Predicting antimicrobial drug consumption using web search data

Consumption of antimicrobial drugs, such as antibiotics, is linked with antimicrobial resistance. Surveillance of antimicrobial drug consumption is therefore an important element in dealing with antimicrobial resistance. Many countries lack sufficient surveillance systems. Usage of web mined data therefore has the potential to improve current surveillance methods. To this end, we study how well antimicrobial drug consumption can be predicted based on web search queries, compared to historical purchase data of antimicrobial drugs.Wepresent twopredictionmodels (linear ElasticNet, and nonlinear Gaussian Processes), which we train and evaluate on almost 6 years ofweekly antimicrobial drug consumption data fromDenmark and web search data fromGoogle Health Trends.We present a novel method of selecting web search queries by considering diseases and drugs linked to antimicrobials, aswell as professional and layman descriptions of antimicrobial drugs, all ofwhichwemine from the open web.Wefindthatpredictionsbasedonwebsearchdataaremarginally more erroneous but overall on a par with predictions based on purchases of antimicrobial drugs. This marginal difference corresponds to <1% point mean absolute error in weekly usage. Best predictions are reported when combining both web search and purchase data. This study contributes a novel alternative solution to the reallife problem of predicting (and hence monitoring) antimicrobial drug consumption, which is particularly valuable in countries/states lacking centralised and timely surveillance systems.