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High variability in model performance of Google relative search volumes in spatially clustered COVID-19 areas of the USA.
Husnayain, Atina; Chuang, Ting-Wu; Fuad, Anis; Su, Emily Chia-Yu.
Afiliación
  • Husnayain A; Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; Department of Biostatistics, Epidemiology and Population Health, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia.
  • Chuang TW; Department of Molecular Parasitology and Tropical Diseases, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
  • Fuad A; Department of Biostatistics, Epidemiology and Population Health, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia.
  • Su EC; Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; Clinical Big Data Research Centre, Taipei Medical University Hospital, Taipei, Taiwan. Electronic address: emilysu@tmu.edu.tw.
Int J Infect Dis ; 109: 269-278, 2021 Aug.
Article en En | MEDLINE | ID: mdl-34273513
OBJECTIVE: Incorporating spatial analyses and online health information queries may be beneficial in understanding the role of Google relative search volume (RSV) data as a secondary public health surveillance tool during pandemics. This study identified coronavirus disease 2019 (COVID-19) clustering and defined the predictability performance of Google RSV models in clustered and non-clustered areas of the USA. METHODS: Getis-Ord General and local G statistics were used to identify monthly clustering patterns. Monthly country- and state-level correlations between new daily COVID-19 cases and Google RSVs were assessed using Spearman's rank correlation coefficients and Poisson regression models for January-December 2020. RESULTS: Huge clusters involving multiple states were found, which resulted from various control measures in each state. This demonstrates the importance of state-to-state coordination in implementing control measures to tackle the spread of outbreaks. Variability in Google RSV model performance was found among states and time periods, possibly suggesting the need to use different frameworks for Google RSV data in each state. Moreover, the sign of correlation can be utilized to understand public responses to control and preventive measures, as well as in communicating risk. CONCLUSION: COVID-19 Google RSV model accuracy in the USA may be influenced by COVID-19 transmission dynamics, policy-driven community awareness and past outbreak experiences.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Motor de Búsqueda / COVID-19 Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Int J Infect Dis Asunto de la revista: DOENCAS TRANSMISSIVEIS Año: 2021 Tipo del documento: Article País de afiliación: Indonesia Pais de publicación: Canadá
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Motor de Búsqueda / COVID-19 Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Int J Infect Dis Asunto de la revista: DOENCAS TRANSMISSIVEIS Año: 2021 Tipo del documento: Article País de afiliación: Indonesia Pais de publicación: Canadá