An agent-based model driven by tropical rainfall to understand the spatio-temporal heterogeneity of a chikungunya outbreak

Dommar, Carlos J.; Lowe, Rachel; Robinson, Marguerite; Rodó, Xavier

doi:10.1016/j.actatropica.2013.08.004

Cited by 37 publications

(38 citation statements)

References 61 publications

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“…The bimodal nature of the outbreak was captured by two studies: Bacaër, by assuming a hypothetical periodic vector abundance [96], and Dommar et al , although only a qualitative fit was reported, by assuming rainfall dependence [98]. …”

Section: Resultsmentioning

confidence: 99%

A large-scale stochastic spatiotemporal model for Aedes albopictus-borne chikungunya epidemiology

et al. 2017

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Chikungunya is a viral disease transmitted to humans primarily via the bites of infected Aedes mosquitoes. The virus caused a major epidemic in the Indian Ocean in 2004, affecting millions of inhabitants, while cases have also been observed in Europe since 2007. We developed a stochastic spatiotemporal model of Aedes albopictus-borne chikungunya transmission based on our recently developed environmentally-driven vector population dynamics model. We designed an integrated modelling framework incorporating large-scale gridded climate datasets to investigate disease outbreaks on Reunion Island and in Italy. We performed Bayesian parameter inference on the surveillance data, and investigated the validity and applicability of the underlying biological assumptions. The model successfully represents the outbreak and measures of containment in Italy, suggesting wider applicability in Europe. In its current configuration, the model implies two different viral strains, thus two different outbreaks, for the two-stage Reunion Island epidemic. Characterisation of the posterior distributions indicates a possible relationship between the second larger outbreak on Reunion Island and the Italian outbreak. The model suggests that vector control measures, with different modes of operation, are most effective when applied in combination: adult vector intervention has a high impact but is short-lived, larval intervention has a low impact but is long-lasting, and quarantining infected territories, if applied strictly, is effective in preventing large epidemics. We present a novel approach in analysing chikungunya outbreaks globally using a single environmentally-driven mathematical model. Our study represents a significant step towards developing a globally applicable Ae. albopictus-borne chikungunya transmission model, and introduces a guideline for extending such models to other vector-borne diseases.

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Section: Resultsmentioning

confidence: 99%

A large-scale stochastic spatiotemporal model for Aedes albopictus-borne chikungunya epidemiology

et al. 2017

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“…Such models are already available for some other vector-borne diseases (Dommar et al, 2014;Favier et al, 2005;Prosper et al, 2012). In view of the ambitious elimination target, it is now timely to include this in models for lymphatic filariasis.…”

Section: Spatial Heterogeneities In the Spread Of Infectionmentioning

confidence: 97%

Modelling Lymphatic Filariasis Transmission and Control: Modelling Frameworks, Lessons Learned and Future Directions

Stolk¹,

Stone²,

Vlas³

2015

Advances in Parasitology

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“…A wide range of modeling approaches, including ordinary and partial differential equations (ODE and PDE) (Enduri and Jolad, 2014; Aldila et al, 2013) as well as agent/individual-based models have also been applied to these questions (Li and Zou, 2009; Isidoro et al, 2011; Chao et al, 2012; Dommar et al, 2014; Manore et al, 2015). Common goals for many of these modeling efforts have been to make quantitative predictions of disease dynamics and to estimate the underlying mechanistic parameters (Chowell et al, 2007; Khan et al, 2014; Ferguson et al, 2016a; Perkins et al, 2016; Johnson et al, 2015).…”

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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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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An agent-based model driven by tropical rainfall to understand the spatio-temporal heterogeneity of a chikungunya outbreak

Cited by 37 publications

References 61 publications

A large-scale stochastic spatiotemporal model for Aedes albopictus-borne chikungunya epidemiology

A large-scale stochastic spatiotemporal model for Aedes albopictus-borne chikungunya epidemiology

Modelling Lymphatic Filariasis Transmission and Control: Modelling Frameworks, Lessons Learned and Future Directions

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

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