Big Data Research in Italy: A Perspective

The aim of this article is to synthetically describe the research projects that a selection of Italian universities is undertaking in the context of big data. Far from being exhaustive, this article has the objective of offering a sample of distinct applications that address the issue of managing huge amounts of data in Italy, collected in relation to diverse domains

Covid-19 Dynamic Monitoring and Real-Time Spatio-Temporal Forecasting

Background: Periodically, humanity is often faced with new and emerging viruses that can be a significant global threat. It has already been over a century post—the Spanish Flu pandemic, and we are witnessing a new type of coronavirus, the SARS-CoV-2, which is responsible for Covid-19. It emerged from the city of Wuhan (China) in December 2019, and within a few months, the virus propagated itself globally now resulting more than 50 million cases with over 1 million deaths. The high infection rates coupled with dynamic population movement demands for tools, especially within a Brazilian context, that will support health managers to develop policies for controlling and combating the new virus. / Methods: In this work, we propose a tool for real-time spatio-temporal analysis using a machine learning approach. The COVID-SGIS system brings together routinely collected health data on Covid-19 distributed across public health systems in Brazil, as well as taking to under consideration the geographic and time-dependent features of Covid-19 so as to make spatio-temporal predictions. The data are sub-divided by federative unit and municipality. In our case study, we made spatio-temporal predictions of the distribution of cases and deaths in Brazil and in each federative unit. Four regression methods were investigated: linear regression, support vector machines (polynomial kernels and RBF), multilayer perceptrons, and random forests. We use the percentage RMSE and the correlation coefficient as quality metrics. / Results: For qualitative evaluation, we made spatio-temporal predictions for the period from 25 to 27 May 2020. Considering qualitatively and quantitatively the case of the State of Pernambuco and Brazil as a whole, linear regression presented the best prediction results (thematic maps with good data distribution, correlation coefficient >0.99 and RMSE (%) <4% for Pernambuco and around 5% for Brazil) with low training time: [0.00; 0.04 ms], CI 95%. / Conclusion: Spatio-temporal analysis provided a broader assessment of those in the regions where the accumulated confirmed cases of Covid-19 were concentrated. It was possible to differentiate in the thematic maps the regions with the highest concentration of cases from the regions with low concentration and regions in the transition range. This approach is fundamental to support health managers and epidemiologists to elaborate policies and plans to control the Covid-19 pandemics

Climate Change and Highland Malaria: Fresh Air for a Hot Debate

In recent decades, malaria has become established in zones at the margin of its previous distribution, especially in the highlands of East Africa. Studies in this region have sparked a heated debate over the importance of climate change in the territorial expansion of malaria, where positions range from its neglect to the reification of correlations as causes. Here, we review studies supporting and rebutting the role of climatic change as a driving force for highland invasion by malaria. We assessed the conclusions from both sides of the argument and found that evidence for the role of climate in these dynamics is robust. However, we also argue that over-emphasizing the importance of climate is misleading for setting a research agenda, even one which attempts to understand climate change impacts on emerging malaria patterns. We review alternative drivers for the emergence of this disease and highlight the problems still calling for research if the multidimensional nature of malaria is to be adequately tackled. We also contextualize highland malaria as an ongoing evolutionary process. Finally, we present Schmalhausen's law, which explains the lack of resilience in stressed systems, as a biological principle that unifies the importance of climatic and other environmental factors in driving malaria patterns across different spatio-temporal scales

Leveraging Mobility Flows from Location Technology Platforms to Test Crime Pattern Theory in Large Cities

