Applied Mathematics and Nonlinear Sciences (Jul 2023)

Seroprevalence of brucellosis infection in sheep in China during 2003-2022: A systematic review and meta-analysis

  • Wang Rulin,
  • Bai Yunli,
  • Li Yinfei,
  • Zhou Weiguang

DOI
https://doi.org/10.2478/amns.2023.2.01133
Journal volume & issue
Vol. 8, no. 2
pp. 3335 – 3354

Abstract

Read online

Brucellosis is an important zoonotic chronic infectious disease caused by Brucella. The disease mainly infects animals, such as sheep, cattle, pigs, and dogs, it can also infect humans, and sheep are the most seriously infected animals in China. The incidence of brucellosis has been rising annually in recent years, which has resulted in significant financial losses for the sheep business. Therefore, this systematic review and meta-analysis aimed to assess the seroprevalence of brucellosis infection in sheep in China from 2003 to October 2022. A sum of 92 relevant articles were retrieved from three Chinese databases and three English databases, and they were analyzed by RStudio software using a random-effects model. In accordance with the conclusions, the pooled incidence of brucellosis infection in sheep was 1.09% in China. Regarding the time distribution, sheep’s brucellosis seroprevalence was the highest from 2012 to 2016 (1.83%, 95% confidence interval (CI): 0.94-3.01) and the seroprevalence was the lowest during 2016-2022 (0.81%, 95% CI: 0.43-1.31). The regional distribution revealed that Northeast China has the highest seroprevalence (2.94%, 95% CI: 0.07-9.81), while that was the lowest in the East China (0.23%, 95%CI: 0.05-0.52). Among different provinces, Hebei Province had the highest incidence (17.41%, 95% CI: 17.41-21.77), and it was the lowest in Guangdong Province (0.08%, 95% CI: 0.02-0.18). Meta-analysis revealed that brucellosis infection was widely spread in sheep in China, thus, In order to lessen the financial losses and risks to human health brought on by brucellosis infection, it is vital to increase the control of the disease in animals.

Keywords