Effects of Data Aggregation on Time Series Analysis of Seasonal Infections

Tania M. Alarcon Falconi, Bertha Estrella, Fernando Sempértegui and Elena N. Naumova
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Tania M. Alarcon Falconi: Division of Nutrition Epidemiology and Data Science, Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA 02111, USA
Bertha Estrella: Department of Immunology, Faculty of Medical Sciences, Central University, Quito 170136, Ecuador
Fernando Sempértegui: Department of Immunology, Faculty of Medical Sciences, Central University, Quito 170136, Ecuador
Elena N. Naumova: Division of Nutrition Epidemiology and Data Science, Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA 02111, USA

IJERPH, 2020, vol. 17, issue 16, 1-21

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 estimates of trend, amplitude, and peak timing for daily and weekly data regardless of the disease. However, estimates of peak timing of the more common infections changed by as much as 2.5 months when controlling for monthly data irregularities. Building a systematic model that controls for data irregularities is essential to accurately characterize temporal patterns of infections. With the urgent need to characterize temporal patterns of novel infections, such as COVID-19, this tutorial is timely and highly valuable for experts in many disciplines.

Keywords: data aggregation; time series; harmonic regression; seasonality; infectious diseases (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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