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Effects of Data Aggregation on Time Series Analysis of Seasonal Infections

Author

Listed:
  • 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)

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.

Suggested Citation

  • Tania M. Alarcon Falconi & Bertha Estrella & Fernando Sempértegui & Elena N. Naumova, 2020. "Effects of Data Aggregation on Time Series Analysis of Seasonal Infections," IJERPH, MDPI, vol. 17(16), pages 1-21, August.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:16:p:5887-:d:398668
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    References listed on IDEAS

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    1. Katarina Ureña-Castro & Silvia Ávila & Mariela Gutierrez & Elena N. Naumova & Rolando Ulloa-Gutierrez & Alfredo Mora-Guevara, 2019. "Seasonality of Rotavirus Hospitalizations at Costa Rica’s National Children’s Hospital in 2010–2015," IJERPH, MDPI, vol. 16(13), pages 1-13, June.
    2. Pavel S. Stashevsky & Irina N. Yakovina & Tania M. Alarcon Falconi & Elena N. Naumova, 2019. "Agglomerative Clustering of Enteric Infections and Weather Parameters to Identify Seasonal Outbreaks in Cold Climates," IJERPH, MDPI, vol. 16(12), pages 1-19, June.
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    Citations

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    Cited by:

    1. Yutong Zhang & Ryan B. Simpson & Lauren E. Sallade & Emily Sanchez & Kyle M. Monahan & Elena N. Naumova, 2022. "Evaluating Completeness of Foodborne Outbreak Reporting in the United States, 1998–2019," IJERPH, MDPI, vol. 19(5), pages 1-19, March.
    2. Anastasia Marshak & Aishwarya Venkat & Helen Young & Elena N. Naumova, 2021. "How Seasonality of Malnutrition Is Measured and Analyzed," IJERPH, MDPI, vol. 18(4), pages 1-12, February.
    3. Elena N. Naumova & Ryan B. Simpson & Bingjie Zhou & Meghan A. Hartwick, 2022. "Global seasonal and pandemic patterns in influenza: An application of longitudinal study designs," International Statistical Review, International Statistical Institute, vol. 90(S1), pages 82-95, December.

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