Unfortunately, reliable predictions are difficult to make during the early stages of an outbreak. A stochastic discrete time epidemic model was applied to predict the development of outbreaks of meningococcal disease in 'closed' populations such as military garrisons or boarding schools, which are further divided into subgroups called 'units'. The performance of the method was assessed by using a simulated epidemic in a 'garrison' of 20 units, with 68 men in each. Predictions of the weekly number of disease cases, the number of carriers, and the number of new infections were computed. Simulations suggest that these predictions can be improved if temporal observations on asymptomatic carriers in different units are utilized together with observed time series of the disease.
Predictions based only on the observed numbers of disease cases are generally very inaccurate.
To improve their quality it is essential that adequate temporal data are collected on the invasive disease cases in all units, and also on the asymptomatic carriers in those units where disease cases occurred, as well as in a few units without disease. Exploiting fully such information requires probabilistic epidemic models and computer intensive Markov chain Monte Carlo methods. To appear in Risk Analysis, 2002. Ranta J, Hovi T, Arjas E. Poliovirus surveillance by examining sewage water specimens. Studies on detection probability using simulation models.