Synchrony, waves, and spatial hierarchies in the spread of influenza.
Reviewed: 10/07/11
There are 3 common subtypes of the influenza virus that have been affecting national health in the USA for 40 years or more; A/H3N2, A/H1N1, and B. The authors of this paper used mortality data for Pheumonia and Influenza over this time range to analyze the inter-state travel and timing of flue epidemics and pandemics. Ranking states by hte size of their populations and noting more intense pandemic years, they found that their is more synchrony in the epidemic as the size of the state population increases and the degree of intensity for the epidemic increases. They also found more synchrony between states that were in total a shorter distance from one another. This synchrony was measured in the correlation of peek epidemic weeks, a metric of timing, and through correlations of death measurements, a metric of intensity.
Mortality was used as a metric rather than cases reported because of the high probability when using number of reported cases that some person was either misdiagnosed with the disease or that the case was never reported at all due to low degree of symptoms. Using mortality does tend to make the predictions for the more severe subtype, A/H3N2, more accurate, but the researchers attempted to correct for sampling error in their measurements, and their results still showed that more intense mortality impacts were associated with the severer subtype and that seasons dominated by this subtype over the past 40yrs had more synchrony over all the states (as measured by standard deviation from the national average) in the timing of the disease.
Some of the most interesting results to come from this study however, are the results linking adult workflows to national and interstate trends in disease spread. Several types of movement were modeled, including air travel, rare long-distance events, and simple Euclidean movements, however when all of these movement types were adjusted for one another, workflow surfaces as the only significant correlation for disease phase and intensity. This is a pretty interesting result as it leads to the notion that adults and workflow dynamics are more important for regional spread of disease, not schoolchildren. Schoolchildren may be responsible for more local aspects of spread, but they do not play as large a role in the national dynamics. Incorporating age structure into other disease systems, including animal, plant, and marine could have similarly fascinating results, and should be investigated in the future. As long as their is some sort of inherent age structure in movements of natural populations, this dynamic should at least be considered as a mechanism for movement, whether or not it turns out to be significant to disease spread models.
This study and the variables used to mark disease trends were generally coarse; they were unable to get down to intra-state disease flows. However, the study was able to efficiently use simple predictors to model synchrony and timing at the national scale. This included results showing how initial disease foci in large states with a high degree of international capitalism, like California, tend to spread disease faster and lead to a higher degree of synchrony nationwide when compared to smaller less connected states such as Wyoming. It can be noted however, that their is a certain capping out effect for large states in the synchrony of events, indicating that higher transmission rates that are an inherent property of the virus itself may be more important than the population size of the initially infected disease focus.
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