Share this article on:

Transmissibility of Seasonal and Pandemic Influenza in a Cohort of Households in Hong Kong in 2009

Klick, Brendana; Nishiura, Hiroshia,b,c; Ng, Sophiaa; Fang, Vicky J.a; Leung, Gabriel M.a; Peiris, J. S. Malikd; Cowling, Benjamin J.a

doi: 10.1097/EDE.0b013e3182302e8e
Infectious Disease

Background: The household secondary attack proportion (SAP) is commonly used to measure the transmissibility of an infectious disease.

Methods: We analyzed the final outbreak size distributions of pandemic A(H1N1), seasonal A(H1N1), and A(H3N2) infections identified in paired sera collected from members of 117 Hong Kong households in April and in August–October 2009.

Results: The estimated community probability of infection overall was higher for children than adults; the probability was similar for pandemic A(H1N1) and seasonal A(H3N2) influenza. The household SAP for pandemic A(H1N1) was higher in children than in adults, whereas for seasonal A(H3N2), it was similar in children and adults. The estimated SAPs were similar for seasonal A(H3N2) and pandemic A(H1N1) after excluding persons with higher baseline antibody titers from analysis.

Conclusions: Pandemic and seasonal influenza A viruses had similar age-specific transmissibility in a cohort of initially uninfected households, after adjustment for baseline immunity.


From the aSchool of Public Health, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China; bPRESTO, Japan Science and Technology Agency, Saitama, Japan; cTheoretical Epidemiology, University of Utrecht, Utrecht, The Netherlands; and dDepartment of Microbiology, The University of Hong Kong, Hong Kong Special Administrative Region, China.

Submitted 22 February 2011; accepted 27 May 2011; posted 30 August 2011.

B.J.C. is currently at School of Public Health, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China.

Supported by the Harvard Center for Communicable Disease Dynamics from the National Institute of General Medical Sciences (grant no. U54 GM088558), the Research Fund for the Control of Infectious Disease, Food and Health Bureau, Government of the Hong Kong SAR (grant no. PHE-2), and the Area of Excellence Scheme of the Hong Kong University Grants Committee (grant no. AoE/M-12/06). Supported by a Hong Kong PhD Fellowship from the Research Grants Council, Hong Kong (to B.K.), and JST PRESTO program (to H.N.).

B.J.C. has received study funding from MedImmune Inc., a manufacturer of influenza vaccines. The authors report no other conflicts of interest.

Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (

Correspondence: Benjamin J. Cowling, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Units 624–7, Cyberport 3, Pokfulam, Hong Kong. E-mail:

The household secondary attack proportion (SAP) is often used to characterize the transmissibility of an infectious disease. This measure is defined as the probability that a susceptible person will be infected by someone in his or her household who has already been infected.1,2 Estimates of the SAP for 2009 pandemic influenza A(H1N1) ranged from 9% to 30%, depending on age, study location, household size, and method of ascertainment.3–11 Estimates for seasonal influenza A and B have varied from 5% to 60%.11–19 There are few direct comparisons of the transmissibility of pandemic and seasonal influenza. In a previous study, we conducted a transmission study in 99 households in Hong Kong, each including an index case with confirmed influenza. We found similar estimates of the SAP for seasonal and pandemic influenza based on laboratory and clinical outcomes.11 However, in that study, index cases were recruited after presentation in an outpatient setting, and selection bias could have led to overestimates of the household SAP.

We report here on 117 households followed through the summer 2009 influenza season and the pandemic in Hong Kong.20 We use estimates of infection status of persons clustered within households to infer and compare the risk of infection with seasonal influenza A(H1N1) (sH1N1) and A(H3N2) (sH3N2) and pandemic A (H1N1) (pH1N1) from the community versus from within the households.

Back to Top | Article Outline


Between October and December 2008, we recruited 119 families to a randomized trial of the direct and indirect benefits of influenza vaccination.20 One child in each household was randomized to receive seasonal influenza vaccine or placebo control. We collected baseline sera from everyone aged 6 years or older, and also from all participants in April 2009, and then during the period from August 2009 to October 2009. Household report of acute respiratory illnesses (defined as at least 2 of temperature ≥37.8°C, cough, headache, sore throat, phlegm, or myalgia) was obtained through daily symptom diaries, participant report to a study hotline, and telephone interviews at biweekly intervals. When illness was reported through a study hotline or biweekly interviews, a study nurse visited the household to collect nose and throat swabs for confirmation of influenza virus infection. All analyses reported here are based on the follow-up period from April 2009 to August–October 2009, which included a period of seasonal influenza circulation, followed by the pandemic.20 We collected sera from 425 people in 117 of the 119 families during this period.

Paired sera were tested for antibody responses to A/Brisbane/59/2007 (sH1N1) and A/Brisbane/10/2007 (sH3N2) by hemagglutination-inhibition (HI) assays, and for antibody responses to A/California/04/2009 (pH1N1) by viral microneutralization (VN) assays using standard methods.20 VN tests, rather than HI tests, were used for pH1N1 based on studies showing that the former could better discriminate pH1N1 infection.11,20,21 A 4-fold or greater rise in antibody titers was considered to indicate influenza infection.22,23 Both pH1N1 and sH3N2 were widely circulating in Hong Kong during the summer of 2009.20 Rises in antibody titers against more than one strain could be associated with cross-reactive antibody responses to a single infection, or with infection by more than one strain during the follow-up period of 4–6 months. Twelve persons with a 4-fold or greater rise in antibody titers to more than one strain were classified as having only one infection based on corresponding laboratory confirmation, infections in other family members, and dates of acute respiratory illnesses (eAppendix Table 1,

We specified a statistical model that explicitly accounted for the probabilities of influenza infection from the community and from within the household.19,24,25 We extended the model to differentiate the risk of infection for adults aged 15 years or older and children aged 14 years or younger. The model allowed estimation of the community probability of infection, which can be interpreted as the probability of infection from the community during the period from April to October 2009.12 The model estimated the community probability of infection and the SAP for both children and adults, based on the possible transmission chains that could have led to the observed final infection status of each person within a household. Additional technical details of our statistical methods, including the likelihood function used and justification for the choice of priors, are provided in the eAppendix (

We included data on infections with pH1N1, sH1N1, and sH3N2 during the follow-up period, and fitted separate transmission parameters for each strain. We fitted the models using Markov Chain Monte Carlo methods.26,27 We used semi-informative beta(1.5, 6) priors for the SAPs, beta(1.2, 6) for the community probability of infection with pH1N1 and sH3N2 (which were prevalent during the study period),20 and beta(1.5,30) for the community probability of infection with sH1N1 (which was less prevalent in the community during the study period) (eAppendix, Those missing one or both sera specimens were considered to have unknown infection status; this was explicitly incorporated into the modeling algorithm.28

Because the model assumed that everyone was susceptible to infection, additional analyses were performed to examine how reduced susceptibility might affect the results. Antibody titers of 1:40 or higher by HI assays have been associated with 50% protection against infection.29–31 Therefore, for each strain, we fitted the model on 2 subgroups, those with baseline titers 1:160 or lower and those with 1:40 or lower, assuming that persons with elevated titers were immune to infection.

We performed 3 sensitivity analyses. First, we excluded everyone who had received seasonal vaccine. Second, because seasonal influenza circulated before pH1N1 and we had previously found some evidence consistent with broad temporary immunity,20 we assumed that those infected with seasonal influenza were immune to pH1N1 and excluded them from the model. Third, we repeated analyses permitting anyone with more than one 4-fold or greater rise in antibody titers to have had more than one infection.

