Using Routine Surveillance Data to Estimate the Epidemic Potential of Emerging Zoonoses: Application to the Emergence of US Swine Origin Influenza A H3N2v Virus

<div><p>Background</p><p>Prior to emergence in human populations, zoonoses such as SARS cause occasional infections in human populations exposed to reservoir species. The risk of widespread epidemics in humans can be assessed by monitoring the reproduction number <i>R</i> (average number of persons infected by a human case). However, until now, estimating <i>R</i> required detailed outbreak investigations of human clusters, for which resources and expertise are not always available. Additionally, existing methods do not correct for important selection and under-ascertainment biases. Here, we present simple estimation methods that overcome many of these limitations.</p> <p>Methods and Findings</p><p>Our approach is based on a parsimonious mathematical model of disease transmission and only requires data collected through routine surveillance and standard case investigations. We apply it to assess the transmissibility of swine-origin influenza A H3N2v-M virus in the US, Nipah virus in Malaysia and Bangladesh, and also present a non-zoonotic example (cholera in the Dominican Republic). Estimation is based on two simple summary statistics, the proportion infected by the natural reservoir among detected cases (<i>G</i>) and among the subset of the first detected cases in each cluster (<i>F</i>). If detection of a case does not affect detection of other cases from the same cluster, we find that <i>R</i> can be estimated by 1−<i>G</i>; otherwise <i>R</i> can be estimated by 1−<i>F</i> when the case detection rate is low. In more general cases, bounds on <i>R</i> can still be derived.</p> <p>Conclusions</p><p>We have developed a simple approach with limited data requirements that enables robust assessment of the risks posed by emerging zoonoses. We illustrate this by deriving transmissibility estimates for the H3N2v-M virus, an important step in evaluating the possible pandemic threat posed by this virus.</p> <p> <i>Please see later in the article for the Editors' Summary</i></p> </div

Impact of uncertainty on the case detection rate and the overdispersion parameter on estimates of the reproduction number R.

<p>1−<i>F</i> always acts as a lower bound for <i>R</i>. Furthermore, an upper bound for <i>R</i> can be obtained if it is possible to specify an upper bound for the case detection rate and a lower bound for the overdispersion parameter <i>k</i> (see <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001399#pmed.1001399.s009" target="_blank">Text S1</a>). The figure shows lower and upper bound for R as a function of . We specify which corresponds to the SARS scenario with superspreading events.</p

Trade-off between bias and precision for the estimator 1−<i>F</i> of the reproduction number <i>R</i>.

<p><b>(A)</b> Absolute bias, standard deviation and root mean square error (RMSE) of estimator 1−<i>F</i> as a function of the case detection rate of the surveillance system, in a scenario with reproduction number <i>R</i> = 0.5, overdispersion parameter <i>k</i> = 0.5, and where <i>n</i> = 10,000 clusters occur in the country. Optimum trade-off between bias and precision is obtained when RMSE is minimum. (B) Optimum case detection rate as a function of the reproduction number <i>R</i>, for different values of overdispersion parameter <i>k</i> and of the number <i>n</i> of clusters. (C) Bias at the optimum case detection rate.</p

Is <i>R≥1</i> for H3N2v-M?

<p>The values of the overdispersion parameter <i>k</i> and case detection rate ρ under which we can reject the hypothesis that <i>R</i>≥1 are indicated with a black dot, and parameter values for which the assumption cannot be rejected are shown in red.</p

Estimate (95% CI) of <i>R</i> for all strains, the H3N2v-M variant, and variants other than H3N2v-M, for different scenarios of detection and overdispersion in the offspring distribution.

<p>We consider three scenarios for the case-to-case variation in infectiousness: high (i.e., most transmission events are caused by a small proportion of cases like for SARS; k  =  0.16), medium (k  =  0.5), and low (k  =  5) <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001399#pmed.1001399-CDC7" target="_blank">[12]</a>.</p>a<p><i>n</i> is the number of clusters. <i>F</i> is the proportion of first detected cases in each cluster that were infected by the reservoir. The number of first detected cases that were infected by the reservoir was 20 for all strains, three for H3N2v-M variant, and 17 for variants other than H3N2v-M.</p

Selection bias and the estimation of the reproduction number <i>R</i>.

<p>(A) True distribution of cluster sizes (red) and distribution for clusters that are detected (blue), for <i>R</i> = 0.7. (B) Asymptotic estimate of the reproduction number as a function of the true reproduction number, derived from distribution of cluster sizes of detected clusters. The detection rate is set to ρ = 1% and the overdispersion parameter of the offspring distribution <i>k</i> = 0.5.</p

Description and univariate analysis of risk factors for mortality among HIV non-infected and HIV-infected children <24 months of age hospitalized with LRTI.

<p>*<i>p<0.20;</i></p><p>**<i>p<0.05.</i></p>†<p> <i>Missing observations for >10%;</i></p

Distribution of RISC score stratified by mortality for children <24 months hospitalized with LRTI, (A) without HIV infection and (B) with HIV infection.

<p>Distribution of RISC score stratified by mortality for children <24 months hospitalized with LRTI, (A) without HIV infection and (B) with HIV infection.</p

Distribution of RISC scores and screening performance in children <24 months of age with and without HIV infection, hospitalized with LRTI in Soweto, South Africa 1998–2001.

<p>*For mortality, using a cutoff at the corresponding score value.</p

RISC scoring system.

<p>RISC scoring system.</p