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; Zhao, Cheng; Tiseo, Katie; Pires, João; Boeckel, Thomas P. Van
Predictive Mapping of Antimicrobial Resistance for Escherichia coli, Salmonella, and Campylobacter in Food-Producing Animals, Europe, 2000–2021 Journal Article
In: Emerging Infectious Diseases, pp. 96-104, 2024.
Abstract | Links | BibTeX | Tags: AMR (Antimicrobial Resistance), OpenDataSet
@article{nokey,
title = {Predictive Mapping of Antimicrobial Resistance for Escherichia coli, Salmonella, and Campylobacter in Food-Producing Animals, Europe, 2000–2021},
author = {Ranya Mulchandani and Cheng Zhao and Katie Tiseo and João Pires and Thomas P. Van Boeckel},
url = {https://wwwnc.cdc.gov/eid/article/30/1/22-1450_article},
doi = {10.3201/eid3001.221450},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Emerging Infectious Diseases},
pages = {96-104},
abstract = {In Europe, systematic national surveillance of antimicrobial resistance (AMR) in food-producing animals has been conducted for decades; however, geographic distribution within countries remains unknown. To determine distribution within Europe, we combined 33,802 country-level AMR prevalence estimates with 2,849 local AMR prevalence estimates from 209 point prevalence surveys across 31 countries. We produced geospatial models of AMR prevalence in Escherichia coli, nontyphoidal Salmonella, and Campylobacter for cattle, pigs, and poultry. We summarized AMR trends by using the proportion of tested antimicrobial compounds with resistance >50% and generated predictive maps at 10 × 10 km resolution that disaggregated AMR prevalence. For E. coli, predicted prevalence rates were highest in southern Romania and southern/eastern Italy; for Salmonella, southern Hungary and central Poland; and for Campylobacter, throughout Spain. Our findings suggest that AMR distribution is heterogeneous within countries and that surveillance data from below the country level could help with prioritizing resources to reduce AMR.},
keywords = {AMR (Antimicrobial Resistance), OpenDataSet},
pubstate = {published},
tppubtype = {article}
}
Arınık, Nejat; Bortel, Wim Van; Boudoua, Bahdja; Busani, Luca; Decoupes, Rémy; Interdonato, Roberto; Kafando, Rodrique; van Kleef, Esther; Roche, Mathieu; Syed, Mehtab Alam; Teisseire, Maguelonne
An annotated dataset for event-based surveillance of antimicrobial resistance Journal Article
In: ScienceDirect, 2023.
Abstract | Links | BibTeX | Tags: AMR (Antimicrobial Resistance), OpenDataSet, Text mining
@article{nokey,
title = {An annotated dataset for event-based surveillance of antimicrobial resistance},
author = {Nejat Arınık and Wim Van Bortel and Bahdja Boudoua and Luca Busani and Rémy Decoupes and Roberto Interdonato and Rodrique Kafando and Esther van Kleef and Mathieu Roche and Mehtab Alam Syed and Maguelonne Teisseire
},
url = {https://www.sciencedirect.com/science/article/pii/S2352340922010733?via%3Dihub},
doi = {10.1016/j.dib.2022.108870},
year = {2023},
date = {2023-02-08},
urldate = {2023-02-08},
journal = {ScienceDirect},
abstract = {This paper presents an annotated dataset used in the MOOD Antimicrobial Resistance (AMR) hackathon, hosted in Montpellier, June 2022. The collected data concerns unstructured data from news items, scientific publications and national or international reports, collected from four event-based surveillance (EBS) Systems, i.e. ProMED, PADI-web, HealthMap and MedISys. Data was annotated by relevance for epidemic intelligence (EI) purposes with the help of AMR experts and an annotation guideline. Extracted data were intended to include relevant events on the emergence and spread of AMR such as reports on AMR trends, discovery of new drug-bug resistances, or new AMR genes in human, animal or environmental reservoirs. This dataset can be used to train or evaluate classification approaches to automatically identify written text on AMR events across the different reservoirs and sectors of One Health (i.e. human, animal, food, environmental sources, such as soil and waste water) in unstructured data (e.g. news, tweets) and classify these events by relevance for EI purposes.
},
keywords = {AMR (Antimicrobial Resistance), OpenDataSet, Text mining},
pubstate = {published},
tppubtype = {article}
}
Tiseo, Katie; Huber, Laura; Gilbert, Marius; Robinson, Timothy P.; Boeckel, Thomas P. Van
Global Trends in Antimicrobial Use in Food Animals from 2017 to 2030 Journal Article
In: Antibiotics, vol. 9, no. 12, 2020, ISSN: 2079-6382.
Abstract | Links | BibTeX | Tags: AMR (Antimicrobial Resistance), OpenDataSet
@article{antibiotics9120918,
title = {Global Trends in Antimicrobial Use in Food Animals from 2017 to 2030},
author = {Katie Tiseo and Laura Huber and Marius Gilbert and Timothy P. Robinson and Thomas P. Van Boeckel},
url = {https://www.mdpi.com/2079-6382/9/12/918},
doi = {10.3390/antibiotics9120918},
issn = {2079-6382},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {Antibiotics},
volume = {9},
number = {12},
abstract = {Demand for animal protein is rising globally and has been facilitated by the expansion of intensive farming. However, intensive animal production relies on the regular use of antimicrobials to maintain health and productivity on farms. The routine use of antimicrobials fuels the development of antimicrobial resistance, a growing threat for the health of humans and animals. Monitoring global trends in antimicrobial use is essential to track progress associated with antimicrobial stewardship efforts across regions. We collected antimicrobial sales data for chicken, cattle, and pig systems in 41 countries in 2017 and projected global antimicrobial consumption from 2017 to 2030. We used multivariate regression models and estimated global antimicrobial sales in 2017 at 93,309 tonnes (95% CI: 64,443, 149,886). Globally, sales are expected to rise by 11.5% in 2030 to 104,079 tonnes (95% CI: 69,062, 172,711). All continents are expected to increase their antimicrobial use. Our results show lower global antimicrobial sales in 2030 compared to previous estimates, owing to recent reports of decrease in antimicrobial use, in particular in China, the world's largest consumer. Countries exporting a large proportion of their production are more likely to report their antimicrobial sales data than countries with small export markets.},
keywords = {AMR (Antimicrobial Resistance), OpenDataSet},
pubstate = {published},
tppubtype = {article}
}