Complex behaviour in a dengue model with a seasonally varying vector population

McLennan-Smith, Timothy A.; Mercer, G. N.

doi:10.1016/j.mbs.2013.11.003

Cited by 28 publications

(14 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2. We chose this model mainly because of its simplicity as well as its potential to be used for intervention design and epidemic prediction accounting for environment factors (Yang et al, 2009a, 2009b; Pinho et al, 2010; Poletti et al, 2011; Yakob and Clements, 2013; Erickson et al, 2010; McLennan-Smith and Mercer, 2014; Sardar et al, 2016). The model includes the disease transmission process between the human ( h ) and mosquito ( m ) populations.…”

Section: Methodsmentioning

confidence: 99%

“…Building on these results, we examine the identifiability of a simple compartmental model based on the Ross-Macdonald framework with various scenarios of measurement assumption (Newton and Reiter, 1992). This model is commonly used for both theoretical (Coutinho et al, 2005; Coutinhoa et al, 2006; Garba et al, 2008; Dumont et al, 2008) and applied epidemiological studies in a wide range of settings (Burattini et al, 2008; Yang and Ferreira, 2008; Chiroleu and Dumont, 2010; Pinho et al, 2010; Poletti et al, 2011; Sardar et al, 2016), and is often used in an expanded form where temperature or environmental dependence is explicitly included (Bartley et al, 2002; Yang et al, 2009a; Erickson et al, 2010; McLennan-Smith and Mercer, 2014). We consider the structural and practical identifiability of this model in the baseline case without explicit environmental drivers, using dengue incidence data in Kaohsiung, Taiwan as a case study.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Kao

Eisenberg

2018

Epidemics

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Kao

Eisenberg

2018

Epidemics

View full text Add to dashboard Cite

show abstract

“…43 Building on these results, we examine the identifiability of a simple compartmental model based 44 on the Ross-Macdonald framework with various scenarios of measurement assumption [51]. This 45 model is commonly used for both theoretical [52][53][54][55] and applied epidemiological studies in a wide 46 range of settings [56][57][58][59][60][61], and is often used in an expanded form where temperature or environmental 47 dependence is explicitly included [62][63][64][65]. We consider the structural and practical identifiability of 48 this model in the baseline case without explicit environmental drivers, using dengue incidence data in 49 Kaohsiung, Taiwan as a case study.…”

Section: Author Summarymentioning

confidence: 99%

“…64 The model and analyses were implemented in Python 2.7.10, with code available at https://github. 65 com/epimath/dengue_model.…”

mentioning

confidence: 99%

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

Kao

Eisenberg

2017

Preprint

View full text Add to dashboard Cite

Background. Mathematical modeling has an extensive history in vector-borne disease epidemiology, and is increasingly used for prediction, intervention design, and understanding mechanisms. Many of these 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.Methodology/Principle Findings. Identifiability methods allow 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 mosquitoborne disease, using 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, which can lead to incorrect predictions of the effects of interventions. However, in spite of 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.Conclusions. While we only consider the simplest case for the model, without explicit environmental drivers, we show that a commonly used model of vector-borne disease is unidentifiable from human 1 Introduction 1 Arboviral diseases are a global threat of increasing importance. Particularly for diseases propagated 2 by Aedes mosquitoes, such as dengue, chikungunya, and Zika [1, 2], incidences have been increasing 3 at alarming rates worldwide, with over approximately 3.9 billion individuals believed to be at risk 4 for dengue infection alone [3][4][5]. These increases are primarily attributed to the habitat expansion 5 of Aedes spp. caused by changes in anthropogenic land use and human movement [6-11]. Given 6 the ecology and life-cycle of Aedes mosquitoes, the transmission dynamics of these mosquito-borne 7 2 diseases are heavily driven by complicated interactions between environmental factors [12-17]. These 8 factors, combined with human behavior and transmission dynamics, make vector-borne diseases 9highly complex-presenting both challenges and opportunities for mathematical modeling [18][19][20]. 10 Modeling has increasingly been viewed as a useful tool to quantify these complex transmission 11 systems by integrating various data sources and specifying nonlinear mechanistic relationships and 12 feedbacks. Numerous recent efforts at combating mosquito-borne diseases have directly incorporate...

show abstract

“…This method continued through increasing the control parameter and the new resulting discrete points were plotted in the bifurcation diagrams versus the altered control parameters. For a full discussion on the bifurcation diagram, the Lyapunov exponent spectrum and their utilization in order to study the bubble dynamics, one can refer to [49][50][51][52][53].…”

Section: Bifurcation Diagramsmentioning

confidence: 99%

Effect of magnetic field on the radial pulsations of a gas bubble in a non-Newtonian fluid

Behnia

Mobadersani

Yahyavi

et al. 2015

Chaos, Solitons & Fractals

View full text Add to dashboard Cite

a b s t r a c tDynamics of acoustically driven bubbles' radial oscillations in viscoelastic fluids are known as complex and uncontrollable phenomenon indicative of highly active nonlinear as well as chaotic behavior. In the present paper, the effect of magnetic fields on the non-linear behavior of bubble growth under the excitation of an acoustic pressure pulse in non-Newtonian fluid domain has been investigated. The constitutive equation [Upper-Convective Maxwell (UCM)] was used for modeling the rheological behaviors of the fluid. Due to the importance of the bubble in the medical applications such as drug, protein or gene delivery, blood is assumed to be the reference fluid. It was found that the magnetic field parameter (B) can be used for controlling the nonlinear radial oscillations of a spherical, acoustically forced gas bubble in nonlinear viscoelastic media. The relevance and importance of this control method to biomedical ultrasound applications were highlighted. We have studied the dynamic behavior of the radial response of the bubble before and after applying the magnetic field using Lyapunov exponent spectra, bifurcation diagrams and time series. A period-doubling bifurcation structure was predicted to occur for certain values of the parameters effects. Results indicated its strong impact on reducing the chaotic radial oscillations to regular ones.

show abstract

Complex behaviour in a dengue model with a seasonally varying vector population

Cited by 28 publications

References 33 publications

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

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

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

Effect of magnetic field on the radial pulsations of a gas bubble in a non-Newtonian fluid

Contact Info

Product

Resources

About