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.
Key words: Disease modeling, deterministic, stochastic, risk of infection, infectiousness, basic reproduction number.
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