Characterizing the Qatar advanced-phase SARS-CoV-2 epidemic
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Abstract
The overarching objective of this study was to provide the descriptive epidemiology of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic in Qatar by addressing specific research questions through a series of national epidemiologic studies. Sources of data were the centralized and standardized national databases for SARS-CoV-2 infection. By July 10, 2020, 397,577 individuals had been tested for SARS-CoV-2 using polymerase-chain-reaction (PCR), of whom 110,986 were positive, a positivity cumulative rate of 27.9% (95% CI 27.8–28.1%). As of July 5, case severity rate, based on World Health Organization (WHO) severity classification, was 3.4% and case fatality rate was 1.4 per 1,000 persons. Age was by far the strongest predictor of severe, critical, or fatal infection. PCR positivity of nasopharyngeal/oropharyngeal swabs in a national community survey (May 6–7) including 1,307 participants was 14.9% (95% CI 11.5–19.0%); 58.5% of those testing positive were asymptomatic. Across 448 ad-hoc testing campaigns in workplaces and residential areas including 26,715 individuals, pooled mean PCR positivity was 15.6% (95% CI 13.7–17.7%). SARS-CoV-2 antibody prevalence was 24.0% (95% CI 23.3–24.6%) in 32,970 residual clinical blood specimens. Antibody prevalence was only 47.3% (95% CI 46.2–48.5%) in those who had at least one PCR positive result, but 91.3% (95% CI 89.5–92.9%) among those who were PCR positive > 3 weeks before serology testing. Qatar has experienced a large SARS-CoV-2 epidemic that is rapidly declining, apparently due to growing immunity levels in the population.
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Because a small proportion of Qatar's population is 50 years of age or older, the findings may not be generalizable to countries with a larger elderly proportion.
“With the small proportion of the population of Qatar being 50 years of age or older,10 our findings may not be generalizable to other countries in which elderly citizens constitute a larger proportion of the population.”
From Effects of Previous Infection and Vaccination on Symptomatic Omicron Infections (2022) · cites this paper as [10]
Cases and controls were exact-matched by sex, 10-year-age group, nationality, and calendar week of PCR test to control for established differences in risk of exposure to SARS-CoV-2 infection in Qatar.
“Cases (persons infected with BA.1, BA.2, or any-Omicron-subvariant) and controls (uninfected persons) were exact-matched by sex, 10-year-age group, nationality, and calendar week of polymerase chain reaction (PCR) test to control for established differences in the risk of exposure to SARS-CoV-2 infection in Qatar15–19.”
From Duration of mRNA vaccine protection against SARS-CoV-2 Omicron BA.1 and BA.2 subvariants in Qatar (2022) · cites this paper as [15]
Case numbers were insufficient to estimate the duration of protection against COVID-19 hospitalization and death for each Omicron subvariant separately.
“With the lower severity of Omicron infections24,25 and the young population of Qatar15,26, case numbers were insufficient to estimate the duration of protection against COVID-19 hospitalization and death for each subvariant separately.”
From Duration of mRNA vaccine protection against SARS-CoV-2 Omicron BA.1 and BA.2 subvariants in Qatar (2022) · cites this paper as [15]
Matching was implemented to control for established differences in the risk of exposure to SARS-CoV-2 infection in Qatar.
“Matching was implemented to control for established differences in the risk of exposure to SARS-CoV-2 infection in Qatar15–19.”
From Duration of mRNA vaccine protection against SARS-CoV-2 Omicron BA.1 and BA.2 subvariants in Qatar (2022) · cites this paper as [15]
Qatar has young, international demographics: only 9% of the population is aged 50 years or older and 89% are international expatriates from over 150 countries.
“Qatar has young, international demographics. Only 9% of Qatar’s population is ≥50 years of age and 89% are international expatriates from over 150 countries15,41.”
