Towards a data-driven characterization of behavioral changes induced by the seasonal flu

In this work, we aim to determine the main factors driving self-initiated behavioral changes during the seasonal flu. To this end, we designed and deployed a questionnaire via Influweb, a Web platform for participatory surveillance in Italy, during the 2017 − 18 and 2018 − 19 seasons. We collected 599 surveys completed by 434 users. The data provide socio-demographic information, level of concerns about the flu, past experience with illnesses, and the type of behavioral changes voluntarily implemented by each participant. We describe each response with a set of features and divide them in three target categories. These describe those that report i) no (26%), ii) only moderately (36%), iii) significant (38%) changes in behaviors. In these settings, we adopt machine learning algorithms to investigate the extent to which target variables can be predicted by looking only at the set of features. Notably, 66% of the samples in the category describing more significant changes in behaviors are correctly classified through Gradient Boosted Trees. Furthermore, we investigate the importance of each feature in the classification task and uncover complex relationships between individuals’ characteristics and their attitude towards behavioral change. We find that intensity, recency of past illnesses, perceived susceptibility to and perceived severity of an infection are the most significant features in the classification task and are associated to significant changes in behaviors. Overall, the research contributes to the small set of empirical studies devoted to the data-driven characterization of behavioral changes induced by infectious disease

Biochemical, computer, and spectroscopic techniques applied to the study of prions and of combinations of antineoplastic drugs

This thesis collects the work I have done during the three-year PhD Course. The results obtained are divided according to the research topics addressed: · Drug discovery of anticancer agents and development of synergistic associations (Part I); · Studies on the prion structure and the pathogenesis of prion diseases (Part II). Studies referring to the Part I have been carried out at the University of Cagliari and were focalized on the evaluation and experimental validation of the method known as Artificial Neural Network (ANN), which allows to determine, and also to predict, types and degree of interaction of two or more drugs in combination with one another(s). I have successfully applied the ANN approach to combinations of two cytotoxic compounds, i.e. cis-platinum (CDDP), a potent antineoplastic used in the therapy of some types of carcinoma, with Cu(II) complexes that were previously shown to be endowed with potent cytotoxic activity in vitro (Pivetta et al., J Inorg Biochem. 2011 and 2012). Binary combinations comprising CDDP and Cu(II) complexes revealed (Part I, Chapter I) a strong synergistic cytotoxic effect against leukemia cell lines (Pivetta et al., Talanta 2013). The synergistic effect was confirmed in CDDP-resistant leukemia and ovarian cancer cell lines (Part I, Chapter II). Investigations on molecular bases of the CDDP – Cu(II) complexes synergism are in progress. As for the Part II of my research, I firstly investigated the mechanisms underlying the process of generation of pathogenic prion protein (PrPSc). These studies were carried out in part at the University of Cagliari, and in part at the Rocky Mountains Laboratories (RML) of the NIAID/NIH in USA (Hamilton, Montana), during my 14-month research internship as supplemental visiting fellow in a graduate partnership program. The studies carried out in Cagliari have investigated 1H NMR modifications of brain metabolite profiles in sheep from a Sardinian farm hit by natural Scrapie with the aim to discriminate infected vs. Uninfected, and early vs. late phases of the prion infection (Part II, Chapter I). The overall results, obtained by different chemometric tools, were able to describe a metabolite profile of scrapie-infected sheep brain, with and without clinical sign, different to that of healthy ones, and to suggest Ala as a biomarker of PrPSc deposition (Scano et al., J Molecular Biosystem, 2015) Studies conducted at the RML of NIAID/NIH have addressed the structural features of PrPSc in order to identify regions of the protein involved in the process of misfolding (Part II, Chapter II). So far, data suggest that i) the N-terminal portion of the prion protein may have a role in the modulation of the protein aggregation process, typical of prion diseases, while ii) the central portion seems capable to undergo aggregation into fibrils even in the absence of other regions of the protein. However, further studies are needed to confirm the proposed role in promoting/hindering the misfolding and subsequent aggregation process of the various protein regions

Forecasting seasonal influenza fusing digital indicators and a mechanistic disease model

