Machine learning to fight antibiotic resistance in farmed chickens

Date: 
Friday, August 2, 2019
Source: 
University of Nottingham

A new research project to improve the health of farmed chickens in China and reduce the risk of disease and antibiotic resistance transferring to human populations has been launched today.

The FARMWATCH project will use machine learning to find new ways to identify and pinpoint disease in poultry farms, reducing the need for antibiotic treatment and lowering the risk of antibiotic resistance transferring to consumers.

The £1.5m project is a partnership between researchers from the University of Nottingham’s School of Veterinary Medicine and Science and the China National Center for Food Safety Risk Assessment. Funded by Innovate UK and the Chinese Ministry of Science and Technology (MoST), the researchers are also collaborating with commercial partners, Nimrod Veterinary Products in the UK and New Hope Liuhe in China.

Antibiotics in poultry production

The rapid increase in poultry production to meet growing demand in China has resulted in extensive and indiscriminate use of antibiotics. This has led to a worrying increase in cases of  antibiotic resistance (ABR) diagnosed in animals and, as a result,  humans, via direct contact, environmental contamination, and food consumption.

With antibiotic resistance now one of the most threatening issues worldwide, effective and rapid diagnostics of bacterial infection in chicken farming can reduce the need for antibiotics, which will reduce epidemics and ABR.

The researchers in Nottingham will be working with colleagues in China to take thousands of samples from the animals, humans and environment of nine farms, in three Chinese provinces over three years. This complex ‘big’ data will be analysed for new diagnostic biomarkers that will predict and detect bacterial infection, insurgence of ABR, and transfer to humans. This data will then allow early intervention and treatment, reducing spread and the need for antibiotics.

...follow the link to read the article in full on the University of Nottingham website...