Spatiotemporal dynamics in the early stages of the 2009 A/H1N1 influenza pandemic

Analysing livestock network data for infectious disease control: an argument for routine data collection in emerging economies

Livestock movements are an important mechanism of infectious disease transmission. Where these are well recorded, network analysis tools have been used to successfully identify system properties, highlight vulnerabilities to transmission, and inform targeted surveillance and control. Here we highlight the main uses of network properties in understanding livestock disease epidemiology and discuss statistical approaches to infer network characteristics from biased or fragmented datasets. We use a 'hurdle model' approach that predicts (i) the probability of movement and (ii) the number of livestock moved to generate synthetic 'complete' networks of movements between administrative wards, exploiting routinely collected government movement permit data from northern Tanzania. We demonstrate that this model captures a significant amount of the observed variation. Combining the cattle movement network with a spatial between-ward contact layer, we create a multiplex, over which we simulated the spread of 'fast' ( R₀ = 3) and 'slow' ( R₀ = 1.5) pathogens, and assess the effects of random versus targeted disease control interventions (vaccination and movement ban). The targeted interventions substantially outperform those randomly implemented for both fast and slow pathogens. Our findings provide motivation to encourage routine collection and centralization of movement data to construct representative networks. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. This theme issue is linked with the earlier issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.

How prepared are we for cross-border outbreaks? An exploratory analysis of cross-border response networks for outbreaks of multidrug resistant microorganisms in the Netherlands and Germany

Background

The emergence and spread of multidrug resistant microorganisms is a serious threat to transnational public health. Therefore, it is vital that cross-border outbreak response systems are constantly prepared for fast, rigorous, and efficient response. This research aims to improve transnational collaboration by identifying, visualizing, and exploring two cross-border response networks that are likely to unfold during outbreaks involving the Netherlands and Germany.

Methods

Quantitative methods were used to explore response networks during a cross-border outbreak of carbapenem resistant Enterobacteriaceae in healthcare settings. Eighty-six Dutch and German health professionals reflected on a fictive but realistic outbreak scenario (response rate ≈ 70%). Data were collected regarding collaborative relationships between stakeholders during outbreak response, prior working relationships, and trust in the networks. Network analysis techniques were used to analyze the networks on the network level (density, centralization, clique structures, and similarity of tie constellations between two networks) and node level (brokerage measures and degree centrality).

Results

Although stakeholders mainly collaborate with stakeholders belonging to the same country, transnational collaboration is present in a centralized manner. Integration of the network is reached, since several actors are beneficially positioned to coordinate transnational collaboration. However, levels of trust are moderately low and prior-existing cross-border working relationships are sparse.

Conclusion

Given the explored network characteristics, we conclude that the system has a promising basis to achieve effective coordination. However, future research has to determine what kind of network governance form might be most effective and efficient in coordinating the necessary cross-border response activity. Furthermore, networks identified in this study are not only crucial in times of outbreak containment, but should also be fostered in times of non-crisis.

Virus evolution and transmission in an ever more connected world

The frequency and global impact of infectious disease outbreaks, particularly those caused by emerging viruses, demonstrate the need for a better understanding of how spatial ecology and pathogen evolution jointly shape epidemic dynamics. Advances in computational techniques and the increasing availability of genetic and geospatial data are helping to address this problem, particularly when both information sources are combined. Here, we review research at the intersection of evolutionary biology, human geography and epidemiology that is working towards an integrated view of spatial incidence, host mobility and viral genetic diversity. We first discuss how empirical studies have combined viral spatial and genetic data, focusing particularly on the contribution of evolutionary analyses to epidemiology and disease control. Second, we explore the interplay between virus evolution and global dispersal in more depth for two pathogens: human influenza A virus and chikungunya virus. We discuss the opportunities for future research arising from new analyses of human transportation and trade networks, as well as the associated challenges in accessing and sharing relevant spatial and genetic data.

Inferring Contagion Patterns in Social Contact Networks with Limited Infection Data

The spread of infectious disease is an inherently stochastic process. As such, real time control and prediction methods present a significant challenge. For diseases which spread through direct human interaction, (e.g., transferred from infected to susceptible individuals) the contagion process can be modeled on a social-contact network where individuals are represented as nodes, and contacts between individuals are represented as links. The model presented in this paper seeks to identify the infection pattern which depicts the current state of an ongoing outbreak. This is accomplished by inferring the most likely paths of infection through a contact network under the assumption of partially available infection data. The problem is formulated as a bi-linear integer program, and heuristic solution methods are developed based on sub-problems which can be solved much more efficiently. The heuristic performance is presented for a range of randomly generated networks and different levels of information. The model results, which include the most likely set of infection spreading contacts, can be used to provide insight into future epidemic outbreak patterns, and aid in the development of intervention strategies.

