Relating phylogenetic trees to transmission trees of infectious disease outbreaks.

Transmission events are the fundamental building blocks of the dynamics of any infectious disease. Much about the epidemiology of a disease can be learned when these individual transmission events are known or can be estimated. Such estimations are difficult and generally feasible only when detailed epidemiological data are available. The genealogy estimated from genetic sequences of sampled pathogens is another rich source of information on transmission history. Optimal inference of transmission events calls for the combination of genetic data and epidemiological data into one joint analysis. A key difficulty is that the transmission tree, which describes the transmission events between infected hosts, differs from the phylogenetic tree, which describes the ancestral relationships between pathogens sampled from these hosts. The trees differ both in timing of the internal nodes and in topology. These differences become more pronounced when a higher fraction of infected hosts is sampled. We show how the phylogenetic tree of sampled pathogens is related to the transmission tree of an outbreak of an infectious disease, by the within-host dynamics of pathogens. We provide a statistical framework to infer key epidemiological and mutational parameters by simultaneously estimating the phylogenetic tree and the transmission tree. We test the approach using simulations and illustrate its use on an outbreak of foot-and-mouth disease. The approach unifies existing methods in the emerging field of phylodynamics with transmission tree reconstruction methods that are used in infectious disease epidemiology.

Finding evidence for local transmission of contagious disease in molecular epidemiological datasets.

Surveillance systems of contagious diseases record information on cases to monitor incidence of disease and to evaluate effectiveness of interventions. These systems focus on a well-defined population; a key question is whether observed cases are infected through local transmission within the population or whether cases are the result of importation of infection into the population. Local spread of infection calls for different intervention measures than importation of infection. Besides standardized information on time of symptom onset and location of cases, pathogen genotyping or sequencing offers essential information to address this question. Here we introduce a method that takes full advantage of both the genetic and epidemiological data to distinguish local transmission from importation of infection, by comparing inter-case distances in temporal, spatial and genetic data. Cases that are part of a local transmission chain will have shorter distances between their geographical locations, shorter durations between their times of symptom onset and shorter genetic distances between their pathogen sequences as compared to cases that are due to importation. In contrast to generic clustering algorithms, the proposed method explicitly accounts for the fact that during local transmission of a contagious disease the cases are caused by other cases. No pathogen-specific assumptions are needed due to the use of ordinal distances, which allow for direct comparison between the disparate data types. Using simulations, we test the performance of the method in identifying local transmission of disease in large datasets, and assess how sensitivity and specificity change with varying size of local transmission chains and varying overall disease incidence.

A sign of superspreading in tuberculosis: highly skewed distribution of genotypic cluster sizes.

BACKGROUND: Molecular typing is a valuable tool for gaining insight into spread of Mycobacterium tuberculosis. Typing allows for clustering of cases whose isolates share an identical genotype, revealing epidemiologic relatedness. Observed distributions of genotypic cluster sizes of tuberculosis (TB) are highly skewed. A possible explanation for this skewness is the concept of "superspreading": a high heterogeneity in the number of secondary cases caused per infectious individual. Superspreading has been previously found for diseases such as severe acute respiratory syndrome and smallpox, where the entire transmission tree is known. So far, no method exists to relate superspreading to the distribution of genotypic cluster sizes. METHODS: We quantified heterogeneity in secondary infections per infectious individual by describing this number as a negative binomial distribution. The dispersion parameter k is a measure of superspreading; standard (homogeneous) models use values of k ≥ 1, whereas small values of k imply superspreading. We estimated this negative binomial dispersion parameter for TB in the Netherlands, using the genotypic cluster size distribution for all 8330 cases of culture confirmed, pulmonary TB diagnosed between 1993 and 2007 in the Netherlands. RESULTS: The dispersion parameter k was estimated at 0.10 (95% confidence interval = 0.09-0.12), well in the range of values consistent with superspreading. Simulation studies showed the method reliably estimates the dispersion parameter across a range of scenarios and parameter values. CONCLUSION: Heterogeneity in the number of secondary cases caused per infectious individual is a plausible explanation for the observed skewness in genotypic cluster size distribution of TB.

Genetic data provide evidence for wind-mediated transmission of highly pathogenic avian influenza.

