Assessment of Google Trends terms reporting allergies and the grass pollen season in Ukraine

Abstract

Background: Grass pollen allergy is an important trigger for the development of respiratory disorders. Defining the grass pollen season onset is critical for correct allergy diagnosis and personalized therapy. The development of a pan-European sentinel network for allergic diseases has raised the problem of translating the Google search terms into different European languages as well as defining specific pollen season characteristics in different regions. Grass pollen allergy was investigated due to high allergenicity and wide expansion of grass pollen in Europe. Objectives: The aim of this study was to examine which translations of "hay fever", "grass", and "rhinitis" could be used in the native Cyrillic languages, especially in Ukrainian and Russian, and to compare the seasonality of allergic respiratory queries in Ukraine with the grass pollen counts. Methods: Google Trends (GT) was used to search Google queries concerning grass pollen allergy: "allergy", "hay fever", "runny nose", "grass", "asthma", and "pollen". The Cyrillic terms in Ukrainian and Russian were used. The search was done for the period from 2013 to 2017. Pollen collection from 2013 to 2016 was conducted using volumetric methods. Average daily temperatures were obtained from http://gismeteo.ua. Correlations were assessed by Spearman (R) test. Results: The Ukrainian Google users searched the Cyrillic equivalents for "runny nose", "grass", and "asthma". Due to the GT queries profile, Ukraine had a "D" pattern according to the classification, developed by Bousquet J et al (2017). In Ukraine, the Poaceae pollen season generally occurred between the second ten-day period of May and the last ten-day period of July. The Poaceae pollen season started with a concentration of pollen grains of 8.0 m À3. This concentration provoked the growth of GT "grass", "allergy", "hay fever", and "asthma" queries. Conclusions: The terms "grass", "allergy", "hay fever", and "asthma" (in their Cyrillic equivalents) are required in Ukraine to account for the grass pollen exposure by GT. The study of GT may be a useful tool to make an assessment of the grass pollen season for the prevention and minimization of exposure to significant grass pollen concentrations.

Can crowdsourced data help to optimize atopic dermatitis treatment? Comparing web search data and environmental data in Germany

“…Previous studies have shown that web search analyses can help to predict medically relevant seasons or to identify unmet needs of patients. For example, web searches correlated with the pollen season or the occurrence of certain infectious diseases 19–21 . Thus, the aim of this study was to analyze AD‐related online search behavior in Germany as a whole and in 16 German cities, focusing on seasonal trends, meteorological influences, and geographic differences to identify on a population‐based level potential environmental trigger factors and unmet needs to optimize treatment options and allocation of healthcare resources.…”

Infodemiology: Using Google Trends as a Research Tool During the Covid-19 Pandemic

“…У якості зручного інструменту інфодеміології протягом тривалого часу активно використовується ресурс Google Trends [11,12,13], зокрема і для дослідження алергічних [15,16,17,18] та інфекційних хвороб [19,20].…”

“…Окремої уваги заслуговують можливості Google Trends як інструмента досліджень алергічних хвороб [15]. Попередніми нашими дослідженнями виявлені кириличні відповідники для прогнозування сезонів алергії на пилок трав, амброзію та альтернарію в Україні [28,29,30].…”

A Mathematical Model for Prognosis of the Covid-19 Incidence in Ukraine Using Google Trends Resources in Real-Time and for the Future Period

“…Комбінація ключових слів пошукових запитів Google ККС була обрана, керуючись наступним: на початкових етапах дослідження термін «COVID-19» не набув ще широкого вжитку у населення України [9], а також вивчення кириличних відповідників пошукових запитів Google дозволяє надійно прогнозувати розвиток хвороб у загальнонаціональному масштабі [15,16].…”