Article

Complete treatment of uncertainties in a model for dengue R0 estimation.

Programa de Computação Científica, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.
Cadernos de Saúde Pública (impact factor: 0.89). 05/2008; 24(4):853-61. pp.853-61
Source: PubMed

ABSTRACT In real epidemic processes, the basic reproduction number R0 is the combined outcome of multiple probabilistic events. Nevertheless, it is frequently modeled as a deterministic function of epidemiological variables. This paper discusses the importance of adequate treatment of uncertainties in such models. This is done by comparing two methods of uncertainty analysis: Monte Carlo uncertainty analysis (MCUA) and the Bayesian melding (BM) method. These methods are applied to a model for the determination of R0 of dengue fever based on entomological parameters. The BM was shown to provide a complete treatment of the uncertainties associated with model parameters. In contrast to MCUA, the incorporation of uncertainties led to realistic posterior distributions for parameter and variables. The incorporation, by the BM, of all the available information, from observational data to expert opinions, allows for the constructive use of uncertainties generating informative posterior distributions for all of the model's components that are coherent as a set.

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Keywords

adequate treatment
 
available information
 
basic reproduction number R0
 
Bayesian melding
 
combined outcome
 
complete treatment
 
dengue fever
 
deterministic function
 
entomological parameters
 
expert opinions
 
informative posterior distributions
 
model parameters
 
model's components
 
Monte Carlo uncertainty analysis
 
multiple probabilistic events
 
paper discusses
 
real epidemic processes
 
realistic posterior distributions
 
uncertainties
 
uncertainty analysis