Abstract
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 reveal asymmetric transmission patterns between age groups in an epidemic. STE provides a ranking of which age groups may 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 are 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 to 19 year-olds indicates that school-aged children were likely the most important transmitters of infection during the autumn wave of the 2009 pandemic in the USA. 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 rates can explain the observed differences in STE.
Highlights
Age is a key predictor of a person’s rate of both acquiring [1,2,3,4,5,6] and transmitting [1,7,8] influenza
Our work demonstrates that symbolic transfer entropy (STE) could serve as an important tool for the detailed epidemiological analysis of age structure, especially as Electronic medical records (EMRs) data become more prevalent
Time series of weekly influenza-like illness (ILI) incidence are created by extracting claims with a direct mention of influenza, or fever combined with a respiratory symptom, or febrile viral illness (ICD-9 487488 OR [780.6 and (462 or 786.2)] OR 079.99), following Viboud et al (2014) [17]
Summary
Age is a key predictor of a person’s rate of both acquiring [1,2,3,4,5,6] and transmitting [1,7,8] influenza. Influenza incidence estimates from online search platforms and social media websites like Google [19] and Twitter [20] can provide massive amounts of data, but the reliability of these sources has been called into question, and they lack detailed age information [21] Dedicated online platforms such as FluNearYou in the USA and FluSurvey in the UK, which gather reports of ILI symptoms from community volunteers [22,23], hold some promise for supplementing traditional ILI data streams [24,25,26], but represent a relatively small convenience sample of the population. While other data sources exist, EMRs offer a relatively promising and so-far underused source of fine-scale data on influenza incidence in the USA [17,21]
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