MOOD project is at the forefront of European research of infectious disease surveillance and modelling from a data science perspective, investigating the impact of global warming on disease outbreaks, and proposing innovations for building of One Health systems across Europe and the world.
In the table below all publications to which the MOOD project contributed are listed. Use the filter to select the most relevant articles.
1.
Emanuele Gustani-Buss Francesco Parino, Trevor Bedford
Integrating dynamical modeling and phylogeographic inference to characterize global influenza circulation Journal Article
In: PNAS Nexus, vol. 4, iss. 1, 2024.
Abstract | Links | BibTeX | Tags: HPAI (Avian Influenza)
@article{Parino2024,
title = {Integrating dynamical modeling and phylogeographic inference to characterize global influenza circulation},
author = {Francesco Parino, Emanuele Gustani-Buss, Trevor Bedford, Marc A. Suchard, N´ıdia Sequeira Trovao, Andrew Rambaut, Vittoria Colizza, Chiara Poletto, Philippe Lemey},
doi = {https://doi.org/10.1093/pnasnexus/pgae561},
year = {2024},
date = {2024-12-17},
journal = {PNAS Nexus},
volume = {4},
issue = {1},
abstract = {Global seasonal influenza circulation involves a complex interplay between local (seasonality, demography, host immunity) and global factors (international mobility) shaping recurrent epidemic patterns. No studies so far have reconciled the two spatial levels, evaluating the coupling between national epidemics, considering heterogeneous coverage of epidemiological, and virological data, integrating different data sources. We propose a novel-combined approach based on a dynamical model of global influenza spread (GLEAM), integrating high-resolution demographic, and mobility data, and a generalized linear model of phylogeographic diffusion that accounts for time-varying migration rates. Seasonal migration fluxes across countries simulated with GLEAM are tested as phylogeographic predictors to provide model validation and calibration based on genetic data. Seasonal fluxes obtained with a specific transmissibility peak time and recurrent travel outperformed the raw air-transportation predictor, previously considered as optimal indicator of global influenza migration. Influenza A subtypes supported autumn–winter reproductive number as high as 2.25 and an average immunity duration of 2 years. Similar dynamics were preferred by influenza B lineages, with a lower autumn–winter reproductive number. Comparing simulated epidemic profiles against FluNet data offered comparatively limited resolution power.},
keywords = {HPAI (Avian Influenza)},
pubstate = {published},
tppubtype = {article}
}
Global seasonal influenza circulation involves a complex interplay between local (seasonality, demography, host immunity) and global factors (international mobility) shaping recurrent epidemic patterns. No studies so far have reconciled the two spatial levels, evaluating the coupling between national epidemics, considering heterogeneous coverage of epidemiological, and virological data, integrating different data sources. We propose a novel-combined approach based on a dynamical model of global influenza spread (GLEAM), integrating high-resolution demographic, and mobility data, and a generalized linear model of phylogeographic diffusion that accounts for time-varying migration rates. Seasonal migration fluxes across countries simulated with GLEAM are tested as phylogeographic predictors to provide model validation and calibration based on genetic data. Seasonal fluxes obtained with a specific transmissibility peak time and recurrent travel outperformed the raw air-transportation predictor, previously considered as optimal indicator of global influenza migration. Influenza A subtypes supported autumn–winter reproductive number as high as 2.25 and an average immunity duration of 2 years. Similar dynamics were preferred by influenza B lineages, with a lower autumn–winter reproductive number. Comparing simulated epidemic profiles against FluNet data offered comparatively limited resolution power.
2.
Pierre Pompidor Trevennec, Samira Bououda
MUST-AI: Multisource Surveillance Tool - Avian Influenza Journal Article
In: Procedia Computer Science, vol. 246, pp. 3034-3043, 2024.
