Temporal variability and social heterogeneity in disease transmission: the case of SARS in Hong Kong.
The extent to which self-adopted or intervention-related changes in behaviors affect the course of epidemics remains a key issue for outbreak control. This study attempted to quantify the effect of such changes on the risk of infection in different settings, i.e., the community and hospitals. The 2002-2003 severe acute respiratory syndrome (SARS) outbreak in Hong Kong, where 27% of cases were healthcare workers, was used as an example. A stochastic compartmental SEIR (susceptible-exposed-infectious-removed) model was used: the population was split into healthcare workers, hospitalized people and general population. Super spreading events (SSEs) were taken into account in the model. The temporal evolutions of the daily effective contact rates in the community and hospitals were modeled with smooth functions. Data augmentation techniques and Markov chain Monte Carlo (MCMC) methods were applied to estimate SARS epidemiological parameters. In particular, estimates of daily reproduction numbers were provided for each subpopulation. The average duration of the SARS infectious period was estimated to be 9.3 days (+/-0.3 days). The model was able to disentangle the impact of the two SSEs from background transmission rates. The effective contact rates, which were estimated on a daily basis, decreased with time, reaching zero inside hospitals. This observation suggests that public health measures and possible changes in individual behaviors effectively reduced transmission, especially in hospitals. The temporal patterns of reproduction numbers were similar for healthcare workers and the general population, indicating that on average, an infectious healthcare worker did not infect more people than any other infectious person. We provide a general method to estimate time dependence of parameters in structured epidemic models, which enables investigation of the impact of control measures and behavioral changes in different settings.
Cori
Anne
A
INSERM, Paris, France. cori@u707.jussieu.fr
Boëlle
Pierre-Yves
PY
Thomas
Guy
G
Leung
Gabriel M
GM
Valleron
Alain-Jacques
AJ
D000465:Algorithms; D017714:Community-Acquired Infections / Q000453:epidemiology / Q000635:transmission* / Q000821:virology; D003428:Cross Infection / Q000453:epidemiology / Q000635:transmission* / Q000821:virology; D004196:Disease Outbreaks; D006282:Health Personnel; D006723:Hong Kong / Q000453:epidemiology; D006801:Humans; D017758:Infectious Disease Transmission, Patient-to-Professional; D017757:Infectious Disease Transmission, Professional-to-Patient; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D045473:Severe acute respiratory syndrome-related coronavirus; D045169:Severe Acute Respiratory Syndrome / Q000453:epidemiology / Q000635:transmission*; D013269:Stochastic Processes; D013997:Time Factors
Int J Occup Environ Health. 2004 Oct-Dec;10(4):421-7
15702757
Am J Trop Med Hyg. 2005 Jul;73(1):17-25
16014825
Proc Biol Sci. 2004 Nov 7;271(1554):2223-32
15539347
Emerg Infect Dis. 2006 Jan;12(1):110-3
16494726
Am J Epidemiol. 2004 Oct 15;160(8):719-28
15466494
Emerg Infect Dis. 2004 Feb;10(2):280-6
15030698
Emerg Infect Dis. 1995 Jan-Mar;1(1):7-15
8903148
Emerg Infect Dis. 2004 Feb;10(2):269-76
15030696
CMAJ. 2003 Aug 19;169(4):285-92
12925421
Ann Intern Med. 2004 Nov 2;141(9):662-73
15520422
Science. 2003 Jun 20;300(5627):1966-70
12766207
Science. 2003 Jun 20;300(5627):1961-6
12766206
Philos Trans R Soc Lond B Biol Sci. 2004 Jul 29;359(1447):1091-105
15306395
N Engl J Med. 2003 May 15;348(20):1986-94
12682352
Lancet. 2003 May 24;361(9371):1767-72
12781535
Proc Biol Sci. 2007 Mar 7;274(1610):611-7
17254984
PLoS One. 2007 Aug 22;2(8):e758
17712406
J Theor Biol. 2003 Sep 7;224(1):1-8
12900200
Am J Epidemiol. 2007 Aug 1;166(3):355-63
17493952
Proc Biol Sci. 2003 Oct 7;270(1528):1979-89
14561285
Nature. 2005 Nov 17;438(7066):355-9
16292310