A Data-Driven Prediction Method for an Early Warning of Coccidiosis in Intensive Livestock Systems: A Preliminary Study

Simple Summary The development of new methods, able to promptly detect the onset of the infection, is highly important to control parasitic infections of the intestinal tract (coccidiosis) in poultry. The early detection of this disease would reduce the use of anticoccidial and antimicrobials drugs, thus lowering the risk of antibiotic resistance. A data-driven machine learning algorithm was built to relate air quality data to the time of enteric disorders. The results show that this procedure has great potential to be used as a rapid technique to detect coccidiosis. Abstract Coccidiosis is still one of the major parasitic infections in poultry. It is caused by protozoa of the genus Eimeria, which cause concrete economic losses due to malabsorption, bad feed conversion rate, reduced weight gain, and increased mortality. The greatest damage is registered in commercial poultry farms because birds are reared together in large numbers and high densities. Unfortunately, these enteric pathologies are not preventable, and their diagnosis is only available when the disease is full-blown. For these reasons, the preventive use of anticoccidials-some of these with antimicrobial action-is a common practice in intensive farming, and this type of management leads to the release of drugs in the environment which contributes to the phenomenon of antibiotic resistance. Due to the high relevance of this issue, the early detection of any health problem is of great importance to improve animal welfare in intensive farming. Three prototypes, previously calibrated and adjusted, were developed and tested in three different experimental poultry farms in order to evaluate whether the system was able to identify the coccidia infection in intensive poultry farms early. For this purpose, a data-driven machine learning algorithm was built, and specific critical values of volatile organic compounds (VOCs) were found to be associated with abnormal levels of oocystis count at an early stage of the disease. This result supports the feasibility of building an automatic data-driven machine learning algorithm for an early warning of coccidiosis.