Limits...
DengueME: A Tool for the Modeling and Simulation of Dengue Spatiotemporal Dynamics †

View Article: PubMed Central - PubMed

ABSTRACT

The prevention and control of dengue are great public health challenges for many countries, particularly since 2015, as other arboviruses have been observed to interact significantly with dengue virus. Different approaches and methodologies have been proposed and discussed by the research community. An important tool widely used is modeling and simulation, which help us to understand epidemic dynamics and create scenarios to support planning and decision making processes. With this aim, we proposed and developed DengueME, a collaborative open source platform to simulate dengue disease and its vector’s dynamics. It supports compartmental and individual-based models, implemented over a GIS database, that represent Aedes aegypti population dynamics, human demography, human mobility, urban landscape and dengue transmission mediated by human and mosquito encounters. A user-friendly graphical interface was developed to facilitate model configuration and data input, and a library of models was developed to support teaching-learning activities. DengueME was applied in study cases and evaluated by specialists. Other improvements will be made in future work, to enhance its extensibility and usability.

No MeSH data available.


Related in: MedlinePlus

Sensitivity analysis using DengueME: (a) sensitivity of the predicted epidemic curve to variations in the biting rate parameter and; (b) recovery rate parameter. The Y-axis is the proportion infected in the human population. The black dashed line is the model output with default values (as used by Nishiura (2006) [106]).
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC5036753&req=5

ijerph-13-00920-f007: Sensitivity analysis using DengueME: (a) sensitivity of the predicted epidemic curve to variations in the biting rate parameter and; (b) recovery rate parameter. The Y-axis is the proportion infected in the human population. The black dashed line is the model output with default values (as used by Nishiura (2006) [106]).

Mentions: file containing the values of parameters for each simulation step. This was done using the interface of Dengue VDE. Then, using the software R (Version 3.3.0) [107], graphics were created to visualize the sensitivity of the epidemic curve to these parameters (Figure 7). It is clear that the epidemic curve is more sensitive to variations in biting rate than in recovery rate. In the future, additional features (e.g., automated execution of batch simulations using value ranges for the parameters) will be developed to help users with sensitivity analyses.


DengueME: A Tool for the Modeling and Simulation of Dengue Spatiotemporal Dynamics †
Sensitivity analysis using DengueME: (a) sensitivity of the predicted epidemic curve to variations in the biting rate parameter and; (b) recovery rate parameter. The Y-axis is the proportion infected in the human population. The black dashed line is the model output with default values (as used by Nishiura (2006) [106]).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5036753&req=5

ijerph-13-00920-f007: Sensitivity analysis using DengueME: (a) sensitivity of the predicted epidemic curve to variations in the biting rate parameter and; (b) recovery rate parameter. The Y-axis is the proportion infected in the human population. The black dashed line is the model output with default values (as used by Nishiura (2006) [106]).
Mentions: file containing the values of parameters for each simulation step. This was done using the interface of Dengue VDE. Then, using the software R (Version 3.3.0) [107], graphics were created to visualize the sensitivity of the epidemic curve to these parameters (Figure 7). It is clear that the epidemic curve is more sensitive to variations in biting rate than in recovery rate. In the future, additional features (e.g., automated execution of batch simulations using value ranges for the parameters) will be developed to help users with sensitivity analyses.

View Article: PubMed Central - PubMed

ABSTRACT

The prevention and control of dengue are great public health challenges for many countries, particularly since 2015, as other arboviruses have been observed to interact significantly with dengue virus. Different approaches and methodologies have been proposed and discussed by the research community. An important tool widely used is modeling and simulation, which help us to understand epidemic dynamics and create scenarios to support planning and decision making processes. With this aim, we proposed and developed DengueME, a collaborative open source platform to simulate dengue disease and its vector’s dynamics. It supports compartmental and individual-based models, implemented over a GIS database, that represent Aedes aegypti population dynamics, human demography, human mobility, urban landscape and dengue transmission mediated by human and mosquito encounters. A user-friendly graphical interface was developed to facilitate model configuration and data input, and a library of models was developed to support teaching-learning activities. DengueME was applied in study cases and evaluated by specialists. Other improvements will be made in future work, to enhance its extensibility and usability.

No MeSH data available.


Related in: MedlinePlus