Crime has been previously explained by social characteristics of the residential population and, as stipulated by crime pat- tern theory, might also be linked to human movements of non- residential visitors. Yet a full empirical validation of the latter is lacking. The prime reason is that prior studies are limited to aggregated statistics of human visitors rather than mobility flows and, because of that, neglect the temporal dynamics of individual human movements. As a remedy, we provide the first work which studies the ability of granular human mo- bility in describing and predicting crime concentrations at an hourly scale. For this purpose, we propose the use of data from location technology platforms. This type of data allows us to trace individual transitions and, therefore, we succeed in distinguishing different mobility flows that (i) are incom- ing or outgoing from a neighborhood, (ii) remain within it, or (iii) refer to transitions where people only pass through the neighborhood. Our evaluation infers mobility flows by lever- aging an anonymized dataset from Foursquare that includes almost 14.8 million consecutive check-ins in three major U.S. cities. According to our empirical results, mobility flows are significantly and positively linked to crime. These findings advance our theoretical understanding, as they provide con- firmatory evidence for crime pattern theory. Furthermore, our novel use of digital location services data proves to be an effective tool for crime forecasting. It also offers unprece- dented granularity when studying the connection between hu- man mobility and crime

How do genetic relatedness and spatial proximity shape African swine fever infections in wild boar?

The importance of social and spatial structuring of wildlife populations for disease spread, though widely recognized, is still poorly understood in many host-pathogen systems. In particular, system-specific kin relationships among hosts can create contact heterogeneities and differential disease transmission rates. Here, we investigate how distance-dependent infection risk is influenced by genetic relatedness in a novel host-pathogen system: wild boar (Sus scrofa) and African swine fever (ASF).We hypothesized that infection risk would correlate positively with proximity and relatedness to ASF-infected individuals but expected those relationships to weaken with the distance between individuals due to decay in contact rates and genetic similarity.We genotyped 323 wild boar samples (243 ASF-negative and 80 ASF-positive) collected in north-eastern Poland in 2014–2016 and modelled the effects of geographic distance, genetic relatedness and ASF virus transmission mode (direct or carcass-based) on the probability of ASF infection. Infection risk was positively associated with spatial proximity and genetic relatedness to infected individuals with generally stronger effect of distance. In the high-contact zone (0–2 km), infection risk was shaped by the presence of infected individuals rather than by relatedness to them. In the medium-contact zone (2–5 km), infection risk decreased but was still associated with relatedness and paired infections were more frequent among relatives. At farther distances, infection risk further declined with relatedness and proximity to positive individuals, and was 60% lower among un-related individuals in the no-contact zone (33% in10–20 km) compared among relatives in the high-contact zone (93% in 0–2 km). Transmission mode influenced the relationship between proximity or relatedness and infection risk. Our results indicate that the presence of nearby infected individuals is most important for shaping ASF infection rates through carcass-based transmission, while relatedness plays an important role in shaping transmission rates between live animals

Regional and temporal characteristics of bovine tuberculosis of cattle in Great Britain

Bovine tuberculosis (TB) is a chronic disease in cattle that causes a serious food security challenge to the agricultural industry in terms of dairy and meat production. In GB, Scotland has had a risk based surveillance testing policy under which high risk herds are tested frequently, and in Sept 2009 was officially declared as TB free. Wales have had an annual or more frequent testing policy for all cattle herds since Jan 2010, while in England several herds are still tested every 4 years except some high TB prevalence areas where annual testing is applied. Time series analysis using publicly available data for total tests on herds, total cattle slaughtered, new herd incidents, and herds not TB free, were analysed globally for GB and locally for the constituent regions of Wales, Scotland, West, North, and East England. After detecting trends over time, underlying regional differences were compared with the testing policies in the region. Total cattle slaughtered are decreasing in Wales, Scotland and West England, but increasing in the North and East English regions. New herd incidents, i.e., disease incidence, are decreasing in Wales, Scotland, West English region, but increasing in North and East English regions. Herds not TB free, are increasing in West, North, and East English regions, while they are decreasing in Wales and Scotland. Total cattle slaughtered were positively correlated with total tests in the West, North, and East English regions, with high slopes of regression. There was no correlation between total cattle slaughtered and total tests on herds in Wales indicating that herds are tested frequent enough in order to detect all likely cases and so control TB. The main conclusion of the analysis conducted here is that more frequent testing is leading to lower TB infections in cattle both in terms of TB prevalence as well as TB incidence.Comment: (in press) Stochastic Environmental Research and Risk Assessment (2015