key: cord-0801571-5xtc2odp authors: Abu-Raddad, L. J.; Chemaitelly, H.; Ayoub, H. H.; Al Kanaani, Z.; Al Khal, A.; Al Kuwari, E.; Butt, A. A.; Coyle, P.; Latif, A. N.; Owen, R. C.; Abdul Rahim, H. F.; 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, S. M.; Bertollini, R. title: Characterizing the Qatar advanced-phase SARS-CoV-2 epidemic date: 2020-07-19 journal: nan DOI: 10.1101/2020.07.16.20155317 sha: f241329b325a0391aac83915ea2c29f4112c3778 doc_id: 801571 cord_uid: 5xtc2odp ABSTRACT Background: Qatar has a population of 2.8 million, over half of whom are expatriate craft and manual workers (CMW). We aimed to characterize the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic in Qatar. Methods: A series of epidemiologic studies were conducted including analysis of the national SARS-CoV-2 PCR testing and hospitalization database, community surveys assessing current infection, ad-hoc PCR testing campaigns in workplaces and residential areas, serological testing for antibody on blood specimens collected for routine clinical screening/management, national Coronavirus Diseases 2019 (COVID-19) death registry, and a mathematical model. Results: By July 10, 397,577 individuals had been PCR tested for SARS-CoV-2, of whom 110,986 were positive, a positivity cumulative rate of 27.9% (95% CI: 27.8-28.1%). PCR positivity of nasopharyngeal 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 PCR 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 it was 91.3% (95% CI: 89.5-92.9%) among those who were PCR positive >3 weeks before serology testing. There were substantial differences in exposure to infection by nationality and sex, reflecting risk differentials between the CMW and urban populations. As of July 5, case severity rate, based on the WHO severity classification, was 3.4% and case fatality rate was 1.4 per 1,000 persons. Model-estimated daily number of infections and active-infection prevalence peaked at 31,040 and 8.0%, respectively, on May 20 and May 21. Attack rate (ever infection) was estimated at 61.3% on July 12. R0 ranged between 1.45-1.68 throughout the epidemic. Rt was estimated at 0.70 on June 15, which was hence set as onset date for easing of restrictions. Age was by far the strongest predictor of severe, critical, or fatal infection. Conclusions: Qatar has experienced a large SARS-CoV-2 epidemic that is rapidly declining, apparently due to exhaustion of susceptibles. The epidemic demonstrated a classic susceptible-infected-recovered SIR dynamics with a rather stable R0 of about 1.6. The young demographic structure of the population, in addition to a resourced public health response, yielded a milder disease burden and lower mortality than elsewhere. Qatar is an Arabian Gulf country of 2.8 million people that has been affected by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. The first documented case of SARS-CoV-2 community transmission was identified on March 6, 2020 and its source was linked soon after to a cluster of over 300 infections among expatriate craft and manual workers (CMW) living in high-density housing accommodations. Using mathematical modeling, the cluster size suggested the infection may have been circulating for at least 4 weeks prior to cluster identification. Restrictive social and physical distancing and other public health measures were immediately imposed in the whole country. By July 11, 103,128 SARS-CoV-2 infections had been laboratory-confirmed, at a rate of 36,729 per million population-one of the highest worldwide. 1 Following the World Health Organization (WHO) guidelines, Qatar adopted a "testing, tracing, and isolation" approach, as the backbone of the national response, 2 implementing a country-wide active contact tracing and testing using polymerase chain reaction (PCR). A total of 409,199 tests have been conducted for SARS-COV-2 using PCR as of July 11, at a rate of 145,736 per million population-one of the highest worldwide. 1 Qatar has a unique demographic and residential dwellings structure 3 that proved critical in understanding SARS-CoV-2 epidemiology. Of the total population, 3 89% are expatriates from over 150 countries, 4-6 most of whom live in the capital city, Doha. 3 About 60% of the population consists of CMW, typically working in mega-development projects. 7 This "labor" population is predominantly young (20-49 years of age), male, and single, living generally in communal housing accommodations akin to dormitories. 