 Early Characterization of the Severity and Transmissibility of Pandemic Influenza Using Clinical Episode Data from Multiple PopulationsPete Riley, Michal Ben-Nun, Jon A Linker, Angelia A Cost, Jose L Sanchez, Dylan George, David P Bacon and Steven Riley PLOS Computational Biology, 2015, vol. 11, issue 9, 1-15 Abstract: The potential rapid availability of large-scale clinical episode data during the next influenza pandemic suggests an opportunity for increasing the speed with which novel respiratory pathogens can be characterized. Key intervention decisions will be determined by both the transmissibility of the novel strain (measured by the basic reproductive number R0) and its individual-level severity. The 2009 pandemic illustrated that estimating individual-level severity, as described by the proportion pC of infections that result in clinical cases, can remain uncertain for a prolonged period of time. Here, we use 50 distinct US military populations during 2009 as a retrospective cohort to test the hypothesis that real-time encounter data combined with disease dynamic models can be used to bridge this uncertainty gap. Effectively, we estimated the total number of infections in multiple early-affected communities using the model and divided that number by the known number of clinical cases. Joint estimates of severity and transmissibility clustered within a relatively small region of parameter space, with 40 of the 50 populations bounded by: pC, 0.0133–0.150 and R0, 1.09–2.16. These fits were obtained despite widely varying incidence profiles: some with spring waves, some with fall waves and some with both. To illustrate the benefit of specific pairing of rapidly available data and infectious disease models, we simulated a future moderate pandemic strain with pC approximately ×10 that of 2009; the results demonstrating that even before the peak had passed in the first affected population, R0 and pC could be well estimated. This study provides a clear reference in this two-dimensional space against which future novel respiratory pathogens can be rapidly assessed and compared with previous pandemics.Author Summary: The ever-increasing availability of timely, large-scale clinical episode data can, in principle, dramatically shorten the time required to characterize the transmission and severity of novel respiratory pathogens, which, in turn, can be used to inform key intervention decisions. We investigated 50 distinct military populations during the 2009 influenza pandemic to test the hypothesis that real-time encounter data combined with disease dynamic models can be used to jointly determine the transmissibility of the novel strain (described by the basic reproductive number R0) and its individual-level severity (described by the proportion pC of infections that result in clinical cases). To illustrate the use of such a procedure, we simulated a future moderate pandemic strain with pC approximately ×10 that of 2009, which demonstrated that even before the peak had passed in the first affected population, R0 and pC could be well estimated. These results provide a clear reference in this two-dimensional space against which future novel respiratory pathogens can be rapidly compared, establishing a firm baseline for describing the relative severity of future emerging respiratory pathogens. Date: 2015 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1004392 DOI: 10.1371/journal.pcbi.1004392 Access Statistics for this article More articles in PLOS Computational Biology from Public Library of Science |
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