Seasonal trends in COVID-19 cases, hospitalizations, and mortality in the United States and Europe
Open accessPublished version of a preprint
Abstract
Determining whether SARS-CoV-2 exhibits seasonality like other respiratory viruses is critical for public health planning. We evaluated whether COVID-19 rates follow a seasonal pattern using time series models. We used time series decomposition to extract the annual seasonal component of COVID-19 case, hospitalization, and mortality rates from March 2020 through December 2022 for the United States and Europe. Models were adjusted for a country-specific stringency index to account for confounding by various interventions. Despite year-round disease activity, we identified seasonal spikes in COVID-19 from approximately November through April for all outcomes and in all countries. Our results support employing annual preventative measures against SARS-CoV-2, such as administering seasonal booster vaccines in a similar timeframe as those in place for influenza. Whether certain high-risk individuals may need more than one COVID-19 vaccine booster dose each year will depend on factors like vaccine durability against severe illness and levels of year-round disease activity.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Seasonal trends in COVID-19 cases are more pronounced in European countries than in countries such as Israel and Qatar, which may affect VE estimates.
“Another reason to include only European studies is that seasonal trends in COVID-19 cases are more pronounced than in countries such as Israel and Qatar [13, 14], which may also affect the VE estimate.”
From Effectiveness of COVID-19 vaccines against SARS-CoV-2 infection and severe outcomes in adults: a systematic review and meta-analysis of European studies published up to 22 January 2024 (2025) · cites this paper as [13]
The Results and Discussion report seasonal spikes in COVID-19 cases in Europe from approximately November through April. The paper does not compare Europe with Israel or Qatar, and its Discussion says the findings are not generalizable beyond the United States and Europe. It discusses vaccine timing, but not whether these seasonal differences affect vaccine-effectiveness estimates. (LLM, conf=0.88)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Nalbandian, A., Sehgal, K., Gupta, A., Madhavan, M. V., McGroder, C., Stevens, J. S., Cook, J. R., Nordvig, A. S., Shalev, D., Sehrawat, T. S., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01283-z · Cited by 4,862 · see citing works
A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker)
Hale, T., Angrist, N., Goldszmidt, R., Kira, B., Petherick, A., Phillips, T., Webster, S., Cameron-Blake, E., Hallas, L., Majumdar, S., et al. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01079-8 · Cited by 4,578 · see citing works
Extrapulmonary manifestations of COVID-19
Gupta, A., Madhavan, M. V., Sehgal, K., Nair, N., Mahajan, S., Sehrawat, T. S., Bikdeli, B., Ahluwalia, N., Ausiello, J. C., Wan, E. Y., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0968-3 · Cited by 3,185 · see citing works
Grohskopf, L. A., Sokolow, L. Z., Broder, K. R., Walter, E. B., Fry, A. M., & Jernigan, D. B. (2018) · MMWR Recommendations and Reports · doi:10.15585/mmwr.rr6703a1 · Cited by 1,635 · see citing works
Seasonality of Respiratory Viral Infections
Moriyama, M., Hugentobler, W., & Iwasaki, A. (2020) · Annual Review of Virology · doi:10.1146/annurev-virology-012420-022445 · Cited by 1,281 · see citing works
