ArticleBMC infectious diseases2025
Traffic-driven spatio-temporal prediction for fine-scale epidemic outbreaks in China.
Article in BMC infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
backgroundAs the high connectivity creates favorable conditions for the infectious diseases, understanding the dynamics of epidemic spread across regions is significant. However, existing models often focus on single dimensions and specific factors under strict controls, but generally overlook regional differences and interactions.
methodsIn this research, we constructed a spatio-temporal forecasting model based on multiple modeling architectures, specifically designed to predict the post-lockdown epidemic's transmission at a fine scale across prefecture-level cities nationwide. The model integrates comprehensive factors and constructs a structured graph based on transportation patterns and characteristic variables across municipal regions in China. To further enhance its ability to capture spatial interactions among regions, the model integrates human mobility into edge weight calculations to optimize the adjacency matrix.
resultsWe analyzed the prediction errors across different urban clusters, provinces, folds, and forecast durations, revealing spatial variations and consistent error reductions. The results demonstrate that our model can accurately predict the spatio-temporal spread of the epidemic across 309 Chinese prefecture-level cities with a high correlation coefficient (r = 0.94) validated through extensive cross-validation.
conclusionsOur proposed approach integrates transportation patterns and human mobility into edge weight calculations to enhance spatial connectivity. This post-lockdown simulation across cities in China offers a fine-grained analytical scale previously unexplored, and the comprehensive analysis provides enhanced insights into epidemic dynamics and transmission patterns, supporting the future public health strategies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.