Evidence map›Paper›PMID 36777897›Full record

ArticleChina CDC weekly2023

Optimization of Population-Level Testing, Contact Tracing, and Isolation in Emerging COVID-19 Outbreaks: a Mathematical Modeling Study - Tonghua City and Beijing Municipality, China, 2021-2022.

Zengmiao Wang, Ruixue Wang, Peiyi Wu, Bingying Li, Yidan Li, Yonghong Liu, Xiaoli Wang, Peng Yang, Huaiyu Tian

Abstract read
In one paragraph

Article in China CDC weekly, 2023. 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

9 authors.

Zengmiao WangState Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China.
Ruixue WangSchool of National Safety and Emergency Management, Beijing Normal University, Beijing, China.
Peiyi WuState Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China.
Bingying LiState Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China.
Yidan LiState Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China.
Yonghong LiuBeijing Center for Disease Prevention and Control, Beijing, China.
Xiaoli WangBeijing Center for Disease Prevention and Control, Beijing, China.
Peng YangBeijing Center for Disease Prevention and Control, Beijing, China.
Huaiyu TianState Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The transmissibility of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant poses challenges for the existing measures containing the virus in China. In response, this study investigates the effectiveness of population-level testing (PLT) and contact tracing (CT) to help curb coronavirus disease 2019 (COVID-19) resurgences in China. Methods: Two transmission dynamic models (i.e. with and without age structure) were developed to evaluate the effectiveness of PLT and CT. Extensive simulations were conducted to optimize PLT and CT strategies for COVID-19 control and surveillance. Results: Urban Omicron resurgences can be controlled by multiple rounds of PLT, supplemented by CT - as long as testing is frequent. This study also evaluated the time needed to detect COVID-19 cases for surveillance under different routine testing rates. The results show that there is a 90% probability of detecting COVID-19 cases within 3 days through daily testing. Otherwise, it takes around 7 days to detect COVID-19 cases at a 90% probability level if biweekly testing is used. Routine testing applied to the age group 21-60 for COVID-19 surveillance would achieve similar performance to that applied to all populations. Discussion: Our analysis evaluates potential PLT and CT strategies for COVID-19 control and surveillance.

Indexed as

Contact tracingOptimizationPopulation-level testingSARS-CoV-2 variants

Identifiers

PMID36777897
PMCPMC9902757

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
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.