Evidence map›Paper›PMID 38130878›Full record

ArticleInfectious Disease Modelling2024

Forecast of peak infection and estimate of excess deaths in COVID-19 transmission and prevalence in Taiyuan City, 2022 to 2023.

Jia-Lin Wang, Xin-Long Xiao, Fen-Fen Zhang, Xin Pei, Ming-Tao Li, Ju-Ping Zhang, Juan Zhang, Gui-Quan Sun

Abstract read
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Article in Infectious Disease Modelling, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

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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

8 authors.

Jia-Lin WangComplex Systems Research Center, Shanxi University, Taiyuan, 030006, China.
Xin-Long XiaoComplex Systems Research Center, Shanxi University, Taiyuan, 030006, China.
Fen-Fen ZhangSchool of Mathematics, North University of China, Taiyuan, 030051, China.
Xin PeiCollege of Mathematics, Taiyuan University of Technology, Taiyuan, 030024, China.
Ming-Tao LiCollege of Mathematics, Taiyuan University of Technology, Taiyuan, 030024, China.
Ju-Ping ZhangComplex Systems Research Center, Shanxi University, Taiyuan, 030006, China.
Juan ZhangComplex Systems Research Center, Shanxi University, Taiyuan, 030006, China.
Gui-Quan SunSchool of Mathematics, North University of China, Taiyuan, 030051, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, with the method of epidemic dynamics, we assess the spread and prevalence of COVID-19 after the policy adjustment of prevention and control measure in December 2022 in Taiyuan City in China, and estimate the excess population deaths caused by COVID-19. Based on the transmission mechanism of COVID-19 among individuals, a dynamic model with heterogeneous contacts is established to describe the change of control measures and the population's social behavior in Taiyuan city. The model is verified and simulated by basing on reported case data from November 8th to December 5th, 2022 in Taiyuan city and the statistical data of the questionnaire survey from December 1st to 23rd, 2022 in Neijiang city. Combining with reported numbers of permanent residents and deaths from 2017 to 2021 in Taiyuan city, we apply the dynamic model to estimate theoretical population of 2022 under the assumption that there is no effect of COVID-19. In addition, we carry out sensitivity analysis to determine the propagation character of the Omicron strain and the effect of the control measures. As a result of the study, it is concluded that after adjusting the epidemic policy on December 6th, 2022, three peaks of infection in Taiyuan are estimated to be from December 22nd to 31st, 2022, from May 10th to June 1st, 2023, and from September 5th to October 13th, 2023, and the corresponding daily peaks of new cases can reach 400 000, 44 000 and 22 000, respectively. By the end of 2022, excess deaths can range from 887 to 4887, and excess mortality rate can range from 3.06% to 14.82%. The threshold of the infectivity of the COVID-19 variant is estimated 0.0353, that is if the strain infectivity is above it, the epidemic cannot be control with the previous normalization measures.

Indexed as

00001111COVID-19Dynamic modelEpidemic peakEpidemic scaleExcess deathTaiyuan city

Identifiers

PMID38130878
PMCPMC10733700

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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.