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Hans Nesse-Global Health-SIR Model

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MATHEMATICAL MODELLING OF ENDEMIC INFECTIOUS DISEASES (Ebola) Introduction An introduction to disease dynamics. Diseases are an unavoidable part of a human’s life. Namely, the flu or the common cold has very minor symptoms as compared to those like Ebola, Malaria, and AIDS etc. The way these diseases spread have cause a lot of worry and fear amongst us due to the fact that some get severely infected while the others manage to be completely immune. Humans in the past have tried to study the manner in which these diseases have spread and have managed to develop a mathematical method in order to derive and make predictions. Prediction of the spread of diseases through mathematics involves dealing with data and linking it with biological …show more content…

The reason I chose to investigate this topic is because; I am planning to pursue a career in medicine and the statistics and working of the spread of an epidemic greatly interests me. Studying how researchers and epidemiologists come to a conclusion whether or not a disease will turn into an impending epidemic would help in my later years studying in the field of medicine. Exploration Mathematical Modeling of Ebola Brief History The Ebola virus was first discovered in the year 1976, and the most recent outbreak in West Africa (March 2014) is considered the largest and most complex outbreak since. The numbers of deaths in the 2014 outbreak are far greater than all the previous outbreaks combined. It has spread to countries starting in Guinea to various other land borders; Sierra Leone and Liberia, These being the most severely affected due to weak health systems and lack of human and infrastructural …show more content…

The model of this scenario is shown below The model displays the SIR model relationship for Liberia with a total population of 4mill, 2mill out of the population are infected and an initial recovered value of 0 within the span of 50 days. This model is greatly similar to that of the one on Guinea, with the decrease in infected and susceptible from the very start and until the end of 50 days and an increase in the recovered hence the value of dI⁄dt is negative from the very beginning until he end. The following scenarios were all hypothetical; therefore there are various limitations involved with the methods and equations used in order to get the respective results, These limitations are mentioned below. Conclusion Although most of the values, equations, and diagrams in the following investigation are all assumptions, one can get a minor idea of how an epidemic spreads through a fixed population. The reasons of the inaccuracy of the values would be due to a number of complications, like, deaths, babies being born and the variations in immune systems within the population. Taking into consideration factors such as gaining immunity or the population being vaccinated could alter the values used in these

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