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Article

Assessing Seasonality Variation with Harmonic Regression: Accommodations for Sharp Peaks

by
Kavitha Ramanathan
1,
Mani Thenmozhi
1,
Sebastian George
2,
Shalini Anandan
3,
Balaji Veeraraghavan
3,
Elena N. Naumova
4,5 and
Lakshmanan Jeyaseelan
1,*
1
Department of Biostatistics, Christian Medical College, Vellore 632002, India
2
Department of Statistics, St. Thomas College, Palai, Kerala 686575, India
3
Department of Clinical Microbiology, Christian Medical College, Vellore 632004, India
4
Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA 02111, USA
5
Department of Gastrointestinal Sciences, Christian Medical College, Vellore 632004, India
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(4), 1318; https://doi.org/10.3390/ijerph17041318
Submission received: 25 December 2019 / Revised: 6 February 2020 / Accepted: 13 February 2020 / Published: 18 February 2020
(This article belongs to the Special Issue Infectious Disease Modeling in the Era of Complex Data)
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Abstract

The use of the harmonic regression model is well accepted in the epidemiological and biostatistical communities as a standard procedure to examine seasonal patterns in disease occurrence. While these models may provide good fit to periodic patterns with relatively symmetric rises and falls, for some diseases the incidence fluctuates in a more complex manner. We propose a two-step harmonic regression approach to improve the model fit for data exhibiting sharp seasonal peaks. To capture such specific behavior, we first build a basic model and estimate the seasonal peak. At the second step, we apply an extended model using sine and cosine transform functions. These newly proposed functions mimic a quadratic term in the harmonic regression models and thus allow us to better fit the seasonal spikes. We illustrate the proposed method using actual and simulated data and recommend the new approach to assess seasonality in a broad spectrum of diseases manifesting sharp seasonal peaks.
Keywords: time series; harmonic regression; seasonality; infectious disease; ARIMA/SARIMA; trends time series; harmonic regression; seasonality; infectious disease; ARIMA/SARIMA; trends

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MDPI and ACS Style

Ramanathan, K.; Thenmozhi, M.; George, S.; Anandan, S.; Veeraraghavan, B.; Naumova, E.N.; Jeyaseelan, L. Assessing Seasonality Variation with Harmonic Regression: Accommodations for Sharp Peaks. Int. J. Environ. Res. Public Health 2020, 17, 1318. https://doi.org/10.3390/ijerph17041318

AMA Style

Ramanathan K, Thenmozhi M, George S, Anandan S, Veeraraghavan B, Naumova EN, Jeyaseelan L. Assessing Seasonality Variation with Harmonic Regression: Accommodations for Sharp Peaks. International Journal of Environmental Research and Public Health. 2020; 17(4):1318. https://doi.org/10.3390/ijerph17041318

Chicago/Turabian Style

Ramanathan, Kavitha, Mani Thenmozhi, Sebastian George, Shalini Anandan, Balaji Veeraraghavan, Elena N. Naumova, and Lakshmanan Jeyaseelan. 2020. "Assessing Seasonality Variation with Harmonic Regression: Accommodations for Sharp Peaks" International Journal of Environmental Research and Public Health 17, no. 4: 1318. https://doi.org/10.3390/ijerph17041318

APA Style

Ramanathan, K., Thenmozhi, M., George, S., Anandan, S., Veeraraghavan, B., Naumova, E. N., & Jeyaseelan, L. (2020). Assessing Seasonality Variation with Harmonic Regression: Accommodations for Sharp Peaks. International Journal of Environmental Research and Public Health, 17(4), 1318. https://doi.org/10.3390/ijerph17041318

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