2008
DOI: 10.1590/s0102-311x2008000400016
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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… Show more

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Cited by 14 publications
(20 citation statements)
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“…From equation (4) the basic reproduction number (R 0 ) obtained was 2.45. The data suggested that there was a good match among values obtained directly from the growth rate estimated with BEAST, the one found using the force of infection and, the epidemiological estimates of 3.36 previously estimated [37] from the cases' doubling time.…”
Section: Resultssupporting
confidence: 63%
“…From equation (4) the basic reproduction number (R 0 ) obtained was 2.45. The data suggested that there was a good match among values obtained directly from the growth rate estimated with BEAST, the one found using the force of infection and, the epidemiological estimates of 3.36 previously estimated [37] from the cases' doubling time.…”
Section: Resultssupporting
confidence: 63%
“…However, the likelihood that local parasite transmission dynamics will differ from one community to another means that reliably estimating the values of these thresholds will require the efficient fitting of models to site-specific infection data. Such data-driven model-based estimation is also necessitated by the often large number of uncertainties associated with the model structure, parameterization (especially when such models are characterized by a relatively large number of parameters, as is typical with dynamic parasite transmission models) and prediction [12-16]. For these reasons, the widespread use of process-based models for guiding parasite control based on theoretical predictions has so far been limited.…”
Section: Introductionmentioning
confidence: 99%
“…Thus, in recent years an increasing focus in work relating to dynamic process-based models for practical applications has been on the development and application of fitting procedures that can allow the use of information from available data to refine and update initially assigned model parameter values [12-15,17,18]. …”
Section: Introductionmentioning
confidence: 99%
“…Such methods, to be effective, must strive to be as comprehensible as possible in the treatment of all identifiable sources of uncertainty related to a given mathematical representation of a biological system [5]. In practice, however, many uncertainty analysis methods fall short of this ideal.…”
Section: Introductionmentioning
confidence: 99%