Researchers have discovered that social media analysis can reveal patterns in seasonal allergies. Over 25% of Americans suffer from seasonal allergies, but how the prevalence of these allergies varies across space and time remains unclear, as allergies seldom warrant visits to healthcare providers. To address this issue, Elias Stallard-Olivera and Noah Fierer mined Twitter and Google searches from 2016-2020 to extrapolate spatial and temporal allergy patterns.
The researchers used a natural language processing model to sort posts that indicated symptoms from posts that included keywords but did not indicate the presence of symptoms. The authors validated their data against emergency department (ED) visits for manifestations of seasonal allergies and found reasonably strong relationships between online activity about seasonal allergies and ED records. They then used the resulting model to infer seasonal allergy patterns across all major metropolitan areas in the United States.
The resulting maps show a strong national pulse of allergy symptoms in March-May, with a wave of low-grade human misery beginning in the Southeast and ending in the Northeast and Upper Midwest. Interannual variability is considerable. According to the authors, anomalous spikes of allergy symptoms, such as one recorded in Los Angeles County in June 2018, could indicate booms in specific pollens or molds.
Conclusion
The researchers believe that their method could assist with predictive modeling of seasonal allergies and allergen exposures. By analyzing social media posts and Google searches, they can infer seasonal allergy patterns across the United States and identify anomalous spikes of allergy symptoms, which could help predict and prevent future allergy outbreaks.
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