8 The remaining 40% of the population constitutes 7 Iceland 11 (Text S1), with interviews administered in English or Arabic, depending on the respondent's preference. We further report the results of two other limited-scope community PCR testing surveys conducted among single CMW in a lockdown zone of Doha, where the first cluster of infections was identified. In the first one conducted between March 22-27, 5,120 individuals were sampled and tested using nasopharyngeal and oropharyngeal swabs. In a second survey in the same community on April 6-7, 886 individuals were sampled and tested. These two surveys were intended to be based on probability-based sampling, but logistical challenges forced a pragmatic, yet still heterogeneous by location, convenience sampling. A national SARS-CoV-2 testing database was compiled including the individual-level PCR testing outcomes of 390 ad-hoc testing campaigns of 22,834 individuals invited randomly to participate in a variety of workplaces in different economic sectors, and the outcomes of 58 adhoc testing campaigns covering 3,881 individuals invited randomly to participate in a variety of residential areas (often where CMW live). These testing campaigns were initiated by the Ministry of Public Health (MOPH) shortly after the identification of the first cluster in March, but accelerated in subsequent weeks. The available database covers all testing conducted up to June 4. SARS-CoV-2 serological testing for antibody was performed on a convenience sample of residual blood specimens collected for routine clinical screening or clinical management from 32,970 outpatient and inpatient departments at HMC for a variety of health conditions, between 8 May 12-July 12. Specimens were unlinked of identifying information about prior PCR testing before blood collection. The sample under-represented the CMW population as they receive outpatient healthcare primarily at customized healthcare centers operated by the Qatar Red Crescent Society. The database was subsequently linked to the national SARS-CoV-2 PCR testing and hospitalization database to conduct additional analyses linking PCR and antibody test results. COVID-19 deaths were extracted from a national COVID-19 mortality validation database, a centralized registry of COVID-19-related deaths per WHO classification, 12 compiled at HMC. The database comprises information on COVID-19 deaths from February 26-July 10, 2020. PCR testing was conducted at HMC Central Laboratory, the national reference laboratory, or at Sidra Medicine Laboratory, following standardized protocols. Nasopharyngeal and/or oropharyngeal swabs (Huachenyang Technology, China) were collected and placed in Universal Transport Medium (UTM). Aliquots of UTM were: extracted on the QIAsymphony platform (QIAGEN, USA) and tested with real-time reverse-transcription PCR (RT-qPCR) using the TaqPath™ COVID-19 Combo Kit (Thermo Fisher Scientific, USA) on a ABI 7500 FAST (ThermoFisher, USA); extracted using a custom protocol 13 on a Hamilton Microlab STAR (Hamilton, USA) and tested using the AccuPower SARS-CoV-2 Real-Time RT-PCR Kit (Bioneer, Korea) on a ABI 7500 FAST; or loaded directly to a Roche cobas® 6800 system and assayed with the cobas® SARS-CoV-2 Test (Roche, Switzerland). The first assay targets the S, N, and ORF1ab regions of the virus; the second targets the virus' RdRp and E-gene regions; and the third targets the ORF1ab and E-gene regions. 9 Serological testing was performed using the Roche Elecsys ® Anti-SARS-CoV-2 (Roche, Switzerland), an electrochemiluminescence immunoassay that uses a recombinant protein representing the nucleocapsid (N) antigen for the determination of antibodies against SARS-CoV-2. Qualitative anti-SARS-CoV-2 results were generated following the manufacturer's instructions 14 (reactive: cutoff index ≥1.0 vs. non-reactive: cutoff index <1.0). Frequency distributions were generated to describe the demographic/clinical profile of tested individuals. Where applicable, probability weights were applied to adjust for unequal sample selection using the Qatar population distribution by sex, age group, and nationality. 4, 6, 15 Chisquare test and univariable logistic regressions were implemented to explore associations. Odds ratios (ORs), 95% confidence intervals (CIs), and p-values were reported. Covariates with pvalue ≤0.1 in univariable regression analysis were considered possibly associated with the outcome variables, and were thus included in the multivariable analysis for estimation of adjusted odds ratios (AORs) and associated 95% CIs and p-values. Covariates with p-value ≤0.05 in the multivariable model were considered as predictors of the outcome. Where relevant, the pooled mean for SARS-CoV-2 PCR positivity was estimated using randomeffects meta-analysis. To this end, variances of measures were first stabilized using a Freeman-Tukey type arcsine square-root transformation. 16, 17 Measures were then weighted using the inverse-variance method, 17, 18 prior to being pooled using a DerSimonian-Laird random-effects model. 19 Factors associated with higher PCR positivity and sources of between-study heterogeneity were then identified using random-effects meta-regression, applying the same criteria used for conventional regression analysis (described above). 