ArticleCities (London, England)2021
Spread of COVID-19 in China: analysis from a city-based epidemic and mobility model.
Article in Cities (London, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
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
42 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Spatio-Temporal COVID-19 Modeling: A Global Systematic Review of Data Integration, Equity, and Lessons for Pandemic Preparedness.International journal of environmental research and public health · 2026Pooled it
- Systematic prediction of spatiotemporal transmission of potential respiratory pandemics in China.Infectious Disease Modelling · 2026Article
- The cross-city mobility patterns and incubation period of the early-stage chikungunya fever outbreak in Guangdong Province, China, in 2025.PLoS neglected tropical diseases · 2026Article
- Leveraging machine learning for accurate forecasting of pulmonary tuberculosis epidemics in a coastal city in China.Tropical medicine and health · 2026Article
- Remarks on a Scaling Theory of Spread of COVID-19 with an Application to the Case of Bulgaria.Entropy (Basel, Switzerland) · 2026Article
- A novel framework for inferring dynamic infectious disease transmission with graph attention: a COVID-19 case study in Korea.BMC public health · 2025Article
- Multi-region infectious disease prediction modeling based on spatio-temporal graph neural network and the dynamic model.PLoS computational biology · 2025Article
- Evaluating the Demand for Nucleic Acid Testing in Different Scenarios of COVID-19 Transmission: A Simulation Study.Infectious diseases and therapy · 2024Article
- Analyzing the impact of COVID-19 on consumption behaviors through recession and recovery patterns.Scientific reports · 2024Article
- Measurement of economic resilience under the COVID-19 based on nighttime light remote sensing: Case of Hubei province.PloS one · 2024Article
- Article
- Towards a constructed order of co-governance: Understanding the state-society dynamics of neighbourhood collaborative responses to COVID-19 in urban China.Urban studies (Edinburgh, Scotland) · 2023Article
- Modelling the roles of visitor flows and returning migrants in the spatial diffusion of COVID-19 from Wuhan city in China.Applied geography (Sevenoaks, England) · 2023Article
- Urban form and COVID-19 cases and deaths in Greater London: An urban morphometric approach.Environment and planning. B, urban analytics and city science · 2023Article
- Understanding the impact of mobility on COVID-19 spread: A hybrid gravity-metapopulation model of COVID-19.PLoS computational biology · 2023Article
- Systematic literature review on impacts of COVID-19 pandemic and corresponding measures on mobility.Transportation · 2023Article
- The impact of traffic control measures on the spread of COVID-19 within urban agglomerations based on a modified epidemic model.Cities (London, England) · 2023Article
- The Effectiveness of Mobility Restrictions on Controlling the Spread of COVID-19 in a Resistant Population.International journal of environmental research and public health · 2023Article
- Simulating exposure-related human mobility behavior at the neighborhood-level under COVID-19 in Porto Alegre, Brazil.Cities (London, England) · 2023Article
- Sustainable planning in Wuhan City during COVID-19: an analysis of influential factors, risk profiles, and clustered patterns.Frontiers in public health · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding the processes and mechanisms of the spatial spread of epidemics is essential for making reasonable judgments on the development trends of epidemics and for adopting effective containment measures. Using multi-agent network technology and big data on population migration, this paper constructed a city-based epidemic and mobility model (CEMM) to stimulate the spatiotemporal of COVID-19. Compared with traditional models, this model is characterized by an urban network perspective and emphasizes the important role of intercity population mobility and high-speed transportation networks. The results show that the model could simulate the inter-city spread of COVID-19 at the early stage in China with high precision. Through scenario simulation, the paper quantitatively evaluated the effect of control measures "city lockdown" and "decreasing population mobility" on containing the spatial spread of the COVID-19 epidemic. According to the simulation, the total number of infectious cases in China would have climbed to 138,824 on February 2020, or 4.46 times the real number, if neither of the measures had been implemented. Overall, the containment effect of the lockdown of cities in Hubei was greater than that of decreasing intercity population mobility, and the effect of city lockdowns was more sensitive to timing relative to decreasing population mobility.
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.