Evidence map›Paper›PMID 39649243›Full record

ArticleInfectious Disease Modelling2025

Evaluation of wastewater percent positive for assessing epidemic trends - A case study of COVID-19 in Shangrao, China.

Jing Wang, Haifeng Zhou, Wentao Song, Lingzhen Xu, Yaoying Zheng, Chen You, Xiangyou Zhang, Yeshan Peng, Xiaolan Wang, Tianmu Chen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Infectious Disease Modelling, 2025. 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.

Jing WangState Key Laboratory of Vaccines for Infectious Diseases, XiangAn Biomedicine Laboratory, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, National Innovation Platform for Industry-Education Integration in Vaccine Research, School of Public Health, Xiamen University, China.
Haifeng ZhouShangrao Center for Disease Control and Prevention, Shangrao City, Jiangxi Province, China.
Wentao SongState Key Laboratory of Vaccines for Infectious Diseases, XiangAn Biomedicine Laboratory, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, National Innovation Platform for Industry-Education Integration in Vaccine Research, School of Public Health, Xiamen University, China.
Lingzhen XuShangrao Center for Disease Control and Prevention, Shangrao City, Jiangxi Province, China.
Yaoying ZhengShangrao Center for Disease Control and Prevention, Shangrao City, Jiangxi Province, China.
Chen YouShangrao Center for Disease Control and Prevention, Shangrao City, Jiangxi Province, China.
Xiangyou ZhangShangrao Center for Disease Control and Prevention, Shangrao City, Jiangxi Province, China.
Yeshan PengShangrao Center for Disease Control and Prevention, Shangrao City, Jiangxi Province, China.
Xiaolan WangShangrao Center for Disease Control and Prevention, Shangrao City, Jiangxi Province, China.
Tianmu ChenState Key Laboratory of Vaccines for Infectious Diseases, XiangAn Biomedicine Laboratory, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, National Innovation Platform for Industry-Education Integration in Vaccine Research, School of Public Health, Xiamen University, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aims to assess the feasibility of evaluating the COVID-19 epidemic trend through monitoring the positive percentage of SARS-CoV-19 RNA in wastewater. Method: The study collected data from January to August 2023, including the number of reported cases, the positive ratio of nucleic acid samples in sentinel hospitals, the incidence rate of influenza-like symptoms in students, and the positive ratio of wastewater samples in different counties and districts in Shangrao City. Wastewater samples were obtained through grabbing and laboratory testing was completed within 24 h. The data were then normalized using Z-score normalization and analyzed for lag time and correlation using the xcorr function and Spearman correlation coefficient. Results: A total of 2797 wastewater samples were collected. The wastewater monitoring study, based on sampling point distribution, was divided into two phases. Wuyuan County consistently showed high levels of positive ratio in wastewater samples in both phases, reaching peak values of 91.67% and 100% respectively. The lag time analysis results indicated that the peak positive ratio in all wastewater samples in Shangrao City appeared around 2 weeks later compared to the other three indicators. The correlation analysis revealed a strong linear correlation across all four types of data, with Spearman correlation coefficients ranging from 0.783 to 0.977, all of which were statistically significant. Conclusion: The positive ratio of all wastewater samples in Shangrao City accurately reflected the COVID-19 epidemic trend from January to August 2023. This study confirmed the lag effect of wastewater percent positive and its strong correlation with the reported incidence rate and the positive ratio of nucleic acid samples in sentinel hospitals, supporting the use of wastewater percent positive monitoring as a supplementary tool for infectious disease surveillance in the regions with limited resources.

Indexed as

CorrelationEpidemic trendLag timePositive ratioWastewater-based epidemiologyWastewater percent positive

Identifiers

PMID39649243
PMCPMC11625299

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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.