11 rate increased rapidly starting from March and peaked at 45.1% on May 22, after which it has been declining and was 17.8% on July 9. Adjusted odds of PCR positivity were 1.6-fold (95% CI: 1.5-1.6) higher in males compared to females ( Table 1) . Odds of PCR positivity varied by nationality and were highest among Nepalese (AOR: 4.5; 95% CI: 4.3-4.6) and Bangladeshis (AOR: 3.9; 95% CI: 3.8-4.0) and lowest among Qataris (AOR: 0.57; 95% CI: 0.55-0.59), compared to other nationalities. A time trend was observed with odds of PCR positivity gradually increasing (Table 1) , consistent with an exponentially growing epidemic, to reach 8.6-fold (95% CI: 7.9-9.4) higher in May 24-30 compared to the beginning of the epidemic, but rapidly declining thereafter to be only 2.8fold (95% CI: 2.5-3.0) higher in July 05-10. A total of 1,307 individuals participated in the PCR community survey conducted on May 6-7 (Table 2 ). There were differences in age, nationality, and educational attainment between the participants who were randomly invited and those who participated through the open announcement, but the differences were not major (Table S3) . No differences were observed by sex or occupation. 12 history of contact with an index case ( Table 2 ). The differences were pronounced for nationality, occupation, presence of symptoms, and seeking medical attention. The proportion of asymptomatic individuals was 58.5% in those testing positive and 79.5% in those testing negative (p<0.001; Table 3 ). While there was no evidence for an association with reporting one or two symptoms, reporting three or more symptoms was significantly associated with SARS-CoV-2 infection (Table 3) . Associations with specific symptoms are summarized in Table S4 -infection was significantly associated with fever, fatigue, muscle ache, other respiratory symptoms, nausea/vomiting, loss of sense of smell, and loss of sense of taste. However, <9.5% of those positive reported each of these individual symptoms apart from fever which was reported by 28.6% of those positive. An analysis of PCR cycle threshold (Ct) values' association with presence of symptoms was performed with the understanding that PCR positivity does not only reflect recent active infection, but also time after recovery, as PCR tests are sensitive enough to pick even non-viable viral fragments even for weeks following recovery from active infection. 20, 21 Table S5 shows the association between PCR Ct value and presence of symptoms. Odds of having a Ct value <25 (proxy for recent infection 22 ) were five-fold higher among symptomatic compared to asymptomatic individuals. In the two other (limited-scope) community surveys conducted among single CMW in a lockdown zone of Doha, where the first cluster of infections was identified, PCR positivity was first assessed at 1.4% (95% CI: 1.1-1.8%) on March 22-27 in a sample comprising 5,120 individuals, and at 4.4% (95% CI: 3.2-6.0%) on April 6-7 in a sample comprising 886 individuals. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. Table S6 shows the pooled mean PCR point prevalence across these testing campaigns and over time. The pooled mean was 17.6% (95% CI: 9.3-27.7%) in March, but testing campaigns then were focused in the specific neighborhood where the first cluster was identified. Starting from April, testing was expanded throughout Qatar. PCR point prevalence increased steadily and rapidly consistent with exponential growth from about 5% in early April to a peak of 23% by mid to late May, after which the prevalence started declining. Table 4 shows the results of the multivariable meta-regression analysis of PCR point prevalence across these testing campaigns. There was no evidence for differences in PCR point prevalence between the workplaces and residential areas, consistent with a widely disseminated epidemic. However, there was strong evidence for a rapidly growing epidemic from March up to the third week of May, after which the epidemic started declining. A total of 32,970 individuals were tested for SARS-CoV-2 antibodies with the blood specimens drawn between May 12-July 12, with a median day of June 28. A total of 5,448 individuals had detectable antibodies. The weighted (for total population of Qatar) antibody prevalence was 24.0% (95% CI: 23.3-24.6%). All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. There were large differences in antibody prevalence by sex and nationality. After controlling for confounders (Table 5) , adjusted odds of antibody positivity were 2.9-fold (95% CI: 2.6-3.2) higher in males compared to females, and were highest among Bangladeshis (AOR: 6.8; 95% CI: 5.9-8.0) and Nepalese (AOR: 6.6; 95% CI: 5.7-7.8), but lowest among Qataris (AOR: 0.7; 95% CI: 0.6-0.8), compared to other nationalities. Adjusted odds of antibody positivity increased incrementally with age. Linking the antibody testing database to the