Effects of Data Aggregation on Time Series Analysis of Seasonal Infections.

ABSTRACT: Time series analysis in epidemiological studies is typically conducted on aggregated counts, although data tend to be collected at finer temporal resolutions. The decision to aggregate data is rarely discussed in epidemiological literature although it has been shown to impact model results. We present a critical thinking process for making decisions about data aggregation in time series analysis of seasonal infections. We systematically build a harmonic regression model to characterize peak timing and amplitude of three respiratory and enteric infections that have different seasonal patterns and incidence. We show that irregularities introduced when aggregating data must be controlled during modeling to prevent erroneous results. Aggregation irregularities had a minimal impact on the estim

SUBMITTER: Alarcon Falconi TM 

PROVIDER: S-EPMC7460497 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature