Global migration of influenza A viruses in swine

Abstract

The complex and unresolved evolutionary origins of the 2009 H1N1 influenza pandemic exposed major gaps in our knowledge of the global spatial ecology and evolution of influenza A viruses in swine (swIAVs). Here we undertake an expansive phylogenetic analysis of swIAV sequence data and demonstrate that the global live swine trade strongly predicts the spatial dissemination of swIAVs, with Europe and North America acting as sources of viruses in Asian countries. In contrast, China has the world's largest swine population but is not a major exporter of live swine, and is not an important source of swIAVs in neighbouring Asian countries or globally. A meta-population simulation model incorporating trade data predicts that the global ecology of swIAVs is more complex than previously thought, and the United States and China's large swine populations are unlikely to be representative of swIAV diversity in their respective geographic regions, requiring independent surveillance efforts throughout Latin America and Asia.

Fig. 1. Modeled global swine distributions

Digital layers from Gridded Livestock of the World (GLW) (version 2.01), downloaded from the publically available Livestock Geo-Wiki database (http://www.livestock.geo-wiki.org) and manually edited in QGIS v.1.7.0. Swine densities are represented by black shading.

Fig. 2. Inter-continental migration events of swIAVs

Circles represent the country of origin, based on the estimates summarized in the MCC tree, and are shaded accordingly. Lines represent the inferred time period of inter-continental transmission, within a level of uncertainty, inferred from the estimated date of ancestral nodes on the MCC tree. Triangles represent clades resulting from onwards transmission of the introduced viruses, are shaded by the country of destination, and extend as far forward in time as the most recently sampled virus. Numbers of introduction (1–18) correspond to the clade numbers on the phylogenies (Fig. 3 and Supplementary Figs. 1–7). The asterisks indicate that additional HA and NA swIAV sequence data was used to estimate the timing of introduction 18. Countries/regions are abbreviated as follows: CHN = China (including Hong Kong SAR and Taiwan), THA = Thailand, VNM = Vietnam, KOR = South Korea, JPN = Japan, MEX = Mexico, and EUR = Europe.

Fig. 3. MCC trees of the NA lineages in swine

Time-scaled Bayesian MCC trees inferred for the NA segment for the three major swine virus lineages: (a) avian-origin Eurasian N1 swIAV lineage, the (b) classical N1 swIAV lineage, and the (c) multiple human seasonal virus-origin N2 swIAV lineages circulating in swine. Branches of human seasonal H3N2 influenza virus origin are shaded grey in (c), while branches associated with viruses from swine are shaded by country of origin: Argentina = brown; Canada = red; China (including Hong Kong SAR and Taiwan) = yellow; Europe = black; Japan = pink; Mexico = light blue; South Korea = green; Thailand = orange; USA = dark blue; Vietnam = purple. Posterior probabilities > 0.8 are included for key nodes, and international migration events that are supported by high posterior probabilities and long branch lengths are labeled according to Fig. 2.

Fig. 4. Heat-map of swIAV migration between locations

Countries are listed in order of increasing geographical distance from Argentina (ARG). MEX = Mexico, USA = United States, CAN = Canada, EUR = Europe, JPN = Japan, CHN = China (including Hong Kong SAR and Taiwan), KOR = South Korea, THA = Thailand, VNM = Vietnam. The intensity of the color (red = high; white = low) reflects the number of ‘Markov jump’ counts inferred over the totality of phylogenies (all segments, all lineages) from one location to another (asymmetrical). Markov jump counts measure the number of inferred location state transitions, modelled by a continuous-time Markov chain process, that occur along the branches of the phylogeny. For clarity the heat-map has been divided into four sections representing (a) viral migration events within the Americas and between the Americas and Europe; (b) migrations from the Americas/Europe to Asia; (c) migrations from Asia to the Americas/Europe; and (d) migrations between Asian countries.

Fig. 5. The support and contribution of swIAV diffusion predictors among 9 countries

Twelve predictors were considered: geographical distance (km), volume of live swine trade, 1996–2012 (USD), swine population size for the years 1969–2010, the total number of imports of live swine during 1969–2010, the total number of swine exports during 1969–2010, the percent change in swine population size from 1969–2010, and the number of sequences available from a given country for our analysis. ‘O’ refers to the swine population of origin, and ‘d’ refers to the swine population of destination. Support for each predictor is represented by an inclusion probability that is estimated as the posterior expectation for the indicator variable associated with each predictor (E[δ]). The contribution of each predictor is represented by the mean and credible intervals of the GLM coefficients (β) on a log scale conditional on the predictor being included in the model (β|δ=1). See Supplementary Fig. 8 for MP and NS results.

Fig. 6. Maps of simulated spread of influenza viruses via live swine trade flows

Simulated spread of an influenza virus from 5 seed countries (shaded in black) to 146 countries for which live swine trade is available from the United Nations Commodity Trade Statistics Database (available at http://comtrade.un.org) (a–e). Probability of an outbreak in the invaded country is shaded from white (probability of 0) to red (probability of 1). The probability of co-invasion by both a virus seeded in North America (Canada and the United States) and Europe also is shaded from white (probability of 0) to red (probability of 1) (f). Arrows represent the direction of viral dissemination for countries with a probability of an outbreak > 0.25 (see Supplementary Table 5 for a complete list of all outbreak probabilities by country).