Abstract | Links | BibTeX | Tags: HPAI (Avian Influenza)
@article{nokey,
title = {MUST-AI: Multisource Surveillance Tool - Avian Influenza},
author = {"Carlene Trevennec, Pierre Pompidor, Samira Bououda, Julien Rabatel, Mathieu
Roche"},
doi = {https://doi.org/10.1016/j.procs.2024.09.718},
year = {2024},
date = {2024-11-28},
journal = {Procedia Computer Science},
volume = {246},
pages = {3034-3043},
abstract = {The multisource surveillance tool (MUST) is a platform for collecting, gathering, and visualizing different sources of information related to health events and highly pathogenic avian influenza in mammals (HPAIM). MUST-AI constitutes the first part of the MUST tool, which centralizes health information relating to cases of HPAIM since January 1, 2021, and comes from 3 different notification sources, an official notification source confirmed by public health institutions (i.e., WAHIS) and two other alternative unofficial sources that collect events from online media (PADI-web) and expert networks (ProMED). Owing to the use of natural language processing (NLP) algorithms, HPAIM events are represented on an interactive map associated with a graph that represents their distribution over a given time interval. This paper presents new tools and approaches for data fusion and experiments for selecting data to integrate into MUST that are related to HPAIM events.
},
keywords = {HPAI (Avian Influenza)},
pubstate = {published},
tppubtype = {article}
}
The multisource surveillance tool (MUST) is a platform for collecting, gathering, and visualizing different sources of information related to health events and highly pathogenic avian influenza in mammals (HPAIM). MUST-AI constitutes the first part of the MUST tool, which centralizes health information relating to cases of HPAIM since January 1, 2021, and comes from 3 different notification sources, an official notification source confirmed by public health institutions (i.e., WAHIS) and two other alternative unofficial sources that collect events from online media (PADI-web) and expert networks (ProMED). Owing to the use of natural language processing (NLP) algorithms, HPAIM events are represented on an interactive map associated with a graph that represents their distribution over a given time interval. This paper presents new tools and approaches for data fusion and experiments for selecting data to integrate into MUST that are related to HPAIM events.
3.
Valdano, Eugenio; Colombi, Davide; Poletto, Chiara; Colizza, Vittoria
Epidemic graph diagrams as analytics for epidemic control in the data-rich era Journal Article
In: Nature Communications, 2023.
Abstract | Links | BibTeX | Tags: HPAI (Avian Influenza), OpenDataSet
@article{nokey,
title = {Epidemic graph diagrams as analytics for epidemic control in the data-rich era},
author = {Eugenio Valdano and Davide Colombi and Chiara Poletto and Vittoria Colizza},
url = {https://www.nature.com/articles/s41467-023-43856-1#citeas},
doi = {10.1038/s41467-023-43856-1},
year = {2023},
date = {2023-12-20},
urldate = {2023-12-20},
journal = {Nature Communications},
abstract = {COVID-19 highlighted modeling as a cornerstone of pandemic response. But it also revealed that current models may not fully exploit the high-resolution data on disease progression, epidemic surveillance and host behavior, now available. Take the epidemic threshold, which quantifies the spreading risk throughout epidemic emergence, mitigation, and control. Its use requires oversimplifying either disease or host contact dynamics. We introduce the epidemic graph diagrams to overcome this by computing the epidemic threshold directly from arbitrarily complex data on contacts, disease and interventions. A grammar of diagram operations allows to decompose, compare, simplify models with computational efficiency, extracting theoretical understanding. We use the diagrams to explain the emergence of resistant influenza variants in the 2007–2008 season, and demonstrate that neglecting non-infectious prodromic stages of sexually transmitted infections biases the predicted epidemic risk, compromising control. The diagrams are general, and improve our capacity to respond to present and future public health challenges.},
keywords = {HPAI (Avian Influenza), OpenDataSet},
pubstate = {published},
tppubtype = {article}
}
COVID-19 highlighted modeling as a cornerstone of pandemic response. But it also revealed that current models may not fully exploit the high-resolution data on disease progression, epidemic surveillance and host behavior, now available. Take the epidemic threshold, which quantifies the spreading risk throughout epidemic emergence, mitigation, and control. Its use requires oversimplifying either disease or host contact dynamics. We introduce the epidemic graph diagrams to overcome this by computing the epidemic threshold directly from arbitrarily complex data on contacts, disease and interventions. A grammar of diagram operations allows to decompose, compare, simplify models with computational efficiency, extracting theoretical understanding. We use the diagrams to explain the emergence of resistant influenza variants in the 2007–2008 season, and demonstrate that neglecting non-infectious prodromic stages of sexually transmitted infections biases the predicted epidemic risk, compromising control. The diagrams are general, and improve our capacity to respond to present and future public health challenges.
