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.
Mulchandani, Ranya; Wang, Yu; Gilbert, Marius; Boeckel, Thomas P Van
Global trends in antimicrobial use in food-producing animals: 2020 to 2030 Journal Article
In: PLOS Global Public Health, vol. 3, no. 2, pp. e0001305, 2023.
Abstract | Links | BibTeX | Tags: ASF (African Swine Fever), OpenDataSet
@article{nokey,
title = {Global trends in antimicrobial use in food-producing animals: 2020 to 2030},
author = {Ranya Mulchandani and Yu Wang and Marius Gilbert and Thomas P Van Boeckel},
editor = {Ismail Ayoade Odetokun, University of Ilorin, NIGERIA},
url = {https://journals.plos.org/globalpublichealth/article?id=10.1371/journal.pgph.0001305#abstract0},
doi = {10.1371/journal.pgph.0001305},
year = {2023},
date = {2023-02-01},
urldate = {2023-02-01},
journal = {PLOS Global Public Health},
volume = {3},
number = {2},
pages = {e0001305},
abstract = {Use of antimicrobials in farming has enabled the growth of intensive animal production and helped in meeting the global increase in demand for animal protein. However, the widespread use of veterinary antimicrobials drives antimicrobial resistance, with important consequences for animal health, and potentially human health. Global monitoring of antimicrobial use is essential: first, to track progress in reducing the reliance of farming on antimicrobials. Second, to identify countries where antimicrobial-stewardship efforts should be targeted to curb antimicrobial resistance. Data on usage of antimicrobials in food animals were collected from 42 countries. Multivariate regression models were used in combination with projections of animal counts for cattle, sheep, chicken, and pigs from the Food and Agriculture Organization to estimate global antimicrobial usage of veterinary antimicrobials in 2020 and 2030. Maps of animal densities were used to identify geographic hotspots of antimicrobial use. In each country, estimates of antimicrobial use (tonnes) were calibrated to match continental-level reports of antimicrobial use intensity (milligrams per kilogram of animal) from the World Organization for Animal Health, as well as country-level reports of antimicrobial use from countries that made this information publicly available. Globally, antimicrobial usage was estimated at 99,502 tonnes (95% CI 68,535–198,052) in 2020 and is projected, based on current trends, to increase by 8.0% to 107,472 tonnes (95% CI: 75,927–202,661) by 2030. Hotspots of antimicrobial use were overwhelmingly in Asia (67%), while <1% were in Africa. Findings indicate higher global antimicrobial usage in 2030 compared to prior projections that used data from 2017; this is likely associated with an upward revision of antimicrobial use in Asia/Oceania (~6,000 tonnes) and the Americas (~4,000 tonnes). National-level reporting of antimicrobial use should be encouraged to better evaluate the impact of national policies on antimicrobial use levels.},
keywords = {ASF (African Swine Fever), OpenDataSet},
pubstate = {published},
tppubtype = {article}
}
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}
}