Dynamics of dengue disease with human and vector mobility

Enduri, Murali Krishna; Jolad, Shivakumar

doi:10.1016/j.sste.2018.03.001

Cited by 34 publications

(19 citation statements)

References 45 publications

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“…Specifically, the analysis of space-time scan statistics reflected significant clusters located in Pearl River Delta regions and the border area of Yunnan Province. The results of global spatial autocorrelation showed the spatiotemporal heterogeneity of dengue cases, with significant spatial correlation that occurred in 2008, 2010, 2012, 2014 and 2017, which is presumably due to the suitable climate and environmental conditions, human activities and vector density (Mweya et al, 2016;Li et al, 2018;Enduri and Jolad, 2018;Schmidt et al, 2011). Moreover, LISA analysis identified the high-high spots were mainly concentrated in some counties of Guangdong and Yunnan Provinces, which was in accordance with the results of space-time scan statistics.…”

Section: Discussionsupporting

confidence: 79%

Spatiotemporal patterns and determinants of dengue at county level in China from 2005–2017

Liu¹,

Sun²,

Liu³

et al. 2018

International Journal of Infectious Diseases

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Section: Discussionsupporting

confidence: 79%

Spatiotemporal patterns and determinants of dengue at county level in China from 2005–2017

Liu¹,

Sun²,

Liu³

et al. 2018

International Journal of Infectious Diseases

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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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“…Another important age group are young men and women who spend significant proportions of time in places of work and in commercials sites, neither of which were included as interventions sites. A growing body of evidence [22,[60][61][62]has shown that human movement is an important consideration when analysing the effectiveness of vector interventions and understanding dengue epidemiology. Previous studies have shown that transmission of dengue virus appears to be largely…”

Section: Plos Onementioning

confidence: 99%

Effectiveness of an intervention for Aedes aegypti control scaled-up under an inter-sectoral approach in a Colombian city hyper-endemic for dengue virus

et al. 2020

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Aedes aegypti transmitted arboviral diseases are of significant importance in Colombia, particularly since the 2014/2015 introduction of chikungunya and Zika in the Americas and the increasing spread of dengue. In response, the Colombian government initiated the scalingup of a community-based intervention under inter and multi-sector partnerships in two out of four sectors in Girardot, one of the most hyper-endemic dengue cities in the country. Using a quasi-experimental research design a scaled-up community-led Aedes control intervention was assessed for its capacity to reduce dengue from January 2010 to August 2017 in Girardot, Colombia. Reported dengue cases, and associated factors were analysed from available data sets from the Colombian disease surveillance systems. We estimated the reduction in dengue cases before and after the intervention using, Propensity Score Matching and an Autoregressive Moving Average model for robustness. In addition, the differences in dengue incidence among scaling-up phases (pre-implementation vs sustainability) and between treatment groups (intervention and control areas) were modelled. Evidence was found in favour of the intervention, although to maximise impact the scaling-up of the intervention should continue until it covers the remaining sectors. It is expected that a greater impact of the intervention can be documented in the next outbreak of dengue in Girardot.

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Dynamics of dengue disease with human and vector mobility

Cited by 34 publications

References 45 publications

Spatiotemporal patterns and determinants of dengue at county level in China from 2005–2017

Spatiotemporal patterns and determinants of dengue at county level in China from 2005–2017

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

Effectiveness of an intervention for Aedes aegypti control scaled-up under an inter-sectoral approach in a Colombian city hyper-endemic for dengue virus

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