4.
Valentin, Sarah; Boudoua, Bahdja; Sewalk, Kara; Arınık, Nejat; Roche, Mathieu; Lancelot, Renaud; Arsevska, Elena
Dissemination of information in event-based surveillance, a case study of Avian Influenza Journal Article
In: PLoS ONE, 2023.
Abstract | Links | BibTeX | Tags: HPAI (Avian Influenza), OpenDataSet, Text mining
@article{nokey,
title = {Dissemination of information in event-based surveillance, a case study of Avian Influenza},
author = {Sarah Valentin and Bahdja Boudoua and Kara Sewalk and Nejat Arınık and Mathieu Roche and Renaud Lancelot and Elena Arsevska },
url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0285341},
doi = {10.1371/journal.pone.0285341},
year = {2023},
date = {2023-09-05},
urldate = {2023-09-05},
journal = {PLoS ONE},
abstract = {Event-Based Surveillance (EBS) tools, such as HealthMap and PADI-web, monitor online news reports and other unofficial sources, with the primary aim to provide timely information to users from health agencies on disease outbreaks occurring worldwide. In this work, we describe how outbreak-related information disseminates from a primary source, via a secondary source, to a definitive aggregator, an EBS tool, during the 2018/19 avian influenza season. We analysed 337 news items from the PADI-web and 115 news articles from HealthMap EBS tools reporting avian influenza outbreaks in birds worldwide between July 2018 and June 2019. We used the sources cited in the news to trace the path of each outbreak. We built a directed network with nodes representing the sources (characterised by type, specialisation, and geographical focus) and edges representing the flow of information. We calculated the degree as a centrality measure to determine the importance of the nodes in information dissemination. We analysed the role of the sources in early detection (detection of an event before its official notification) to the World Organisation for Animal Health (WOAH) and late detection. A total of 23% and 43% of the avian influenza outbreaks detected by the PADI-web and HealthMap, respectively, were shared on time before their notification. For both tools, national and local veterinary authorities were the primary sources of early detection. The early detection component mainly relied on the dissemination of nationally acknowledged events by online news and press agencies, bypassing international reporting to the WAOH. WOAH was the major secondary source for late detection, occupying a central position between national authorities and disseminator sources, such as online news. PADI-web and HealthMap were highly complementary in terms of detected sources, explaining why 90% of the events were detected by only one of the tools. We show that current EBS tools can provide timely outbreak-related information and priority news sources to improve digital disease surveillance.
Figures},
keywords = {HPAI (Avian Influenza), OpenDataSet, Text mining},
pubstate = {published},
tppubtype = {article}
}
Event-Based Surveillance (EBS) tools, such as HealthMap and PADI-web, monitor online news reports and other unofficial sources, with the primary aim to provide timely information to users from health agencies on disease outbreaks occurring worldwide. In this work, we describe how outbreak-related information disseminates from a primary source, via a secondary source, to a definitive aggregator, an EBS tool, during the 2018/19 avian influenza season. We analysed 337 news items from the PADI-web and 115 news articles from HealthMap EBS tools reporting avian influenza outbreaks in birds worldwide between July 2018 and June 2019. We used the sources cited in the news to trace the path of each outbreak. We built a directed network with nodes representing the sources (characterised by type, specialisation, and geographical focus) and edges representing the flow of information. We calculated the degree as a centrality measure to determine the importance of the nodes in information dissemination. We analysed the role of the sources in early detection (detection of an event before its official notification) to the World Organisation for Animal Health (WOAH) and late detection. A total of 23% and 43% of the avian influenza outbreaks detected by the PADI-web and HealthMap, respectively, were shared on time before their notification. For both tools, national and local veterinary authorities were the primary sources of early detection. The early detection component mainly relied on the dissemination of nationally acknowledged events by online news and press agencies, bypassing international reporting to the WAOH. WOAH was the major secondary source for late detection, occupying a central position between national authorities and disseminator sources, such as online news. PADI-web and HealthMap were highly complementary in terms of detected sources, explaining why 90% of the events were detected by only one of the tools. We show that current EBS tools can provide timely outbreak-related information and priority news sources to improve digital disease surveillance.