Back to Top | Article Outline


Table 1 shows the baseline characteristics of the study population and the proportion of influenza infections in each group. eAppendix Table 2 ( shows the numbers of infections in children and adults overall, and in the subgroups with lower baseline antibody titers. There were high levels of baseline antibody against both sH1N1 and sH3N2, particularly among children; 64% of children had baseline antibody titers higher than 1:40 against sH1N1 and 63% against sH3N2. Four adults and no children had baseline antibody levels above 1:40 against pH1N1. eAppendix Figures 1–3 ( show the prior and posterior distributions for model parameters in these 3 analyses.



Table 2 shows the subtype-specific estimates of community probabilities of infection and SAPs for children and adults. For pH1N1, estimates were higher in children than in adults for both the community probability of infection (0.18 vs. 0.06) and the SAP (0.15 vs. 0.07). Among children, the community probability of infection and SAP was lower for sH3N2 than for pH1N1 (0.09 vs. 0.18 for the community probability of infection and 0.07 vs. 0.15 for the SAP [P = 0.02 and >0.27, respectively]). However, when only those with lower levels (≤1:160 and ≤1:40) of baseline antibody were considered susceptible to infection, there was little evidence of differences between SAPs for pandemic and seasonal viruses in either children or adults. χ2 goodness-of-fit tests indicate that all models had satisfactory fit (all P > 0.1).



When we performed sensitivity analysis by restricting our analysis to only those with antibody titers less than 1:160 and 1:40 and who did not receive seasonal vaccine, parameter estimates were similar to those using the entire sample (eAppendix Table 3, Moreover, when we restricted analysis to those who had not been infected with seasonal influenza, parameter estimates for pH1N1 were similar to those using the entire sample (eAppendix Table 4, When individuals (eAppendix Table 2, were allowed to have more than one infection rather than classifying them to one infection, all estimates of the community probability of infection and SAP were similar (eAppendix Table 5,

Back to Top | Article Outline


Our results suggest similar patterns of household infection and transmission for sH3N2 and pH1N1 viruses in Hong Kong during the summer and early fall of 2009.11 The estimated SAPs for pH1N1 were consistent with estimates of confirmed infection reported in the literature.5,6,9–11 Although unadjusted community probability of infection and SAP estimates were lower for seasonal influenza than for pandemic influenza, the estimates for the sH3N2 and pH1N1 viruses generally were similar, once we restricted our analysis to a subgroup with low immunity to seasonal influenza. This suggests that the household transmission potential of pH1N1 and sH3N2 would not differ greatly in equally susceptible populations (Table 2). SAP estimates in adults were slightly lower for pH1N1 compared with sH3N2, although in the subgroup analyses excluding those with high baseline antibody levels, this difference was inconsistent. A number of other studies have demonstrated decreasing risk of pH1N1 infection with increasing age.4,10,11,21,32,33

One limitation of our study is that the models assumed that everyone included in analyses was susceptible to infection. We conducted subanalyses assuming that persons with higher levels of baseline antibody were immune to infection. Due to the study design, 1 child in some families received influenza vaccine in September–October 2008, and a small proportion of other household members also reported receipt of vaccine.20 Of the children who had received vaccine, 70% had baseline titers higher than 1:40 against sH1N1 in April 2009, and 74% had elevated titers against sH3N2; thus, they were removed from at least one subgroup analysis. Although humoral antibody is imperfectly correlated with immunity,20 it is plausible that models restricting the analysis to the subgroup with low baseline antibody titers (≤1:40) should represent a group largely susceptible to infection.

