 17 (2021). doi:10.1371/journal.pcbi.1009087
DOI: 10.1371/journal.pcbi.1009087
Metrics:
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journals.plos.org | ISTI Repository | CNR ExploRA