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Epidemiological Investigation, Risk Factors, Spatial-Temporal Cluster, and Epidemic Trend Analysis of Pseudorabies Virus Seroprevalence in China (2017 to 2021)
Pseudorabies virus (PRV) is a double-stranded linear DNA virus capable of infecting various animals, including humans. We collected blood samples from 14 provinces in China between December 2017 and May 2021 to estimate PRV seroprevalence. The PRV gE antibody was detected using the enzyme-linked imm...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Society for Microbiology
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10269690/ https://www.ncbi.nlm.nih.gov/pubmed/37227271 http://dx.doi.org/10.1128/spectrum.05297-22 |
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author | Zhao, Pengfei Wang, Yu Zhang, Pengfei Du, Fen Li, Jianhai Wang, Chaofei Fang, Rui Zhao, Junlong |
author_facet | Zhao, Pengfei Wang, Yu Zhang, Pengfei Du, Fen Li, Jianhai Wang, Chaofei Fang, Rui Zhao, Junlong |
author_sort | Zhao, Pengfei |
collection | PubMed |
description | Pseudorabies virus (PRV) is a double-stranded linear DNA virus capable of infecting various animals, including humans. We collected blood samples from 14 provinces in China between December 2017 and May 2021 to estimate PRV seroprevalence. The PRV gE antibody was detected using the enzyme-linked immunosorbent assay (ELISA). Logistic regression analysis identified potential risk factors associated with PRV gE serological status at the farm level. Spatial-temporal clusters of high PRV gE seroprevalence were explored using SaTScan 9.6 software. Time-series data of PRV gE seroprevalence were modeled using the autoregressive moving average (ARMA) method. A Monte Carlo sampling simulation based on the established model was performed to analyze epidemic trends of PRV gE seroprevalence using @RISK software (version 7.0). A total of 40,024 samples were collected from 545 pig farms across China. The PRV gE antibody positivity rates were 25.04% (95% confidence interval [CI], 24.61% to 25.46%) at the animal level and 55.96% (95% CI, 51.68% to 60.18%) at the pig farm level. Variables such as farm geographical division, farm topography, African swine fever (ASF) outbreak, and porcine reproductive and respiratory syndrome virus (PRRSV) control in pig farms were identified as risk factors for farm-level PRV infection. Five significant high-PRV gE seroprevalence clusters were detected in China for the first time, with a time range of 1 December 2017 to 31 July 2019. The monthly average change value of PRV gE seroprevalence was −0.826%. The probability of a monthly PRV gE seroprevalence decrease was 0.868, while an increase was 0.132. IMPORTANCE PRV is a critical pathogen threatening the global swine industry. Our research fills knowledge gaps regarding PRV prevalence, infection risk factors, spatial-temporal clustering of high PRV gE seroprevalence, and the epidemic trend of PRV gE seroprevalence in China in recent years. These findings are valuable for the clinical prevention and control of PRV infection and suggest that PRV infection is likely to be successfully controlled in China. |
format | Online Article Text |
id | pubmed-10269690 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Society for Microbiology |
record_format | MEDLINE/PubMed |
spelling | pubmed-102696902023-06-16 Epidemiological Investigation, Risk Factors, Spatial-Temporal Cluster, and Epidemic Trend Analysis of Pseudorabies Virus Seroprevalence in China (2017 to 2021) Zhao, Pengfei Wang, Yu Zhang, Pengfei Du, Fen Li, Jianhai Wang, Chaofei Fang, Rui Zhao, Junlong Microbiol Spectr Research Article Pseudorabies virus (PRV) is a double-stranded linear DNA virus capable of infecting various animals, including humans. We collected blood samples from 14 provinces in China between December 2017 and May 2021 to estimate PRV seroprevalence. The PRV gE antibody was detected using the enzyme-linked immunosorbent assay (ELISA). Logistic regression analysis identified potential risk factors associated with PRV gE serological status at the farm level. Spatial-temporal clusters of high PRV gE seroprevalence were explored using SaTScan 9.6 software. Time-series data of PRV gE seroprevalence were modeled using the autoregressive moving average (ARMA) method. A Monte Carlo sampling simulation based on the established model was performed to analyze epidemic trends of PRV gE seroprevalence using @RISK software (version 7.0). A total of 40,024 samples were collected from 545 pig farms across China. The PRV gE antibody positivity rates were 25.04% (95% confidence interval [CI], 24.61% to 25.46%) at the animal level and 55.96% (95% CI, 51.68% to 60.18%) at the pig farm level. Variables such as farm geographical division, farm topography, African swine fever (ASF) outbreak, and porcine reproductive and respiratory syndrome virus (PRRSV) control in pig farms were identified as risk factors for farm-level PRV infection. Five significant high-PRV gE seroprevalence clusters were detected in China for the first time, with a time range of 1 December 2017 to 31 July 2019. The monthly average change value of PRV gE seroprevalence was −0.826%. The probability of a monthly PRV gE seroprevalence decrease was 0.868, while an increase was 0.132. IMPORTANCE PRV is a critical pathogen threatening the global swine industry. Our research fills knowledge gaps regarding PRV prevalence, infection risk factors, spatial-temporal clustering of high PRV gE seroprevalence, and the epidemic trend of PRV gE seroprevalence in China in recent years. These findings are valuable for the clinical prevention and control of PRV infection and suggest that PRV infection is likely to be successfully controlled in China. American Society for Microbiology 2023-05-25 /pmc/articles/PMC10269690/ /pubmed/37227271 http://dx.doi.org/10.1128/spectrum.05297-22 Text en Copyright © 2023 Zhao et al. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Article Zhao, Pengfei Wang, Yu Zhang, Pengfei Du, Fen Li, Jianhai Wang, Chaofei Fang, Rui Zhao, Junlong Epidemiological Investigation, Risk Factors, Spatial-Temporal Cluster, and Epidemic Trend Analysis of Pseudorabies Virus Seroprevalence in China (2017 to 2021) |
title | Epidemiological Investigation, Risk Factors, Spatial-Temporal Cluster, and Epidemic Trend Analysis of Pseudorabies Virus Seroprevalence in China (2017 to 2021) |
title_full | Epidemiological Investigation, Risk Factors, Spatial-Temporal Cluster, and Epidemic Trend Analysis of Pseudorabies Virus Seroprevalence in China (2017 to 2021) |
title_fullStr | Epidemiological Investigation, Risk Factors, Spatial-Temporal Cluster, and Epidemic Trend Analysis of Pseudorabies Virus Seroprevalence in China (2017 to 2021) |
title_full_unstemmed | Epidemiological Investigation, Risk Factors, Spatial-Temporal Cluster, and Epidemic Trend Analysis of Pseudorabies Virus Seroprevalence in China (2017 to 2021) |
title_short | Epidemiological Investigation, Risk Factors, Spatial-Temporal Cluster, and Epidemic Trend Analysis of Pseudorabies Virus Seroprevalence in China (2017 to 2021) |
title_sort | epidemiological investigation, risk factors, spatial-temporal cluster, and epidemic trend analysis of pseudorabies virus seroprevalence in china (2017 to 2021) |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10269690/ https://www.ncbi.nlm.nih.gov/pubmed/37227271 http://dx.doi.org/10.1128/spectrum.05297-22 |
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