An evolutionary computing approach for parameter estimation investigation of a model for cholera

Akman, Olcay; Schaefer, Elsa

doi:10.1080/17513758.2015.1039608

Cited by 19 publications

(24 citation statements)

References 35 publications

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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 [4–6,8,10,18,22–26]. …”

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%

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Model distinguishability and inference robustness in mechanisms of cholera transmission and loss of immunity

Lee

Kelly

Ochocki

et al. 2017

Journal of Theoretical Biology

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Mathematical models of cholera and waterborne disease vary widely in their structures, in terms of transmission pathways, loss of immunity, and a range of other features. These differences can affect model dynamics, with different models potentially yielding different predictions and parameter estimates from the same data. Given the increasing use of mathematical models to inform public health decision-making, it is important to assess model distinguishability (whether models can be distinguished based on fit to data) and inference robustness (whether inferences from the model are robust to realistic variations in model structure). In this paper, we examined the effects of uncertainty in model structure in the context of epidemic cholera, testing a range of models with differences in transmission and loss of immunity structure, based on known features of cholera epidemiology. We fit these models to simulated epidemic and long-term data, as well as data from the 2006 Angola epidemic. We evaluated model distinguishability based on fit to data, and whether the parameter values, model behavior, and forecasting ability can accurately be inferred from incidence data. In general, all models were able to successfully fit to all data sets, both real and simulated, regardless of whether the model generating the simulated data matched the fitted model. However, in the long-term data, the best model fits were achieved when the loss of immunity structures matched those of the model that simulated the data. Two parameters, one representing person-to-person transmission and the other representing the reporting rate, were accurately estimated across all models, while the remaining parameters showed broad variation across the different models and data sets. The basic reproduction number (ℛ0) was often poorly estimated even using the correct model, due to practical unidentifiability issues in the waterborne transmission pathway which were consistent across all models. Forecasting efforts using noisy data were not successful early in the outbreaks, but once the epidemic peak had been achieved, most models were able to capture the downward incidence trajectory with similar accuracy. Forecasting from noise-free data was generally successful for all outbreak stages using any model. Our results suggest that we are unlikely to be able to infer mechanistic details from epidemic case data alone, underscoring the need for broader data collection, such as immunity/serology status, pathogen dose response curves, and environmental pathogen data. Nonetheless, with sufficient data, conclusions from forecasting and some parameter estimates were robust to variations in the model structure, and comparative modeling can help to determine how realistic variations in model structure may affect the conclusions drawn from models and data.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model distinguishability and inference robustness in mechanisms of cholera transmission and loss of immunity

Lee

Kelly

Ochocki

et al. 2017

Journal of Theoretical Biology

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“…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%

“…We would claim that this model has little mechanistic merit in its description of cholera transmission, and we include it as a check of our model comparison technique. In Section 5, we will show that in some cases, the fit of this quite possibly inappropriate model to the data we have Parameters chosen using Genetic Algorithms approach (Akman & Schaefer, 2015) Figure 1. The possibly too-simple SIR model is included in our analysis as a control and is described in Section 3.2.1.…”

Section: Model 1: Sirmentioning

confidence: 99%

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Examination of models for cholera: insights into model comparison methods

Akman

Corby

Schaefer

2016

Letters in Biomathematics

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This article provides an overview of the Akaike and Bayesian Information Criteria as applied to the setting of deterministic modelling, with the perspective that this may be a useful tool for biomathematics researchers whose primary interests lie in the analysis of compartmental models. We additionally examine a wide range mechanistic and parameter assumptions in the cholera literature through the unifying lens of model selection criteria. Five models for cholera are considered using multiple model selection formulations, and implications for cholera modelling and for model selection criteria are discussed. ARTICLE HISTORY

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