Predictive health intelligence: Potential, limitations and sense making

We discuss the new paradigm of predictive health intelligence, based on the use of modern deep learning algorithms and big biomedical data, along the various dimensions of: a) its potential, b) the limitations it encounters, and c) the sense it makes. We conclude by reasoning on the idea that viewing data as the unique source of sanitary knowledge, fully abstracting from human medical reasoning, may affect the scientific credibility of health predictions

Excess mortality and COVID-19 deaths in Italy: A peak comparison study

During a sanitary crisis, excess mortality measures the number of all-cause deaths, beyond what we would have expected if that crisis had not occurred. The high number of COVID-19 deaths started a debate in Italy with two opposite positions: those convinced that COVID-19 deaths were not by default excess deaths, because many COVID-19 deaths were not correctly registered, with most being attributable to other causes and to the overall crisis conditions; and those who presented the opposite hypothesis. We analyzed the curve of the all-cause excess mortality, during the period of January 5, 2020–October 31, 2022, compared to the curve of the daily confirmed COVID-19 deaths, investigating the association between excess mortality and the recurrence of COVID-19 waves in Italy. We compared the two curves looking for the corresponding highest peaks, and we found that 5 out of the 6 highest peaks (83.3%) of the excess mortality curve have occurred, on average, just a week before the concomitant COVID-19 waves hit their highest peaks of daily deaths (Mean 6.4 days; SD 2.4 days). This temporal correspondence between the moments when the excess mortality peaked and the highest peaks of the COVID-19 deaths, provides further evidence in favor of a positive correlation between COVID-19 deaths and all-cause excess mortality

Fashion, Digital Technologies, and AI. Is the 2020 Pandemic Really Driving a Paradigm Shift?

Is the COVID-19 pandemic going to force the fashion industry to rethink herself and push it to embrace digital technologies more massively than before? The answer is most likely \u201cyes\u201d, but the question is somewhat ill-posed. In fact, the fashion world, especially haute couture, has always been very keen to innovation and to digital technology. Even before the current situation, there have been experiments that encompass every part of the fashion ecosystem, including smarter supply chain and manufacturing, design of new materials, new ways of presenting fashion with digitally augmented shows. While other businesses are hardly learning that digital is the way to go, the fashion world seems to have found this insight a long time ago and has been a fertile field for digital applications for a long time. For example, the commercial model has already shifted from being centered around retailers to being heavily reliant on online shopping. Not only this, but we are also seeing an increasing number of so-called digital native fashion brands, that is brands designed from the ground up to be entities of the digital world. This new way of selling fashion has been leveraging big data for some years now. Nonetheless, the abrupt change in our life dictated by the global advent of COVID-19, with the measures taken to mitigate it, like quarantine for example, is most certainly having an further effect on this industry, at all levels, from haute couture to fast fashion, from big brands to small ones. Some few examples include big fashion shows, where dazzling set pieces and parties are no longer possible, replaced by internet live streams. Even big fairs are now hosted as online events, with many brands launching digital applications that allow customers to try clothes virtually. All this considered, while it is certainly true that what happened in 2020 has had the primary effect of relegating retail stores almost to mere warehouses, with the catastrophic possibility they can even disappear in the foreseeable future, yet we believe that the correct question to ask is whether this phenomenon has just started now or has simply accelerated with the onset of the COVID-19 pandemic.In this paper, we favor this second hypothesis, and maintain that the current shift in the fashion industry practices and priorities follow a trend started may years ago, that the spread of the virus has only emphasized

The new york city covid‐19 spread in the 2020 spring: A study on the potential role of particulate using time series analysis and machine learning

This study investigates the potential association between the daily distribution of the PM2,5 air pollutant and the initial spreading of COVID‐19 in New York City. We study the period from 4 March to 22 March 2020, and apply our analysis to all five counties, including the city, plus seven neighboring counties, including both urban and peripheral districts. Using the Granger causality methodology, and considering the maximum lag period (14 days) between infection and the correspondent diagnosis, we found that the time series of the new daily infections registered in those 12 counties appear to correlate to the time series of the concentrations of the PM2.5 particulate circulating in the air, with 33 over 36 statistical tests with a p‐value less than 0.005, thus confirming such a hypothesis. Moreover, looking for further confirmation of this association, we train four different machine learning algorithms on a portion of those time series. These are able to predict that the number of the new daily infections would have surpassed a given infections threshold for the remaining portion of the series, with an average accuracy ranging from 84% to 95%, depending on the algorithm and/or on the specific county under observation. This is similar to other results obtained from several polluted urban areas, e.g., Wuhan, Xiaogan, and Huanggang in China, and Northern Italy. Our study provides further evidence that ambient air pollutants can be associated with a daily COVID‐19 infection incidence

