Similar results were also reported by several studies in the USA ( Bento et al., 2020 Panuganti et al., 2020 Yuan et al., 2020). In the case of COVID-19, various studies in the early phase of the outbreak suggested that Google searches peaked earlier than newly confirmed cases ( Effenberger et al., 2020 Strzelecki, 2020) and correlated well with the rise of COVID-19-related data ( Husnayain et al., 2020a,b Li et al., 2020 Ortiz-Martínez et al., 2020). It may capture wider population events than conventional surveillance systems ( Milinovich et al., 2014), as people who are ill may not contact local healthcare facilities, but they may still search for online health information. This approach was part of an infodemiological study that examined the determinants and distributions of health information for public health purposes ( Eysenbach, 2006). ![]() These online search data potentially depict patterns of information-seeking behaviours that represent the public's concerns, awareness or restlessness ( Ayyoubzadeh et al., 2020 Husnayain et al., 2020a). These data are collected during information-seeking activities on Google search engines that are normalized during a specified period ( Google, 2020). Google RSVs are emerging digital data that are being used as a secondary public health surveillance tool during the COVID-19 pandemic. However, few studies have analysed a year of COVID-19 spatiotemporal patterns along with temporal predictability performances of Google relative search volume (RSV) models in clustered and non-clustered areas. State-level studies also characterized emerging clusters ( Cordes and Castro, 2020 Maroko et al., 2020 Ramírez and Lee, 2020). Disease mapping also enables targeted public health responses ( Oster et al., 2020b) through assessment of the distribution of high-risk areas and their progression throughout the outbreak period ( Desjardins et al., 2020).Ĭountrywide analyses have described COVID-19 clusters in the USA ( CDC COVID-19 Response Team, 2020 Dasgupta et al., 2020 Desjardins et al., 2020 Oster et al., 2020a,b), vulnerability assessments ( Snyder and Parks, 2020 Wang et al., 2020) and spatial modelling which employed various explanatory variables ( Mollalo et al., 2020 Andersen et al., 2021) for the first 3–6 months of the outbreak. Most of these studies dealt with cluster detection analyses, a necessary approach in allocating resources, implementing strict control measures, and evaluating currently implemented policies ( Desjardins et al., 2020). During the outbreak, multiple studies have discussed COVID-19 spatial patterns in the USA using both state- ( Cordes and Castro, 2020 Maroko et al., 2020 Ramírez and Lee, 2020) and county-level analyses ( CDC COVID-19 Response Team, 2020 Dasgupta et al., 2020 Desjardins et al., 2020 Mollalo et al., 2020 Oster et al., 2020a,b Snyder and Parks, 2020 Wang et al., 2020 Andersen et al., 2021). Spatial spread is one of the most important aspects in understanding disease epidemics ( Franch-Pardo et al., 2020), including the coronavirus disease 2019 (COVID-19) pandemic. Moreover, the sign of correlation can be utilized to understand public responses to control and preventive measures, as well as in communicating risk.Ĭonclusion: 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. 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. This demonstrates the importance of state-to-state coordination in implementing control measures to tackle the spread of outbreaks. Results: Huge clusters involving multiple states were found, which resulted from various control measures in each state. 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. Methods: Getis-Ord General and local G statistics were used to identify monthly clustering patterns. ![]() 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. 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.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |