Analysis of Factors Influencing Swine Fever Transmission using Machine Learning, Ecuador

This research focuses on implementing machine learning models of swine fever incidence in Ecuador to assess the impact in order to understand the dynamics, patterns, and trends in swine fever transmission. This study highlights the importance of information technologies, through data science and encompassing the disciplines of statistics and mathematics, in veterinary epidemiology, which has its origins in the health crisis caused by the COVID-19 pandemic. The analysis includes the collection, analysis, and systematization of environmental, social, and economic-operational variables related to transmission foci, highlighting that altitude, precipitation, temperature, density of swine production units, and economic activity (trade operations) strongly influence virus transmission. The findings show that the basic reproduction number (R0), which is less than, suggests a downward trend in the disease, which benefits public health. It is concluded that combining veterinary epidemiological data with mathematical models is crucial for developing effective of swine fever in Ecuador.