national SARS-CoV-2 PCR testing and hospitalization database identified 17,062 individuals with available PCR and antibody testing results. Antibody prevalence was 6.2% (95% CI: 5.7-6.7%) in those who had all PCR negative results. Antibody prevalence was 47.3% (95% CI: 46.2-48.5%) in those who had at least one PCR positive result. Figure 1 shows antibody prevalence versus the time difference between the first positive PCR test and the antibody test. Overall, antibody positivity was >88% among those who had their first positive PCR test >15 days before the antibody test. Antibody positivity declined steadily the closer is the PCR test date to the antibody test date, consistent with a weeks-long delay between onset of infection and development of detectable antibodies. Tables S7-S9 show basic socio-demographic associations with each of severe and critical infection and COVID-19 death (all per WHO classification), 9, 12 respectively, as of June 25. After controlling for confounders, age was, by far, the strongest predictor of each of these outcomes, and more so for critical infection and death (Fig. 2) . Risk of serious disease and death increased immensely for those >50 years of age, who are a small minority in the population of Qatar (8.8%). All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 19, 2020. . https://doi.org/10.1101/2020.07. 16.20155317 doi: medRxiv preprint Males had 1.6-fold higher odds for developing severe and critical infection compared to females (Tables S7-S8 ), but no difference was observed for death, possibly because of the small number of deaths (Table S9) . Overall, there were no major differences in these outcomes by nationality, but there was evidence for higher risk of serious disease for Bangladeshis and Filipinos, and lower risk for Indians. The odds of disease and mortality declined with testing period, but this may just reflect the lagging time between onset of infection and disease/mortality. Figure 3A shows the crude case severity rate versus time defined as the cumulative number of severe and critical infections over the cumulative number of laboratory-confirmed infections. The crude case severity rate was mostly stable, assessed at 3.4% on July 5. As of July 10, the crude case fatality rate was 0.14%. Figures 4A-4D show, respectively, the model predictions versus time for SARS-CoV-2 incidence, active-infection prevalence, PCR positivity prevalence (that is also accounting for the prolonged PCR positivity duration 20, 21 ) , and attack rate (ever infection) in the total population, All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 19, 2020. . https://doi.org/10.1101/2020.07.16.20155317 doi: medRxiv preprint 16 assuming continuation of current levels of social and physical distancing for the coming weeks. Incident infections peaked at 31,040 on May 20, active-infection prevalence peaked at 8.0% on May 21, and PCR positivity prevalence peaked at 24.8% on June 1 st -there was a 10-day time shift between the epidemic peak based on active-infection prevalence and the peak based on PCR positivity prevalence. The attack rate was estimated at 61.3% on July 12, that is a total of 1.68 million persons have been ever infected. Of these, only 103,128 have been laboratoryconfirmed-a diagnosis rate of 6.1%. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 19, 2020. . The SARS-CoV-2 epidemic in Qatar was investigated using different lines of epidemiological evidence and study methodologies, utilizing a centralized and standardized national data-capture system and a national response that emphasized broad testing and provision of early, rapid, standardized, and universal healthcare. Strikingly, these independent lines of evidence converged on similar and consistent findings: Qatar has experienced a pervasive but heterogeneous SARS-CoV-2 epidemic, that is already declining rapidly, apparently due to exhaustion of susceptibles. While the overall epidemic at the national level demonstrated classic susceptible-infectedrecovered "SIR" dynamics with an R0 of about 1.6, the national epidemic implicitly included two linked and overlapping but different sub-epidemics. The first affected the labor population, which constitutes the majority of Qatar's population, and grew rapidly before peaking and then starting to decline. The second and slowly growing sub-epidemic affected the urban population, and appears to be plateauing if not declining slowly, with potential for growth if the easing of the social and physical distancing restrictions proceeds too quickly. Epidemic intensity in Qatar reflected the unique demographic and residential dwelling structure in this country. The most affected subpopulation was that of the majority population of single CMW living in shared housing accommodations, where workers at a given workplace not only work together during the day, but also typically live