ArticleInternational journal of environmental research and public health2020
Effects of Data Aggregation on Time Series Analysis of Seasonal Infections.
Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Investigating seasonal patterns in enteric infections: a systematic review of time series methods.Epidemiology and infection · 2022Pooled it
- Seasonality of underweight among infants 1-11 months old in Niger: an exploratory analysis of data from a cluster-randomised trial.BMJ global health · 2025Trial
- Recommendations for the analysis of leading indicators in epidemic surveillance.PLOS digital health · 2026Review
- The Peacemaker Fellowship and Urban Gun Violence in Richmond, California.JAMA network open · 2026Article
- Seasonal and meteorological determinants of district level acute watery diarrhea incidence in Fiji, 2016-2023: a time-series analysis.BMC public health · 2026Article
- A longitudinal analysis of the prevalence of restrictive interventions involving women with mental health conditions, learning disabilities or autism in mental health services in England.Frontiers in psychiatry · 2026Article
- Deconstructing the effects of stochasticity on transmission of hospital-acquired infections in ICUs.Royal Society open science · 2023Article
- Global seasonal and pandemic patterns in influenza: An application of longitudinal study designs.International statistical review = Revue internationale de statistique · 2022Article
- Evaluating Completeness of Foodborne Outbreak Reporting in the United States, 1998-2019.International journal of environmental research and public health · 2022Article
- How Seasonality of Malnutrition Is Measured and Analyzed.International journal of environmental research and public health · 2021Review
- The Effect of Interdependences of Referral Behaviors on the Quality of Ambulatory Care: Evidence from Taiwan.Risk management and healthcare policy · 2021Article
- Modeling the Global Dynamic Contagion of COVID-19.Frontiers in public health · 2021Article
- Article
- Seasonal synchronization of foodborne outbreaks in the United States, 1996-2017.Scientific reports · 2020Article
- An analecta of visualizations for foodborne illness trends and seasonality.Scientific data · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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.
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