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

Document Type : Original Article


1 Assistant professor, Department of Epidemiology and Biostatistics, School of Public Health, Shahrekord University of Medical Sciences, Shahrekord, Iran

2 Department of Public Health Behbahan Faculty of Medical Sciences, Behbahan, Iran

3 Student Research Committee, School of Nursing and Midwifery, Sabzevar University of Medical Sciences, Sabzevar, Iran

4 Student Research Committee, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran

5 Alimentary Tract Research Center, Imam Khomeini Hospital Clinical Research Development Unit, Jundishapur University of Medical Sciences, Ahvaz, Iran

6 School of Management and Medical Informatics, Shiraz University of Medical Sciences, Shiraz, Iran

7 Assistant professor, Modeling in Health Research Center, Shahrekord University of Medical Sciences, Shahrekord, Iran -Assistant professor, Department of Epidemiology and Biostatistics, School of Public Health, Shahrekord University of Medical Sciences, Shahrekord, 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.


Olivera P, Danese S, Jay N, Natoli G, Peyrin-Biroulet L. Big data in IBD: a look into the future. Nat Rev Gastroenterol Hepatol. 2019;16(5):312-21. doi: 10.1038/s41575-019-0102-5.
2. Lu SQ, Xie G, Chen Z, Han X, editors. The management of application of big data in internet of thing in environmental protection in China. 13 August 2015. Redwood: 2015 IEEE First International Conference on Big Data Computing Service and Applications; 2015. doi: 10.1109/BigDataService.2015.68.
3. Sarowar MG, Kamal MS, Dey N. Internet of Things and its impacts in computing intelligence: a comprehensive review IoT application for big data. Big Data Analytics for Smart and Connected Cities. 2019:103-36. doi: 10.4018/978-1-52256207-8.ch005.
4. Eckmanns T, Fuller H, Roberts SL. Digital epidemiology and global health security; an interdisciplinary conversation. Life Sci Soc Policy. 2019;15(1):2. doi: 10.1186/s40504-019-0091-8.
5. Murdoch TB, Detsky AS. The inevitable application of big data to health care. JAMA. 2013;309(13):1351-2. doi: 10.1001/jama.2013.393.
6. Cervellin G, Comelli I, Lippi G. Is Google Trends a reliable tool for digital epidemiology? Insights from different clinical settings. J Epidemiol Glob Health. 2017;7(3):185-9. doi: 10.1016/j.jegh.2017.06.001.
7. Raub M. Bots, bias and big data: artificial intelligence, algorithmic bias and disparate impact liability in hiring practices. Ark L Rev.2018;71:529.
8. Griffin GP, Mulhall M, Simek C, Riggs WW. Mitigating bias in big data for transportation. Journal of Big Data Analytics in Transportation. 2020;2(1):49-59. doi: 10.1007/s42421-020-00013-0.
9. Lippi G, Mattiuzzi C, Cervellin G. Is Digital Epidemiology the Future of Clinical Epidemiology? J Epidemiol Glob Health. 2019;9(2):146. doi: 10.2991/jegh.k.190314.003.
10. Budd J, Miller BS, Manning EM, Lampos V, Zhuang M, Edelstein M, et al. Digital technologies in the public-health response to COVID-19. Nat Med. 2020;26(8):1183-92. doi: 10.1038/s41591-020-1011-4.
11. Tarkoma S, Alghnam S, Howell MD. Fighting pandemics with digital epidemiology. EClinicalMedicine. 2020;26:100512. doi: 10.1016/j.eclinm.2020.100512.
12. Khan N, Faisal S. Epidemiology of Corona virus in the world and its effects on the China economy. Available at SSRN 3548292. 2020. doi: 10.2139/ssrn.3548292.
13. Park YE. Research evidence for reshaping global energy strategy based on trend-based approach of big data analytics in the corona era. Energy Strategy Reviews. 2022;41:100835. doi: 10.1016/j.esr.2022.100835.
14. Salathe M, Freifeld CC, Mekaru SR, Tomasulo AF, Brownstein JS. Influenza A (H7N9) and the importance of digital epidemiology. N Engl J Med. 2013;369(5):401-4. doi: 10.1056/NEJMp1307752.
15. Fagherazzi G, Goetzinger C, Rashid MA, Aguayo G, Huiart L. Digital health strategies to fight COVID-19 around the globe: challenges and recommendations. J Med Internet Res. 2020;46(3):1-8. doi: 10.2196/preprints.19284.
16. Walker A, Hopkins C, Surda P. Use of Google Trends to investigate loss-of-smell-related searches during the COVID-19 outbreak. Int Forum Allergy Rhinol. 2020;10(7):839-47. doi: 10.1002/alr.22580.
17. Lippi G, Mattiuzzi C, Cervellin G. Google search volume predicts the emergence of COVID-19 outbreaks. Acta Biomed. 2020;91(3):e2020006. doi: 10.23750/abm.v91i3.10030.