Python Application to SEIR Model of the Spread of Malaria
Abstract
Python is the next breakthrough in natural science computing because it enables users to do more and better science. Research on the Susceptible-Exposed-Infected-Recovered-Susceptible (SEIRS) disease spread model has received a lot of attention, but with different factors, and the article aims to apply Python in simulating SEIRS-type Malaria spread data with handling/treatment parameters in other classes. They are exposed to the assumption that individuals who recover from Malaria may become susceptible to transmission of the disease. The data processing simulation aims to see whether Malaria will develop into an epidemic. The use of Python code will make it easier to detect outbreaks. The model for spreading Malaria involves four classes: susceptible, infected but not yet active, infected, and recovered. The simulation data is the number of malaria sufferers in 2017 from the Mimika District Health Service, Indonesia. Mimika Regency is the region with the highest number of malaria cases at 29.12% of all malaria cases in Indonesia. The equilibrium point is determined using a SEIRS-type mathematical model. Data processing with simulations in the SEIRS model obtains a primary reproduction number (Ro) of 0.078 and R0 < 1, so the disease will not become an epidemic.
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References
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