Recombination is one of the determinants of genetic diversity in the foot-and-mouth disease virus (FMDV). FMDV sequences have a mosaic structure caused by extensive intra- and inter-serotype recombination, with the exception of the capsid-encoding region. While these genome-wide patterns of broad-scale recombination are well studied, not much is known about the patterns of recombination that may exist within infected hosts. In addition, detection of recombination among viruses evolving at the within-host level is challenging due to the similarity of the sequences and the limitations in differentiating recombination from point mutations. Here, we present the first analysis of recombination events between closely related FMDV sequences occurring within buffalo hosts. The detection of these events was made possible by the occurrence of co-infection of two viral swarms with about 1% nucleotide divergence. We found more than 15 recombination events, unequally distributed across eight samples from different animals. The distribution of these events along the FMDV genome was neither uniform nor related to the phylogenetic distribution of recombination breakpoints, suggesting a mismatch between within-host evolutionary pressures and long-term selection for infectivity and transmissibility.

In the early stages of an outbreak, the term ‘pandemic’ can be used to communicate about infectious disease risk, particularly by those who wish to encourage a large-scale public health response. However, the term lacks a widely accepted quantitative definition. We show that, under alternate quantitative definitions of ‘pandemic’, an epidemiological metapopulation model produces different estimates of the probability of a pandemic. Critically, we show that using different definitions alters the projected effects of key parameters—such as inter-regional travel rates, degree of pre-existing immunity, and heterogeneity in transmission rates between regions—on the risk of a pandemic. Our analysis provides a foundation for understanding the scientific importance of precise language when discussing pandemic risk, illustrating how alternative definitions affect the conclusions of modelling studies. This serves to highlight that those working on pandemic preparedness must remain alert to the variability in the use of the term ‘pandemic’, and provide specific quantitative definitions when undertaking one of the types of analysis that we show to be sensitive to the pandemic definition.

When vaccinating a large population in response to an invading pathogen, it is often necessary to prioritize some individuals to be vaccinated first. One way to do this is to choose individuals to vaccinate based on their location. Methods for this prioritization include strategies that target those regions most at risk of importing the pathogen, and strategies that target regions with high centrality on the travel network. We use a simple infectious disease epidemic model to compare a risk-targeting strategy to two different centrality-targeting strategies based on betweenness centrality and random walk percolation centrality, respectively. We find that the relative effectiveness of these strategies in reducing the total number of infections varies with the basic reproduction number of the pathogen, travel rates, structure of the travel network and vaccine availability. We conclude that when a pathogen has high spreading capacity, or when vaccine availability is limited, centrality-targeting strategies should be considered as an alternative to the more commonly used risk-targeting strategies.

When vaccinating a large population in response to an invading pathogen, it is often necessary to prioritise some individuals to be vaccinated first. One way to do this is to choose individuals to vaccinate based on their location. Methods for this prioritisation include strategies which target those regions most at risk of importing the pathogen, and strategies which target regions with high centrality on the travel network. We use a simple infectious disease epidemic model to compare a risk-targeting strategy to two different centrality-targeting strategies based on betweenness centrality and random walk percolation centrality, respectively. We find that the relative effectiveness of these strategies in reducing the total number of infections varies with the basic reproduction number of the pathogen, travel rates, structure of the travel network, and vaccine availability. We conclude that, when a pathogen has high spreading capacity, or when vaccine availability is limited, centrality-targeting strategies should be considered as an alternative to the more commonly used risk-targeting strategies.

Modern phylogeography aims at reconstructing the geographic diffusion of organisms based on their genomic sequences and spatial information. Phylogeographic approaches usually ignore the possibility of recombination, which decouples the evolutionary and geographic histories of different parts of the genome. Genomic regions of recombining or reassorting pathogens often originate and evolve at different times and locations, which characterised their unique spatial histories. Measuring the extent of these differences requires new methods to compare geographic information on phylogenetic trees reconstructed from different parts of the genome. Here we develop for the first time a set of measures of phylogeographic incompatibility aimed at detecting differences between geographical histories in terms of distances between phylogeographies. We study the effect of varying demography and recombination on phylogeographic incompatibilities using coalescent simulations. We further apply these measures to the evolutionary history of human and livestock pathogens, either reassorting or recombining, such as the Victoria and Yamagata lineages of influenza B and the O/Ind-2001 foot-and-mouth disease virus strain. Our results reveal diverse geographical paths of diffusion that characterise the origins and evolutionary histories of different viral genes and genomic segments. phylogeography, recombination, viral evolution

In the early stages of an outbreak, the term ‘pandemic’ can be used to communicate about infectious disease risk, particularly by those who wish to encourage a large-scale public health response. However, the term lacks a widely accepted quantitative definition. We show that, under alternative quantitative definitions of ‘pandemic’, an epidemiological metapopulation model produces different estimates of the probability of a pandemic. Critically, we show that using different definitions alters the projected effects on the pandemic risk of key parameters such as inter-regional travel rates, degree of pre-existing immunity, and heterogeneity of transmission rates between regions. Our analysis provides a foundation for understanding the scientific importance of precise language when discussing pandemic risk, illustrating how alternate definitions affect the conclusions of modelling studies. This serves to highlight that those working on pandemic preparedness must remain alert to the variability in the use of the term ‘pandemic’, and provide specific quantitative definitions when undertaking one of the types of analysis that we show to be sensitive to the pandemic definition.