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 are listed all MOOD publications. Use the filter to select the most relevant articles.
Hammer, Charlotte C; Dub, Timothee; Luomala, Oskari; Sane, Jussi
In: Eurosurveillance, vol. 27, no. 4, 0000.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Is clinical primary care surveillance for tularaemia a useful addition to laboratory surveillance? An analysis of notification data for Finland, 2013 to 2019},
author = {Hammer, Charlotte C and Dub, Timothee and Luomala, Oskari and Sane, Jussi},
url = {https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2022.27.4.2100098},
doi = {https://doi.org/10.2807/1560-7917.ES.2022.27.4.2100098},
journal = {Eurosurveillance},
volume = {27},
number = {4},
abstract = {Background
In Finland, surveillance of tularaemia relies on laboratory-confirmed case notifications to the National infectious Diseases Register (NIDR).
Aim
The aim of the study was to assess the suitability and usefulness of clinical surveillance as an addition to laboratory notification to improve tularaemia surveillance in Finland.
Methods
We retrieved NIDR tularaemia surveillance and primary healthcare data on clinically diagnosed tularaemia cases in Finland between 2013 and 2019. We compared incidences, demographic distributions and seasonal trends between the two data sources.
Results
The median annual incidence was 0.6 (range: 0.1–12.7) and 0.8 (range: 0.6–7.2) per 100,000 for NIDR notifications and primary healthcare notifications, respectively. Cases reported to NIDR were slightly older than cases reported to primary healthcare (median: 53 years vs 50 years, p = 0.04), but had similar sex distribution. Seasonal peaks differed between systems, both in magnitude and in timing. On average, primary healthcare notifications peaked 3 weeks before NIDR. However, peaks in NIDR were more pronounced, for example in 2017, monthly incidence per 100,000 of NIDR notifications peaked at 12.7 cases in September, while primary healthcare notifications peaked at 7.2 (1.8 ratio) in August.
Conclusions
Clinically diagnosed cases provide a valuable additional data source for surveillance of tularaemia in Finland. A primary healthcare-based system would allow for earlier detection of increasing incidences and thereby for early warning of outbreaks. This is crucial in order to implement targeted control and prevention measures as early as possible.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In Finland, surveillance of tularaemia relies on laboratory-confirmed case notifications to the National infectious Diseases Register (NIDR).
Aim
The aim of the study was to assess the suitability and usefulness of clinical surveillance as an addition to laboratory notification to improve tularaemia surveillance in Finland.
Methods
We retrieved NIDR tularaemia surveillance and primary healthcare data on clinically diagnosed tularaemia cases in Finland between 2013 and 2019. We compared incidences, demographic distributions and seasonal trends between the two data sources.
Results
The median annual incidence was 0.6 (range: 0.1–12.7) and 0.8 (range: 0.6–7.2) per 100,000 for NIDR notifications and primary healthcare notifications, respectively. Cases reported to NIDR were slightly older than cases reported to primary healthcare (median: 53 years vs 50 years, p = 0.04), but had similar sex distribution. Seasonal peaks differed between systems, both in magnitude and in timing. On average, primary healthcare notifications peaked 3 weeks before NIDR. However, peaks in NIDR were more pronounced, for example in 2017, monthly incidence per 100,000 of NIDR notifications peaked at 12.7 cases in September, while primary healthcare notifications peaked at 7.2 (1.8 ratio) in August.
Conclusions
Clinically diagnosed cases provide a valuable additional data source for surveillance of tularaemia in Finland. A primary healthcare-based system would allow for earlier detection of increasing incidences and thereby for early warning of outbreaks. This is crucial in order to implement targeted control and prevention measures as early as possible.
Valentin, Sarah; Arsevska, Elena; Rabatel, Julien; Falala, Sylvain; Mercier, Alizé; Lancelot, Renaud; Roche, Mathieu
PADI-web 3.0: A new framework for extracting and disseminating fine-grained information from the news for animal disease surveillance Journal Article
In: One Health, vol. 13, pp. 100357, 0000, ISSN: 2352-7714.