Figures
Figures
5.
Borm, Steven Van; Boseret1, Géraldine; Dellicour1, Simon; Steensels, Mieke; Roupie, Virginie; Vandenbussche, Frank; Mathijs, Elisabeth; Vilain, Aline; Driesen, Michèle; Dispas, Marc; Delcloo, Andy W.; Lemey, Philippe; Mertens, Ingeborg; Gilbert, Marius; Lambrecht, Bénédicte; van den Berg, Thierry
2023.
Abstract | Links | BibTeX | Tags: HPAI (Avian Influenza), OpenDataSet
@bachelorthesis{nokey,
title = {Combined Phylogeographic Analyses and Epidemiologic Contact Tracing to Characterize Atypically Pathogenic Avian Influenza (H3N1) Epidemic, Belgium, 2019},
author = {Steven Van Borm and Géraldine Boseret1 and Simon Dellicour1 and Mieke Steensels and Virginie Roupie and Frank Vandenbussche and Elisabeth Mathijs and Aline Vilain and Michèle Driesen and Marc Dispas and Andy W. Delcloo and Philippe Lemey and Ingeborg Mertens and Marius Gilbert and Bénédicte Lambrecht and Thierry van den Berg},
url = {https://wwwnc.cdc.gov/eid/article/29/2/22-0765_article},
doi = {10.3201/eid2902.220765},
year = {2023},
date = {2023-02-01},
urldate = {2023-02-01},
journal = {Center for Disease Control and Prevention},
volume = {19},
number = {2},
abstract = {The high economic impact and zoonotic potential of avian influenza call for detailed investigations of dispersal dynamics of epidemics. We integrated phylogeographic and epidemiologic analyses to investigate the dynamics of a low pathogenicity avian influenza (H3N1) epidemic that occurred in Belgium during 2019. Virus genomes from 104 clinical samples originating from 85% of affected farms were sequenced. A spatially explicit phylogeographic analysis confirmed a dominating northeast to southwest dispersal direction and a long-distance dispersal event linked to direct live animal transportation between farms. Spatiotemporal clustering, transport, and social contacts strongly correlated with the phylogeographic pattern of the epidemic. We detected only a limited association between wind direction and direction of viral lineage dispersal. Our results highlight the multifactorial nature of avian influenza epidemics and illustrate the use of genomic analyses of virus dispersal to complement epidemiologic and environmental data, improve knowledge of avian influenza epidemiologic dynamics, and enhance control strategies.},
keywords = {HPAI (Avian Influenza), OpenDataSet},
pubstate = {published},
tppubtype = {bachelorthesis}
}
The high economic impact and zoonotic potential of avian influenza call for detailed investigations of dispersal dynamics of epidemics. We integrated phylogeographic and epidemiologic analyses to investigate the dynamics of a low pathogenicity avian influenza (H3N1) epidemic that occurred in Belgium during 2019. Virus genomes from 104 clinical samples originating from 85% of affected farms were sequenced. A spatially explicit phylogeographic analysis confirmed a dominating northeast to southwest dispersal direction and a long-distance dispersal event linked to direct live animal transportation between farms. Spatiotemporal clustering, transport, and social contacts strongly correlated with the phylogeographic pattern of the epidemic. We detected only a limited association between wind direction and direction of viral lineage dispersal. Our results highlight the multifactorial nature of avian influenza epidemics and illustrate the use of genomic analyses of virus dispersal to complement epidemiologic and environmental data, improve knowledge of avian influenza epidemiologic dynamics, and enhance control strategies.
6.
Bonacina, Francesco; Boëlle, Pierre-Yves; Colizza, Vittoria; Lopez, Olivier; Thomas, Maud; Poletto, Chiara
Global patterns and drivers of influenza decline during the COVID-19 pandemic Journal Article
In: 2023.
