Predicting Dynamics of an Infectious Disease

In a broader sense, disease is either infectious or not infectious. Such a classification purely rely weather a disease can be passed or transmitted between individuals or not. Infectious diseases are those diseases that can spread or transmitted easily and influenza is an example. Non- infectious diseases seem to develop over an individual’s lifespan and are not transmittable as such. Disease such as arthritis is an example of non-infectious disease. Classification of diseases is not an easy touch and in principle we have to admit to some overlapping situations. Some infectious diseases although not necessary for developing can be a cause for non-infectious disease such as cancer. There is however a clear distinction in carrying out epidemiological study for each disease type. The epidemiologic interest in studying non-infectious disease lies primarily on identifying the risk factors that are prominent in the chance of developing a particular disease. This could for example be learning the risk of contracting lung cancer attributed to smoking. In studying infectious disease on contrary, the epidemiologic interest would be identifying the infectious cause, origin and pattern of the disease in a population of interest. A study of disease outbreak in a local population where the primary risk factor for catching an infectious disease is the presence of an outbreak itself is example in this case [1]. This paper focuses on learning the great predictive power of models for infectious disease at population scale over the short period of time. The paper is a build-up on the same idea to examine infectious disease transmission mechanisms from host to another caused by micro parasitic pathogens. The paper does not cover cases where a disease is transmitted by macro parasite intermediate vectors enter the dynamic of the model. The examination of the model is based on constant and susceptible healthy, infected and immune population without no vital (birth and death) statistics.