| 英文摘要 |
Objectives. Coronavirus disease 2019 (COVID-19) is an atypical pneumonia caused by SARS-CoV-2 infection, which is continuing to spread worldwide. Nowadays, nucleic acid testing is the golden standard to diagnosis COVID-19. However, technique limitation remains in some less-developed countries and remote areas. Therefore, we aim to develop a surveillance system suitable for monitoring epidemic outbreaks and assessing public opinion.
Methods. We used Pearson coefficients to analyze the correlation between COVID-19 epidemic data (confirmed case) and online search query data.
Results. Based on our approach, we noted that keywords“COVID-19”,“corona”,“PPE (Personal protective equipment)”,“community infection”,“smell”exhibited good correlation between Google Trends query data and COVID-19 incidence; keywords“pneumonia”,“corona virus”,“mask”,“quarantine”,“fever”,“taste”,“dyspnea”, exhibited moderate correlation. Furthermore, some keywords displayed high correlation with 1-week lag COVID-19 incidence, including“corona”,“PPE”,“community infection”,“quarantine”and“ECMO”.
Conclusion. These results suggested that Google Trends can be a potential surveillance tool for COVID-19 outbreaks in Taiwan. Online search activity indicates what people concerned during epidemic outbreaks, even if they do not visit hospitals. This finding prompted us to analyze the big data and serve as useful tools to monitor social media during an epidemic. These media usage reflects infectious disease trends more quickly than does traditional reporting. |
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