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Epidemiological determinants of spread of causal agent of severe acute respiratory syndrome in Hong Kong
Open accessAbstract
Background Health authorities worldwide, especially in the Asia Pacific region, are seeking effective public-health interventions in the continuing epidemic of severe acute respiratory syndrome (SARS). We assessed the epidemiology of SARS in Hong Kong.Methods We included 1425 cases reported up to April 28, 2003. An integrated database was constructed from several sources containing information on epidemiological, demographic, and clinical variables. We estimated the key epidemiological distributions: infection to onset, onset to admission, admission to death, and admission to discharge. We measured associations between the estimated case fatality rate and patients' age and the time from onset to admission.Findings After the initial phase of exponential growth, the rate of confirmed cases fell to less than 20 per day by April 28. Public-health interventions included encouragement to report to hospital rapidly after the onset of clinical symptoms, contact tracing for confirmed and suspected cases, and quarantining, monitoring, and restricting the travel of contacts. The mean incubation period of the disease is estimated to be 6.4 days (95% CI 5.2-7.7). The mean time from onset of clinical symptoms to admission to hospital varied between 3 and 5 days, with longer times earlier in the epidemic. The estimated case fatality rate was 13.2% (9.8-16.8) for patients younger than 60 years and 43.3% (35.2-52.4) for patients aged 60 years or older assuming a parametric gamma distribution. A non-parametric method yielded estimates of 6.8% (4.0-9.6) and 55.0% (45.3-64.7), respectively. Case clusters have played an important part in the course of the epidemic.Interpretation Patients' age was strongly associated with outcome. The time between onset of symptoms and admission to hospital did not alter outcome, but shorter intervals will be important to the wider population by restricting the infectious period before patients are placed in quarantine.
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“However, there were substantial variations among studies, with no clear indication of what factors might drive such heterogeneity (appendix 1 pp 58, 65), except that the delay reduced over time because of improved population awareness.75”
From Severe acute respiratory syndrome (SARS) mathematical models and disease parameters: a systematic review (2025) · cites this paper as [75]
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Epidemiologic Clues to SARS Origin in China
Xu, R.-H., He, J., Evans, M. R., Peng, G.-W., Field, H. E., Yu, D.-W., Lee, C.-K., Luo, H.-M., Lin, W.-S., Lin, P., et al. (2004) · Emerging infectious diseases · doi:10.3201/eid1006.030852 · Cited by 327
“Other 2 (9) 49 (11) 14 (7) 18 (9) 83 (10)”
ResultInfluentialEctodomain shedding of angiotensin converting enzyme 2 in human airway epithelia
Jia, H. P., Look, D. C., Tan, P., Shi, L., Hickey, M. A., Gakhar, L., Chappell, M. C., Wohlford-Lenane, C., & McCray, P. B. (2009) · American Journal of Physiology-Lung Cellular and Molecular Physiology · doi:10.1152/ajplung.00071.2009 · Cited by 334
Case-Fatality Risk Estimates for COVID-19 Calculated by Using a Lag Time for Fatality
Wilson, N., Kvalsvig, A., Telfar-Barnard, L., & Baker, M. G. (2020) · Emerging infectious diseases · doi:10.3201/eid2606.200320 · Cited by 316
“However, given the critical need to consider time lags to death when calculating casefatality risk (3), we used time lags from a recent study from China (4).”
MethodDetection of SARS Coronavirus in Patients with Suspected SARS
Chan, K. H., Poon, L. L. M., Cheng, V. C., Guan, Y., Hung, I. F., Kong, J. H. B., Yam, L. Y.-C., Seto, W., Yuen, K., & Peiris, J. S. M. (2004) · Emerging infectious diseases · doi:10.3201/eid1002.030610 · Cited by 313
Self-citationModerate mutation rate in the SARS coronavirus genome and its implications
Zhao, Z., Li, H., Wu, X., Zhong, Y., Zhang, K., Zhang, Y.-P., Boerwinkle, E., & Fu, Y.-X. (2004) · BMC Evolutionary Biology · doi:10.1186/1471-2148-4-21 · Cited by 286
HIV protease inhibitor nelfinavir inhibits replication of SARS-associated coronavirus
Yamamoto, N., Yang, R., Yoshinaka, Y., Amari, S., Nakano, T., Cinatl, J. J., Rabenau, H. F., Doerr, H. W., Hunsmann, G., Otaka, A., et al. (2004) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2004.04.083 · Cited by 304
“An overall estimate of case fatality reached 14–15% as reported by WHO [4] and the mortality rate in people older than 60 years could be as high as 43–55% [5].”
BackgroundThe Human/Animal Interface: Emergence and Resurgence of Zoonotic Infectious Diseases
Greger, M. (2007) · Critical Reviews in Microbiology · doi:10.1080/10408410701647594 · Cited by 317
“Also known as human T-cell leukemia/lymphoma virus, HTLV1 can have a long latency period before triggering an aggressive hematological malignancy in 1 in 1,500 adult HTLV-1 carriers per year (CDC 1987).”
BackgroundInfluentialWang, S., Dai, T., Qin, Z., Pan, T. S., Chu, F., Lou, L., Zhang, L., Yang, B., Huang, H., Lu, H., et al. (2021) · Nature Cell Biology · doi:10.1038/s41556-021-00710-0 · Cited by 290
“It is known that human coronavirus 229E induces high levels of IFN and thus exhibits mild virulence 65 , whereas SARS-CoV and MERS-CoV utilize multiple mechanisms to repress the IFN response, contributing to their high lethality 66–68 .”
Predicting epidemics on directed contact networks
Ancel, L. W., Newman, M. E. J., & Pourbohloul, B. (2005) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2005.10.004 · Cited by 283
Li, T., Qiu, Z., Zhang, L., Han, Y., He, W., Liu, Z., Ma, X., Fan, H., Lu, W., Xie, J., et al. (2004) · The Journal of Infectious Diseases · doi:10.1086/381535 · Cited by 279
“rently estimated to be 13.2% for patients !60 years of age and 43.3% for patients у60 of age [11] .”
BackgroundInfluentialThe Impact of the SARS Epidemic on the Utilization of Medical Services: SARS and the Fear of SARS
Chang, H.-J., Huang, N., Lee, C.-H., Hsu, Y., Hsieh, C., & Chou, Y. (2004) · American Journal of Public Health · doi:10.2105/ajph.94.4.562 · Cited by 276
Methods for estimating the case fatality ratio for a novel, emerging infectious disease
Ghani, A. C., Donnelly, C. A., Cox, D. R., Griffin, J. T., Fraser, C., Lam, T. H., Ho, L. M., Chan, W. S., Anderson, R. M., Hedley, A. J., et al. (2005) · American Journal of Epidemiology · doi:10.1093/aje/kwi230 · Cited by 252
“For SARS, one of the most important factors determining the case fatality ratio is age (14, 15).”
BackgroundMethodInfluentialSelf-citationSystematic Review: A Century of Inhalational Anthrax Cases from 1900 to 2005
Holty, J.-E. C., Bravata, D. M., Liu, H., Olshen, R. A., McDonald, K. M., & Owens, D. K. (2006) · Annals of Internal Medicine · doi:10.7326/0003-4819-144-4-200602210-00009 · Cited by 273
MacIntyre, C. R., Wang, Q., Cauchemez, S., Seale, H., Dwyer, D. E., Yang, P., Shi, W., Gao, Z., Pang, X., Zhang, Y., et al. (2011) · Influenza and Other Respiratory Viruses · doi:10.1111/j.1750-2659.2011.00198.x · Cited by 257
Self-citationDetection of SARS-associated Coronavirus in Throat Wash and Saliva in Early Diagnosis
Wang, W., Chen, S., Liu, I.-J., Chen, Y., Chen, H., Yang, C., Chen, P., Yeh, S., Kao, C., Huang, L., et al. (2004) · Emerging infectious diseases · doi:10.3201/eid1007.031113 · Cited by 270
“Since sneezing and rhinorrhea are not common symptoms of SARS, and cough with sputum is only seen in the later stage of infection (1) (2) (3) 13) , oral droplets generated during talking may represent an important route of transmission.”
