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.E STIMATING who infected whom for an outbreak of an infectious disease can provide valuable insights. Estimated transmission trees have been used to evaluate effectiveness of intervention measures (Ferguson et al. 2001;Keeling et al. 2003;Wallinga and Teunis 2004;Heijne et al. 2009), to quantify superspreading (Lloyd-Smith et al. 2005), to estimate key parameters (Haydon et al. 2003;Heijne et al. 2012;Hens et al. 2012), and to identify mechanisms of transmission (Spada et al. 2004;Ypma et al. 2013). Transmission trees can be statistically reconstructed using epidemiological data from outbreak investigations, such as time of symptom onset, geographical location, and social ties; these data generally must be very detailed to allow for accurate reconstructions.For many pathogens, in particular RNA viruses, evolutionary processes occur on the same timescale as epidemiological processes (Holmes et al. 1995;Pybus and Rambaut 2009). This makes it possible to draw conclusions about epidemiology from genetic analysis. The field that infers epidemiological characteristics from genetic sequences by simultaneously considering host dynamics and pathogen genetics has been dubbed "phylodynamics" (Grenfell et al. 2004). In practical applications researchers have considered a specific epidemiological model dependent on the phylogenetic tree inferred from sequence data, simultaneously estimating mutational and epidemiological parame...
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.
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 tradeoff 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.evolution ͉ pathogen ͉ spatial model ͉ spatial patterns C urrent theory on pathogen evolution places much emphasis on physiological (or life-history) trade-offs that relate virulence, infectiousness, mode of transmission, and immune clearance (1-6). These trade-offs, motivated by a supposed functional link between two (or more) traits, specify that evolutionary improvements in one trait are necessarily accompanied by a decline in another (7,8). One of the most commonly made trade-off assumptions is that increased production of transmission stages causes increased host mortality and thereby shortens the infection period (9). Where traits can evolve independently, nonspatial theory typically predicts selection for maximal transmissibility and infection period, thus maximizing the number of secondary infections (i.e., the number of new infections an infected host causes). It is commonly held, however, that the benefits of increased transmission and the associated penalties of virulence and shorter infection are balanced so that the number of secondary infections is maximized at intermediate transmissibility and virulence (2). In simple nonspatial models, this evolutionary maximization corresponds to selection for maximal basic reproductive ratio R 0 (10), i.e., the expected number of secondary infections in an unexposed population [but note that this result depends on absence of multiple infections (5) and vertical transmission (11, 12)]. The current popularity of tradeoffs in studies of pathogen evolution stems from the fact that they provide a possible explanation for selection for intermediate virulence and transmissibility (8), and that they can be used to predict pathogen evolution in response to human interventions such as the use of imperfect vaccines (4) or improved hygiene (13). However, the exact shape (and even existence) of trade-offs is unknown for many diseases (14).A growing body of work reports on the role of spatial pattern formation on evolutionary pro...
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%.
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.
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