Abstract | Links | BibTeX | Tags: Covid-19 (Coronavirus), HPAI (Avian Influenza), OpenDataSet
@article{nokey,
title = {Global patterns and drivers of influenza decline during the COVID-19 pandemic},
author = {Francesco Bonacina and Pierre-Yves Boëlle and Vittoria Colizza and Olivier Lopez and Maud Thomas and Chiara Poletto
},
url = {https://www.ijidonline.com/article/S1201-9712(22)00682-8/fulltext},
doi = {10.1016/j.ijid.2022.12.042},
year = {2023},
date = {2023-01-03},
urldate = {2023-01-03},
abstract = {Objectives
The influenza circulation reportedly declined during the COVID-19 pandemic in many countries. The occurrence of this change has not been studied worldwide nor its potential drivers.
Methods
The change in the proportion of positive influenza samples reported by country and trimester was computed relative to the 2014-2019 period using the FluNet database. Random forests were used to determine predictors of change from demographical, weather, pandemic preparedness, COVID-19 incidence, and pandemic response characteristics. Regression trees were used to classify observations according to these predictors.
Results
During the COVID-19 pandemic, the influenza decline relative to prepandemic levels was global but heterogeneous across space and time. It was more than 50% for 311 of 376 trimesters-countries and even more than 99% for 135. COVID-19 incidence and pandemic preparedness were the two most important predictors of the decline. Europe and North America initially showed limited decline despite high COVID-19 restrictions; however, there was a strong decline afterward in most temperate countries, where pandemic preparedness, COVID-19 incidence, and social restrictions were high; the decline was limited in countries where these factors were low. The “zero-COVID” countries experienced the greatest decline.
Conclusion
Our findings set the stage for interpreting the resurgence of influenza worldwide.},
keywords = {Covid-19 (Coronavirus), HPAI (Avian Influenza), OpenDataSet},
pubstate = {published},
tppubtype = {article}
}
Objectives
The influenza circulation reportedly declined during the COVID-19 pandemic in many countries. The occurrence of this change has not been studied worldwide nor its potential drivers.
Methods
The change in the proportion of positive influenza samples reported by country and trimester was computed relative to the 2014-2019 period using the FluNet database. Random forests were used to determine predictors of change from demographical, weather, pandemic preparedness, COVID-19 incidence, and pandemic response characteristics. Regression trees were used to classify observations according to these predictors.
Results
During the COVID-19 pandemic, the influenza decline relative to prepandemic levels was global but heterogeneous across space and time. It was more than 50% for 311 of 376 trimesters-countries and even more than 99% for 135. COVID-19 incidence and pandemic preparedness were the two most important predictors of the decline. Europe and North America initially showed limited decline despite high COVID-19 restrictions; however, there was a strong decline afterward in most temperate countries, where pandemic preparedness, COVID-19 incidence, and social restrictions were high; the decline was limited in countries where these factors were low. The “zero-COVID” countries experienced the greatest decline.
Conclusion
Our findings set the stage for interpreting the resurgence of influenza worldwide.
The influenza circulation reportedly declined during the COVID-19 pandemic in many countries. The occurrence of this change has not been studied worldwide nor its potential drivers.
Methods
The change in the proportion of positive influenza samples reported by country and trimester was computed relative to the 2014-2019 period using the FluNet database. Random forests were used to determine predictors of change from demographical, weather, pandemic preparedness, COVID-19 incidence, and pandemic response characteristics. Regression trees were used to classify observations according to these predictors.
Results
During the COVID-19 pandemic, the influenza decline relative to prepandemic levels was global but heterogeneous across space and time. It was more than 50% for 311 of 376 trimesters-countries and even more than 99% for 135. COVID-19 incidence and pandemic preparedness were the two most important predictors of the decline. Europe and North America initially showed limited decline despite high COVID-19 restrictions; however, there was a strong decline afterward in most temperate countries, where pandemic preparedness, COVID-19 incidence, and social restrictions were high; the decline was limited in countries where these factors were low. The “zero-COVID” countries experienced the greatest decline.
Conclusion
Our findings set the stage for interpreting the resurgence of influenza worldwide.
7.
Valentin, Sarah; Arsevska, Elena; Mercier, Alizé; Falala, Sylvain; Rabatel, Julien; Lancelot, Renaud; Roche, Mathieu
PADI-web: An Event-Based Surveillance System for Detecting, Classifying and Processing Online News Conference
Human Language Technology. Challenges for Computer Science and Linguistics, vol. 12598, Springer International Publishing, 2022, ISBN: 978-3-030-66526-5.