From Duration of mRNA vaccine protection against SARS-CoV-2 Omicron BA.1 and BA.2 subvariants in Qatar (2022) · cites this paper as [15]
With only 9% of Qatar's population aged 50 years or older, the findings may not be generalizable to other countries in which elderly citizens constitute a larger proportion of the total population.
“With only 9% of Qatar’s population ≥50 years of age15,41, our findings may not be generalizable to other countries in which elderly citizens constitute a larger proportion of the total population.”
From Duration of mRNA vaccine protection against SARS-CoV-2 Omicron BA.1 and BA.2 subvariants in Qatar (2022) · cites this paper as [15]
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Self-citationImmune Imprinting and Protection against Repeat Reinfection with SARS-CoV-2
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Self-citationEffectiveness of mRNA-1273 and BNT162b2 Vaccines in Qatar
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Self-citationCovid-19 Vaccine Protection among Children and Adolescents in Qatar
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Self-citationOne Year of SARS-CoV-2: Genomic Characterization of COVID-19 Outbreak in Qatar
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Self-citationSARS-CoV-2 infection hospitalization, severity, criticality, and fatality rates in Qatar
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Self-citationA review of mathematical model-based scenario analysis and interventions for COVID-19
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Self-citationInfection fatality rate of COVID-19 in community-dwelling elderly populations
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Differential protection against SARS-CoV-2 reinfection pre- and post-Omicron
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“Qatar had a significant first wave of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic that peaked in late May 2020 [1,2].”
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Self-citationReinforcement learning-based decision support system for COVID-19
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Self-citationAll-cause and COVID-19 mortality in Qatar during the COVID-19 pandemic
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Self-citationMental health during COVID‐19 in Qatar
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Bivalent mRNA-1273.214 vaccine effectiveness against SARS-CoV-2 omicron XBB* infections
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Two prolonged viremic SARS-CoV-2 infections with conserved viral genome for two months
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Self-citationProtection of natural infection against reinfection with SARS-CoV-2 JN.1 variant
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Self-citationHistory of primary-series and booster vaccination and protection against Omicron reinfection
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Self-citationSynthesis, Phase Behavior and Computational Simulations of a Pyridyl-Based Liquid Crystal System
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Self-citationCOVID-19 primary series and booster vaccination and potential for immune imprintingPreprint
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Self-citationSARS-CoV-2 vaccine effectiveness in immunosuppressed kidney transplant recipients
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Self-citationSeroprevalence of herpes simplex virus type 1 and type 2 among the migrant workers in Qatar
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Self-citationMechanisms underlying the loss of migratory behaviour in a long‐lived bird
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Self-citationAntigen B modulates anti‐inflammatory cytokines in the EAE model of multiple sclerosis
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Self-citationPrevalence of hepatitis B and C viruses among migrant workers in Qatar
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Self-citationHashing for secure optical information compression in a heterogeneous convolutional neural network