Outbreaks of highly pathogenic avian influenza in poultry can cause severe economic damage and represent a public health threat. Development of efficient containment measures requires an understanding of how these influenza viruses are transmitted between farms. However, the actual mechanisms of interfarm transmission are largely unknown. Dispersal of infectious material by wind has been suggested, but never demonstrated, as a possible cause of transmission between farms. Here we provide statistical evidence that the direction of spread of avian influenza A(H7N7) is correlated with the direction of wind at date of infection. Using detailed genetic and epidemiological data, we found the direction of spread by reconstructing the transmission tree for a large outbreak in the Netherlands in 2003. We conservatively estimate the contribution of a possible wind-mediated mechanism to the total amount of spread during this outbreak to be around 18%.

Different transmission patterns in the early stages of the influenza A(H1N1)v pandemic: a comparative analysis of 12 European countries.

Following the emergence of a novel strain of influenza A(H1N1) in Mexico and the United States in April 2009, its epidemiology in Europe during the summer was limited to sporadic and localised outbreaks. Only the United Kingdom experienced widespread transmission declining with school holidays in late July. Using statistical modelling where applicable we explored the following causes that could explain this surprising difference in transmission dynamics: extinction by chance, differences in the susceptibility profile, age distribution of the imported cases, differences in contact patterns, mitigation strategies, school holidays and weather patterns. No single factor was able to explain the differences sufficiently. Hence an additive mixed model was used to model the country-specific weekly estimates of the effective reproductive number using the extinction probability, school holidays and weather patterns as explanatory variables. The average extinction probability, its trend and the trend in absolute humidity were found to be significantly negatively correlated with the effective reproduction number - although they could only explain about 3% of the variability in the model. By comparing the initial epidemiology of influenza A (H1N1) across different European countries, our analysis was able to uncover a possible role for the timing of importations (extinction probability), mixing patterns and the absolute humidity as underlying factors. However, much uncertainty remains. With better information on the role of these epidemiological factors, the control of influenza could be improved.

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 vaccination.

Improved tracing of hepatitis B virus transmission chains by phylogenetic analysis based on C region sequences.

An effective vaccine is available for the hepatitis B virus (HBV), which is a very contagious human pathogen. The prevalence of chronic HBV infection is very low in the Netherlands (<0.5%), and no universal vaccination is in place. Instead, a program of vaccination for targeted groups at high risk of HBV exposure has been implemented. Because transmission of HBV can occur by various routes, the effectiveness of this targeted vaccination strategy is difficult to assess. Molecular typing data for the surface protein encoding gene of HBV isolates, in combination with epidemiological data, provide some insight into the main transmission routes. Due to the low mutation rate of the HBV genome, many isolates have identical S region sequences, which hampers phylogenetic analysis and identification of transmission chains. The molecular epidemiological analysis of acute HBV isolates based on the surface and core protein encoding regions were compared. The nucleotide diversity found in the C region was statistically significant greater (1.5 times) than in the S region, and phylogenetic analysis based on the C region showed a higher resolution. C region analysis resulted in an almost 50% reduction of genotype A isolates with identical sequences. C region analysis also indicated that no long-chain transmission of genotype D strains is occurring in the Netherlands, as all genotype D isolates have unique C region sequences. Defining the goals of molecular typing of HBV isolates should precede the choice for phylogenetic analysis on the basis of either C or S region sequences.

Emergent trade-offs and selection for outbreak frequency in spatial epidemics.

Nonspatial theory on pathogen evolution generally predicts selection for maximal number of secondary infections, constrained only by supposed physiological trade-offs between pathogen infectiousness and virulence. Spread of diseases in human populations can, however, exhibit large scale patterns, underlining the need for spatially explicit approaches to pathogen evolution. Here, we show, in a spatial model where all pathogen traits are allowed to evolve independently, that evolutionary trajectories follow a single relationship between transmission and clearance. This trade-off relation is an emergent system property, as opposed to being a property of pathogen physiology, and maximizes outbreak frequency instead of the number of secondary infections. We conclude that spatial pattern formation in contact networks can act to link infectiousness and clearance during pathogen evolution in the absence of any physiological trade-off. Selection for outbreak frequency offers an explanation for the evolution of pathogens that cause mild but frequent infections.