Prognose

Prognose (German for “forecast” or “prognosis”) designates a systematic prediction about future states based on the analysis of current and past data. The term is used across scientific, medical, economic and technical fields, where it denotes the application of quantitative or qualitative methods to estimate the likely development of a variable or a system.

In medicine, a prognosis refers to the anticipated course and outcome of a disease, often expressed in terms of probability of recovery, disease progression or survival time. Clinicians combine epidemiological statistics, patient characteristics and disease-specific factors to formulate a medical prognose, which guides therapeutic decisions and patient counseling.

Economic prognoses involve forecasting indicators such as gross domestic product, inflation, unemployment or market demand. Analysts employ models ranging from simple trend extrapolation to complex econometric simulations that incorporate stochastic variables, policy scenarios and external shocks. Accuracy depends on data quality, model assumptions and the volatility of the underlying economy.

Meteorology and climatology use prognoses to predict weather patterns and longer‑term climate trends. Numerical weather prediction models solve fluid dynamics equations on a grid, assimilating observational data from satellites, radars and surface stations. The output is a probabilistic forecast that is regularly updated as new data become available.

Methodologically, prognostic techniques can be divided into deterministic models, which produce a single expected outcome, and probabilistic models, which assign likelihoods to a range of possible futures. Emerging approaches incorporate machine learning algorithms that detect nonlinear relationships in large datasets, improving predictive power in domains such as finance, epidemiology and energy consumption.

Criticism of prognoses focuses on inherent uncertainties, model over‑fitting and the influence of unforeseen events. Consequently, most professional practice accompanies forecasts with confidence intervals, scenario analyses and regular revisions as new information emerges.