Spatiotemporal disparities in regional public risk perception of COVID-19 using Bayesian Spatiotemporally Varying Coefficients (STVC) series models across Chinese cities.

Content Provider	Europe PMC
Author	Song, Chao Yin, Hao Shi, Xun Xie, Mingyu Yang, Shujuan Zhou, Junmin Wang, Xiuli Tang, Zhangying Yang, Yili Pan, Jay
Copyright Year	2022
Abstract	Regional public attention has been critical during the COVID-19 pandemic, impacting the effectiveness of sub-national non-pharmaceutical interventions. While studies have focused on public attention at the national level, sub-national public attention has not been well investigated. Understanding sub-national public attention can aid local governments in designing regional scientific guidelines, especially in large countries with substantial spatiotemporal disparities in the spread of infections. Here, we evaluated the online public attention to the COVID-19 pandemic using internet search data and developed a regional public risk perception index (PRPI) that depicts heterogeneous associations between local pandemic risk and public attention across 366 Chinese cities. We used the Bayesian Spatiotemporally Varying Coefficients (STVC) model, a full-map local regression for estimating spatiotemporal heterogeneous relationships of variables, and improved it to the Bayesian Spatiotemporally Interacting Varying Coefficients (STIVC) model to incorporate space–time interaction non-stationarity at spatial or temporal stratified scales. COVID-19 daily cases (median contribution 82.6%) was the most critical factor affecting public attention, followed by urban socioeconomic conditions (16.7%) and daily population mobility (0.7%). After adjusting national and provincial impacts, city-level influence factors accounted for 89.4% and 58.6% in spatiotemporal variations of public attention. Spatiotemporal disparities were substantial among cities and provinces, suggesting that observing national-level public dynamics alone was insufficient. Multi-period PRPI maps revealed clusters and outlier cities with potential public panic and low health literacy. Bayesian STVC series models are systematically proposed and provide a multi-level spatiotemporal heterogeneous analytical framework for understanding collective human responses to major public health emergencies and disasters.
Related Links	https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC9148270&blobtype=pdf
Journal	International Journal of Disaster Risk Reduction [Int J Disaster Risk Reduct]
Volume Number	77
DOI	10.1016/j.ijdrr.2022.103078
PubMed Central reference number	PMC9148270
PubMed reference number	35664453
e-ISSN	22124209
Language	English
Publisher	The Authors. Published by Elsevier Ltd.
Publisher Date	2022-05-29
Access Restriction	Open
Rights License	Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. © 2022 The Authors
Subject Keyword	Regional public risk perception Multi-level spatiotemporal heterogeneity Spatiotemporal non-stationary regression STVC Internet search engine COVID-19
Content Type	Text
Resource Type	Article
Subject	Geology Geotechnical Engineering and Engineering Geology Safety Research