Abstract:
Disease outbreaks can be devastating to many nations. Interventions of all kinds are run alongside
treatments or preventive measures. The effectiveness of the measures taken depends on the number of
individuals who are susceptible, exposed, infectious or recovered. These in turn are a function of the
nature of the disease: The mode of transmission, the risk of infection reproduction number and
incubation period among others. Cases of epidemics are often difficult to contain owing to the fact that
no one knows the dynamics of such diseases. In the absence of information the preparation and
response to such disease is erratic and may entirely depend on predictions made. Epidemiologists
and/or statisticians use the available data and parameterize the key indicators of a disease, do
simulations so as to enable them make estimations. Wrong prediction or modeling may lead to huge
variation in the predicted values and hence under-preparedness or over-preparedness. Both cases are
costly. When properly done modeling can and has become extremely useful. This study reviews two
approaches to modeling: The deterministic and the stochastic with merits and demerits of each
discussed. The importance of modeling is also reviewed. It was found out that stochastic models are
inevitably suitable when the population is small. For larger population the effect of randomness
becomes negligible and hence deterministic approach which is relatively easier to model may be used.
Stochastic model, as it was noticed, is also good in giving the range of the much desired numbers at
every category of the disease.
