Early characterization of the severity and transmissibility of pandemic influenza using clinical episode data from multiple populations
Author(s)
Type
Journal Article
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.
Date Issued
2015-09-24
Date Acceptance
2015-06-09
Citation
PLOS Computational Biology, 2015, 11 (9)
ISSN
1553-734X
Publisher
Public Library of Science
Journal / Book Title
PLOS Computational Biology
Volume
11
Issue
9
Copyright Statement
This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication
License URL
Sponsor
Medical Research Council (MRC)
Grant Number
MR/K010174/1B
Subjects
Science & Technology
Life Sciences & Biomedicine
Biochemical Research Methods
Mathematical & Computational Biology
Biochemistry & Molecular Biology
NEW-YORK-CITY
SYNDROMIC SURVEILLANCE
INFECTION
DISEASES
VIRUS
RISK
Computational Biology
Databases, Factual
Humans
Influenza, Human
Models, Biological
Pandemics
Bioinformatics
06 Biological Sciences
08 Information And Computing Sciences
01 Mathematical Sciences
Publication Status
Published
Article Number
e1004392