Virus Commuters

USA Travel In increasingly mobile modern societies, long-distance transmission can rapidly spread pathogens. A new study suggests that both airline and commuter road travel influence flu virus distribution in the continental USA.

When viruses invade naïve host populations and are propagated predominantly by local transmission, we expect to observe wave-like spread across geographic space. As viruses evolve rapidly, because of their high mutation rate, these wave-like patterns of local transmission (from person-to-person and village-to-village) should generate wave-like patterns of genetic variation where the geographic distance between locations and the genetic distance between variants is positively correlated. In today’s world, however, transmission patterns are more complicated, as human pathogens also travel by road, rail, and air. To examine how genetic variation correlates with spatial distribution in a highly mobile society, scientists explored whether measures of distance defined by airline and commuter transportation networks can explain the population genetic structure of seasonal influenza viruses within the USA.

Analyzing the travel networks, the researchers calculated that during the flu season, approximately 1.6 million people travel along the interstate aviation network per day, and that most US states are well connected to most other states. More people (over 3.8 million) travel daily across the interstate ground travel commuter network, but the vast majority of connections here occur between neighboring states, and more so in the Eastern than in the Western USA.

They conclude that while recent findings have shown that the aviation network plays an important role in the world-wide transmission of seasonal influenza, their results suggest that when population structure is detectable, it is the commuter network that is of greater importance at more regional scales. Discussing the public health implications, the researchers say that the detection of network structure implies that patterns of epidemic spread are, to some extent, predictable and point out that the absence of predictability is problematic for the design of containment strategies, since it suggests that the annual seasonal spread of influenza within countries is highly variable and depends heavily on chance events.

The Role of Human Transportation Networks in Mediating the Genetic Structure of Seasonal Influenza in the United States. (2015) PLoS Pathog 11(6): e1004898. doi: 10.1371/journal.ppat.1004898
Recent studies have demonstrated the importance of accounting for human mobility net- works when modeling epidemics in order to accurately predict spatial dynamics. However, little is known about the impact these movement networks have on the genetic structure of pathogen populations and whether these effects are scale-dependent. We investigated how human movement along the aviation and commuter networks contributed to intra-sea- sonal genetic structure of influenza A epidemics in the continental United States using spa- tially-referenced hemagglutinin nucleotide sequences collected from 2003–2013 for both the H3N2 and H1N1 subtypes. Comparative analysis of these transportation networks re- vealed that the commuter network is highly spatially-organized and more heavily traveled than the aviation network, which instead is characterized by high connectivity between all state pairs. We found that genetic distance between sequences often correlated with dis- tance based on interstate commuter network connectivity for the H1N1 subtype, and that this correlation was not as prevalent when geographic distance or aviation network con- nectivity distance was assessed against genetic distance. However, these patterns were not as apparent for the H3N2 subtype at the scale of the continental United States. Finally, although sequences were spatially referenced at the level of the US state of collection, a community analysis based on county to county commuter connections revealed that com- muting communities did not consistently align with state geographic boundaries, emphasiz- ing the need for the greater availability of more specific sequence location data. Our results highlight the importance of utilizing host movement data in characterizing the underlying ge- netic structure of pathogen populations and demonstrate a need for a greater understanding of the differential effects of host movement networks on pathogen transmission at various spatial scales.

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