TY - JOUR
T1 - Addressing Learning Needs on the Use of Metagenomics in Antimicrobial Resistance Surveillance
AU - Ribeiro Duarte, Ana Sofia
AU - Stärk, Katharina D. C.
AU - Munk, Patrick
AU - Leekitcharoenphon, Pimlapas
AU - Bossers, Alex
AU - Luiken, Roosmarijn
AU - Sarrazin, Steven
AU - Lukjancenko, Oksana
AU - Pamp, Sünje Johanna
AU - Bortolaia, Valeria
AU - Nissen, Jakob Nybo
AU - Kirstahler, Philipp
AU - Van Gompel, Liese
AU - Poulsen, Casper Sahl
AU - Kaas, Rolf Sommer
AU - Hellmér, Maria
AU - Hansen, Rasmus Borup
AU - Gomez, Violeta Munoz
AU - Hald, Tine
PY - 2020
Y1 - 2020
N2 - One Health surveillance of antimicrobial resistance (AMR) depends on a harmonized method for detection of AMR. Metagenomics-based surveillance offers the possibility to compare resistomes within and between different target populations. Its potential to be embedded into policy in the future calls for a timely and integrated knowledge dissemination strategy. We developed a blended training (e-learning and a workshop) on the use of metagenomics in surveillance of pathogens and AMR. The objectives were to highlight the potential of metagenomics in the context of integrated surveillance, to demonstrate its applicability through hands-on training and to raise awareness to bias factors1. The target participants included staff of competent authorities responsible for AMR monitoring and academic staff. The training was organized in modules covering the workflow, requirements, benefits and challenges of surveillance by metagenomics. The training had 41 participants. The face-to-face workshop was essential to understand the expectations of the participants about the transition to metagenomics-based surveillance. After revision of the e-learning, we released it as a Massive Open Online Course (MOOC), now available at https://www.coursera.org/learn/metagenomics. This course has run in more than 20 sessions, with more than 3,000 learners enrolled, from more than 120 countries. Blended learning and MOOCs are useful tools to deliver knowledge globally and across disciplines. The released MOOC can be a reference knowledge source for international players in the application of metagenomics in surveillance.
AB - One Health surveillance of antimicrobial resistance (AMR) depends on a harmonized method for detection of AMR. Metagenomics-based surveillance offers the possibility to compare resistomes within and between different target populations. Its potential to be embedded into policy in the future calls for a timely and integrated knowledge dissemination strategy. We developed a blended training (e-learning and a workshop) on the use of metagenomics in surveillance of pathogens and AMR. The objectives were to highlight the potential of metagenomics in the context of integrated surveillance, to demonstrate its applicability through hands-on training and to raise awareness to bias factors1. The target participants included staff of competent authorities responsible for AMR monitoring and academic staff. The training was organized in modules covering the workflow, requirements, benefits and challenges of surveillance by metagenomics. The training had 41 participants. The face-to-face workshop was essential to understand the expectations of the participants about the transition to metagenomics-based surveillance. After revision of the e-learning, we released it as a Massive Open Online Course (MOOC), now available at https://www.coursera.org/learn/metagenomics. This course has run in more than 20 sessions, with more than 3,000 learners enrolled, from more than 120 countries. Blended learning and MOOCs are useful tools to deliver knowledge globally and across disciplines. The released MOOC can be a reference knowledge source for international players in the application of metagenomics in surveillance.
KW - Survellance
KW - Metagenomics
KW - MOOC
KW - Antimicrobial resistance
KW - One health
U2 - 10.3389/fpubh.2020.00038
DO - 10.3389/fpubh.2020.00038
M3 - Journal article
C2 - 32158739
SN - 2296-2565
VL - 8
JO - Frontiers in Public Health
JF - Frontiers in Public Health
M1 - 38
ER -