Fuzzy model identification of dengue epidemic in Colombia based on multiresolution analysis

Torres, C C M Andrés; Barguil, Samier; Melgarejo, Miguel; Olarte, Andres

doi:10.1016/j.artmed.2013.11.008

Cited by 27 publications

(16 citation statements)

References 20 publications

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“…For the time series analysis of dengue case counts associated with meteorological variables, diverse methodologies have been employed, including auto-regressive integrated moving average (ARIMA) models [7] [8] [9] [10] [11] [12] [13] [14] [15], Poisson multivariate regression forecasting models [16] [17] [18], distributed lag non-linear models (DLNM) [19] [20], decision trees with cross-validation [21], multiresolution analysis and fuzzy systems [22], stepwise negative binomial multivariate linear regression analysis [23], wavelet time series analysis [24], probabilistic random walks [25] [26], and dynamic generalized linear models (DGLM) [27] [28] [29]. …”

Section: Introductionmentioning

confidence: 99%

Bayesian dynamic modeling of time series of dengue disease case counts

2017

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The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model’s short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order random walk time-varying coefficients. We applied Markov Chain Monte Carlo simulations for parameter estimation, and deviance information criterion statistic (DIC) for model selection. We assessed the short-term predictive performance of the selected final model, at several time points within the study period using the mean absolute percentage error. The results showed the best model including first-order random walk time-varying coefficients for calendar trend and first-order random walk time-varying coefficients for the meteorological variables. Besides the computational challenges, interpreting the results implies a complete analysis of the time series of dengue with respect to the parameter estimates of the meteorological effects. We found small values of the mean absolute percentage errors at one or two weeks out-of-sample predictions for most prediction points, associated with low volatility periods in the dengue counts. We discuss the advantages and limitations of the dynamic Poisson models for studying the association between time series of dengue disease and meteorological variables. The key conclusion of the study is that dynamic Poisson models account for the dynamic nature of the variables involved in the modeling of time series of dengue disease, producing useful models for decision-making in public health.

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Section: Introductionmentioning

confidence: 99%

Bayesian dynamic modeling of time series of dengue disease case counts

2017

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“…The abundance of Ae. albopictus varies with seasons and regions [33], thus we introduced two seasonal parameters c s and c c and used a trigonometric function to simulate them according to the previous study [34]. The calculation of parameter c s and c c was as follows, τ and T refer to simulation delay of the initial time in the whole season, and the time span of the season cycle respectively.…”

Section: Parameter Estimationmentioning

confidence: 99%

Dengue fever transmission between construction site and its surrounding communities in China

Liu

Zhang

Cheng

et al. 2020

Preprint

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Background: Because of the urbanization and the lack of basic mosquito control facilities, constructions sites might increase the transmission risk of dengue. For example, the outbreak in Zhanjiang Prefecture, China, 2018, started at a construction site, then spread to surrounding communities. This study aims to investigate how to effectively reduce the dengue transmission risk related to construction sites.Methods: The study was based on an outbreak of dengue fever (DF) occurred in Zhanjiang Prefecture, Guangdong Province in 2018. We simulated the transmission of dengue virus between human and mosquitoes at the construction site and in the neighboring community separately, as well between them by assuming human movement between the construction site and the neighboring community. Susceptible-Exposed-Infectious/Asymptomatic-Recovered (SEIAR) model was used for the human, while SEI model was used for the mosquitoes. We obtained the transmissibility parameters by fitting the model to observed number of incident cases before local interventions started and the coefficient of determination (R2) was used to quantify the significance of the fit. We then evaluated the effectiveness of different intervention scenarios targeting at reducing the transmissibility between different human and mosquito subpopulations at different locations (i.e. construction sites and community) quantified by the total attack rate (TAR) and duration of the outbreak (DO).Results: A total of 467 cases of dengue fever were reported in Zhanjiang Prefecture in 2018, among which 102 were located at a construction site and 131 were located in the surrounding community. The values of R2 were 0.829 (P < 0.001) and 0.878 (P < 0.001) for the construction site and the community, respectively. The modelling outcome indicated that without interventions, the number of cases on the construction site would reach to 156, yielding a total attack rate (TAR) of 31.25% (95% confidence interval [CI]: 27.18%- 35.31%). In the community, the number of the cases would be much more than the reported data and up to 10796, yielding a TAR of 21.59% (95%CI: 21.23%- 21.95%). When the transmission route from mosquitoes to people is cut off in the community, the number of cases in the community would decrease to a minimum of 33 compared with other situations we simulated, yielding a TAR of 0.068% (95%CI: 0.05%- 0.09%) and a duration of outbreak (DO) of 60 days. When the transmission route from infectious mosquitoes in the community and the construction site to susceptible people on the construction site is cut off at the same time, the number of cases in the construction site would drop to a minimum of 74, yielding a TAR of 14.88% (95%CI: 11.76%- 18.00%) and a DO of 66 days.Conclusions: Construction sites may facilitate the dengue outbreaks into neighboring communities, SEIAR-SEI model can be efficiently applied to simulate outbreaks of dengue fever. Cutting off the transmission routes and properly controlling vectors in communities is an effective measure to control this disease.

