Estimated epidemiologic parameters and morbidity associated with pandemic H1N1 influenza

Data collection

We collected individual-level data on laboratory-confirmed cases of pandemic H1N1 influenza in the province of Ontario, Canada, with a reported date of symptom onset between Apr. 13 and June 20, 2009. This cutoff was chosen because individual-level reporting of cases of pandemic H1N1 influenza in Ontario was stopped after this date.

We obtained the data from the province’s Integrated Public Health Information System. This system incorporates data provided by public health units engaged in the investigation of initial laboratory-confirmed cases of pandemic H1N1 influenza. For these cases, information was available on age, date of symptom onset and, for patients admitted to hospital, the date of admission and discharge and the outcome at discharge. Records included source of exposure if known (e.g., travel to Mexico for early cases), earliest possible exposure and, in a subset of cases, most recent possible exposure before onset of symptoms.

Estimation of incubation period

We restricted this analysis to case records with both an earliest and most recent date of exposure to an infectious case or locale, as well as a recorded date of symptom onset. The most likely date of exposure was estimated as the midpoint between the earliest and most recent dates of exposure. We assessed the goodness-of-fit of log-normal and Weibull distributions to observed distributions of incubation periods, with goodness-of-fit evaluated using graphic log–log plots.

Estimation of duration of symptoms, risk of hospital admission and case-fatality rate

We estimated the duration of symptoms (the interval between onset of illness and resolution of symptoms) using methods similar to those used to evaluate the period of incubation. We restricted our analysis to include cases with a recorded date of symptom onset before June 21, 2009, and a documented date of symptom resolution. We evaluated the association between age and time to resolution of symptoms using the log-rank test and through construction of Cox proportional hazards models.

Because the absence of a documented date of symptom resolution could signify either ongoing illness or missing data on symptom resolution, we performed sensitivity analyses in which missing dates of symptom resolution were randomly replaced based on the log-normal probability distribution of available durations of symptoms; we considered dates after June 21, 2009, as “censored” for the purposes of these analyses.

To estimate the risk of hospital admission and case-fatality rates, we used competing-risk survival models. 1 Such models can be used to estimate the predicted cumulative incidence of a given outcome over time if other “competing” outcomes do not occur. In the model evaluating risk of hospital admission, the competing outcomes were admission to hospital, death without admission to hospital (as occurred in a handful of cases), recovery without admission to hospital and “censored” (for cases in which none of the outcomes had occurred by the final date of evaluation). We used a “ratio of hazards” approach to account for temporal effects, allowing for the projection of likely long-term hospital stays and case-fatality rate. For a more detailed description of this approach and its derivation, see Appendix 1 (available at www.cmaj.ca/cgi/content/full/cmaj.091807/DC1).

Simulation of disease transmission

We used a Markov Chain Monte Carlo model to simulate disease transmission under varying assumptions around duration of infectiousness, latent period (infected, but noninfectious) and likelihood of asymptomatic infection. We assumed that people would become infected at a rate dependent on the number of infectious people in the Ontario population and the duration of infectiousness. We used data on history of travel to Mexico to classify imported cases. These cases were assumed to contribute to the transmission of infection; we used dates of symptom onset for these cases to model the initial introduction of pandemic H1N1 influenza into the Ontario population. The structure of the model is outlined in Appendix 2 (available at www.cmaj.ca/cgi/content/full/cmaj.091807/DC1).

We performed 1000 simulations in which daily reported counts of imported and locally acquired cases were assumed to Poisson-random variates. We generated best-fit values for R₀, latent period and infectious period for each simulation using an optimization routine that minimized the sum-of-squares difference between model projections and the observed epidemic curve. From the simulations, we estimated mean parameter values and 95% credible intervals.

Incubation period

A total of 316 cases contained sufficient information on earliest exposure and disease onset to estimate incubation periods. The median time from exposure to symptom onset was 4 days (Figure 3). The distribution of estimated incubation periods followed a log-normal distribution (mean incubation 4.3 days, 95% CI 2.6–6.6 days).

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Figure 3: Estimates of the incubation period (time from exposure to onset of symptoms) in 316 laboratory-confirmed cases of pandemic H1N1 influenza for whom these data were available. Estimates were calculated as the time between exposure date (midpoint of earliest and most recent exposure) and date of symptom onset. The blue curve represents the best-fit log-normal distribution of observed data.

