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We developed an extension of a standard outbreak model, the simple stochastic epidemic, which accounts for societal learning.
To construct our influenza outbreak model, we adapted selected characteristics of St .Olaf College in Northfield, Minnesota, a residential liberal arts college that has previously participated in a study of the burden of influenza-like illness among college and university students.
The outbreak model is a lognormal epicurve of Sartwell [ 26].
The algorithm is applied to the simple outbreak model described next.
We added four soil variables to the set of eight environmental layers used in the previous study to test the influence of soil parameters on the outbreak model.
Inclusion of available soil variables into our anthrax outbreak model resulted in subtle changes in the likelihood of the pathogen in areas of northern Kazakhstan, but did not substantially change the extent of geographic predictions or results of rule set analyses [ 26].
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The validation of a disease outbreak simulation model is challenging given the poorly defined and variable nature of outbreaks.
"The challenge is that outbreaks are actually extremely rare events, but with terrible consequences," says Nicholas Grassly, an epidemiologist at Imperial College London who has done his own outbreak modeling.
Outbreak models typically assume that there are four groups of people: those who are susceptible, those who have been infected but are not contagious yet, those who are sick and can transmit the virus, and those who have recovered.
[1], [4], [27] [30] Understanding this heterogeneity is important, because modeling studies demonstrate that the models incorporating variability in transmission differ substantially from standard outbreak models, and, in these models, individually targeted interventions are much more effective than untargeted interventions.
Their simulations are based on less realistic deterministic outbreak models.
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