Evidence map›Paper›PMID 35649981›Full record

ArticleChaos (Woodbury, N.Y.)2022

Propagation dynamics and control policies of COVID-19 pandemic at early stages: Implications on future resurgence response.

Ni Dong, Xiangyang Guan, Jin Zhang, Hanchu Zhou, Jie Zhang, Xiaobo Liu, Yichen Sun, Pengpeng Xu, Qin Li, Xingjie Hao

Abstract read
In one paragraph

Article in Chaos (Woodbury, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

10 authors.

Ni DongSchool of Transportation and Logistics, National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu, Sichuan 611756, China.
Xiangyang GuanDepartment of Civil and Environmental Engineering, University of Washington, Seattle, Washington 98105, USA.ORCID 0000-0002-4821-6551
Jin ZhangSchool of Transportation and Logistics, National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu, Sichuan 611756, China.
Hanchu ZhouSchool of Data Science, City University of Hong Kong, Hong Kong 999077, China.ORCID 0000-0002-6305-7907
Jie ZhangSchool of Transportation and Logistics, National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu, Sichuan 611756, China.
Xiaobo LiuSchool of Transportation and Logistics, National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu, Sichuan 611756, China.
Yichen SunSchool of Transportation and Logistics, National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu, Sichuan 611756, China.ORCID 0000-0002-7399-6107
Pengpeng XuDepartment of Civil Engineering, The University of Hong Kong, Hong Kong 999077, China.ORCID 0000-0001-9826-4212
Qin LiDepartment of Nephrology, Chengdu Second People's Hospital, Chengdu, Sichuan 610021, China.
Xingjie HaoDepartment of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.

Funding

A three-population three-scale social network model to assess disease dispersionR01GM108731 · NIGMS · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI BIAN, LING, CHEN, CYNTHIA · 2015 to 2019
$2.7M
NIGMS NIH HHS R01 GM108731
6 · The paper itself

Abstract

The spreading of novel coronavirus (SARS-CoV-2) has gravely impacted the world in the last year and a half. Understanding the spatial and temporal patterns of how it spreads at the early stage and the effectiveness of a governments' immediate response helps our society prepare for future COVID-19 waves or the next pandemic and contain it before the spreading gets out of control. In this article, a susceptible-exposed-infectious-removed model is used to model the city-to-city spreading patterns of the disease at the early stage of its emergence in China (from December 2019 to February 2020). Publicly available reported case numbers in 312 Chinese cities and between-city mobility data are leveraged to estimate key epidemiological characteristics, such as the transmission rate and the number of infectious people for each city. It is discovered that during any given time period, there are always only a few cities that are responsible for spreading the disease to other cities. We term these few cities as transmission centers. The spatial and temporal changes in transmission centers demonstrate predictable patterns. Moreover, rigorously designed experiments show that in controlling the disease spread in a city, non-pharmaceutical interventions (NPIs) implemented at transmission centers are more effective than the NPI implemented in the city itself. These findings have implications on the control of an infectious disease at the early stage of its spreading: implementing NPIs at transmission centers at early stages is effective in controlling the spread of infectious diseases.

Indexed as

Communicable DiseasesCOVID-19HumansPandemicsPolicySARS-CoV-2

Identifiers

PMID35649981
PMCPMC9064399

What OpenQuestion holds

Textmetadata
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.