Abstract | Links | BibTeX | Tags: ASF (African Swine Fever), HPAI (Avian Influenza), Text mining
@conference{@InProceedings{10.1007/978-3-030-66527-2_7,
title = {PADI-web: An Event-Based Surveillance System for Detecting, Classifying and Processing Online News},
author = {Sarah Valentin and Elena Arsevska and Alizé Mercier and Sylvain Falala and Julien Rabatel and Renaud Lancelot and Mathieu Roche},
editor = {Vetulani, Zygmunt and Paroubek, Patrick and Kubis, Marek},
url = {https://link.springer.com/chapter/10.1007/978-3-030-66527-2_7},
doi = {https://doi.org/10.1007/978-3-030-66527-2_7},
isbn = {978-3-030-66526-5},
year = {2022},
date = {2022-12-31},
urldate = {2022-12-31},
booktitle = {Human Language Technology. Challenges for Computer Science and Linguistics},
volume = {12598},
pages = {87-101},
publisher = {Springer International Publishing},
abstract = {The Platform for Automated Extraction of Animal Disease Information from the Web (PADI-web) is a multilingual text mining tool for automatic detection, classification, and extraction of disease outbreak information from online news articles. PADI-web currently monitors the Web for nine animal infectious diseases and eight syndromes in five animal hosts. The classification module is based on a supervised machine learning approach to filter the relevant news with an overall accuracy of 0.94. The classification of relevant news between 5 topic categories (confirmed, suspected or unknown outbreak, preparedness and impact) obtained an overall accuracy of 0.75. In the first six months of its implementation (January--June 2016), PADI-web detected 73{%} of the outbreaks of African swine fever; 20{%} of foot-and-mouth disease; 13{%} of bluetongue, and 62{%} of highly pathogenic avian influenza. The information extraction module of PADI-web obtained F-scores of 0.80 for locations, 0.85 for dates, 0.95 for diseases, 0.95 for hosts, and 0.85 for case numbers},
keywords = {ASF (African Swine Fever), HPAI (Avian Influenza), Text mining},
pubstate = {published},
tppubtype = {conference}
}
The Platform for Automated Extraction of Animal Disease Information from the Web (PADI-web) is a multilingual text mining tool for automatic detection, classification, and extraction of disease outbreak information from online news articles. PADI-web currently monitors the Web for nine animal infectious diseases and eight syndromes in five animal hosts. The classification module is based on a supervised machine learning approach to filter the relevant news with an overall accuracy of 0.94. The classification of relevant news between 5 topic categories (confirmed, suspected or unknown outbreak, preparedness and impact) obtained an overall accuracy of 0.75. In the first six months of its implementation (January--June 2016), PADI-web detected 73{%} of the outbreaks of African swine fever; 20{%} of foot-and-mouth disease; 13{%} of bluetongue, and 62{%} of highly pathogenic avian influenza. The information extraction module of PADI-web obtained F-scores of 0.80 for locations, 0.85 for dates, 0.95 for diseases, 0.95 for hosts, and 0.85 for case numbers
8.
Arınık, Nejat; Interdonato, Roberto; Roche, Mathieu; Teissere, Maguelonne
Inferring the transmission dynamics of Avian Influenza from news and environmental data Conference
NetSci (Network Science Society) , 2022.
Links | BibTeX | Tags: HPAI (Avian Influenza)
@conference{nokey,
title = {Inferring the transmission dynamics of Avian Influenza from news and environmental data},
author = {Nejat Arınık and Roberto Interdonato and Mathieu Roche and Maguelonne Teissere},
url = {https://hal.science/hal-03936660/document},
year = {2022},
date = {2022-11-01},
urldate = {2022-11-01},
booktitle = {NetSci (Network Science Society) },
keywords = {HPAI (Avian Influenza)},
pubstate = {published},
tppubtype = {conference}
}
9.
Schaeffer, Camille; Interdonato, Roberto; Lancelot, Renaud; Roche, Mathieu; Teisseire, Maguelonne
Labeled entities from social media data related to avian influenza disease Journal Article Forthcoming
In: Data in Brief, vol. 43, pp. 108317, Forthcoming, ISSN: 2352-3409.