Antibody evasion by SARS-CoV-2 Omicron subvariants BA.2.12.1, BA.4 and BA.5
Wang, Q., Guo, Y., Iketani, S., Nair, M. S., Li, Z., Mohri, H., Wang, M., Yu, J., Bowen, A. D., Chang, J. Y., et al. (2022) · Nature · doi:10.1038/s41586-022-05053-w · Cited by 768 · see citing works
Pathological sequelae of long-haul COVID
Mehandru, S., & Mérad, M. (2022) · Nature Immunology · doi:10.1038/s41590-021-01104-y · Cited by 649 · see citing works
Influenza Seasonality: Underlying Causes and Modeling Theories
Lofgren, E., Fefferman, N. H., Naumov, Y. N., Gorski, J., & Naumova, E. N. (2006) · Journal of Virology · doi:10.1128/jvi.01680-06 · Cited by 559 · see citing works
Roles of Humidity and Temperature in Shaping Influenza Seasonality
Lowen, A. C., & Steel, J. (2014) · Journal of Virology · doi:10.1128/jvi.03544-13 · Cited by 540 · see citing works
Evolution of enhanced innate immune evasion by SARS-CoV-2
Thorne, L., Bouhaddou, M., Reuschl, A., Zuliani‐Alvarez, L., Polacco, B. J., Pelin, A., Batra, J., Whelan, M. V. X., Hosmillo, M. D. T., Fossati, A., et al. (2021) · Nature · doi:10.1038/s41586-021-04352-y · Cited by 438 · see citing works
Ferdinands, J. M., Rao, S., Dixon, B. E., Mitchell, P. K., DeSilva, M. B., Irving, S. A., Lewis, N., Natarajan, K., Stenehjem, E., Grannis, S. J., et al. (2022) · MMWR. Morbidity and Mortality Weekly Report · doi:10.15585/mmwr.mm7107e2 · Cited by 435 · see citing works
Thompson, M. G., Natarajan, K., Irving, S. A., Rowley, E. A., Griggs, E. P., Gaglani, M., Klein, N. P., Grannis, S. J., DeSilva, M. B., Stenehjem, E., et al. (2022) · MMWR. Morbidity and Mortality Weekly Report · doi:10.15585/mmwr.mm7104e3 · Cited by 398 · see citing works
Protection by a Fourth Dose of BNT162b2 against Omicron in Israel
(2022) · doi:10.1056/nejmoa2201570 · Cited by 376 · see citing works
Grohskopf, L. A., Alyanak, E., Ferdinands, J. M., Broder, K. R., Blanton, L., Talbot, H. K., & Fry, A. M. (2021) · MMWR Recommendations and Reports · doi:10.15585/mmwr.rr7005a1 · Cited by 364 · see citing works
Increasing Temperature and Relative Humidity Accelerates Inactivation of SARS-CoV-2 on Surfaces
Biryukov, J., Boydston, J. A., Dunning, R. A., Yeager, J. J., Wood, S., Reese, A. L., Ferris, A., Miller, D., Weaver, W. G., Zeitouni, N. E., et al. (2020) · mSphere · doi:10.1128/msphere.00441-20 · Cited by 316 · see citing works
Forecasting at ScalePreprint
Taylor, S. J., & Letham, B. (2017) · doi:10.7287/peerj.preprints.3190 · Cited by 301 · see citing works
The role of seasonality in the spread of COVID-19 pandemic
Liu, X., Huang, J., Li, C., Zhao, Y., Wang, D., Huang, Z., & Yang, K. (2021) · Environmental Research · doi:10.1016/j.envres.2021.110874 · Cited by 269 · see citing works
Seasonality and uncertainty in global COVID-19 growth rates
Merow, C., & Urban, M. C. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2008590117 · Cited by 250 · see citing works
Use of At-Home COVID-19 Tests — United States, August 23, 2021–March 12, 2022
Rader, B., Gertz, A., Iuliano, A. D., Gilmer, M., Wronski, L., Astley, C. M., Sewalk, K., Varrelman, T. J., Cohen, J., Parikh, R., et al. (2022) · MMWR. Morbidity and Mortality Weekly Report · doi:10.15585/mmwr.mm7113e1 · Cited by 223 · see citing works
Chadha, M., Hirve, S. S., Bancej, C. M., et al. (2020) · Influenza and Other Respiratory Viruses · doi:10.1111/irv.12726 · Cited by 139 · see citing works
Tartof, S. Y., Slezak, J. M., Puzniak, L., Hong, V. X., Frankland, T. B., Ackerson, B. K., Takhar, H. S., Ogun, O. A., Simmons, S. R., Zamparo, J. M., et al. (2021) · The Lancet Regional Health - Americas · doi:10.1016/j.lana.2022.100198 · Cited by 106 · see citing works