BackgroundThe Effectiveness of Contact Tracing in Emerging Epidemics
Klinkenberg, D., Fraser, C., & Heesterbeek, H. (2006) · PLoS ONE · doi:10.1371/journal.pone.0000012 · Cited by 264
“These parameter values were obtained from literature [2, 3, [12] [13] [14] , assuming that the time to detection consists of the incubation period (time to symptom onset) plus a symptom-to-detection delay, which we assumed to be distributed as observed in the SARS epidemic (average 3.67 days, [2] ).”
BackgroundMethodInfluentialSelf-citationGenerality of the Final Size Formula for an Epidemic of a Newly Invading Infectious Disease
Ma, J., & Earn, D. J. D. (2006) · Bulletin of Mathematical Biology · doi:10.1007/s11538-005-9047-7 · Cited by 263
“It will, however, typically be relevant whenever a new respiratory pathogen emerges, as in the case of the SARS outbreak in 2003 (Donnelly et al., 2003) or the emergence of a new subtype of influenza (Earn et al., 2002).”
BackgroundTanshinones as selective and slow-binding inhibitors for SARS-CoV cysteine proteases
Park, J. Y., Kim, J. H., Kim, Y.-M., Jeong, H. J., Kim, D. W., Park, K. H., Kwon, H.-J., Park, S.-J., Lee, W. S., & Ryu, Y. B. (2012) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2012.07.038 · Cited by 258
The nsp9 Replicase Protein of SARS-Coronavirus, Structure and Functional Insights
Sutton, G. C., Fry, E. E., Carter, L. G., Sainsbury, S., Walter, T., Nettleship, J. E., Berrow, N. S., Owens, R. J., Gilbert, R. J. C., Davidson, A. D., et al. (2004) · Structure · doi:10.1016/j.str.2004.01.016 · Cited by 256
“In the first outbreak ofOxford OX3 7BN SARS in 2003, at least 8000 people were infected andUnited Kingdom there were over 750 fatalities (Donnelly et al., 2003).3 Department of Pathology and Microbiology To date, SARS has been controlled using conventionalSchool of Medical Sciences measures such as…”
MethodHuman infection with avian influenza A H7N9 virus: an assessment of clinical severity
Yu, H., Cowling, B. J., Feng, S., Lau, E. H. Y., Liao, Q., Tsang, T. K., Peng, Z., Wu, P., Liu, F., Fang, V. J., et al. (2013) · The Lancet · doi:10.1016/s0140-6736(13)61207-6 · Cited by 252
Self-citationFaes, C., Abrams, S., Beckhoven, D. V., Meyfroidt, G., Vlieghe, E., & Hens, N. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17207560 · Cited by 248
“The time delay from illness onset to death is important for the estimation of the case fatality ratio [2].”
BackgroundPotential Biases in Estimating Absolute and Relative Case-Fatality Risks during Outbreaks
Lipsitch, M., Donnelly, C. A., Fraser, C., Blake, I. M., Cori, A., Dorigatti, I., Ferguson, N. M., Garske, T., Mills, H. L., Riley, S., et al. (2015) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0003846 · Cited by 247
Self-citationSevere Acute Respiratory Syndrome (SARS)
Dandekar, A. A., & Perlman, S. M. (2006) · Wiley eBooks · doi:10.1038/npg.els.0004025 · Cited by 236
Dynamically Modeling SARS and Other Newly Emerging Respiratory Illnesses
Bauch, C. T., Lloyd‐Smith, J. O., Coffee, M. P., & Galvani, A. P. (2005) · Epidemiology · doi:10.1097/01.ede.0000181633.80269.4c · Cited by 235
Lau, A. L. D., Chi, I., Cummins, R. A., Lee, T. M. C., Chou, K.-L., & Chung, L. W. M. (2008) · Aging & Mental Health · doi:10.1080/13607860802380607 · Cited by 217
“(13.2%; Donnelly et al., 2003).”
