Infectious diseases had major impacts and influences in the human history. Diseases such as Spanish Influenza or the Bubonic Plague have remarkable positions in history. Disease spread models are used to predict outcomes of an epidemic. These models are used to calculate the impact of an infectious disease, funding required for mass vaccinations and data for public health departments. The earliest mathematical model of infectious diseases was created by Daniel Bernoulli in 1766. This model was used to predict the outcome of inoculation against smallpox disease. In the modern world, these models are created using various software programs. The reason why I chose this subject is because I previously worked on some modelling simulations. Also…show more content…
The mortality rate of H1N1 virus is found to be 0.4%. So the 0.4% of the recovered population would die after this disease.
Limitations of SIR Model
The SIR Model is created in 1927 as its said before, so the initial model is bit outdated for numerous reasons. Firstly, the only vector of spread is humans. Vaccination rates, quality medical service, other vectors like rodents, birds are not included in this model. Also the disease always dies out, there is no way for the disease to live continuously. I(∞)=0, because the population will be either recovered or susceptible in (t) infinite. In newer epidemic models, new factors added into to get more accurate results. The population in SIR model is assumed homogenous however in fact old and young people are more vulnerable to diseases. So in the newer models, demographic effects are also put into account.
SIR Model with Vaccination and Treatment
Vaccines are already available for most infectious diseases. In this mathematical disease spread model, the only difference between the SIR Model and this is, vaccination rate of the susceptible population is added into the model. This model can be used for diseases like flu, typhoid, measles which can be prevented by vaccinations. In this model SIR equations change a little because a new parameter is in the play, which is Nu (ν) which is the rate