The availability of novel digital data streams that can be used as proxy for monitoring infectious disease incidence is ushering in a new era for real-time forecast approaches to disease spreading. Here, we propose the first seasonal influenza forecast framework based on a stochastic, spatially structured mechanistic model (individual level microsimulation) initialized with geo-localized microblogging data. The framework provides for more than 600 census areas in the United States, Italy and Spain, the initial conditions for a stochastic epidemic computational model that generates an ensemble of forecasts for the main indicators of the epidemic season: peak time and intensity. We evaluate the forecasts accuracy and reliability by comparing the results from our framework with the data from the official influenza surveillance systems in the US, Italy and Spain in the seasons 2014/15 and 2015/16. In all countries studied, the proposed framework provides reliable results with leads of up to 6 weeks that became more stable and accurate with progression of the season. The results for the United States have been generated in real-time in the context of the Centers for Disease Control and Prevention “Forecasting the Influenza Season Challenge". A characteristic feature of the mechanistic modeling approach is in the explicit estimate of key epidemiological parameters relevant for public health decision-making that cannot be achieved with statistical models not considering the disease dynamic. Furthermore, the presented framework allows the fusion of multiple data streams in the initialization stage and can be enriched with census, weather and socioeconomic data

Collective Response to Media Coverage of the COVID-19 Pandemic on Reddit and Wikipedia: Mixed-Methods Analysis

Background: The exposure and consumption of information during epidemic outbreaks may alter people’s risk perception and trigger behavioral changes, which can ultimately affect the evolution of the disease. It is thus of utmost importance to map the dissemination of information by mainstream media outlets and the public response to this information. However, our understanding of this exposure-response dynamic during the COVID-19 pandemic is still limited. Objective: The goal of this study is to characterize the media coverage and collective internet response to the COVID-19 pandemic in four countries: Italy, the United Kingdom, the United States, and Canada. Methods: We collected a heterogeneous data set including 227,768 web-based news articles and 13,448 YouTube videos published by mainstream media outlets, 107,898 user posts and 3,829,309 comments on the social media platform Reddit, and 278,456,892 views of COVID-19–related Wikipedia pages. To analyze the relationship between media coverage, epidemic progression, and users’ collective web-based response, we considered a linear regression model that predicts the public response for each country given the amount of news exposure. We also applied topic modelling to the data set using nonnegative matrix factorization. Results: Our results show that public attention, quantified as user activity on Reddit and active searches on Wikipedia pages, is mainly driven by media coverage; meanwhile, this activity declines rapidly while news exposure and COVID-19 incidence remain high. Furthermore, using an unsupervised, dynamic topic modeling approach, we show that while the levels of attention dedicated to different topics by media outlets and internet users are in good accordance, interesting deviations emerge in their temporal patterns. Conclusions: Overall, our findings offer an additional key to interpret public perception and response to the current global health emergency and raise questions about the effects of attention saturation on people’s collective awareness and risk perception and thus on their tendencies toward behavioral change.Peer ReviewedPostprint (published version

Social data mining and seasonal influenza forecasts: The FluOutlook platform

FluOutlook is an online platform where multiple data sources are integrated to initialize and train a portfolio of epidemic models for influenza forecast. During the 2014/15 season, the system has been used to provide real-time forecasts for 7 countries in North America and Europe

1h nmr brain metabonomics of scrapie exposed sheep

While neurochemical metabolite modifications, determined by different techniques, have been diffusely reported in human and mice brains affected by transmissible spongiform encephalopathies (TSEs), this aspect has been little studied in the natural animal hosts with the same pathological conditions so far

High diagnostic accuracy of RT-QuIC assay in a prospective study of patients with suspected sCJD

The early and accurate in vivo diagnosis of sporadic Creutzfeldt-Jakob disease (sCJD) is essential in order to differentiate CJD from treatable rapidly progressive dementias. Diagnostic investigations supportive of clinical CJD diagnosis include magnetic resonance imaging (MRI), electroencephalogram (EEG), 14-3-3 protein detection, and/or real-time quaking-induced conversion (RT-QuIC) assay positivity in the cerebrospinal fluid (CSF) or in other tissues. The total CSF tau protein concentration has also been used in a clinical setting for improving the CJD diagnostic sensitivity and specificity. We analyzed 182 CSF samples and 42 olfactory mucosa (OM) brushings from patients suspected of having sCJD with rapidly progressive dementia (RPD), in order to determine the diagnostic accuracy of 14-3-3, the total tau protein, and the RT-QuIC assay. A probable and definite sCJD diagnosis was assessed in 102 patients. The RT-QuIC assay on the CSF samples showed a 100% specificity and a 96% sensitivity, significantly higher compared with 14-3-3 (84% sensitivity and 46% specificity) and tau (85% sensitivity and 70% specificity); however, the combination of RT-QuIC testing of the CSF and OM samples resulted in 100% sensitivity and specificity, proving a significantly higher accuracy of RT-QuIC compared with the surrogate biomarkers in the diagnostic setting of patients with RPD. Moreover, we showed that CSF blood contamination or high protein levels might interfere with RT-QuIC seeding. In conclusion, we provided further evidence that the inclusion of an RT-QuIC assay of the CSF and OM in the diagnostic criteria for sCJD has radically changed the clinical approach towards the diagnosis

