Abstract
Individual-based epidemiology models are increasingly used in the study of influenza epidemics. Several studies on influenza dynamics and evaluation of intervention measures have used the same incubation and infectious period distribution parameters based on the natural history of influenza. A sensitivity analysis evaluating the influence of slight changes to these parameters (in addition to the transmissibility) would be useful for future studies and real-time modeling during an influenza pandemic.In this study, we examined individual and joint effects of parameters and ranked parameters based on their influence on the dynamics of simulated epidemics. We also compared the sensitivity of the model across synthetic social networks for Montgomery County in Virginia and New York City (and surrounding metropolitan regions) with demographic and rural-urban differences. In addition, we studied the effects of changing the mean infectious period on age-specific epidemics. The research was performed from a public health standpoint using three relevant measures: time to peak, peak infected proportion and total attack rate. We also used statistical methods in the design and analysis of the experiments.The results showed that: (i) minute changes in the transmissibility and mean infectious period significantly influenced the attack rate; (ii) the mean of the incubation period distribution appeared to be sufficient for determining its effects on the dynamics of epidemics; (iii) the infectious period distribution had the strongest influence on the structure of the epidemic curves; (iv) the sensitivity of the individual-based model was consistent across social networks investigated in this study and (v) age-specific epidemics were sensitive to changes in the mean infectious period irrespective of the susceptibility of the other age groups. These findings suggest that small changes in some of the disease model parameters can significantly influence the uncertainty observed in real-time forecasting and predicting of the characteristics of an epidemic.
Highlights
Sensitivity analysis is the study of the contribution of different parameters to the uncertainty present in the outcome of a system [1,2]
We explore the following aims: (i) evaluate individual and joint effects, and rank parameters based on influence on simulated epidemics and (ii) compare the sensitivity of the model across age groups and social networks with demographic differences
To perform sensitivity analysis on these three quantities, we explore the mapping 1⁄2xi,z(xi), i~1,2, . . . ,I, where xi represents the parameters: transmissibility, infectious period distribution, and the incubation period distribution [2]. z(xi) are the epidemic curves resulting from different parameter combinations
Summary
Sensitivity analysis is the study of the contribution of different parameters to the uncertainty present in the outcome of a system [1,2]. Various scientific fields use sensitivity and uncertainty analysis to: (i) highlight important and remove irrelevant data, (ii) optimize the design of a system and (iii) rank by importance the influence of various parameters on the behavior of a system [3,4]. The scope of a sensitivity analysis procedure can be local or global. Global analysis is used to evaluate the entire parameter space in addition to interactions between parameters to determine all of the system’s critical points [3,6]. Methods for sensitivity analysis can be either statistical or deterministic [2,7]. Complex systems (models) are computationally expensive which tends to limit the scope of a sensitivity analysis
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