Antimicrobial Resistance (AMR)-Forecast for 30 countries in Europe

Antimicrobial resistance (AMR) has emerged among the most serious public health issues, prompting the creation of worldwide implementation strategies. In this study, the application of seasonal or time-series approaches was suggested for forecasting the unknown percentages of resistance towards othe...

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Main Authors: Mohd Jaffar, Mai Zurwatul Ahlam, Zailan, Aimi Najwa
Format: Article
Published: Zibeline International 2021
Online Access:http://psasir.upm.edu.my/id/eprint/95920/
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author Mohd Jaffar, Mai Zurwatul Ahlam
Zailan, Aimi Najwa
author_facet Mohd Jaffar, Mai Zurwatul Ahlam
Zailan, Aimi Najwa
author_sort Mohd Jaffar, Mai Zurwatul Ahlam
building UPM Institutional Repository
collection Online Access
description Antimicrobial resistance (AMR) has emerged among the most serious public health issues, prompting the creation of worldwide implementation strategies. In this study, the application of seasonal or time-series approaches was suggested for forecasting the unknown percentages of resistance towards other microbial groups for seven microorganisms. Annual data between 2012 and 2019 were acquired from European Centre for Disease Prevention, and Control (ECDC) reports. Microsoft Excel’s function, ‘FORECAST.ETS’, was used for prediction purposes. Then, a brief analysis was done on the forecasted results. Forecasting AMR’s percentage makes it possible to develop a strategy for dealing with any situation that may emerge.
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institution Universiti Putra Malaysia
institution_category Local University
last_indexed 2025-11-15T13:14:05Z
publishDate 2021
publisher Zibeline International
recordtype eprints
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spelling upm-959202023-03-21T04:16:12Z http://psasir.upm.edu.my/id/eprint/95920/ Antimicrobial Resistance (AMR)-Forecast for 30 countries in Europe Mohd Jaffar, Mai Zurwatul Ahlam Zailan, Aimi Najwa Antimicrobial resistance (AMR) has emerged among the most serious public health issues, prompting the creation of worldwide implementation strategies. In this study, the application of seasonal or time-series approaches was suggested for forecasting the unknown percentages of resistance towards other microbial groups for seven microorganisms. Annual data between 2012 and 2019 were acquired from European Centre for Disease Prevention, and Control (ECDC) reports. Microsoft Excel’s function, ‘FORECAST.ETS’, was used for prediction purposes. Then, a brief analysis was done on the forecasted results. Forecasting AMR’s percentage makes it possible to develop a strategy for dealing with any situation that may emerge. Zibeline International 2021 Article PeerReviewed Mohd Jaffar, Mai Zurwatul Ahlam and Zailan, Aimi Najwa (2021) Antimicrobial Resistance (AMR)-Forecast for 30 countries in Europe. Science Heritage Journal, 5 (2). 44 - 48. ISSN 2521-0858; ESSN: 2521-0866 https://www.researchgate.net/publication/357424862_ANTIMICROBIAL_RESISTANCE_AMR-FORECAST_FOR_30_COUNTRIES_IN_EUROPE 10.26480/gws.02.2021.44.48
spellingShingle Mohd Jaffar, Mai Zurwatul Ahlam
Zailan, Aimi Najwa
Antimicrobial Resistance (AMR)-Forecast for 30 countries in Europe
title Antimicrobial Resistance (AMR)-Forecast for 30 countries in Europe
title_full Antimicrobial Resistance (AMR)-Forecast for 30 countries in Europe
title_fullStr Antimicrobial Resistance (AMR)-Forecast for 30 countries in Europe
title_full_unstemmed Antimicrobial Resistance (AMR)-Forecast for 30 countries in Europe
title_short Antimicrobial Resistance (AMR)-Forecast for 30 countries in Europe
title_sort antimicrobial resistance (amr)-forecast for 30 countries in europe
url http://psasir.upm.edu.my/id/eprint/95920/
http://psasir.upm.edu.my/id/eprint/95920/
http://psasir.upm.edu.my/id/eprint/95920/