Cooperative behavior in blockchain-based complementary currency networks through time: The Sarafu case study

The effort to reach the 17 Sustainable Development Goals by the United Nations has incentivized the adoption of IT solutions in many fields. Many systems for sustainable economic development are now relying on a digital form making them more accessible and provides the access to new functionalities. A very interesting example of such systems are complementary currencies i.e. cooperative currency systems that support national economies to provide humanitarian aid and promote sustainable development. While there are many studies on the principles and case studies of successful complementary currencies, many aspects are still unexplored, especially regarding cooperative behavior. Cooperative behavior in these systems is a key aspect, as complementary currencies are often born out of cooperation among members that face a period of crisis or they usually have the objective of creating bonds of reciprocity and integrating social networks between people, which should lead to increased cooperation. However, there is a lack of studies on many aspects of cooperative behavior in complementary currencies, such as how such behavior changes over time, especially in times of a crisis like the COVID-19 pandemic. Moreover how cooperation behavior is affected by time and different geographical locations is still unclear. In this work, we analyze Sarafu, a complementary currency that went digital and now relies on blockchain technology. Sarafu is a successful case of a complementary currency that was used for humanitarian aid during the COVID-19 pandemic. Moreover, Sarafu is a perfect case study for the study of cooperative behavior, as it implements a special type of account, the group account, to support cooperation groups. This feature supports the study of group dynamics and behavior. What we find is that Sarafu users exhibit strong reliance on cooperation groups;we also observe that the interaction of users and cooperation groups is influenced by both time and geographical location. The study of group accounts and in general mechanisms that promote cooperation can be useful for other humanitarian or community development projects. Moreover, similar cooperation enhancers could have an important role in other social development projects, and in general, in any setting where there is a strong need to foster cooperation for reaching social good.

CoViD-19, learning from the past: A wavelet and cross-correlation analysis of the epidemic dynamics looking to emergency calls and Twitter trends in Italian Lombardy region.

The first case of Coronavirus Disease 2019 in Italy was detected on February the 20th in Lombardy region. Since that date, Lombardy has been the most affected Italian region by the epidemic, and its healthcare system underwent a severe overload during the outbreak. From a public health point of view, therefore, it is fundamental to provide healthcare services with tools that can reveal possible new health system stress periods with a certain time anticipation, which is the main aim of the present study. Moreover, the sequence of law decrees to face the epidemic and the large amount of news generated in the population feelings of anxiety and suspicion. Considering this whole complex context, it is easily understandable how people "overcrowded" social media with messages dealing with the pandemic, and emergency numbers were overwhelmed by the calls. Thus, in order to find potential predictors of possible new health system overloads, we analysed data both from Twitter and emergency services comparing them to the daily infected time series at a regional level. Particularly, we performed a wavelet analysis in the time-frequency plane, to finely discriminate over time the anticipation capability of the considered potential predictors. In addition, a cross-correlation analysis has been performed to find a synthetic indicator of the time delay between the predictor and the infected time series. Our results show that Twitter data are more related to social and political dynamics, while the emergency calls trends can be further evaluated as a powerful tool to potentially forecast new stress periods. Since we analysed aggregated regional data, and taking into account also the huge geographical heterogeneity of the epidemic spread, a future perspective would be to conduct the same analysis on a more local basis.

Monitoring emergency calls and social networks for COVID-19 surveillance. To learn for the future: The outbreak experience of the Lombardia region in Italy.

On 18th February the first Italian case of Coronavirus Induced Disease 2019 (COVID19) due to secondary transmission outside China was identified in Codogno, Lombardia region. In the following days the number of cases started to rise not only in Lombardia but also in other Italian regions, although Lombardia remained and it is still the most affected region in Italy. At the moment, 234801 cases have been identified in Italy, out of which 90070 in Lombardia region. The (Severe Acute Respiratory Syndrome Coronavirus 2) SARS CoV 2 outbreak in Italy has been characterized by a massive spread of news coming from both official and unofficial sources leading what has been defined as infodemia, an over-abundance of information - some accurate and some not - that has made hard for people to find trustworthy sources and reliable guidance needed. Infodemia on SARS CoV 2 created the perfect field to build uncertainty in the population, which was scared and not prepared to face this outbreak. It is understandable how the rapid increase of the cases' number , the massive spread of news and the adoption of laws to face this outbreak led to a feeling of anxiety in the population whose everyday life changed very quickly. A way to assess the dynamic burden of social anxiety is a context analysis of major social networks activities over the Internet. To this aim Twitter represents a possible ideal tool since the focused role of the tweets according to the more urgent needs of information and communication rather than general aspects of social projection and debate as in the case of Facebook, which could provide slower responses for the fast individual and social context evolution dynamics.  Aim of the paper is to analyse the most common reasons for calling and outcomes. Furthermore, the joint analysis with Twitter trends related to emergency services might be useful to understand possible correlations with epidemic trends and predict new outbreaks.