Title: Measuring the uncertainties of pandemic influenza

Authors: Jeanne M. Fair; Dennis R. Powell; Rene J. LeClaire; Leslie M. Moore; Michael L. Wilson; Lori R. Dauelsberg; Michael E. Samsa; Sharon M. DeLand; Gary Hirsch; Brian W. Bush

Addresses: Biosecurity and Public Health, Los Alamos National Laboratory, Los Alamos, NM 87545, USA. ' Risk Analysis and Decision Support Systems, Los Alamos National Laboratory, Los Alamos, NM 87545, USA. ' Energy and Infrastructure Analysis, Los Alamos National Laboratory, Los Alamos, NM 87545, USA. ' Statistical Sciences, Los Alamos National Laboratory, Los Alamos, NM 87545, USA. ' Sandia National Laboratories New Mexico, Albuquerque, NM 87185, USA. ' Risk Analysis and Decision Support Systems, Los Alamos National Laboratory, Los Alamos, NM 87545, USA. ' Decision Support and Risk Management, Argonne National Laboratory, Argonne, IL 60439, USA. ' Sandia National Laboratories New Mexico, Albuquerque, NM 87185, USA. ' Creator of Learning Environments, Wayland, MA 01778, USA. ' Energy Analysis, National Renewable Energy Laboratory, Golden, CO 80401, USA

Abstract: It has become critical to assess the potential range of consequences of a pandemic influenza outbreak given the uncertainty about its disease characteristics while investigating risks and mitigation strategies of vaccines, antivirals, and social distancing measures. Here, we use a simulation model and rigorous experimental design with sensitivity analysis that incorporates uncertainty in the pathogen behaviour and epidemic response to show the extreme variation in the consequences of a potential pandemic outbreak in the USA. Using sensitivity analysis we found the most important disease characteristics are the fraction of the transmission that occur prior to symptoms, the reproductive number, and the length of each disease stage. Using data from the historical pandemics and for potential viral evolution, we show that response planning may underestimate the pandemic consequences by a factor of two or more.

Keywords: influenza; flu epidemics; public health epidemiology; pandemics; simulation; sensitivity analysis; uncertainties; uncertainty measurement; risk assessment; mitigation; vaccines; antivirals; social distancing; modelling; experimental design; pathogen behaviour; epidemic response; USA; United States; viral evolution; pandemic consequences.

DOI: 10.1504/IJRAM.2012.047550

International Journal of Risk Assessment and Management, 2012 Vol.16 No.1/2/3, pp.1 - 27

Received: 11 Jun 2011
Accepted: 21 Nov 2011
Published online: 29 Oct 2014 *

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