DOI: 10.1101/19005710Sep 19, 2019Paper

Symbolic transfer entropy reveals the age structure of pandemic influenza transmission from high-volume influenza-like illness data

MedRxiv : the Preprint Server for Health Sciences
Stephen M KisslerJulia R Gog


Existing methods to infer the relative roles of age groups in epidemic transmission can normally only accommodate a few age classes, and/or require data that are highly specific for the disease being studied. Here, symbolic transfer entropy (STE), a measure developed to identify asymmetric transfer of information between stochastic processes, is presented as a way to determine which age groups drive an epidemic. STE provides a ranking of which age groups dominate transmission, rather than a reconstruction of the explicit between-age-group transmission matrix. Using simulations, we establish that STE can identify which age groups dominate transmission, even when there are differences in reporting rates between age groups and even if the data is noisy. Then, the pairwise STE is calculated between time series of influenza-like illness for 12 age groups in 884 US cities during the autumn of 2009. Elevated STE from 5-19 year-olds indicates that school-aged children were the most important transmitters of infection during the autumn wave of the 2009 pandemic in the US. The results may be partially confounded by higher rates of physician-seeking behaviour in children compared to adults, but it is unlikely that differences in reporting...Continue Reading

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