The analysis of the SIR model to study different patterns of future outbreaks of a dangerous disease (Pathogen X) using AI
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Abstract
This study aims to analyze the SIR model (Susceptible-Infectious-Recovered) to better understand how population dynamics could be impacted by an outbreak of a potential future disease called "Pathogen X." Despite the importance of the SIR model, there has been limited research applying it specifically to a disease like Pathogen X. The purpose of this study is to explore how changes in key factors—such as the rate of transmission () and the recovery rate (
)—influence the spread of the disease. By doing this, the study aims to provide valuable insights for controlling and managing future epidemics. Four scenarios were tested: (1) reducing both
and
, (2) reducing
and increasing
, (3) increasing
and reducing
, and (4) increasing both
and
. The findings show that in scenario (1), reducing both
and
slows down the outbreak and lowers the peak number of infections. In scenario (2), the outbreak slows down and the epidemic ends faster. In scenario (3), the disease spreads more quickly, with a higher peak number of infections. Scenario (4) results in a faster and more severe outbreak, but it also concludes more quickly.Ultimately, the results of this study can help guide strategies for controlling and preventing the spread of various diseases in the future.
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