Duration of symptoms

Of the 712 reported cases with documented resolution of symptoms, the median duration of symptoms was 7 days. The median duration was significantly shorter among patients aged less than 18 years (7 days) than among older patients (8 days) (hazard ratio 1.23, 95% confidence interval 1.06–1.44). Intervals between onset and resolution of symptoms followed a log-normal distribution (mean duration 9.3 days, 95% CI 2.6–24.2 days) (Figure 4).

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Figure 4: Estimates of the duration of symptoms (time from onset to resolution of symptoms) in 712 laboratory-confirmed cases of pandemic H1N1 influenza for whom these data were available. The blue curve represents the best-fit log-normal distribution of observed data.

Risk of hospital admission and case-fatality rate

A total of 140 admissions to hospital and 10 deaths were recorded during the 10 weeks of observation. The estimated risk of hospital admission per case was 4.5% (95% CI 3.8%–5.2%). The estimated case-fatality rate was 0.3% (95% CI 0.1%–0.5%). In the competing-risk models used to adjust for recovery and death without admission to hospital, the cumulative risk of admission increased gradually during the first 5 days of illness, to about 3%. The risk was highest among infants (age < 1 year) and among elderly patients (age ≥ 65 years); adolescents were at decreased risk (Table 1). Although adults more than 50 years old comprised only 7% of cases, 7 of 10 deaths occurred in this age group (odds ratio 28.6, 95% CI 7.3–111.2). Using methods described above for the estimation of the case-fatality rate, we estimated an asymptomatic case-fatality rate of 0.002 (95% CI 0.0008–0.0076).

Model-based estimates of transmission parameters

We estimated a mean basic reproductive number (R₀) of 1.31 (95% credible interval 1.25–1.38) (Figure 5); a mean latent period of 2.62 days (95% credible interval 2.28–3.12); and a mean duration of infectiousness of 3.38 days (95% credible interval 2.06–4.69). Using a commonly applied heuristic algorithm to estimate the serial interval 2 (the average time from onset of infectiousness in a case to the onset of infectiousness in a person infected by that case) based on the sum of the latent periods plus half the duration of infectiousness, we estimated the serial interval for pandemic H1N1 influenza to be 4–5 days. The range of R₀ values would correspond with an overall attack rate (symptomatic and asymptomatic) of 20%–50% in the absence of intervention. 3

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Figure 5: Estimates of the basic reproductive number (R₀, number of new cases created by a single primary case in a susceptible population) in the province of Ontario during the wave of pandemic H1N1 influenza in the spring of 2009. Estimates were generated with the use of Markov Chain Monte Carlo simulation modelling. The mean estimate for R₀ is 1.31 (95% credible interval 1.25–1.38).

Limitations

Our study has several limitations. First, our projection of hospital admissions and deaths among laboratory-confirmed cases, and with a limited number of outcomes, introduced wide confidence intervals in the risk estimates. As the pandemic progresses, we will be able to refine these values. However, our initial estimates provide some upper and lower bounds that may be useful for planning, if it is understood that laboratory-confirmed cases represent only a subset of all cases of pandemic H1N1 influenza in the community. Second, our estimates of the duration of symptoms may have been biased downward. Although we attempted to account for missing dates of symptom resolution by using statistical methods, some of the outcomes may have been censored because the patients had prolonged illness. Finally, not all hospital admissions or deaths attributable to influenza will be captured in surveillance data. For seasonal influenza, about 30% of related deaths have been predicted to occur outside of hospital, 20 and the burden of influenza difficult to assess. 21^,22

Conclusion

On the basis of data from initial laboratory-confirmed cases of pandemic H1N1 influenza, our estimates show that the characteristics of this novel influenza virus do not differ substantially from those of seasonal influenza. However, when combined with high attack rates in younger groups, 13 there may be greater absolute numbers of hospital admissions and deaths than are observed in a typical influenza season. Enhanced surveillance during an influenza pandemic, compared with surveillance during seasonal influenza epidemics, could also contribute to such an effect. Our relatively low estimate of R₀ (the number of new cases created by a single primary case in a susceptible population) indicates that effective use of mitigation strategies may substantially reduce the final size of the pandemic.