Background And Aim: Brucellosis is an infectious and zoonotic disease that affects people's health and the economy in most countries. Brucellosis is still prevalent in several Indonesian regions. This study aimed to analyze the correlation between the characteristics, knowledge, attitudes, and practices (KAP) of dairy farmers in Bogor District in supporting brucellosis control and surveillance programs.
Materials And Methods: The study was cross-sectional. Data were collected through interviews with 151 dairy farmers in Bogor Regency, West Java, Indonesia. The outcome is brucellosis surveillance and control practice among dairy farmers, and the variables include individual characteristics, knowledge, and attitudes toward brucellosis surveillance and control. Descriptive analysis and path analysis were used in statistical analysis.
Results: The majority of farmers' knowledge, attitudes and practices were moderate, with the percentages 67.55%, 60.92%, and 41.72% respectively. Formal education, training, and dairy rising length are variables that have a direct and significant impact on knowledge level. Knowledge is the variable that influences the overall level of attitude. Age, knowledge, and attitude are factors that influence the practice of brucellosis surveillance and control.
Conclusion: Although the practice level of brucellosis surveillance and control for dairy farmers in Bogor Regency is moderate, efforts to improve it are still required. The basic effort is critical for increasing farmers' knowledge.
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http://dx.doi.org/10.14202/vetworld.2023.126-133 | DOI Listing |
BMC Microbiol
January 2025
National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
Human brucellosis is a re-emerging disease in Sichuan Province, China. In this study, bacteriology, conventional bio-typing, multi-locus sequence typing (MLST), and multiple locus variable-number tandem repeat analysis (MLVA) were applied to preliminarily characterize the strains in terms of genetic diversity and epidemiological links. A total of 101 Brucella strains were isolated from 16 cities (autonomous prefectures) from 2014 to 2021, and all of the strains were identified as Brucella melitensis bv.
View Article and Find Full Text PDFCurr Microbiol
January 2025
Razi Vaccine and Serum Research Institute (RVSRI), Agricultural Research, Education and Organization (AREEO), Karaj, Iran.
Brucella spp. is the bacterium responsible for brucellosis, a zoonotic infection that affects humans. This disease poses significant health challenges and contributes to poverty, particularly in developing countries.
View Article and Find Full Text PDFVet Med Sci
January 2025
Department of Microbiology, Faculty of Veterinary and Animal Science, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh.
Background: Brucellosis is a zoonotic disease caused by Brucella spp., affecting various animals and humans, leading to significant economic and public health impacts. Traditional diagnostic methods, mainly serological, often fail to detect seronegative carriers, which continue to spread the infection.
View Article and Find Full Text PDFCurr Microbiol
January 2025
Agricultural Research, Education and Extension Organization (AREEO), Razi Vaccine and Serum Research Institute (RVSRI), Karaj, Iran.
Brucellosis, a zoonotic disease caused by Brucella spp. globally, is of great significance not only to livestock but also to public health. The most significant of the twelve species is Brucella melitensis.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830017, China.
Human brucellosis remains a significant public health issue in the Ili Kazak Autonomous Prefecture, Xinjiang, China. To assist local Centers for Disease Control and Prevention (CDC) in promptly formulate effective prevention and control measures, this study leveraged time-series data on brucellosis cases from February 2010 to September 2023 in Ili Kazak Autonomous Prefecture. Three distinct predictive modeling techniques-Seasonal Autoregressive Integrated Moving Average (SARIMA), eXtreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM) networks-were employed for long-term forecasting.
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