Evidence map›Paper›PMID 34574996›Full record

ArticleHealthcare (Basel, Switzerland)2021

Estimation of Human Mobility Patterns for Forecasting the Early Spread of Disease.

Zhengyan Li, Huichun Li, Xue Zhang, Chengli Zhao

Abstract read
In one paragraph

Article in Healthcare (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Privacy by Projection: Federated Population Density Estimation by Projecting on Random Features.Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium · 2023
    Article
  2. Epidemic Diffusion Network of Spain: A Mobility Model to Characterize the Transmission Routes of Disease.International journal of environmental research and public health · 2023
    Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Zhengyan LiCollege of Liberal Arts and Sciences, National University of Defense Technology, Changsha 410073, China.ORCID 0000-0001-5326-1659
Huichun LiCollege of Liberal Arts and Sciences, National University of Defense Technology, Changsha 410073, China.ORCID 0000-0002-6119-0106
Xue ZhangCollege of Liberal Arts and Sciences, National University of Defense Technology, Changsha 410073, China.
Chengli ZhaoCollege of Liberal Arts and Sciences, National University of Defense Technology, Changsha 410073, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Human mobility data are indispensable in modeling large-scale epidemics, especially in predicting the spatial spread of diseases and in evaluating spatial heterogeneity intervention strategies. However, statistical data that can accurately describe large-scale population migration are often difficult to obtain. We propose an algorithm model based on the network science approach, which estimates the travel flow data in mainland China by transforming location big data and airline operation data into network structure information. In addition, we established a simplified deterministic SEIR (Susceptible-Exposed-Infectious-Recovered)-metapopulation model to verify the effectiveness of the estimated travel flow data in the study of predicting epidemic spread. The results show that individual travel distance in mainland China is mainly within 100 km. There is far more travel between prefectures within the same province than across provinces. The epidemic spatial spread model incorporating estimated travel data accurately predicts the spread of COVID-19 in mainland China. The results suggest that there are far more travelers than usual during the Spring Festival in mainland China, and the number of travelers from Wuhan mainly determines the number of confirmed cases of COVID-19 in each prefecture.

Indexed as

COVID-19epidemic modelhuman mobilityinfectious diseasetravel flow

Identifiers

PMID34574996
PMCPMC8468459

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.