Seasonal allergies can be a recurring nightmare, with airborne pollen being one of the root causes. In this study, our data scientists unveil a significant correlation between airborne pollen levels and the demand for antihistamines in Serbia. Using a range of General Linear Models (GLMs), the research constructs models to predict current and future sales across daily, weekly, and monthly intervals.
This research highlights the undeniable influence of pollen on allergic reactions and provides a proactive approach to managing symptoms. The optimal model was attained by projecting the forthcoming month's aggregate sales based on the current month's total pollen count, yielding a commendable R2 score of 0.71.
This study's outcome can empower the healthcare sector to make well-informed choices, enabling the assessment of antihistamine demand by utilizing airborne pollen concentration data.
Download the research paper to learn in detail how extensive data sets and employing advanced statistical techniques helped our data scientists establish a clear connection between pollen count surge and the sales of antihistamine medications.
.svg){kind=icon}
.svg){kind=icon}
