Exploring seasonal pollen probabilities: Ambee’s comparative approach

Pollen allergies are increasingly affecting millions of people worldwide, causing discomfort and impacting daily life. Which is why it’s becoming increasingly important to predict pollen levels accurately. That way, we would be able to help individuals manage their allergies proactively.

However, traditional methods often lack the spatial coverage and real-time capabilities to provide comprehensive insights.

Ambee’s data scientists looked at pollen levels in big cities like Sydney, New York, Lyon, Paris, and London, only to realize that predicting pollen isn’t straightforward. So, we came up with a new way of modeling pollen season probabilities. 

How?

• We used Gamma and Beta distributions to understand the skewed nature of pollen counts.
• We generated synthetic data to explore distribution characteristics over time and across diverse regions.
• We concentrated on weekly fluctuations for clearer insights into seasonal patterns.
• We highlighted the importance of local factors and seasonality in data analysis.

Long story short, we’ve got a smarter way to predict the pollen troubles. Read our research here for all the details.