InfluentialSARS-Coronavirus Open Reading Frame-3a drives multimodal necrotic cell death
Yue, Y., Nabar, N. R., Shi, C.-S., Kamenyeva, O., Xiao, X., Hwang, I., Wang, M., & Kehrl, J. H. (2018) · Cell Death and Disease · doi:10.1038/s41419-018-0917-y · Cited by 232
Model Parameters and Outbreak Control for SARS
Chowell, G., Castillo‐Chávez, C., Fenimore, P. W., Kribs, C. M., Arriola, L. M., & Hyman, J. M. (2004) · Emerging infectious diseases · doi:10.3201/eid1007.030647 · Cited by 230
Ralph, R., Lew, J. M., Zeng, T., Francis, M. E., Xue, B., Roux, M., Ostadgavahi, A. T., Rubino, S., Dawe, N., Al‐Ahdal, M. N., et al. (2020) · The Journal of Infection in Developing Countries · doi:10.3855/jidc.12425 · Cited by 229
Severe Acute Respiratory Syndrome
Christian, M. D., Poutanen, S. M., Loutfy, M. R., Muller, M. P., & Low, D. E. (2004) · Clinical Infectious Diseases · doi:10.1086/420743 · Cited by 224
“R0 is defined as the average number of secondary cases generated by 1 primary case in a susceptible population [10].”
Background
Metadata
- Authors
- Donnelly, C. A., Ghani, A. C., Leung, G. M., Hedley, A. J., Fraser, C., Riley, S., Abu-Raddad, L. J., Ho, L.-M., Thach, T.-Q., Chau, P., Chan, K.-P., Lam, T.-H., Tse, L.-Y., Tsang, T., Liu, S.-H., Kong, J. H., Lau, E. M., Ferguson, N. M., & Anderson, R. M.
- Affiliations
- Imperial College London (GB)
- University of Hong Kong (HK)
- Department of Health (CN)
- Hospital Authority (HK)
- Chinese University of Hong Kong (HK)
- ORCID
- Venue
- The Lancet
- Publisher
- Elsevier BV
- Published
- 2003-05
- Volume
- 361
- Issue
- 9371
- Pages
- 1761-1766
- ISSN
- 0099-5355 (ISSN-L)
- 0140-6736
- PMC
- PMC7112380
- OpenAlex
- W1990049863
- Cited by
- 940
- References
- 3
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 15 · 2023: 18 · 2022: 39 · 2021: 93 · 2020: 154 · 2019: 18 · 2018: 5 · 2017: 10 · 2016: 15 · 2015: 17 · 2014: 15 · 2013: 20 · 2012: 9
- FWCI
- 71.19
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- The Lancet (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- The HKU Scholars Hub (University of Hong Kong) (submittedVersion)
- LSHTM Research Online (London School of Hygiene and Tropical Medicine) (submittedVersion)
- Funders
- Howard Hughes Medical Institute
- Wellcome Trust
- Royal Society
- Medical Research Council
- Topics
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Health Sciences › Medicine › Infectious Diseases › Viral Infections and Outbreaks Research
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Keywords
- SARS, Hong Kong SARS outbreak, incubation period, case fatality rate, age and mortality, contact tracing, quarantine, public health interventions, case clusters, infectious period
- MeSH terms
- Adolescent, Adult, Aged, Child, Child, Preschool, Disease Outbreaks, Female, Hong Kong, Humans, Infant, Male, Middle Aged, Quarantine, Incidence, Survival Rate, Likelihood Functions, Age Distribution, Disease Progression, Severe Acute Respiratory Syndrome
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