together in large dormitories where they share rooms, bathrooms, and cafeteria-style meals. In these settings, the pattern of SARS-CoV-2 transmission showed resemblance to that of influenza outbreaks in schools, 23, 24 and more so to boarding schools, 24 where the options for effective social and physical distancing are reduced. The differences in exposure by nationality reflected the contribution of each nationality to the labor versus urban population. Yet, these differences may also reflect the structure of social All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 19, 2020. . networks in Qatar, as social contacts could be higher among groups who share the same culture, language, and/or national background. Remarkably, while widespread, the infection has been characterized by relatively low case and infection severity and fatality rates (Fig. 3) , which were not well above those of a severe seasonal influenza epidemic. 25, 26 The young age profile of the population, with only 8.8% being >50 years of age, appears to explain great part of the low severity-88.4% of confirmed infections were among those <50 years of age. The fact that the epidemic was most intense in the young and healthy CMW population, as opposed to the urban population where all elderly reside, contributed also to the low severity. Indeed, analyses indicated a strong role for age in disease severity and mortality ( Fig. 2) , with even higher effect sizes than elsewhere, 27-30 possibly because of greater accounting of asymptomatic infection in Qatar. The resourced healthcare system, which was well below the health system threshold even at the epidemic peak, may have also contributed to the low mortality. Emphasis on broad testing coupled with proactive early treatment, such as the treatment of >4,000 cases for pneumonia, may have also limited the number of people who went on to require hospitalization or to develop severe or critical disease. A notable feature of the epidemic, that is also possibly linked to the population's young demographic structure, is the large proportion of infections that were asymptomatic or with minimal/mild symptoms for infection to be suspected. The proportion of infections diagnosed at a health facility, somewhat of a proxy for symptomatic infection, was only 60.1%-nearly 40% of infections were diagnosed through contact tracing or surveillance testing in workplaces or residential areas. Among those PCR positive in the community survey, 58.5% reported no symptoms within the last two weeks (Table 3) . Given a median PCR Ct value of 28.7 among these asymptomatic persons (Table S5) , they are also not likely to develop symptoms following All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 19, 2020. . the survey date, as the infection was probably in an advanced stage, if not essentially cleared. 22 Hospital and isolation facility records show that none of these asymptomatically-infected persons were admitted subsequently with severe or critical disease as of July 10, but there is record indicating that two of them (out of 72) did develop subsequently a symptomatic mild infection. Meanwhile, 20.5% of those PCR negative in the community survey reported at least one symptom, suggesting that among those who were positive and symptomatic, some symptoms may not have been related to SARS-CoV-2 infection (Table 3) . Notably, presence of one or two symptoms was not predictive of infection, but presence of three or more symptoms was strongly predictive (Table 3) . Although several symptoms were associated (and strongly) with infection (Table S4 ), very few infected persons reported them (<10%), apart from fever which was reported by 29% of infected persons. This study has limitations. Symptoms in the community survey were based on self-report, thus introducing potential for recall bias. Although the sampling was intended to be probability-based, most participants were recruited through convenience sampling due to poor response rate suggesting potential for selection bias. However, there was no evidence that PCR positivity differed by method of recruitment (Table 2) . Moreover, observed PCR prevalence was similar in both the community survey and the ad-hoc testing campaigns conducted in diverse workplaces and residential areas ( Table 2 and Table S6 ). Analyses of predictors of severity and mortality were limited in scope, not accounting for relevant covariates, such as comorbidities. COVID-19 mortality was based on confirmed hospital deaths and out of hospital deaths per WHO classification, 12 but such mortality registry may not capture excess deaths caused indirectly by this infection, or deaths misclassified for other causes. Study outcomes may be affected by the sensitivity or specificity of the assays used. However, laboratory methods were based on quality All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 19, 2020. . https://doi.org/10.1101/2020.07. 