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

Chao Song, Hao Yin, Xun Shi, Mingyu Xie, Shujuan Yang, Junmin Zhou, Xiuli Wang, Zhangying Tang, Yili Yang, Jay Pan

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)103078
JournalInternational journal of disaster risk reduction : IJDRR
StatePublished - Jun 1 2022
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2022-06-06
Acknowledgements: We are grateful to Henry Chung (Michigan State University, US) for his ongoing language and academic writing assistance. We appreciate Håvard Rue (King Abdullah University of Science and Technology, Norway), the leading developer of the R-INLA project. We also thank the editors and anonymous reviewers for their constructive comments and valuable suggestions on improving our manuscript.
This work was supported by the National Natural Science Foundation of China [grant numbers 42071379, 71874116, 72074163, 71904104, 72104159, 41701448]; the Sichuan Science and Technology Department [grant numbers 2022YFS0052, 2021YFQ0060, 2020YJ0117]; the Chongqing Science and Technology Bureau [grant number cstc2020jscx-cylhX0001]; the Medical Science and Technology Project of Sichuan Provincial Health Commission [grant number 21PJ067]; the Sichuan Provincial Health Commission Project for Prevention and Treatment of Major Infectious Diseases [grant number 2021zc01]; the Fund for Introducing Talents of Sichuan University [grant number YJ202157]; the Research Center of Sichuan County Economy Development [grant number xy2021018]; and the Chengdu Federation of Social Science Association [grant number 2021ZC003]. The funders had no role in the study design, data collection, analysis, publishing decision, or manuscript preparation.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

ASJC Scopus subject areas

  • Geology
  • Safety Research
  • Geotechnical Engineering and Engineering Geology


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