Abstract | Links | BibTeX | Tags: HPAI (Avian Influenza), OpenDataSet, Text mining
@article{@article{SCHAEFFER2022108317,,
title = {Labeled entities from social media data related to avian influenza disease},
author = {Camille Schaeffer and Roberto Interdonato and Renaud Lancelot and Mathieu Roche and Maguelonne Teisseire},
url = {https://www.sciencedirect.com/science/article/pii/S2352340922005194},
doi = {https://doi.org/10.1016/j.dib.2022.108317},
issn = {2352-3409},
year = {2022},
date = {2022-08-01},
urldate = {2022-08-01},
journal = {Data in Brief},
volume = {43},
pages = {108317},
abstract = {This dataset is composed by spatial (e.g. location) and thematic (e.g. diseases, symptoms, virus) entities concerning avian influenza in social media (textual) data in English. It was created from three corpora: the first one includes 10 transcriptions of YouTube videos and 70 tweets manually annotated. The second corpus is composed by the same textual data but automatically annotated with Named Entity Recognition (NER) tools. These two corpora have been built to evaluate NER tools and apply them to a bigger corpus. The third corpus is composed of 100 YouTube transcriptions automatically annotated with NER tools. The aim of the annotation task is to recognize spatial information such as the names of the cities and epidemiological information such as the names of the diseases. An annotation guideline is provided in order to ensure a unified annotation and to help the annotators. This dataset can be used to train or evaluate Natural Language Processing (NLP) approaches such as specialized entity recognition.},
keywords = {HPAI (Avian Influenza), OpenDataSet, Text mining},
pubstate = {forthcoming},
tppubtype = {article}
}
This dataset is composed by spatial (e.g. location) and thematic (e.g. diseases, symptoms, virus) entities concerning avian influenza in social media (textual) data in English. It was created from three corpora: the first one includes 10 transcriptions of YouTube videos and 70 tweets manually annotated. The second corpus is composed by the same textual data but automatically annotated with Named Entity Recognition (NER) tools. These two corpora have been built to evaluate NER tools and apply them to a bigger corpus. The third corpus is composed of 100 YouTube transcriptions automatically annotated with NER tools. The aim of the annotation task is to recognize spatial information such as the names of the cities and epidemiological information such as the names of the diseases. An annotation guideline is provided in order to ensure a unified annotation and to help the annotators. This dataset can be used to train or evaluate Natural Language Processing (NLP) approaches such as specialized entity recognition.
10.
Camille, Schaeffer; Roberto, Interdonato; Lancelot, Renaud; Roche, Mathieu; Teisseire, Maguelonne.
Social network data and epidemiological intelligence: A case study of avian influenza Conference
vol. 116, 2021.
Abstract | BibTeX | Tags: HPAI (Avian Influenza)
@conference{nokey,
title = {Social network data and epidemiological intelligence: A case study of avian influenza},
author = {Schaeffer Camille and Interdonato Roberto and Lancelot, Renaud and Roche, Mathieu and Teisseire, Maguelonne.},
year = {2021},
date = {2021-11-06},
urldate = {2021-11-06},
journal = {International Journal of Infectious Diseases},
volume = {116},
pages = {99},
abstract = {Event Based Surveillance (EBS) systems detect and monitor diseases by analysing articles from online newspapers and reports from health organizations (e.g. FAO, OIE, etc.). However, they partially integrate data from social networks, even though these data are present in large quantities on the web. The purpose of this study is to exploit social network data, such as Twitter and YouTube, to provide epidemiological and additional information for Avian Influenza surveillance.},
keywords = {HPAI (Avian Influenza)},
pubstate = {published},
tppubtype = {conference}
}
Event Based Surveillance (EBS) systems detect and monitor diseases by analysing articles from online newspapers and reports from health organizations (e.g. FAO, OIE, etc.). However, they partially integrate data from social networks, even though these data are present in large quantities on the web. The purpose of this study is to exploit social network data, such as Twitter and YouTube, to provide epidemiological and additional information for Avian Influenza surveillance.