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Analyzing inherent biases in SARS-CoV-2 PCR and serological epidemiologic metrics
Makhoul, M., Abu-Hijleh, F. M., Seedat, S., Mumtaz, G. R., Chemaitelly, H. S., Ayoub, H. H., & Abu-Raddad, L. J. (2022) · BMC Infectious Diseases · doi:10.1186/s12879-022-07425-z · Cited by 7 · see citing works
Self-citationSukik, L., Chemaitelly, H. S., Ayoub, H. H., Coyle, P. V., Tang, P., Hasan, M. R., Yassine, H. M., Thani, A. A. A., Kanaani, Z. A., Al‐Kuwari, E., et al. (2025) · Open Forum Infectious Diseases · doi:10.1093/ofid/ofaf098 · Cited by 6 · see citing works
Self-citationChemaitelly, H. S., Akhtar, N., Jerdi, S. A., Kamran, S., Joseph, S., Morgan, D., Uy, R. S., Abid, F. B., Al‐Khal, A., Abou‐Samra, A., et al. (2025) · Expert Review of Vaccines · doi:10.1080/14760584.2025.2596676 · Cited by 6 · see citing works
Self-citationAssociation between COVID-19 vaccination and stroke: a nationwide case-control study in Qatar
Chemaitelly, H. S., Akhtar, N., Jerdi, S. A., Kamran, S., Joseph, S., Morgan, D., Uy, R. S., Abid, F. B., Al‐Khal, A., Bertollini, R., et al. (2024) · International Journal of Infectious Diseases · doi:10.1016/j.ijid.2024.107095 · Cited by 6 · see citing works
Self-citationBastaki, K., Dhieb, D., Elanbari, M., Velayutham, D., Khudadad, H., Ghuloum, S., & Jithesh, P. V. (2025) · Journal of Infection and Public Health · doi:10.1016/j.jiph.2025.102789 · Cited by 6 · see citing works
Global Estimates of COVID‐19 Morbidity and Mortality: A Cohort Study and Mathematical Model Analysis
Ayoub, H. H., Chemaitelly, H. S., & Abu-Raddad, L. J. (2025) · Influenza and Other Respiratory Viruses · doi:10.1111/irv.70154 · Cited by 5 · see citing works
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Modeling the population-level impact of treatment on COVID-19 disease and SARS-CoV-2 transmission
Makhoul, M., Abu-Hijleh, F. M., Ayoub, H. H., Seedat, S., Chemaitelly, H. S., & Abu-Raddad, L. J. (2022) · Epidemics · doi:10.1016/j.epidem.2022.100567 · Cited by 5 · see citing works
Self-citationAsymptomatic viruses detectable in saliva in the first year of life: a narrative review
Goh, M., Joy, C., Gillespie, A. N., Soh, Q. R., He, F., & Sung, V. (2023) · Pediatric Research · doi:10.1038/s41390-023-02952-0 · Cited by 5 · see citing works
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Self-citation
- Authors
- Abu-Raddad, L. J., Chemaitelly, H., Ayoub, H. H., Al Kanaani, Z., Al Khal, A., Al Kuwari, E., Butt, A. A., Coyle, P., Jeremijenko, A., Kaleeckal, A. H., Latif, A. N., Owen, R. C., Rahim, H. F. A., Al Abdulla, S. A., Al Kuwari, M. G., Kandy, M. C., Saeb, H., Ahmed, S. N. N., Al Romaihi, H. E., Bansal, D., Dalton, L., Al-Thani, M. H., & Bertollini, R.
- Affiliations
- Cornell University (US)
- Qatar University (QA)
- Hamad Medical Corporation (QA)
- Primary Health Care (QA)
- Ministry of Public Health (QA)
- Corresponding author
- Abu-Raddad, Laith J.
- ORCID
- Venue
- Scientific Reports
- Published
- 2021-03-18
- Volume
- 11
- Issue
- 1
- Pages
- 6233-6233
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC7973743
- OpenAlex
- W3138604848
- Cited by
- 148
- References
- 40
- Citations by year
- 2026: 8 · 2025: 18 · 2024: 18 · 2023: 26 · 2022: 38 · 2021: 33 · 2020: 7
- FWCI
- 11.10
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- medRxiv (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Qatar University QSpace (Qatar University) (submittedVersion)
- Funders
- Qatar National Research Fund (NPRP grant number 9-040-3-008 and NPRP grant number 12S-0216-190094)
- 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
- SARS-CoV-2, COVID-19 epidemic, Qatar, case fatality rate, case severity, age as risk factor, asymptomatic infection, seroprevalence, community survey, herd immunity, epidemic decline
- MeSH terms
- COVID-19, SARS-CoV-2, COVID-19 Nucleic Acid Testing, Adolescent, Adult, Aged, Aged, 80 and over, Child, Child, Preschool, Cross-Sectional Studies, Female, Humans, Infant, Male, Middle Aged, Qatar, Young Adult, Asymptomatic Infections
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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.
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