ArticleRoyal Society open science2022
Dynamic model of respiratory infectious disease transmission by population mobility based on city network.
Article in Royal Society open science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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
10 citing papers in PubMed, 12 citations in OpenAlex.
- A spatio-temporal causal network for multi-scale analysis of infectious respiratory diseases transmission.Infectious Disease Modelling · 2026Article
- Systematic prediction of spatiotemporal transmission of potential respiratory pandemics in China.Infectious Disease Modelling · 2026Article
- Epidemiological characteristics and spatio-temporal analysis of influenza in Sichuan Province from 2015 to 2024.BMC infectious diseases · 2025Article
- Current trends and challenges in the management of foot and mouth disease in Saudi Arabia: A review.Open veterinary journal · 2025Review
- A theoretical epidemiological investigation into the transmission of respiratory infectious diseases during group meals among military personnel based on an individual-based model.Frontiers in public health · 2025Article
- Evaluating the impact of large-scale nucleic acid testing and home quarantine on a novel emerging infectious disease prevention and control: a dynamic modeling approach.Frontiers in public health · 2025Article
- The epidemic forest reveals the spatial pattern of the spread of acute respiratory infections in Jakarta, Indonesia.Scientific reports · 2024Article
- Dynamic model of respiratory infectious disease transmission in urban public transportation systems.Heliyon · 2023Article
- A dynamic model for elevator operation-induced spread of a respiratory infectious disease in an apartment building.Heliyon · 2023Article
- Dynamic model of respiratory infectious disease transmission by population mobility based on city network.Royal Society open science · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In modern societies, newly emerging infectious diseases spread rapidly between regions owing to frequent contact between people, causing considerable social and economic impacts. In this study, first, a scale-free city network was established, and then the shortest path between any two nodes was determined. Second, the movement path of tourists was designed based on the shortest path. Subsequently, every infected person's information, such as the city, infection time, onset and hospitalization, was confirmed based on their movement path. Third, the features of the transmission path and time distribution of the epidemic were characterized after summarizing the information. Finally, the reliability of the model was verified. The number of citizens and tourists in every city remained stable during this time. The results indicated that a larger basic reproduction number (
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.