Temporal analysis of cooperative behaviour in a blockchain for humanitarian aid during the COVID-19 pandemic

In the last few years, there has been a growing interest in the subject of blockchain technology for good. Among the many endeavours, blockchain technology has lately been exploited to build complementary currencies in the sphere of humanitarian aid: currencies that support national economies to provide humanitarian aid and promote development. While there have been numerous research projects on complementary currencies (CCs) and their success, some critical aspects remain largely unexplored. First, even though cooperation is a key factor in the development of these systems, as local communities organize themselves in times of crisis, there is a lack of studies that investigate the cooperative behaviour in these systems and how it changes over time. Besides, there are only a few works studying these currencies during the recent crisis of the COVID-19 pandemic. In this work, we investigate Sarafu, a digital complementary currency based on blockchain technology. To support cooperation, Sarafu implements a special type of account, the group account, thus allowing the study of cooperation groups, that cannot be easily analyzed in other CC systems;furthermore, it was successfully used for humanitarian aid during the COVID-19 pandemic. We find that Sarafu users show strong cooperative behaviour, facilitated by the usage of these group accounts. Furthermore, we observe the increasing importance of cooperation groups over time, as well as differences over time in their spending behaviour. From the analysis, we highlight the presence of cooperation patterns and the importance of group accounts, a takeaway for current and future humanitarian projects. © 2022 ACM.

Epidemic oscillations induced by social network control

Epidemic spreading can be suppressed by the introduction of containment measures such as social distancing and lockdowns. Yet, when such measures are relaxed, new epidemic waves and infection cycles may occur. Here we explore this issue in compartmentalized epidemic models on graphs in presence of a feedback between the infection state of the population and the structure of its social network for the case of discontinuous control. We show that in random graphs the effect of containment measures is simply captured by a renormalization of the effective infection rate that accounts for the change in the branching ratio of the network. In our simple setting, a piece-wise mean-field approximation can be used to derive analytical formulae for the number of epidemic waves and their length. A variant of the model with imperfect information is used to model data of the recent COVID-19 epidemics in the Basque Country and Lombardy, where we estimate the extent of social network disruption during lockdowns and characterize the dynamical trajectories in the phase space.

Predicting the spread of COVID-19 in Italy using machine learning: Do socio-economic factors matter?

We exploit the provincial variability of COVID-19 cases registered in Italy to select the territorial predictors of the pandemic. Absent an established theoretical diffusion model, we apply machine learning to isolate, among 77 potential predictors, those that minimize the out-of-sample prediction error. We first estimate the model considering cumulative cases registered before the containment measures displayed their effects (i.e. at the peak of the epidemic in March 2020), then cases registered between the peak date and when containment measures were relaxed in early June. In the first estimate, the results highlight the dominance of factors related to the intensity and interactions of economic activities. In the second, the relevance of these variables is highly reduced, suggesting mitigation of the pandemic following the lockdown of the economy. Finally, by considering cases at onset of the "second wave", we confirm that the territorial distribution of the epidemic is associated with economic factors.