title: Early Characterization of the Severity and Transmissibility of Pandemic Influenza Using Clinical Episode Data from Multiple Populations.
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.
authors.affiliation: Predictive Science Inc., San Diego, California, United States of America.
authors.forename: Pete
authors.initials: P
authors.lastname: Riley
authors.affiliation: Predictive Science Inc., San Diego, California, United States of America.
authors.forename: Michal
authors.initials: M
authors.lastname: Ben-Nun
authors.affiliation: Predictive Science Inc., San Diego, California, United States of America.
authors.forename: Jon A
authors.initials: JA
authors.lastname: Linker
authors.affiliation: Armed Forces Health Surveillance Center, Silver Spring, Maryland, United States of America.
authors.forename: Angelia A
authors.initials: AA
authors.lastname: Cost
authors.affiliation: Armed Forces Health Surveillance Center, Silver Spring, Maryland, United States of America.
authors.forename: Jose L
authors.initials: JL
authors.lastname: Sanchez
authors.affiliation: Biomedical Advanced Research and Development Authority (BARDA), Assistant Secretary for Preparedness and Response (ASPR), Department of Health and Human Services (HHS), Washington, D.C., United States of America.
authors.forename: Dylan
authors.initials: D
authors.lastname: George
authors.affiliation: Leidos, McLean, Virginia, United States of America.
authors.forename: David P
authors.initials: DP
authors.lastname: Bacon
authors.affiliation: Predictive Science Inc., San Diego, California, United States of America; MRC Centre for Outbreak Analysis and Modelling, Imperial College London, United Kingdom.
authors.forename: Steven
authors.initials: S
authors.lastname: Riley
mesh_terms: D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology / Q000635:transmission; D008954:Models, Biological; D058873:Pandemics / Q000517:prevention & control / Q000706:statistics & numerical data