Estimating dengue type reproduction numbers for two provinces of Sri Lanka during the period 2013–14

Sardar, Tridip; Sasmal, Sourav Kumar; Chattopadhyay, Joydev

doi:10.1080/21505594.2015.1096470

Cited by 10 publications

(8 citation statements)

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“…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%

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

Kao

Eisenberg

2018

Epidemics

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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.

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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

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“…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%

“…Through our simple model, we hope to draw attention 59 to identifiability issues in vector-borne disease models and their implications in the application of 60 models with more complexity. 61…”

Section: Author Summarymentioning

confidence: 99%

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

Kao

Eisenberg

2017

Preprint

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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...

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“…Sardar et al 9 developed a two strain model to approximate the dynamics of dengue infection. Since 2002, dengue is an endemic disease in southeast Asia ravaging the population with two seasonal peaks per year.…”

Section: Brief Review Of Contributionsmentioning

confidence: 99%

Editorial: Mathematical modeling of infectious disease dynamics

Siettos

2016

Virulence

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Estimating dengue type reproduction numbers for two provinces of Sri Lanka during the period 2013–14

Cited by 10 publications

References 30 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

Editorial: Mathematical modeling of infectious disease dynamics

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