2015
DOI: 10.1080/17513758.2015.1039608
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An evolutionary computing approach for parameter estimation investigation of a model for cholera

Abstract: We consider the problem of using time-series data to inform a corresponding deterministic model and introduce the concept of genetic algorithms (GA) as a tool for parameter estimation, providing instructions for an implementation of the method that does not require access to special toolboxes or software. We give as an example a model for cholera, a disease for which there is much mechanistic uncertainty in the literature. We use GA to find parameter sets using available time-series data from the introduction … Show more

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Cited by 19 publications
(24 citation statements)
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“…In addition to standard mass-action transmission [8,14], many models use nonlinear transmission functions for waterborne transmission [4,21] to reflect the dose response for cholera in the water. Models may also include an asymptomatic transmission pathway, a hyperinfectious state for the bacteria immediately after shedding, and other ecological and environmental factors in the environmental reservoir, such as effects of vibriophages, plankton, weather, and climate [46,8,10,18,2226]. …”
Section: Introductionmentioning
confidence: 99%
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“…In addition to standard mass-action transmission [8,14], many models use nonlinear transmission functions for waterborne transmission [4,21] to reflect the dose response for cholera in the water. Models may also include an asymptomatic transmission pathway, a hyperinfectious state for the bacteria immediately after shedding, and other ecological and environmental factors in the environmental reservoir, such as effects of vibriophages, plankton, weather, and climate [46,8,10,18,2226]. …”
Section: Introductionmentioning
confidence: 99%
“…Both of these concepts are important to evaluate in the model-building process when model results are used as the basis for decision making. While parameter uncertainty has been highlighted for cholera outbreaks both in general [26,41] and in Haiti-specific contexts [25,41], model distinguishability is a higher order examination of uncertainty at the model structure level. In previous work [20], we examined identifiability of a very simple but commonly used environmental transmission model of cholera, examining structural (theoretical) and practical identifiability in the context of distinguishing between direct (non-environmentally driven) and indirect (environmentally-driven) cholera transmission.…”
Section: Introductionmentioning
confidence: 99%
“…For each of the 5 × 11 = 55 model-data-set combinations, we found the lowest RSS/n value (where, again, n is the number of data observations for the given data-set). However, our work in exploring methods for parameter selection have convinced us that it is unlikely that differing parameter estimation approaches, or even repeated approaches, are likely to retrieve the same parameter values or goodness of fit (this was explored further in a previous article of ours (Akman & Schaefer, 2015)). Thus, we observe that if we might get 'lucky' in finding a strongly performing data-set for model A, but 'unlucky' for model B.…”
Section: Methods and Resultsmentioning
confidence: 99%
“…There are many approaches to parameter selection. In this work, we first found twenty best-fit parameter sets for each of the 55 data-set?-model combination using genetic algorithms (GA) (Akman & Schaefer, 2015). As explained in the reference, each single run is the result of the evolution over many generations of 5000 randomly chosen parameter sets from the biologically feasible parameter space.…”
Section: Methods and Resultsmentioning
confidence: 99%
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