ABSTRACT
BackgroundCOVID-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. MethodsHFMD 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. ResultsHFMD 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. ConclusionsSignificant 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.
ABSTRACT
BackgroundThe global public health and socioeconomic impacts of coronavirus disease 2019 (COVID-19) have been substantial, making herd immunity by COVID-19 vaccination an important factor for protecting people and retrieving the economy. Among all the countries, Japan became one of the countries with the highest COVID-19 vaccination rate in several months, although the vaccine confidence in Japan is the lowest worldwide. ObjectiveWe attempted to find the reasons for the rapid coronavirus disease 2019 (COVID-19) vaccination in Japan under the lowest vaccine confidence in the world by Twitter analysis. Materials and methodsWe downloaded COVID-19 related Japanese tweets from a large-scale public COVID-19 Twitter chatter dataset within the timeline of February 1, 2021 and September 30, 2021. The daily number of vaccination cases was collected from the official website of the Prime Ministers Office of Japan. After preprocessing, we applied unigram and bigram token analysis, then calculated the cross correlation and Pearson correlation coefficient (r) between the term frequency and daily vaccination cases. Then we identified vaccine sentiments and emotions of tweets and used the topic modeling to look deeper into the dominant emotions. ResultsWe selected 190,697 vaccine-related tweets after filtering. By n-gram token analysis, we discovered the top unigrams and bigrams over the whole period. In all the combinations of the top six unigrams, tweets with both keywords "reserve" and "venue" showed the largest r = 0.912 (P < 0.001) with the daily vaccination cases. In sentiment analysis, negative sentiment overwhelmed positive sentiment, and fear was the dominant emotion across the period. For the latent Dirichlet allocation model on tweets with fear emotion, the two topics were identified as "infect" and "vaccine confidence". The expectation of the number of tweets generated from topic "infect" was larger than "vaccine confidence." ConclusionOur work indicated that awareness of the danger of COVID-19 might increase the willingness to get vaccinated; With sufficient vaccine supply, effective vaccine reservation information delivery may be an important factor for people to get vaccinated; We didnt find evidence for increased vaccine confidence in Japan during the period in our research. We recommend policymakers to share fair and prompt information about the infectious diseases and vaccination, and make efforts on smoother delivery of vaccine-reservation information.
ABSTRACT
BackgroundThe pandemic of COVID-19 is causing a crisis in public health, food systems, and employment. Vaccination is considered as one of the most effective ways for containing the pandemic, but widespread vaccine hesitation on social media may curtail uptake progress. Fully comprehending public sentiment towards the COVID-19 vaccine is critical to building confidence on the vaccines and achieving herd immunity, especially in Japan with inadequate vaccine confidence. ObjectiveThis study aims to explore the opinion and sentiment towards the COVID-19 vaccine in Japanese tweets, before and at the beginning of large-scale vaccinations. MethodsWe collected 144,101 Japanese tweets containing COVID-19 vaccine-related keywords between August 1, 2020, and June 30, 2021. We visualized the trend of number of tweets and identified the critical events that triggered a surge and provided high-frequency unigram and bigram tokens. Also, we performed sentiment analysis and calculated the correlation of number of tweets and positive/negative sentiments with infection, death, and vaccinated cases. we also used the latent Dirichlet allocation (LDA) model to identify topics of tweets. In addition, we conducted analysis on three vaccine brands (Pfizer/Moderna/AstraZeneca). ResultsDaily number of tweets continued growing and the growth accelerated since the large-scale vaccinations in Japan. The sentiment of around 85% tweets were neutral, and the negative sentiment overwhelmed the positive sentiment in the other tweets. Number of tweets strongly correlated (r[≥]0.5) with infection/death/vaccinated cases, and the number of negative tweets correlated strongly with the number of infection/death cases but weakened after the first vaccination in Japan. LDA identified three public-concerned topics: vaccine appointment and distribution strategy; Different vaccines development progress and approval status of countries; Side effects and effectiveness against mutated viruses. Among vaccines of the three manufactures, Pfizer won the most attention and Moderna the least. ConclusionsOur findings indicated that negative sentiment towards vaccines dominated than positive sentiment in Japan. Changes in number of tweets and sentiments might be driven by critical events related to the COVID-19 vaccine, and negative sentiment continued increasing when numerous adverse accidents occurred at the beginning of large-scale vaccinations. Under the negative sentiment, the concerns of three vaccine brands remains effectiveness and safety with slight differences. The policymakers should provide more evidence about the effectiveness and safety of vaccines and optimize the process of vaccinations.