Origin and fate of A/H1N1 influenza in Scotland during 2009

The spread of influenza has usually been described by a ‘density’ model, where the largest centres of population drive the epidemic within a country. An alternative model emphasizing the role of air travel has recently been developed. We have examined the relative importance of the two in the context of the 2009 H1N1 influenza epidemic in Scotland. We obtained genome sequences of 70 strains representative of the geographical and temporal distribution of H1N1 influenza during the summer and winter phases of the pandemic in 2009. We analysed these strains, together with another 128 from the rest of the UK and 292 globally distributed strains, using maximum-likelihood phylogenetic and Bayesian phylogeographical methods. This revealed strikingly different epidemic patterns within Scotland in the early and late parts of 2009. The summer epidemic in Scotland was characterized by multiple independent introductions from both international and other UK sources, followed by major local expansion of a single clade that probably originated in Birmingham. The winter phase, in contrast, was more diverse genetically, with several clades of similar size in different locations, some of which had no particularly close phylogenetic affinity to strains sampled from either Scotland or England. Overall there was evidence to support both models, with significant links demonstrated between North American sequences and those from England, and between England and East Asia, indicating that major air-travel routes played an important role in the pattern of spread of the pandemic, both within the UK and globally.

Influenza A viruses: new research developments

Influenza A viruses are zoonotic pathogens that continuously circulate and change in several animal hosts, including birds, pigs, horses and humans. The emergence of novel virus strains that are capable of causing human epidemics or pandemics is a serious possibility. Here, we discuss the value of surveillance and characterization of naturally occurring influenza viruses, and review the impact that new developments in the laboratory have had on our understanding of the host tropism and virulence of viruses. We also revise the lessons that have been learnt from the pandemic viruses of the past 100 years.

A timescale for evolution, population expansion, and spatial spread of an emerging clone of methicillin-resistant Staphylococcus aureus

Due to the lack of fossil evidence, the timescales of bacterial evolution are largely unknown. The speed with which genetic change accumulates in populations of pathogenic bacteria, however, is a key parameter that is crucial for understanding the emergence of traits such as increased virulence or antibiotic resistance, together with the forces driving pathogen spread. Methicillin-resistant Staphylococcus aureus (MRSA) is a common cause of hospital-acquired infections. We have investigated an MRSA strain (ST225) that is highly prevalent in hospitals in Central Europe. By using mutation discovery at 269 genetic loci (118,804 basepairs) within an international isolate collection, we ascertained extremely low diversity among European ST225 isolates, indicating that a recent population bottleneck had preceded the expansion of this clone. In contrast, US isolates were more divergent, suggesting they represent the ancestral population. While diversity was low, however, our results demonstrate that the short-term evolutionary rate in this natural population of MRSA resulted in the accumulation of measurable DNA sequence variation within two decades, which we could exploit to reconstruct its recent demographic history and the spatiotemporal dynamics of spread. By applying Bayesian coalescent methods on DNA sequences serially sampled through time, we estimated that ST225 had diverged since approximately 1990 (1987 to 1994), and that expansion of the European clade began in 1995 (1991 to 1999), several years before the new clone was recognized. Demographic analysis based on DNA sequence variation indicated a sharp increase of bacterial population size from 2001 to 2004, which is concordant with the reported prevalence of this strain in several European countries. A detailed ancestry-based reconstruction of the spatiotemporal dispersal dynamics suggested a pattern of frequent transmission of the ST225 clone among hospitals within Central Europe. In addition, comparative genomics indicated complex bacteriophage dynamics.

Because fossils of bacteria do not exist or are morphologically indeterminate, the timescales of bacterial evolution are widely unknown. We have investigated the short-term evolution of a particular strain of methicillin-resistant Staphylococcus aureus (MRSA), a notorious cause of hospital-associated infections. By comparing 118 kilobases of DNA from MRSA isolates that had been collected at different points in time, we demonstrate that this strain has accumulated measurable DNA sequence variation within two decades. Further, we exploited this sequence diversity to estimate the short-term evolutionary rate and to date divergence times without paleontological calibration, and to reconstruct the recent demographic expansion and spatial spread of this MRSA.