Self-citationCoronavirus seasonality, respiratory infections and weather
Nichols, G. L., Gillingham, E. L., Macintyre, H. L., Vardoulakis, S., Hajat, S., Sarran, C. E., Amankwaah, D., & Phalkey, R. K. (2021) · BMC Infectious Diseases · doi:10.1186/s12879-021-06785-2 · Cited by 93 · see citing works
A cross-country comparison of Covid-19 containment measures and their effects on the epidemic curves
Cascini, F., Failla, G., Gobbi, C., Pallini, E., Jin, H., Luxi, W., Villani, L., Quentin, W., Boccia, S., & Ricciardi, W. (2022) · BMC Public Health · doi:10.1186/s12889-022-14088-7 · Cited by 57 · see citing works
On the Environmental Determinants of COVID‐19 Seasonality
Choi, Y., Tuel, A., & Eltahir, E. A. B. (2021) · GeoHealth · doi:10.1029/2021gh000413 · Cited by 55 · see citing works
Seasonality of Respiratory Viruses at Northern Latitudes
Hawkes, M., Lee, B. E., Kanji, J. N., et al. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.24650 · Cited by 48 · see citing works
Tartof, S. Y., Slezak, J. M., Puzniak, L., Hong, V. X., Xie, F., Ackerson, B. K., Valluri, S. R., Jódar, L., & McLaughlin, J. M. (2022) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(22)00170-9 · Cited by 33 · see citing works
Self-citationHow health systems approached respiratory viral pandemics over time: a systematic review
Cascini, F., Hoxhaj, I., Zaçe, D., Ferranti, M., Pietro, M. L. D., Boccia, S., & Ricciardi, W. (2020) · BMJ Global Health · doi:10.1136/bmjgh-2020-003677 · Cited by 29 · see citing works
Tartof, S. Y., Slezak, J. M., Puzniak, L., Hong, V. X., Xie, F., Ackerson, B. K., Valluri, S. R., Jódar, L., & McLaughlin, J. M. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4011905 · Cited by 18 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Zhou, G., Dael, N., Verweij, S., Balafas, S., Mubarik, S., Oude Rengerink, K., Pasmooij, A. M. G., van Baarle, D., Mol, P. G. M., de Bock, G. H., et al. (2025) · European Respiratory Review · doi:10.1183/16000617.0222-2024 · Cited by 34 · see citing works
“Another reason to include only European studies is that seasonal trends in COVID-19 cases are more pronounced than in countries such as Israel and Qatar [13, 14], which may also affect the VE estimate.”
- Authors
- Wiemken, T. L., Khan, F., Puzniak, L., Yang, W., Simmering, J., Polgreen, P., Nguyen, J. L., Jodar, L., & McLaughlin, J. M.
- Affiliations
- Pfizer (United States) (US)
- Columbia University (US)
- University of Iowa (US)
- Corresponding author
- Wiemken, Timothy L.
- McLaughlin, John M.
- ORCID
- Venue
- Scientific Reports
- Published
- 2023-03-08
- Volume
- 13
- Issue
- 1
- Pages
- 3886-3886
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC9994397
- OpenAlex
- W4323652706
- Cited by
- 138
- References
- 28
- Citations by year
- 2026: 17 · 2025: 45 · 2024: 62 · 2023: 14
- FWCI
- 29.00
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Pfizer
- Topics
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Keywords
- COVID-19 pandemic, SARS-CoV-2, seasonal variation, seasonal patterns, COVID-19 cases, hospitalization, mortality, time series decomposition, stringency index, booster vaccination, vaccine durability, respiratory viruses, United States, Europe
- MeSH terms
- COVID-19, SARS-CoV-2, COVID-19 Vaccines, Europe, Hospitalization, Humans, Immunization, Secondary, Seasons, United States
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review