Current trends in the diagnosis of infectious diseases in pigs

Objective. This review highlights the latest trends in diagnostic technologies and their application in disease surveillance, outbreak prediction and treatment optimisation. Results. Early and accurate diagnosis of infectious diseases in pigs plays a critical role in safeguarding animal health, maximising productivity and maintaining the economic stability of the pig industry. Infectious diseases in pigs, which can be of bacterial, viral or parasitic origin, often result in significant economic losses due to reduced productivity, increased mortality and the costs associated with disease management. In addition, infectious diseases in pigs are a serious public health concern, especially when zoonotic pathogens are involved, as they can be transmitted to humans and potentially lead to widespread health problems. Therefore, the timely identification and control of these pathogens is of vital importance not only to the pig industry but also to global public health. The rapid development of diagnostic technologies in recent years has had a transformative impact on the detection and control of swine diseases. Molecular diagnostic methods, including polymerase chain reaction (PCR) and next- generation sequencing (NGS), have greatly improved early detection capabilities, allowing veterinarians and farmers to identify pathogens before animals show clinical signs. Such technologies improve disease surveillance by facilitating the rapid identification of infected animals that can be immediately isolated to prevent further spread of disease within herds. This early intervention capability is essential to control outbreaks and minimise their economic and health impact. The integration of these advanced diagnostic methods with tools such as data analytics, bioinformatics and machine learning has opened new horizons in disease management. Through predictive modelling and data analysis, these tools can help predict outbreaks and inform more targeted treatment and prevention strategies. Machine learning algorithms, for example, can process large data sets from multiple sources to more accurately predict disease trends and identify high-risk factors, enabling proactive rather than reactive disease management. This combination of molecular diagnostics and computational tools represents a powerful advance in veterinary medicine, promoting the rapid and strategic response needed to contain infectious diseases in pig populations. However, significant challenges remain, particularly in the context of smallholder farms and resource-poor regions. Many smallholders face barriers to adopting these technologies due to limited financial resources, lack of technical training and inadequate infrastructure. Addressing these challenges is critical to ensuring that advances in diagnostics reach all levels of the industry, promoting more equitable health outcomes and reducing the risk of disease spread across regions and communities. Conclusions. As we move towards a future where technology is more integrated into agriculture and veterinary medicine, ensuring that diagnostic tools are both accessible and affordable for farms of all sizes is critical. Removing the current barriers that limit access to these advanced diagnostics will improve both the health and productivity of pig populations and support broader initiatives to prevent zoonotic disease outbreaks. By promoting the widespread use of these innovations, the pig industry can grow more sustainably while playing a key role in protecting global health. Keywords: Infectious diseases, swine, diagnostic technologies, PCR, next- generation sequencing, molecular diagnostics, disease management, pig industry, zoonoses.