Google Trend as an Early Warning System for Corona Outbreak Investigation in Iran

Introduction: Digital epidemiology is introduced as a major aspect of epidemiology; its sources are digital data and it uses spaces such as Google, YouTube and Twitter as databases. In the recent Covid-19 pandemic, the use of digital epidemiology, as an early warning system, has been considered. This study aimed to investigate the context of Google Trend as an early warning system in the study of coronavirus outbreaks in Iran. Methods: The coronavirus epidemic in Iran started on February 24, 2020, and with some differences to consider the rumors in the community, we consider the date before the announcement of all by February 16, 2020 until November 16, 2021. We searched using keywords related to symptoms such as “fever”, “cough” and “sore throat” and the keyword “corona symptoms”; information was extracted and entered in Microsoft Excel and the keyword chart was drawn according to the date of each wave. Spearman correlation test was performed to find the correlation between keywords in SPSS version 18. Results: The trend chart of the keywords “fever”, “cough” and “sore throat” and the keyword “corona symptoms” in different waves of coronavirus in Iran showed an increase in keyword searches before the onset of the corona epidemic wave. Spearman correlation coefficient between sore throat and fever was 0.645, sore throat and cough 0.775, sore throat and corona symptoms 0.684, between fever and cough keywords 0.435, fever and corona symptoms 0.779 and between keyword cough and corona symptoms 0.503. In all these coefficients, the level of error of the first type was 0.05 significant (P<0.001) Conclusion: Google Trend, a digital epidemiology tool, can be used as an effective early warning system to control the corona pandemic, and this field of epidemiological knowledge with all its limitations needs further research.