Predictive Validation of an Influenza Spread Model
Authored by Ayaz Hyder, David L Buckeridge, Brian Leung
Date Published: 2013
DOI: 10.1371/journal.pone.0065459
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Abstract
Background: Modeling plays a critical role in mitigating impacts of
seasonal influenza epidemics. Complex simulation models are currently at
the forefront of evaluating optimal mitigation strategies at multiple
scales and levels of organization. Given their evaluative role, these
models remain limited in their ability to predict and forecast future
epidemics leading some researchers and public-health practitioners to
question their usefulness. The objective of this study is to evaluate
the predictive ability of an existing complex simulation model of
influenza spread.
Methods and Findings: We used extensive data on past epidemics to
demonstrate the process of predictive validation. This involved
generalizing an individual-based model for influenza spread and fitting
it to laboratory-confirmed influenza infection data from a single
observed epidemic (1998-1999). Next, we used the fitted model and
modified two of its parameters based on data on real-world perturbations
(vaccination coverage by age group and strain type). Simulating
epidemics under these changes allowed us to estimate the deviation/error
between the expected epidemic curve under perturbation and observed
epidemics taking place from 1999 to 2006. Our model was able to forecast
absolute intensity and epidemic peak week several weeks earlier with
reasonable reliability and depended on the method of forecasting-static
or dynamic.
Conclusions: Good predictive ability of influenza epidemics is critical
for implementing mitigation strategies in an effective and timely
manner. Through the process of predictive validation applied to a
current complex simulation model of influenza spread, we provided users
of the model (e.g. public-health officials and policy-makers) with
quantitative metrics and practical recommendations on mitigating impacts
of seasonal influenza epidemics. This methodology may be applied to
other models of communicable infectious diseases to test and potentially
improve their predictive ability.
Tags
Dynamics
predictability
Epidemic
Surveillance
Pandemic influenza
Virus
Infectious-disease
Immunization practices acip
Advisory-committee
Recommendations