Spatiotemporal disparities in regional public risk perception of COVID-19 using Bayesian Spatiotemporally Varying Coefficients (STVC) series models across Chinese cities
Why this work is in the frame
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Bibliographic record
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it