16.20155317 doi: medRxiv preprint 20 commercial platforms, and each diagnostic method was validated in the laboratory before its use. All results, regardless of the laboratory method used, were also consistent with each other, and specificity of the antibody assay, even if not perfect, may not affect the results given the high antibody prevalence. Of note that the specificity of the antibody assay was reported at 99.8% 14 by the manufacturer and at 100% by a validation study by Public Health England. 31 Remarkably, among those who were diagnosed PCR positive or negative >3 weeks before being tested for detectable antibodies (the three weeks to allow for antibodies to be detectable 20 ) , the percent agreement between the PCR outcome and the antibody outcome was 94.4%, affirming the consistency of both the PCR and antibody methods in diagnosing infection. In conclusion, the characterized epidemic of Qatar provides inferences about the epidemiology of this infection. First, SARS-CoV-2 is a highly infectious virus with a large R0, that is considerably larger than that for other typical cold respiratory viruses. [32] [33] [34] Despite the enforced restrictive social and physical distancing measures, that reduced R0 from as much as 3-4 35,36 to about 1.6, R0 was well above 1 leading to such large epidemic. Second, while age has been recognized as an important factor since the beginning of the pandemic, 27,37,38 28-30 it appeared to play even a more critical role in the epidemiology in Qatar than estimated thus far. Not only were serious disease and mortality very strongly linked to being >50 years of age, but most infections in younger persons exhibited no or minimal/mild symptoms. Age may have even played a role in the risk of exposure or susceptibility to the infection (Table 5) , as suggested earlier. 39, 40 These findings may suggest that the epidemic expansion in nations with young populations may lead to milder disease burden than previously thought. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 19, 2020. . All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 19, 2020. * PCR, polymerase chain reaction. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 19, 2020. . We would like to thank Her Excellency Dr. Hanan Al Kuwari, the Minister of Public Health, for her vision, guidance, leadership, and support. We also would like to thank Dr. Saad Al Kaabi, Chair of the System Wide Incident Command and Control (SWICC) Committee for the COVID-19 national healthcare response, for his leadership, analytical insights, and for his instrumental role in enacting the data information systems that made these studies possible. We further extend our appreciation to the SWICC Committee and the Scientific Reference and Research Taskforce (SRRT) members for their informative input, scientific technical advice, and enriching discussions. We also would like to thank Professor David Heymann of the London School of Hygiene & Tropical Medicine and former Assistant Director-General for Health Security and Environment at the World Health Organization for valuable comments and insights on an earlier version of this manuscript. We also would like to acknowledge the efforts of the surveillance team at the Ministry of Public Health as well as all personnel involved in the conduct of the community surveys at the Primary Health Care Centers, Qatar University, Ministry of Public Health, and Hamad Medical Corporation. We further would like to thank all persons who agreed to participate in the community surveys and testing campaigns. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 19, 2020. . Text S1. SARS-CoV-2 population-based survey questionnaire. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 19, 2020. We constructed an age-structured deterministic mathematical model to describe the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission dynamics and disease progression in the population of Qatar (Fig. S1) . The model stratified the population into compartments according to age group (0-9, 10-19, 20-29,…, ≥80 years), infection status (infected, uninfected), early infection stage (asymptomatic/mild, severe, critical), and disease stage (severe disease or critical disease). This model extends our calibrated mathematical model developed earlier to characterize key attributes of the SARS-CoV-2 epidemic in China. 1 Epidemic dynamics were described using age-specific sets of coupled nonlinear differential equations. Each age group, a , denoted a ten-year age band apart from the last category which grouped together all individuals ≥80 years of age. Qatar's population size and demographic structure were based on findings of "The Simplified Census of Population, Housing, and Establishments" conducted by Qatar's Planning and Statistics Authority. 