Noise Is Not Error: Detecting Parametric Heterogeneity Between Epidemiologic Time Series.

ABSTRACT: Mathematical models play a central role in epidemiology. For example, models unify heterogeneous data into a single framework, suggest experimental designs, and generate hypotheses. Traditional methods based on deterministic assumptions, such as ordinary differential equations (ODE), have been successful in those scenarios. However, noise caused by random variations rather than true differences is an intrinsic feature of the cellular/molecular/social world. Time series data from patients (in the case of clinical science) or number of infections (in the case of epidemics) can vary due to both intrinsic differences or incidental fluctuations. The use of traditional fitting methods for ODEs applied to noisy problems implies that deviation from some trend can only be due to error or parametric

SUBMITTER: Romero-Severson EO 

PROVIDER: S-EPMC6052138 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature