Examination of models for cholera: insights into model comparison methods

Abstract

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

Methods for prophylactic management of West Nile virus using a stage‐structured avian host‐vector model with vaccination, larvicide, and adulticide

“…In particular, we found that large population sizes were not effective in achieving successful evolution to an OC vector. Additionally, we found that neither stagnation nor continued improvement between consecutive generations was well approximated by considering the magnitudes of the average functional values across the population, although this latter approach was successful for determining a stopping criteria in prior work focused on the use of GA for parameter estimation (Akman & Schaefer, ; Akman et al., ). Finally, while for the sake of efficiency, some GA explorations have begun with large populations and adapted population sizes to decrease as the optimal value is approached (Akman & Schaefer, ; Hallam, Akman, & Akman, ), some of our numerical runs suggested advantages to increasing population sizes when a local minimum has been reached, albeit generally for incremental improvement in solutions.…”

“…The choice of GA structure and GA parameters that led to effective GA convergence in this setting of control vectors with our rigid binary structure requirements differed significantly from recent explorations of the use of GA to discover optimal parameters for a deterministic model (Akman & Schaefer, ; Akman, Corby, & Schaefer, ). In particular, we found that large population sizes were not effective in achieving successful evolution to an OC vector.…”

Parameter Estimation in Ordinary Differential Equations Modeling via Particle Swarm Optimization

“…The system takes parameters and initial values (some known, some unknown) as input and outputs functions, which can then be compared to the data using some goodness of fit (GOF) metric (e.g., mean squared error). Competing models can be evaluated using frameworks introduced in [3]. If we take the vector of unknown inputs to be a position within search space, then we can apply PSO in order to minimize the model's error.…”

“…Therefore, we will focus on the lesser-used evolutionary algorithms in comparison to Particle Swarm Optimization (PSO). In particular, Genetic Algorithms (GA) have been frequently used to optimize the parameters of ordinary differential equations (ODE) models [2][3][4][5][6][7]. PSO has thus far been underutilized in this area.…”

Direct transmission via households informs models of disease and intervention dynamics in cholera

“…Given how recently epidemiologists have engaged the specifics of direct household transmission, we have yet to fully appreciate the extent to which direct household transmission may alter the dynamics of cholera outbreaks. And while there are many important models of indirect cholera transmission via reservoirs [10][11][12][13][14][15][16][17][18][19][20][21], and some that examine the theoretical implications of direct household transmission [22,23], we remain in the dark with regards to how recent insights on direct household transmission may specifically influence our picture of cholera disease dynamics and interventions.…”