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“…The abundance of Ae. albopictus varies with seasons and regions [34], thus we introduced two seasonal parameters cs and cc and used a trigonometric function to simulate them according to the previous study [35]. The calculation of parameter cs and cc was as follows, According to the reported data, the illness onset date of the infection source was August 17, the peak of the disease spanned along September.…”

Section: Parameter Estimationmentioning

confidence: 99%

Dengue fever transmission between construction site and its surrounding communities in China

Liu

Zhang

Cheng

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Due to more mosquito habitats and the lack of basic mosquito control facilities, construction sites are more likely to have secondary cases after case importation, which may increase the number of cases entering the community and the chance of community transmission. For example, the outbreak in Zhanjiang Prefecture, China, 2018, started at a construction site, then occurred in the surrounding communities. This study aims to investigate how to effectively reduce the dengue transmission risk related to construction sites and the neighboring communities.Methods: The study was based on an outbreak of dengue fever (DF) occurred in Zhanjiang Prefecture, Guangdong Province in 2018. We simulated the transmission of dengue virus between human and mosquitoes at the construction site and in the neighboring community separately, as well between them by assuming human movement between the construction site and the neighboring community. Susceptible-Exposed-Infectious/Asymptomatic-Recovered (SEIAR) model was used for the human, while SEI model was used for the mosquitoes. We obtained the transmissibility parameters by fitting the model to observed number of locally acquired cases before local interventions started and the coefficient of determination (R2) was used to quantify the significance of the fit. We then evaluated the effectiveness of different intervention scenarios targeting at reducing the transmissibility between different human and mosquito subpopulations at different locations (i.e. construction sites and community) quantified by the total attack rate (TAR) and duration of the outbreak (DO).Results: A total of 467 DF cases were reported in Zhanjiang Prefecture in 2018, among which 102 were located at a construction site and 131 were located in the surrounding community. The values of R2 were 0.829 (P < 0.001) and 0.878 (P < 0.001) for the construction site and the community, respectively. The modelling outcome indicated that without interventions, the number of cases on the construction site would reach to 156, yielding a total attack rate (TAR) of 31.25% (95% confidence interval [CI]: 27.18%- 35.31%). In the community, the number of the cases would be much more than the reported data and up to 10796, yielding a TAR of 21.59% (95%CI: 21.23%- 21.95%). When the transmission route from mosquitoes to people is cut off in the community, the number of cases in the community would decrease to a minimum of 33 compared with other situations we simulated, yielding a TAR of 0.068% (95%CI: 0.05%- 0.09%) and a duration of outbreak (DO) of 60 days. When the transmission route from infectious mosquitoes in the community and the construction site to susceptible people on the construction site is cut off at the same time, the number of cases in the construction site would drop to a minimum of 74, yielding a TAR of 14.88% (95%CI: 11.76%- 18.00%) and a DO of 66 days.Conclusions: Without intervention, DF could spread rapidly in the densely populated communities around the construction site. To control the outbreak effectively for both the construction site and the community, interventions need to be taken to reduce the transmission within the community and from the community to the construction site. Only controlling the transmission within the construction site could not reduce the number of cases on the construction site, and controlling the transmission route within the construction site or between the construction site and the community could not lead to a reduction in the number of cases in the community.

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Fuzzy model identification of dengue epidemic in Colombia based on multiresolution analysis

Cited by 27 publications

References 20 publications

Bayesian dynamic modeling of time series of dengue disease case counts

Bayesian dynamic modeling of time series of dengue disease case counts

Dengue fever transmission between construction site and its surrounding communities in China

Dengue fever transmission between construction site and its surrounding communities in China

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