Utilizing Google Trends data to enhance forecasts and monitor long COVID prevalence DOI Creative Commons
Amanda M. Y. Chu, Jenny Tsun Yee Tsang, Sophia Siu Chee Chan

и другие.

Communications Medicine, Год журнала: 2025, Номер 5(1)

Опубликована: Май 16, 2025

Long COVID, the persistent illness following COVID-19 infection, has emerged as a major public health concern since outbreak of pandemic. Effective disease surveillance is crucial for policymaking and resource allocation. We investigated potential utilizing Google Trends data to enhance long COVID symptoms surveillance. Though provides freely available search popularity data, limitations in normalization retrieval restrictions have hindered its predictive capabilities. In our study, we carefully selected 33 terms 20 related topics from list provided by Centers Disease Control Prevention database "scite", calculated their merged volumes using developed statistical method analysis. identify four (ageusia, anosmia, chest pain, headaches) that consistently exhibit increased before "long COVID." Additionally, nine (aching muscle anxiety, clouding consciousness, dizziness, fatigue, myalgia, shortness breath, hypochondriasis) show demonstrate volume (MSV), derived relative downloaded Google, can be used forecast prevalence prediction supporting use methodology risk management regarding COVID. By comprehensive sophisticated analytics, study contributes exploring forecasting monitoring prevalence. These findings methodologies prior knowledge inform future infodemiological epidemiological investigations.

Язык: Английский

Utilizing Google Trends data to enhance forecasts and monitor long COVID prevalence DOI Creative Commons
Amanda M. Y. Chu, Jenny Tsun Yee Tsang, Sophia Siu Chee Chan

и другие.

Communications Medicine, Год журнала: 2025, Номер 5(1)

Опубликована: Май 16, 2025

Long COVID, the persistent illness following COVID-19 infection, has emerged as a major public health concern since outbreak of pandemic. Effective disease surveillance is crucial for policymaking and resource allocation. We investigated potential utilizing Google Trends data to enhance long COVID symptoms surveillance. Though provides freely available search popularity data, limitations in normalization retrieval restrictions have hindered its predictive capabilities. In our study, we carefully selected 33 terms 20 related topics from list provided by Centers Disease Control Prevention database "scite", calculated their merged volumes using developed statistical method analysis. identify four (ageusia, anosmia, chest pain, headaches) that consistently exhibit increased before "long COVID." Additionally, nine (aching muscle anxiety, clouding consciousness, dizziness, fatigue, myalgia, shortness breath, hypochondriasis) show demonstrate volume (MSV), derived relative downloaded Google, can be used forecast prevalence prediction supporting use methodology risk management regarding COVID. By comprehensive sophisticated analytics, study contributes exploring forecasting monitoring prevalence. These findings methodologies prior knowledge inform future infodemiological epidemiological investigations.

Язык: Английский

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