Predictions by early indicators of the time and height of the peaks of yearly influenza outbreaks in Sweden.

Aims: Methods for prediction of the peak of the influenza from early observations are suggested. These predictions can be used for planning purposes.

Methods: In this study, new robust methods are described and applied to weekly Swedish data on influenza-like illness (ILI) and weekly laboratory diagnoses of influenza (LDI).

Statistical surveillance of epidemics: peak detection of influenza in Sweden.

A statistical surveillance system gives a signal as soon as data give enough evidence of an important event. We consider on-line surveillance systems for detecting changes in influenza incidence. One important feature of the influenza cycle is the start of the influenza season, and another one is the change to a decline (the peak).

Modeling influenza incidence for the purpose of on-line monitoring.

We describe and discuss statistical models of Swedish influenza data, with special focus on aspects which are important in on-line monitoring. Earlier suggested statistical models are reviewed and the possibility of using them to describe the variation in influenza-like illness (ILI) and laboratory diagnoses (LDI) is discussed. Exponential functions were found to work better than earlier suggested models for describing the influenza incidence.

Effect of uncertainty about population parameters on pharmacodynamics-based prediction of clinical trial power.

Clinical trial simulation (CTS) may be applied to predict power of intended drug trials on the basis of pharmacokinetic/pharmacodynamic (PKPD) drug models. The validity of such predictions will, among other factors, depend on the degree of uncertainty about population parameters entering the simulation. In the current article, we illustrate how population parameter uncertainty may be incorporated in the overall simulation model, using a worked example to demonstrate our approach.