A simplified method to compute $$R_t$$ R t , the effective reproduction number, is presented. The method relates the value of $$R_t$$ R t to the estimation of the doubling time performed with a local exponential fit. The condition $$R_t=1$$ R t = 1 corresponds to a growth rate equal to zero or equivalently an infinite doubling time. Different assumptions on the probability distribution of the generation time are considered. A simple analytical solution is presented in case the generation time follows a gamma distribution.

In a recent work, we introduced a novel method to compute the effective reproduction number and we applied it to describe the development of the COVID-19 outbreak in Italy. The study is based on the number of daily positive swabs as reported by the Italian Dipartimento di Protezione Civile. Recently, the Italian Istituto Superiore di Sanità made available the data relative of the symptomatic cases, where the reporting date is the date of beginning of symptoms instead of the date of the reporting of the positive swab. In this paper, we will discuss merits and drawbacks of this data, quantitatively comparing the quality of the pandemic indicators computed with the two samples.

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In the last few decades, we have witnessed an increasing focus on safety in the workplace. ICT has always played a leading role in this context. One ICT sector that is increasingly important in ensuring safety at work is the Internet of Things and, in particular, the new architectures referring to it, such as SIoT, MIoT and Sentient Multimedia Systems. All these architectures handle huge amounts of data to extract predictive and prescriptive information. For this purpose, they often make use of Machine Learning. In this paper, we propose a framework that uses both Sentient Multimedia Systems and Machine Learning to support safety in the workplace. After the general presentation of the framework, we describe its specialization to a particular case, i.e., fall detection. As for this application scenario, we describe a Machine Learning based wearable device for fall detection that we designed, built and tested. Moreover, we illustrate a safety coordination platform for monitoring the work environment, activating alarms in case of falls, and sending appropriate advices to help workers involved in falls.

Since November 6th, 2020, Italian regions have been classified according to four levels, corresponding to specific risk scenarios, for which specific restrictive measures have been foreseen. By analyzing the time evolution of the reproduction number R t , we estimate how much different restrictive measures affect R t , and we quantify the combined effect of the diffusion of virus variants and the beginning of the vaccination campaign upon the R t trend. We also compute the time delay between implementation of restrictive measures and the resulting effects. Three different models to describe the effects of restrictive measures are discussed and the results are cross-checked with two different algorithms for the computation of R t .

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Since November 6 th , 2020, Italian regions have been classified according to four levels, corresponding to specific risk scenarios, for which specific restrictive measures have been foreseen. By analyzing the time evolution of the reproduction number Rt, we estimate how much different restrictive measures affect Rt, and we quantify the combined effect of the diffusion of virus variants and the beginning of the vaccination campaign upon the Rt trend. We also compute the time delay between implementation of restrictive measures and the resulting effects. Three different models to describe the effects of restrictive measures are discussed and the results are cross-checked with two different algorithms for the computation of Rt.

One of the key aspects that distinguish TikTok from other social media is the presence of challenges. A challenge is a kind of competition that starts when a user posts a video with certain actions and a certain hashtag and invites other users to replicate the same video in their own way. Most challenges are fun and harmless, but sometimes dangerous challenges are launched as well. The authors of these challenges use various tricks to bypass TikTok’s controls. In this paper, we analyze the lifespans of some TikTok challenges and show how they are very different for non-dangerous and dangerous ones. Then, we deepen our analysis by identifying some time patterns that characterize the two types of challenges. Finally, we test the accuracy of the results obtained on a large set of challenges different from those used during the detection of time patterns. The focus of this paper is the detection of time patterns allowing the classification of challenges in dangerous and non-dangerous ones. This could represent a first step towards an approach for the early detection of dangerous challenges in TikTok.