Biology subjects

Paolotti, D.

Publications and source records attributed to Paolotti, D..

4 recordsLinked to original sources

bioRxiv2024

Association Between Oral Microbiota and Close-Range Proximity in a Primary School

The microbiota is the ensemble of microorganisms inhabiting the human body and it deeply influences human health and well-being. Recent studies showed its interplay with social behavior, suggesting that part of the microbiota might be socially transmissible. In this work, we investigate the association between close-range proximity and the oral microbiota composition in a group of children attending primary school. Unlike most related studies, our cohort comprises non-cohabiting individuals, and we use high-resolution proximity sensors to objectively quantify close-range proximity. Our analysis shows that longer time spent in proximity between pairs of children correlates with a higher similarity between their oral microbiota composition. Building on previous studies on cohabiting individuals, our findings suggest that social contacts beyond the closest social circle may also shape oral microbiota composition, although longitudinal studies are needed to test this hypothesis.

microbiology↗

bioRxiv2021

Integrating digital and field surveillance to complement efforts to manage epidemic diseases of livestock: African swine fever as a case study

The SARS-CoV-2 pandemic has unveiled the importance of stakeholders and ordinary citizens in managing infectious disease emergencies. Efficient management of infectious diseases requires a top-down approach which must be complemented with a bottom-up response to be effective. Here we investigate a novel approach to surveillance for transboundary animal diseases using African Swine fever as a model. We were able to collect data at a population level on information-seeking behavior and at a local level through a targeted questionnaire-based survey to relevant stakeholders such as farmers and veterinary authorities. Our study shows how information-seeking behavior and resulting public attention during an epidemic, can be addressed through novel data streams from digital platforms such as Wikipedia. We also bring evidence on how field surveys aimed at local workers (e.g. farmers) and public authorities remain a crucial tool to assess more in-depth preparedness and awareness among front-line actors. We conclude that they should be used in combination to maximize the outcome of surveillance and prevention activities for selected transboundary animal diseases.

scientific communication and education↗

bioRxiv2018

The impact of news exposure on collective attention in the United States during the 2016 Zika epidemic

In recent years, many studies have drawn attention to the important role of collective awareness and human behaviour during epidemic outbreaks. A number of modelling efforts have investigated the interaction between the disease transmission dynamics and human behaviour change mediated by news coverage and by information spreading in the population. Yet, given the scarcity of data on public awareness during an epidemic, few studies have relied on empirical data. Here, we use fine-grained, geo-referenced data from three online sources - Wikipedia, the GDELT Project and the Internet Archive - to quantify population-scale information seeking about the 2016 Zika virus epidemic in the U.S., explicitly linking such behavioural signal to epidemiological data. Geolocalized Wikipedia pageview data reveal that visiting patterns of Zika-related pages in Wikipedia were highly synchronized across the United States and largely explained by exposure to national television broadcast. Contrary to the assumption of some theoretical models, news volume and Wikipedia visiting patterns were not significantly correlated with the magnitude or the extent of the epidemic. Attention to Zika, in terms of Zika-related Wikipedia pageviews, was high at the beginning of the outbreak, when public health agencies raised an international alert and triggered media coverage, but subsequently exhibited an activity profile that suggests nonlinear dependencies and memory effects in the relation between information seeking, media pressure, and disease dynamics. This calls for a new and more general modelling framework to describe the interaction between media exposure, public awareness and disease dynamics during epidemic outbreaks.

epidemiology↗

bioRxiv2018

Unsupervised Extraction of Epidemic Syndromes from Participatory Influenza Surveillance Self-reported Symptoms

Seasonal influenza surveillance is usually carried out by sentinel general practitioners who compile weekly reports based on the number of influenza-like illness (ILI) clinical cases observed among visited patients. This practice for surveillance is generally affected by two main issues: i) reports are usually released with a lag of about one week or more, ii) the definition of a case of influenza-like illness based on patients symptoms varies from one surveillance system to the other, i.e. from one country to the other. The availability of novel data streams for disease surveillance can alleviate these issues; in this paper, we employed data from Influenzanet, a participatory web-based surveillance project which collects symptoms directly from the general population in real time. We developed an unsupervised probabilistic framework that combines time series analysis of symptoms counts and performs an algorithmic detection of groups of symptoms, hereafter called syndromes. Symptoms counts were collected through the participatory web-based surveillance platforms of a consortium called Influenzanet which is found to correlate with Influenza-like illness incidence as detected by sentinel doctors. Our aim is to suggest how web-based surveillance data can provide an epidemiological signal capable of detecting influenza-like illness temporal trends without relying on a specific case definition. We evaluated the performance of our framework by showing that the temporal trends of the detected syndromes closely follow the ILI incidence as reported by the traditional surveillance, and consist of combinations of symptoms that are compatible with the ILI definition. The proposed framework was able to predict quite accurately the ILI trend of the forthcoming influenza season based only on the available information of the previous years. Moreover, we assessed the generalisability of the approach by evaluating its potentials for the detection of gastrointestinal syndromes. We evaluated the approach against the traditional surveillance data and despite the limited amount of data, the gastrointestinal trend was successfully detected. The result is a real-time flexible surveillance and prediction tool that is not constrained by any disease case definition.\n\nAuthor summaryThis study suggests how web-based surveillance data can provide an epidemiological signal capable of detecting influenza-like illness temporal trends without relying on a specific case definition. The proposed framework was able to predict quite accurately the ILI trend of the forthcoming influenza season based only on the available information of the previous years. Moreover, we assessed the generalisability of the approach by evaluating its potentials for the detection of gastrointestinal syndromes. We evaluated the approach against the traditional surveillance data and despite the limited amount of data, the gastrointestinal trend was successfully detected. The result is a real-time flexible surveillance and prediction tool that is not constrained by any disease case definition.

epidemiology↗