Comparisons of pre-existing high risk conditions of children hospitalized for influenza A pH1N1 with age- matched controls hospitalized for seasonal H1N1 and H3N2, and with children hospitalized for seasonal influenza concurrently during the study period, respectively.

<p>Comparisons of pre-existing high risk conditions of children hospitalized for influenza A pH1N1 with age- matched controls hospitalized for seasonal H1N1 and H3N2, and with children hospitalized for seasonal influenza concurrently during the study period, respectively.</p

Comparison of clinical diagnosis of children hospitalized for influenza A pH1N1 with age- matched controls hospitalized for seasonal H1N1 and H3N2, and with children hospitalized for seasonal influenza concurrently during the study period, respectively.

<p>Comparison of clinical diagnosis of children hospitalized for influenza A pH1N1 with age- matched controls hospitalized for seasonal H1N1 and H3N2, and with children hospitalized for seasonal influenza concurrently during the study period, respectively.</p

Regression model results

The results generated from the above log-linear random effects censored regression model

Bias, RMSE of the estimated excess hospitalization rates from the observed hospitalization rates with laboratory confirmed influenza infections.

<p>Note. QAIC, quasi-Akaike information criterion; QBIC, quasi-Bayesian information criterion; PACF, partial autocorrelation function; GCV, generalized cross validation; RMSE, root-mean-square error.</p

Table 3 scripts

Table 3 script

Bias, Standard error and RMSE of influenza coefficients estimated from the best-fit models selected by different criteria.

<p>Note: Lines of QAIC and QBIC are overlapping when the degrees of freedom (<i>df</i>) range from 2 to 10 per year. Abbreviations: QAIC, quasi-Akaike information criterion; QBIC, quasi-Bayesian information criterion; PACF, partial autocorrelation function; GCV, generalized cross validation; RMSE, root-mean-square error.</p

Figure 1 scripts

Figure 1 script

Matlab function 1

Contain the matlab function to generate results on the model in the main analysis

Matlab function 2

Contain the matlab function to generate results on the additional analysis of exploring the effect of presence of symptom. (a to i respents fever, sore throat, cough, runny nose, phlegm, muscle pain, headache, any three or more of above symptoms and ILI (fever + cough or sough) )

Percentage difference of estimated excess hospitalization rates from the observed admission rates of influenza cases during 2003−2008.

<p>Note: Percentage difference = 100%× (estimated excess hospitalization rate – observed rate)/observed rate.</p