Practical unidentifiability of a simple vector-borne disease model: Implications for parameter estimation and intervention assessment

Abstract

Mathematical modeling has an extensive history in vector-borne disease epidemiology, and is increasingly used for prediction, intervention design, and understanding mechanisms. Many studies rely on parameter estimation to link models and data, and to tailor predictions and counterfactuals to specific settings. However, few studies have formally evaluated whether vector-borne disease models can properly estimate the parameters of interest given the constraints of a particular dataset. Identifiability analysis allows us to examine whether model parameters can be estimated uniquely—a lack of consideration of such issues can result in misleading or incorrect parameter estimates and model predictions. Here, we evaluate both structural (theoretical) and practical identifiability of a commonly used compartmental model of mosquito-borne disease, using the 2010 dengue epidemic in Taiwan as a case study. We show that while the model is structurally identifiable, it is practically unidentifiable under a range of human and mosquito time series measurement scenarios. In particular, the transmission parameters form a practically identifiable combination and thus cannot be estimated separately, potentially leading to incorrect predictions of the effects of interventions. However, in spite of the unidentifiability of the individual parameters, the basic reproduction number was successfully estimated across the unidentifiable parameter ranges. These identifiability issues can be resolved by directly measuring several additional human and mosquito life-cycle parameters both experimentally and in the field. While we only consider the simplest case for the model, we show that a commonly used model of vector-borne disease is unidentifiable from human and mosquito incidence data, making it difficult or impossible to estimate parameters or assess intervention strategies. This work illustrates the importance of examining identifiability when linking models with data to make predictions and inferences, and particularly highlights the importance of combining laboratory, field, and case data if we are to successfully estimate epidemiological and ecological parameters using models.

Mathematical Modeling and Characterization of the Spread of Chikungunya in Colombia

“…However, it is important to remark that we are more interested in their product since the epidemic threshold parameter R 0 is proportional to this product, as we have mentioned before. These results agree with previous results regarding identifiability for epidemic models with vectors [67].…”

Assessing parameter identifiability in compartmental dynamic models using a computational approach: application to infectious disease transmission models

“…Some methods, such as Taylor series methods [15, 16] and differential algebra-based methods [17, 18], require more mathematical analyses, which becomes increasingly complicated as model complexity increases. Other methods rely on constructing the profile likelihood for each of the estimated parameters to assess local structural identifiability [11, 14, 31, 32]. In this method, one of the parameters (θ i ) is fixed across a range of realistic values, and the other parameters are refit to the data using the likelihood function of θ i .…”

“…For instance, model parameters can be estimated by connecting models with observed data through various methods, including least-squares fitting [9], maximum likelihood estimation [10, 11], and approximate Bayesian computation [12, 13]. An important, yet often overlooked step in estimating parameters is examining parameter identifiability – whether a set of parameters can be uniquely estimated from a given model and data set [14]. Lack of identifiability, or non-identifiability, occurs when multiple sets of parameter values yield a very similar model fit to the data.…”

“…Structurally identifiable parameters may still be non-identifiable in practice due to a lack of information in available data. The so-called “practical identifiability” considers real-world data issues: amount of noise in the data and sampling frequency (e.g., data collection process) [14].…”

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