Abstract
Background COVID-19 pandemic affected common disease infections, while the impact on hand, foot, and mouth disease (HFMD) is unclear. Google Trends data is beneficial in approximately real-time statistics and easily accessed, expecting to be used for infection explanation from information-seeking behavior perspectives. We aimed to explain HFMD cases before and during COVID-19 using Google Trends data.
Methods HFMD cases were obtained from the National Institute of Infectious Disease, and Google search data from 2009 to 2021 was downloaded using Google Trends in Japan. Pearson correlation coefficients were calculated between HFMD cases and the search topic “HFMD” from 2009 to 2021. Japanese tweets containing “HFMD” were retrieved to select search terms for further analysis. Search terms were retained with counts larger than 1000 and belonging to ranges of infection sources, susceptible sites, susceptible populations, symptoms, treatment, preventive measures, and identified diseases. Cross-correlation analyses were conducted to detect lag changes between HFMD cases and HFMD search terms before and during COVID-19. Multiple linear regressions with backward elimination processing were used to identify the most significant terms for HFMD explanation.
Results HFMD cases and Google search volume peaked around July in most years without 2020 and 2021. The search topic “HFMD” presented strong correlations with HFMD cases except in 2020 when COVID-19 outbroke. In addition, differences in lags for 73 (72.3%) search terms were negative, might indicating increasing public awareness of HFMD infections during the COVID-19 pandemic. Results of multiple linear regression demonstrated that significant search terms contained the same meanings but expanded informative search content during COVID-19.
Conclusions Significant terms for HFMD cases explanation before and during COVID-19 were different. The awareness of HFMD infection in Japan may improve during the COVID-19 pandemic. Continuous monitoring is important to promote public health and prevent resurgence. Public interest reflected in information-seeking behavior can be helpful for public health surveillance.
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
This work was supported by the Japan Science and Technology for pioneering research initiated by the next generation (SPRING; grant number JPMJSP2110).
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Footnotes
After major revision
Data Availability
All data produced in the present study are available upon reasonable request to the authors
Abbreviations
- HFMD
- hand, foot, and mouth disease
- EV-A71
- enterovirus A71
- CV-A6
- coxsackievirus A6
- CV-A16
- coxsackievirus A16
- NIID
- National Institute of Infectious Disease
- LDE
- logistic differential equation
- L-DNM
- landscape dynamic network marker
- COPD
- chronic obstructive pulmonary disease
- RSV
- relative search volume