Asset Details
Do you wish to reserve the book?
Long-term forecast for antibacterial drug consumption in Germany using ARIMA models
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Long-term forecast for antibacterial drug consumption in Germany using ARIMA models
Do you wish to request the book?
Long-term forecast for antibacterial drug consumption in Germany using ARIMA models
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Long-term forecast for antibacterial drug consumption in Germany using ARIMA models
2025
Overview
The increasing supply shortages of antibacterial drugs presents significant challenges to public health in Germany. This study aims to predict the future consumption of the ten most prescribed antibacterial drugs in Germany up to 2040 using ARIMA (Auto Regressive Integrated Moving Average) models, based on historical prescription data. This analysis also evaluates the plausibility of the forecasts. Our findings represent one of the first long-term national forecasts for antibacterial drug consumption. ARIMA(
0,1,0
), a random walk model with drift, is the best-fitting model to capture trends across all antibacterial drugs. While more complex models offer greater detail, they seem less suitable for long-term forecasting. In a short-term forecast of 5 and 10 years, predictions between significant models vary very little. Predictions indicate increasing DDD-prescriptions for amoxicillin, cefuroxime axetil, amoxicillin clavulanic acid, clindamycin, azithromycin, nitrofurantoin, and ciprofloxacin, while declines are forecasted for doxycycline, phenoxymethylpenicillin, and sulfamethoxazole-trimethoprim. The reliability of the predictions varies. Forecasts for azithromycin, phenoxymethylpenicillin, and sulfamethoxazole-trimethoprim are likely accurate, whereas uncertainties exist for doxycycline, amoxicillin clavulanic acid, nitrofurantoin, and ciprofloxacin, though general trends appear valid. Potential discrepancies may arise in the predictions for amoxicillin, cefuroxime axetil, and clindamycin. These forecasts highlight the urgent need for proactive healthcare planning to prevent future shortages, a problem underscored by recent supply disruptions in Germany. Future research should extend this analysis to the development of bacterial resistance and other frequently used drug classes.
Publisher
Springer Berlin Heidelberg,Springer Nature B.V
