Unravelling transmission trees of infectious diseases by combining genetic and epidemiological data

Knowledge on the transmission tree of an epidemic can provide valuable insights into disease dynamics. The transmission tree can be reconstructed by analysing either detailed epidemiological data (e.g. contact tracing) or, if sufficient genetic diversity accumulates over the course of the epidemic, genetic data of the pathogen. We present a likelihood-based framework to integrate these two data types, estimating probabilities of infection by taking weighted averages over the set of possible transmission trees. We test the approach by applying it to temporal, geographical and genetic data on the 241 poultry farms infected in an epidemic of avian influenza A (H7N7) in The Netherlands in 2003. We show that the combined approach estimates the transmission tree with higher correctness and resolution than analyses based on genetic or epidemiological data alone. Furthermore, the estimated tree reveals the relative infectiousness of farms of different types and sizes. (Résumé d'auteur

Nowcasting pandemic influenza A/H1N1 2009 hospitalizations in the Netherlands

During emerging epidemics of infectious diseases, it is vital to have up-to-date information on epidemic trends, such as incidence or health care demand, because hospitals and intensive care units have limited excess capacity. However, real-time tracking of epidemics is difficult, because of the inherent delay between onset of symptoms or hospitalizations, and reporting. We propose a robust algorithm to correct for reporting delays, using the observed distribution of reporting delays. We apply the algorithm to pandemic influenza A/H1N1 2009 hospitalizations as reported in the Netherlands. We show that the proposed algorithm is able to provide unbiased predictions of the actual number of hospitalizations in real-time during the ascent and descent of the epidemic. The real-time predictions of admissions are useful to adjust planning in hospitals to avoid exceeding their capacity

Molecular sequence data of hepatitis B virus and genetic diversity after vaccination

The effect of vaccination programs on transmission of infectious disease is usually assessed by monitoring programs that rely on notifications of symptomatic illness. For monitoring of infectious diseases with a high proportion of asymptomatic cases or a low reporting rate, molecular sequence data combined with modern coalescent-based techniques offer a complementary tool to assess transmission. Here, the authors investigate the added value of using viral sequence data to monitor a vaccination program that was started in 1998 and was targeted against hepatitis B virus in men who have sex with men in Amsterdam, the Netherlands. The incidence in this target group, as estimated from the notifications of acute infections with hepatitis B virus, was low; therefore, there was insufficient power to show a significant change in incidence. In contrast, the genetic diversity, as estimated from the viral sequence collected from the target group, revealed a marked decrease after vaccination was introduced. Taken together, the findings suggest that introduction of vaccination coincided with a change in the target group toward behavior with a higher risk of infection. The authors argue that molecular sequence data provide a powerful additional monitoring instrument, next to conventional case registration, for assessing the impact of vaccinatio

Graphical representation of the dissimilarity measure between cases.

<p>Shown is a dataset of nine cases and two (one-dimensional) data types. For each of the two data types, the dissimilarity between the two black cases is given as the number of cases in between them (for that data type), including one of the two black cases. This definition ensures the dissimilarity between a case and itself is zero. The total dissimilarity between the black cases is then given as the product of these, here 5*4 = 20.</p

Graphical representation of hierarchical clustering.

<p>Hierarchical clustering sequentially clusters together elements of a set, based on inter-element distances. (A) Representation of a set of six elements. Shown is a minimal spanning tree: the tree that connects all elements minimizing total distance. (B) The clustering provided by hierarchical clustering when using single linkage clustering. Sequentially, the two current subsets with smallest distance are joined together, where the initial subsets are the six elements. This means the distances of clustering on the x-axis in (B) are the distances of the minimal spanning tree in (A). In total five distinct clusters are passed before all elements cluster together.</p

Sensitivity (black) and false positive rate (gray) for analyses on simulated datasets.

<p>For each of nine simulation scenarios, percentage of outbreak (black) and non-outbreak (gray) cases assigned to a putative transmission cluster are shown. In each scenario, ten percent of all cases are an outbreak case. Total expected number of cases is (left column) 1000, (middle column) 500 or (right column) 100. Outbreak cases belong to (top row) one large outbreak, (middle row) small outbreaks caused by 1/10 of cases being contagious with basic reproduction number R = 0.5, (bottom row) very small outbreaks caused by all cases being contagious with R = 0.1. For all scenarios, outbreak cases are distinguishable from unrelated cases. Sensitivity increases with outbreak size, at the cost of an increased false positive rate. Sensitivity and false positive rate improve when the incidence, or equivalently the number of cases in the same region of spacetime, decreases. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069875#pone-0069875-g004" target="_blank">Figure 4</a> corresponds to a simulation from the middle left panel.</p

Different patterns of disease clusters.

<p>Clusters of disease cases caused by a point source (A) show a different pattern than clusters caused by human-to-human transmission of a contagious disease (B). (A) When there is a point source cases tend to be found in the region around it. Modern scan statistics exploiting this pattern have been developed to find evidence of point sources causing disease. (B) When contagious diseases are spread by human-to-human transmission, clusters tend to be more chain-like; the relevant distances are those between pairs of cases rather than between case and point source. Although it is still possible to find clusters in situation (B) with algorithms developed for (A), the problem can be handled more naturally by taking into account the different cluster pattern.</p