A Systematic Literature Review on Machine Learning and Laboratory Techniques for the Diagnosis of African swine fever (ASF)

Abstract

African swine fever (ASF) is a virulent infectious disease of pigs. It can infect domestic and wild pigs, causing severe economic and production losses. The virus can be spread through live or dead pigs and through pork products. Since there is currently no vaccine or treatment method, it poses a major challenge and threat to the pig industry once it breaks out. The results of the investigation show that most existing solutions use laboratory tests to diagnose possible ASF cases. In addition, various machine learning (ML) techniques have been used in the past to diagnose ASF. However, historical review of recent years shows that laboratories have difficulty diagnosing ASF with the required accuracy due to a lack of correlation between causes and effects. Lack of accuracy and incorrect ASF diagnoses by laboratories have proven to be a major problem for pig welfare. Consequently, misdiagnosis of ASF disease can result in severe direct and indirect economic losses to farmers, especially farmers whose income is derived primarily from pig production. While several other researchers have proposed the use of ML for ASF diagnosis, the application of cause-effect relationships between specific viruses and symptoms for ASF diagnosis is still missing. In this systematic literature review, we examine the methods, limitations, and approaches in the existing literature from ML and laboratories for ASF diagnosis. In this review, we evaluate the performance of ML and laboratory techniques for ASF diagnosis. In addition, we compare the performance of the techniques of ML with other statistical approaches such as causal ML and computer vision for ASF diagnosis. In addition, the strengths and weaknesses of ML and laboratory techniques for ASF diagnosis were summarized. A thorough search of relevant databases was performed, and the selected studies were examined using predefined inclusion and exclusion criteria. Nevertheless, the study also indicates an area for improvement, such as the accuracy of ASF diagnosis. The study recommends the use of Causal Reasoning with ML to develop a causal ML model capable of establishing relationships between viruses and symptoms to improve the accuracy of the ASF disease. The application of causal ML is presented as an alternative solution for laboratory diagnosis of ASF, which contributes to the field of the study. In addition, further research could investigate the possible characteristics of ASF, including virus variants originating from the ASF family. The review could provide essential information on ASF datasets based on the interpretation of results obtained from the use of appropriate samples and validated tests in combination with the information from laboratory tests of ASF disease epidemiology, scenario, clinical signs, and lesions produced by different virulence. This review concludes that more studies are needed for improving the accuracy and implementation of the causal ML model for ASF diagnosis in real-time surveillance systems.