Designing human-centric software artifacts with future users: a case study

The quality and quantity of participation supplied by human beings during the different phases of the design and development of a software artifact are central to studies in human-centered computing. With this paper, we have investigated on what kind of experienced people should be engaged to design a new computational artifact, when a participatory approach is adopted. We compared two approaches: the former including only future users (i.e., novices) in the design process, and the latter enlarging the community to expert users. We experimented with the design of a large software artifact, in use at the University of Bologna, engaging almost 1500 users. Statistical methodologies were employed to validate our findings. Our analysis has provided mounting evidence that expert users have contributed to the design of the artifact only by a small amount. Instead, most of the innovative initiatives have come from future users, thus surpassing some traditional limitations that tend to exclude future users from this kind of processes. We here challenge the traditional opinion that expert users provide typically a more reliable contribution in a participatory software design process, demonstrating instead that future users would be often better suited. Along this line of sense, this is the first paper, in the field of human-centric computing, that discusses the relevant question to offer to future users a larger design space, intended as a higher level of freedom given in a software design situation, demarcated by precise design constraints. In this sense, the outcome has been positiv

Questioning the seasonality of SARS-COV-2: a Fourier spectral analysis

OBJECTIVES: To investigate the hypothesis of a seasonal periodicity, driven by climate, in the contagion resurgence of COVID-19 in the period February 2020-December 2021. DESIGN: An observational study of 30 countries from different geographies and climates. For each country, a Fourier spectral analysis was performed with the series of the daily SARS-CoV-2 infections, looking for peaks in the frequency spectrum that could correspond to a recurrent cycle of a given length. SETTINGS: Public data of the daily SARS-CoV-2 infections from 30 different countries and five continents. PARTICIPANTS: Only publicly available data were utilised for this study, patients and/or the public were not involved in any phase of this study. RESULTS: All the 30 investigated countries have seen the recurrence of at least one COVID-19 wave, repeating over a period in the range 3-9 months, with a peak of magnitude at least half as large as that of the highest peak ever experienced since the beginning of the pandemic until December 2021. The distance in days between the two highest peaks in each country was computed and then averaged over the 30 countries, yielding a mean of 190 days (SD 100). This suggests that recurrent outbreaks may repeat with cycles of different lengths, without a precisely predictable seasonality of 1\u2009year. CONCLUSION: Our findings suggest that COVID-19 outbreaks are likely to occur worldwide, with cycles of repetition of variable lengths. The Fourier analysis of 30 different countries has not found evidence in favour of a seasonality that recurs over 1year period, solely or with a precisely fixed periodicity

A human–AI collaboration workflow for archaeological sites detection

This paper illustrates the results obtained by using pre-trained semantic segmentation deep learning models for the detection of archaeological sites within the Mesopotamian floodplains environment. The models were fine-tuned using openly available satellite imagery and vector shapes coming from a large corpus of annotations (i.e., surveyed sites). A randomized test showed that the best model reaches a detection accuracy in the neighborhood of 80%. Integrating domain expertise was crucial to define how to build the dataset and how to evaluate the predictions, since defining if a proposed mask counts as a prediction is very subjective. Furthermore, even an inaccurate prediction can be useful when put into context and interpreted by a trained archaeologist. Coming from these considerations we close the paper with a vision for a Human–AI collaboration workflow. Starting with an annotated dataset that is refined by the human expert we obtain a model whose predictions can either be combined to create a heatmap, to be overlaid on satellite and/or aerial imagery, or alternatively can be vectorized to make further analysis in a GIS software easier and automatic. In turn, the archaeologists can analyze the predictions, organize their onsite surveys, and refine the dataset with new, corrected, annotations

On the Comparison of Two Vehicular Safety Systems in Realistic Highway Scenarios

Abstract The application of wireless VANET technology to accident warning systems is gaining an increasing interest. These systems can significantly increase the safety of daily driving and are based on a technology that is steadily becoming mature. We present an experimental comparison between two effective approaches that cope with realistic scenarios. Both rapidly broadcast alert messages throughout platoons of vehicles, and are based on wireless vehicle-to-vehicle (V2V) communications. However, with one approach an alert message propagates through the farthest relay at each hop, whereas with the other it propagatesusing the farthest spanning relay (i.e., the relay that can retransmit farthest away an alert message). With this study we will see retransmitting through the farthest spanning relay at each hop can improve the performance by a factor of two in terms of propagation delay, in comparison to choosing the farthest relay

Using digital fountains in future IPTV streaming platforms: A future perspective

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