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Prevalence, Time of Infection, and Diversity of Porcine Reproductive and Respiratory Syndrome Virus in China.

Chaosi LiAihua FanZhicheng LiuGang WangLei ZhouHongliang ZhangLv HuangJianfeng ZhangZhendong ZhangYan Zhang
Published in: Viruses (2024)
Porcine reproductive and respiratory syndrome virus (PRRVS) is a major swine viral pathogen that affects the pig industry worldwide. Control of early PRRSV infection is essential, and different types of PRRSV-positive samples can reflect the time point of PRRSV infection. This study aims to investigate the epidemiological characteristics of PRRSV in China from Q4 2021 to Q4 2022, which will be beneficial for porcine reproductive and respiratory syndrome virus (PRRSV)control in the swine production industry in the future. A total of 7518 samples (of processing fluid, weaning serum, and oral fluid) were collected from 100 intensive pig farms in 21 provinces, which covered all five pig production regions in China, on a quarterly basis starting from the fourth quarter of 2021 and ending on the fourth quarter of 2022. Independent of sample type, 32.1% (2416/7518) of the total samples were PCR-positive for PRRSV, including 73.6% (1780/2416) samples that were positive for wild PRRSV, and the remaining were positive for PRRSV vaccine strains. On the basis of the time of infection, 58.9% suckling piglets (processing fluid) and 30.8% weaning piglets (weaning serum) showed PRRSV infection at an early stage (approximately 90% of the farms). The sequencing analysis results indicate a wide range of diverse PRRSV wild strains in China, with lineage 1 as the dominant strain. Our study clearly demonstrates the prevalence, infection stage, and diversity of PRRSV in China. This study provides useful data for the epidemiological understanding of PRRSV, which can contribute to the strategic and systematic prevention and control of PRRSV in China.
Keyphrases
  • early stage
  • escherichia coli
  • risk factors
  • mechanical ventilation
  • squamous cell carcinoma
  • single cell
  • artificial intelligence
  • deep learning
  • current status
  • respiratory tract
  • extracorporeal membrane oxygenation