Links | BibTeX | Tags: Animal disease surveillance, Software, Text mining
@article{@article{VALENTIN2021100357,
title = {PADI-web 3.0: A new framework for extracting and disseminating fine-grained information from the news for animal disease surveillance},
author = {Sarah Valentin and Elena Arsevska and Julien Rabatel and Sylvain Falala and Alizé Mercier and Renaud Lancelot and Mathieu Roche},
url = {https://www.sciencedirect.com/science/article/pii/S2352771421001476},
doi = {https://doi.org/10.1016/j.onehlt.2021.100357},
issn = {2352-7714},
journal = {One Health},
volume = {13},
pages = {100357},
keywords = {Animal disease surveillance, Software, Text mining},
pubstate = {published},
tppubtype = {article}
}
Syed, Mehtab Alam; Arsevska, Elena; Roche, Mathieu; Teisseire, Maguelonne
Feature Selection for Sentiment Classification of COVID-19 Tweets: H-TFIDF Featuring BERT Conference
Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF, INSTICC SciTePress, 0000, ISBN: 978-989-758-552-4.
Links | BibTeX | Tags: Feature selection, sentiment analysis, Text mining, Twitter
@conference{@conference{healthinf22,
title = {Feature Selection for Sentiment Classification of COVID-19 Tweets: H-TFIDF Featuring BERT},
author = {Syed, Mehtab Alam and Arsevska, Elena and Roche, Mathieu and Teisseire, Maguelonne},
url = {https://www.scitepress.org/Link.aspx?doi=10.5220/0010887800003123},
doi = {10.5220/0010887800003123},
isbn = {978-989-758-552-4},
booktitle = {Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF},
pages = {648-656},
publisher = {SciTePress},
organization = {INSTICC},
keywords = {Feature selection, sentiment analysis, Text mining, Twitter},
pubstate = {published},
tppubtype = {conference}
}
Decoupes, Rémy; Kafando, Rodrique; Roche, Mathieu; Maguelonne, Teisseire.
Proceedings of the 24th AGILE Conference on Geographic Information Science, vol. 2, AGILE: GIScience Series 2 0000.
Abstract | Links | BibTeX | Tags: pandemic situation, social networks
@conference{nokey,
title = {H-TFIDF: What makes areas specific over time in the massive flow of tweets related to the covid pandemic?},
author = {Decoupes, Rémy and Kafando, Rodrique and Roche, Mathieu and Teisseire. Maguelonne},
editor = { Panagiotis Partsinevelos, Phaedon Kyriakidis, and Marinos Kavouras},
url = {https://agile-giss.copernicus.org/articles/2/2/2021/agile-giss-2-2-2021.pdf},
doi = {https://doi.org/10.5194/agile-giss-2-2-2021},
booktitle = {Proceedings of the 24th AGILE Conference on Geographic Information Science},
volume = {2},
series = {AGILE: GIScience Series 2},
abstract = {Data produced by social networks may contain weak
signals of possible epidemic outbreaks. In this paper,
we focus on Twitter data during the waiting period before the appearance of COVID-19 first cases outside
China. Among the huge flow of tweets that reflects a global growing concern in all countries, we propose to analyze such data with an adaptation of the TF-IDF measure. It allows the users to extract the discriminant vocabularies used across time and space. The results are then discussed to show how the specific spatiotemporal anchoring of the extracted terms make it possible to follow the crisis dynamics on different scales of time and space.},
keywords = {pandemic situation, social networks},
pubstate = {published},
tppubtype = {conference}
}
signals of possible epidemic outbreaks. In this paper,
we focus on Twitter data during the waiting period before the appearance of COVID-19 first cases outside
China. Among the huge flow of tweets that reflects a global growing concern in all countries, we propose to analyze such data with an adaptation of the TF-IDF measure. It allows the users to extract the discriminant vocabularies used across time and space. The results are then discussed to show how the specific spatiotemporal anchoring of the extracted terms make it possible to follow the crisis dynamics on different scales of time and space.