Design of a Simulation Model for the Diagnosis of Classical Swine Fever Virus in Ecuadorian Farms

Classical swine fever (CSF) is a disease that slows down animal production and international trade; therefore, its identification is key in pig farms to take the relevant health measures. Therefore, the objective of this research was to design a Susceptible-Exposed-Infected-Recovered (SEIR) simulation model to carry out epidemiological modeling for the identification of outbreaks of classical swine fever in the Sierra Region of Ecuador, using Python software and historical data on incidences of this disease in the provinces of the Ecuadorian highlands, considering the variables pig population, initial number of exposed pigs, initial number of infected, number of pigs removed, contagion rate (α), transmission rate (β), and recovery rate (γ). The results show that the SEIR model allowed us to determine that the population of susceptible (healthy) pigs decreases over time until reaching zero. This decrease in susceptibility occurred during the first 15 days, which shows that this is the time necessary to infect the entire population with an infected person. Therefore, the exposed population increases during the 15 days that the total infection process lasts and then decreases. It is also identified that throughout these five years of analysis of the past, it has been increasing from 2015 to 2019, which hurt the yields and productivity of pig farms in the Ecuadorian mountains.