2 Life expectancy was obtained from the United Nations World Population Prospects database. 3 Disease 2019 (COVID-19) mortality was assumed to occur in individuals that are in the critical disease stage, as informed by current understanding of SARS-CoV-2 epidemiology. 4 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. Figure S1 . Schematic diagram describing the basic structure of the SARS-CoV-2 mathematical model for Qatar. 5 The following equations were used to describe the transmission dynamics in the total population: The definitions of population variables and symbols used in the equations are listed in Table S1 . Here,  is the average rate of infectious contacts. The probability that an individual in the a age group will mix with an individual in the a age group is determined by an age-mixing matrix, , aa  H , given by (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 19, 2020. . All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 19, 2020. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 19, 2020. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 19, 2020. . Planning and Statistics Authority-State of Qatar. The Simplified Census of Population Qatar Population (Live) Planning and Statistics Authority-State of Qatar. Labor force sample survey 10. World Health Organization. Population-based age-stratified seroepidemiological investigation protocol for COVID-19 virus infection Spread of SARS-CoV-2 in the Icelandic Population World Health Organization. International guidelines for certification and classification (coding) of COVID-19 as cause of death High-throughput extraction of SARS-CoV-2 RNA from nasopharyngeal swabs using solid-phase reverse immobilization beads. medRxiv 2020:2020.04.08.20055731. 14. The Roche Group. Roche's COVID-19 antibody test receives FDA Emergency Use Authorization and is available in markets accepting the CE mark Transformations Related to the Angular and the Square Root The Inverse of the Freeman -Tukey Double Arcsine Transformation Meta-analysis of prevalence Meta-analysis in clinical trials Interpreting Diagnostic Tests for SARS-CoV-2 Humoral immune response and prolonged PCR positivity in a cohort of 1343 SARS-CoV 2 patients in the New York City region Attack rates assessment of the 2009 pandemic H1N1 influenza A in children and their contacts: a systematic review and meta-analysis Estimating influenza disease burden from populationbased surveillance data in the United States Systematic Assessment of Multiple Routine and Near Real-Time Indicators to Classify the Severity of Influenza Seasons and Pandemics in the United States Estimating the burden of SARS-CoV-2 in France. Science 2020:eabc3517. 28. 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Evaluation of Roche Elecsys AntiSARS-CoV-2 serology assay for the detection of anti-SARS-CoV-2 antibodies Estimates of the reproduction number for seasonal, pandemic, and zoonotic influenza: a systematic review of the literature Estimation of the reproductive number of the Spanish flu epidemic in Estimates of the reproduction numbers of Spanish influenza using morbidity data Estimation of the basic reproduction number, average incubation time, asymptomatic infection rate, and case fatality rate for COVID-19: Meta-analysis and sensitivity analysis Novel Coronavirus Pneumonia Emergency Response Epidemiology Team Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72314 Cases From the Chinese Center for Disease Control and Prevention Characterizing key attributes of the epidemiology of COVID-19 in China: Model-based estimations Age could be driving variable SARS-CoV-2 epidemic trajectories worldwide PCR, polymerase chain reaction; OR, odds ratio. * Estimates are weighted by age and nationality ** Target assayed using the TaqPath™ COVID-19 Combo Kit † Target assayed using the AccuPower SARS-CoV-2 Real-Time RT-PCR Kit ‡ Ct values for N protein were used, and complemented by Ct values for E-gene in case of missing values. All individuals with Ct values for S protein or ORF1ab protein also had values for N protein * Testing campaign sample with sample size <10 were grouped together or with the testing campaign sample that has the lowest sample size ≥10 † Q: the Cochran's Q statistic is a measure assessing the existence of heterogeneity in effect size (here, SARS-CoV-2 PCR prevalence) across studies ‡ I 2 : a measure assessing the magnitude of between-study variation that is due to differences in effect size (here, SARS-CoV-2 PCR prevalence) across studies rather than chance. § Prediction interval: a measure estimating the 95% interval of the distribution of true effect sizes (here, SARS-CoV-2 PCR prevalence measures) Planning and Statistics Authority-State of Qatar. The Simplified Census of Population, Housing & Establishments United Nations Department of Economic and Social Affairs Population Dynamics. 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