Three other technical issues should be noted. First, the study ended before the end of pH1N1 circulation in Hong Kong34; however, some pH1N1 infections may have occurred after the end of our study, potentially leading us to underestimate the community probability of infection for pH1N1. Second, the HI and VN assays have imperfect sensitivity and specificity,35 and infection status may have been misclassified.11 Finally, although the community probability of infection and SAP has straightforward epidemiologic interpretations, the risk of community infection in the adopted model is separately estimated from that in households. Further work is needed to link estimates of household transmissibility with more detailed transmission dynamics.36

Back to Top | Article Outline


We thank Calvin Cheng, Winnie Lim, Teresa So and Kevin Yau for assistance in subject recruitment and follow-up. We thank Kwok-Hung Chan, Daniel Chu and Edward Ma for assistance with laboratory testing. We thank Philip O'Neill of the University of Nottingham for helpful comments on the statistical methods.

Back to Top | Article Outline


1. Halloran ME, Preziosi MP, Chu H. Estimating vaccine efficacy from secondary attack rates. J Am Stat Assoc. 2003;98:38–46.
2. Cannell JJ, Zasloff M, Garland CF, Scragg R, Giovannucci E. On the epidemiology of influenza. Virol J. 2008;5:29.
3. Yang Y, Sugimoto JD, Halloran ME, et al. The transmissibility and control of pandemic influenza A (H1N1) virus. Science. 2009;326:729–733.
4. Cauchemez S, Donnelly CA, Reed C, et al. Household transmission of 2009 pandemic influenza A (H1N1) virus in the United States. N Engl J Med. 2009;361:2619–2627.
5. Sikora C, Fan S, Golonka R, et al. Transmission of pandemic influenza A (H1N1) 2009 within households: Edmonton, Canada. J Clin Virol. 2010;49:90–93.
6. Komiya N, Gu Y, Kamiya H, et al. Household transmission of pandemic 2009 influenza A (H1N1) virus in Osaka, Japan in May 2009. J Infect. 2010;61:284–288.
7. Doyle TJ, Hopkins RS. Low secondary transmission of 2009 pandemic influenza A (H1N1) in households following an outbreak at a summer camp: relationship to timing of exposure. Epidemiol Infect. 2010;1–7.
8. Liu SL, Zhang ZR, Wang C, et al. 2009 pandemic characteristics and controlling experiences of influenza H1N1 virus 1 year after the inception in Hangzhou, China. J Med Virol. 2010;82:1985–1995.
9. Suess T, Buchholz U, Dupke S, et al. Shedding and transmission of novel influenza virus A/H1N1 infection in households—Germany, 2009. Am J Epidemiol. 2010;171:1157–1164.
10. Sugimoto JD, Borse NN, Ta ML, et al. The effect of age on transmission of 2009 pandemic influenza A (H1N1) in a camp and associated households. Epidemiol 2011;22:180–187.
11. Cowling BJ, Chan KH, Fang VJ, et al. Comparative epidemiology of pandemic and seasonal influenza A in households. N Engl J Med. 2010;362:2175–2184.
12. Longini IM Jr, Koopman JS, Monto AS, Fox JP. Estimating household and community transmission parameters for influenza. Am J Epidemiol. 1982;115:736–751.
13. Jennings LC, Miles JA. A study of acute respiratory disease in the community of Port Chalmers. II: influenza A/Port Chalmers/1/73: intrafamilial spread and the effect of antibodies to the surface antigens. J Hyg (Lond). 1978;81:67–75.
14. Longini IM Jr, Monto AS. Efficacy of virucidal nasal tissues in interrupting familial transmission of respiratory agents. A field trial in Tecumseh, Michigan. Am J Epidemiol. 1988;128:639–644.
15. Parker R, Loewen N, Skowronski D. Experience with oseltamivir in the control of a nursing home influenza B outbreak. Can Commun Dis Rep. 2001;27:37–40.
16. Cowling BJ, Chan KH, Fang VJ, et al. Facemasks and hand hygiene to prevent influenza transmission in households: a cluster randomized trial. Ann Intern Med. 2009;151:437–446.
