Seasonal Patterns and Periodicity of COVID-19 Outbreaks in the United States: Discoveries from a Spectral Analysis Study


The global burden of COVID-19 has been profound, with the United States experiencing a substantial share of cases and fatalities. Unlike other respiratory viruses, the seasonality of COVID-19 has been ambiguous, leading to a pressing need for definitive research in this area. Initial observations suggested that surges in COVID-19 cases were not merely responses to social interactions or public health interventions but followed a more intrinsic, periodic pattern. A new study published in Frontiers in Public Health and conducted by El Hussain Shamsa and Professor Kezhong Zhang from the Center for Molecular Medicine and Genetics at the Wayne State University School of Medicine alongside Dr. Ali Shamsa from the Medical College of Wisconsin, the researchers investigated the seasonality and periodicity of COVID-19 outbreaks in the United States.

The team collected daily, county-level COVID-19 incidence data across the contiguous United States for three full seasonal years, from March 2020 to March 2023. This data was sourced from reliable databases such as 1Point3Acres and the New York Times, which are known for their accurate, real-time updates of COVID-19 cases and deaths. The core methodology the authors employed in the study was spectral analysis using Fast Fourier Transform (FFT) algorithms. This technique helps in identifying frequency components of a signal and is particularly useful in analyzing periodic patterns in time series data. Briefly, the daily case data was transformed to smooth out noise and normalize the scale. They used log-transformation to stabilize the variance across the time series and centered the data around its mean to better highlight variations. The researchers applied the FFT to the transformed data to decompose it into its frequency components. This allowed them to identify the dominant frequencies and periods of COVID-19 case surges. Additionally, they then constructed a periodogram from the FFT results, which is a plot showing the strength (amplitude) of each frequency component. Peaks in the periodogram indicate the dominant periods of COVID-19 outbreaks. Following the spectral analysis, they used statistical methods to validate the findings and ensure the robustness of the detected periodic signals against random fluctuations in the data.

The authors’ spectral analysis revealed several key periods at which COVID-19 cases peaked. In the annual Peak (366 days), this dominant period indicates a major outbreak roughly once a year, typically aligning with early to mid-winter. This finding is consistent with the behavior of other respiratory viruses, which often show winter seasonality. On the other hand, the triannual Peak (146.4 days) is another significant finding was the period of about 146.4 days, suggesting that smaller, yet significant, outbreaks occur approximately three times a year. This pattern was observed with peaks in late winter, late spring, and early autumn. They also reported additional smaller peaks at 183 days and 122 days which suggest further sub-annual cycles, although these were less pronounced compared to the annual and triannual cycles. Further research is needed to investigate the mechanisms driving the periodicity of COVID-19. Additionally, it is important to determine whether these patterns hold over longer periods and across different geographical regions. Additionally, integrating data on viral mutations, vaccination rates, and mobility data could refine the predictions of COVID-19 seasonality.

In conclusion, the comprehensive study led by Professor Kezhong Zhang is an important advancement in the epidemiological understanding of COVID-19 by illustrating its periodic outbreak patterns in the United States. The detailed spectral analysis showed COVID-19 to align with other respiratory viruses like influenza and RSV, which are known to follow seasonal trends significantly affecting transmission dynamics. The findings of annual and triannual peaks in COVID-19 outbreaks enriches our scientific understanding and also enhances public health preparedness. For instance, authorities can strategize vaccination timing, allocate resources more effectively, and implement targeted public health interventions to counter anticipated surge periods. Moreover, the predictability inferred from the study could improve existing predictive models, thereby aiding in precise planning and policy formulation to mitigate the virus’s impact.

About the author

Dr. El Hussain Shamsa is a resident physician undergoing Internal Medicine training at University Hospitals Cleveland Medical Center/Case Western University with the career goal to become a Cardiologist and physician-scientist. His passion for research developed early in his educational career during his pursuit of a bachelor’s in science degree in Mathematics and Biochemistry at the University of Michigan. After his graduation, Hussain continued to develop his research portfolio during his medical schooling at Wayne State University School of Medicine. With his background in mathematics, biostatistics, and computer programming, he undertook various translational/clinical and epidemiological research projects on molecular factors in fatty liver disease, fine airborne particulate matter (PM2.5) in cardiovascular disease, environmental and public health research on the COVID-19 pandemic, and computational cardiology projects.

About the author

Dr. Kezhong Zhang obtained his Ph.D. at the Institute of Genetics of Fudan University in 1998.  Following completion of his Ph.D., Dr. Zhang did his postdoctoral training at the University of Michigan Medical School, where he started his research journey in exploring endoplasmic reticulum (ER) stress and Unfolded Protein Response (UPR).  Dr. Zhang is currently Professor of Molecular Medicine and Genetics and of Biochemistry, Microbiology, and Immunology at Wayne State University. His research interest is focused on cellular stress signaling originated from intracellular organelles, such as ER, mitochondria, and lysosome, which are associated with metabolic disease, autoimmune disease, and cancer. Dr. Zhang is also interested in studying the impact of environmental and social-economic factors in health and disease.


Shamsa EH, Shamsa A and Zhang K (2023) Seasonality of COVID-19 incidence in the United States. Front. Public Health 11:1298593. doi: 10.3389/fpubh.2023.1298593

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