Collective response to the media coverage of COVID-19 pandemic on Reddit and Wikipedia: mixed-methods analysis

Background: The exposure and consumption of information during epidemic outbreaks may alter risk perception, trigger behavioral changes, and ultimately affect the evolution of the disease. It is thus of the uttermost importance to map information dissemination by mainstream media outlets and public response. However, our understanding of this exposure-response dynamic during COVID-19 pandemic is still limited. Objective: The goal of this work is to provide a characterization of media coverage and online collective response to COVID-19 pandemic in four countries: Italy, United Kingdom, United States, and Canada. Methods: We collect a heterogeneous dataset including 227’768 online news articles and 13’448 YouTube videos published by mainstream media, 107’898 users posts and 3’829’309 comments on the social media platform Reddit, and 278’456’892 views to COVID-19 related Wikipedia pages. Results: Our results show that public attention, quantified as users activity on Reddit and active searches on Wikipedia pages, is mainly driven by media coverage and declines rapidly, while news exposure and COVID-19 incidence remain high. Furthermore, by using an unsupervised, dynamical topic modeling approach, we show that while the attention dedicated to different topics by media and online users are in good accordance, interesting deviations emerge in their temporal patterns. Conclusions: Overall, our findings offer an additional key to interpret public perception and response to the current global health emergency and raise questions about the effects of attention saturation on collective awareness, risk perception and thus on tendencies towards behavioural change

Combining participatory influenza surveillance with modeling and forecasting

Background: Influenza outbreaks affect millions of people every year and its surveillance is usually carried out in developed countries through a network of sentinel doctors who report the weekly number of Influenza-like Illness cases observed among the visited patients. Monitoring and forecasting the evolution of these outbreaks supports decision makers in designing effective interventions and allocating resources to mitigate their impact. Objectives: Describe the existing participatory surveillance approaches that have been used for modeling and forecasting of the seasonal influenza epidemic, and how they can help strengthen real-time epidemic science and provide a more rigorous understanding of epidemic conditions. Methods: We describe three different participatory surveillance systems, WISDM (Widely Internet Sourced Distributed Monitoring), InfluenzaNet and Flu Near You (FNY), and show how modeling and simulation can be or has been combined with participatory disease surveillance to: i) measure the non-response bias in a participatory surveillance sample using WISDM; and ii) nowcast and forecast influenza activity in different parts of the world (using InfluenzaNet and Flu Near You). Results: WISDM based results measure the participatory and sample bias for three epidemic metrics i.e. attack rate, peak infection rate, and time-to-peak, and find the participatory bias to be the largest component of the total bias. InfluenzaNet platform shows that digital participatory surveillance data combined with a realistic data-driven epidemiological model can provide both short-term and long-term forecasts of epidemic intensities; and the ground truth data lie within the 95 percent confidence intervals for most weeks. The statistical accuracy of the ensemble forecasts increase as the season progresses. The Flu Near You platform shows that participatory surveillance data provide accurate short-term flu activity forecasts and influenza activity predictions. The correlation of the HealthMap Flu Trends estimates with the observed CDC ILI rates is 0.99 for 2013-2015. Additional data sources lead to an error reduction of about 40% when compared to the estimates of the model that only incorporates CDC historical information. Conclusions: While the advantages of participatory surveillance, compared to traditional surveillance, include its timeliness, lower costs, and broader reach, it is limited by a lack of control over the characteristics of the population sample. Modeling and simulation can help overcome this limitation as well as provide real-time and long term forecasting of Influenza activity in data poor parts of the world