17. Nishiura H, Chowell G. Household and community transmission of the Asian influenza A (H2N2) and influenza B viruses in 1957 and 1961. Southeast Asian J Trop Med Public Health. 2007;38:1075–1083.
18. Longini IM Jr, Monto AS, Koopman JS. Statistical procedures for estimating the community probability of illness in family studies: rhinovirus and influenza. Int J Epidemiol. 1984;13:99–106.
19. Longini IM Jr, Koopman JS, Haber M, Cotsonis GA. Statistical inference for infectious diseases. Risk-specific household and community transmission parameters. Am J Epidemiol. 1988;128:845–859.
20. Cowling BJ, Ng S, Ma ES, et al. Protective efficacy of seasonal influenza vaccination against seasonal and pandemic influenza virus infection during 2009 in Hong Kong. Clin Infect Dis. 2010;51:1370–1379.
21. Wu JT, Ma ES, Lee CK, et al. The infection attack rate and severity of 2009 pandemic H1N1 influenza in Hong Kong. Clin Infect Dis. 2010;51:1184–1191.
22. Masurel N. Swine influenza virus and the recycling of influenza-A viruses in man. Lancet. 1976;2:244–247.
23. Subbarao EK, Kawaoka Y, Ryan-Poirier K, Clements ML, Murphy BR. Comparison of different approaches to measuring influenza A virus-specific hemagglutination inhibition antibodies in the presence of serum inhibitors. J Clin Microbiol. 1992;30:996–999.
24. Longini IM Jr, Koopman JS. Household and community transmission parameters from final distributions of infections in households. Biometrics. 1982;38:115–126.
25. Ludwig D. Final size distribution for epidemics. Math Biosci. 1975;23:33–46.
26. O'Neill PD, Balding DJ, Becker NG, Eerola M, Mollison D. Analyses of infectious disease data from household outbreaks by Markov chain Monte Carlo methods. J Roy Statist Soc Ser C. 2000;49:517–542.
27. Robert CP, Casella G. Monte Carlo Statistical Methods. Springer Texts in Statistics. 2nd ed. New York: Springer; 2004.
28. O'Neill PD. A tutorial introduction to Bayesian inference for stochastic epidemic models using Markov chain Monte Carlo methods. Math Biosci. 2002;180:103–114.
29. Hobson D, Curry RL, Beare AS, Ward-Gardner A. The role of serum haemagglutination-inhibiting antibody in protection against challenge infection with influenza A2 and B viruses. J Hyg (Lond). 1972;70:767–777.
30. Hannoun C, Megas F, Piercy J. Immunogenicity and protective efficacy of influenza vaccination. Virus Res. 2004;103:133–138.
31. Coudeville L, Bailleux F, Riche B, et al. Relationship between haemagglutination-inhibiting antibody titres and clinical protection against influenza: development and application of a Bayesian random-effects model. BMC Med Res Methodol. 2010;10:18.
32. Miller E, Hoschler K, Hardelid P, et al. Incidence of 2009 pandemic influenza A H1N1 infection in England: a cross-sectional serological study. Lancet. 2010;375:1100–1108.
33. Deng Y, Pang XH, Yang P, et al. Serological survey of 2009 H1N1 influenza in residents of Beijing, China. Epidemiol Infect. 2011;139:52–58.
34. Cowling BJ, Lau MS, Ho LM, et al. The effective reproduction number of pandemic influenza: prospective estimation. Epidemiol. 2010;21:842–846.
35. Rothbarth PH, Groen J, Bohnen AM, de Groot R, Osterhaus AD. Influenza virus serology—a comparative study. J Virol Methods. 1999;78:163–169.
36. Ball FG, Lyne OD. Epidemics among a population of households. In: Castillo-Chavez C, Blower S, Driessche P, Kirschner D, Yakubu A-A, eds. Mathematical Approaches for Emerging and Reemerging Infectious Diseases. IMA Volumes in Mathematics and Its Applications. New York: Springer; 2002:115–142.

Supplemental Digital Content

Back to Top | Article Outline
© 2011 Lippincott Williams & Wilkins, Inc.