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- 1. Automata Network Simulator Applied to the Epidemiology of Urban Dengue Fever Paper’s Authors: Henrique F. Gagliardi, Fabrcio A.B. da Silva and Domingos Alves Presented by: Hector Cuesta Arvizu Center for Computational Epidemiology and Response Analysis University of North TexasHector Cuesta (CeCERA) Automata Network Simulator Urban Dengue Fever September 19, 2011 1 / 17
- 2. Outline Introduction (why this is important?). Dengue Fever. Cellular Automata (...the computational part). Model (Epidemiology of Urban Dengue Fever). Automata Network Simulator. Conclusions. Hector Cuesta (CeCERA) Automata Network Simulator Urban Dengue Fever September 19, 2011 2 / 17
- 3. IntroductionWhy is this important? Dengue Fever are the most common mosquito-borne viral disease in the world. Globally, there are an estimated 50 to 100 million cases of Dengue Fever. 2.5 billion people are at risk world-wide, in most tropical countries. In the last 20 years, Dengue transmission and frequency of dengue epidemics has increase greatly. It can be fatal. Hector Cuesta (CeCERA) Automata Network Simulator Urban Dengue Fever September 19, 2011 3 / 17
- 4. Dengue FeverDengue FeverWhat is Dengue Fever? Dengue fever is an illness caused by infection with a virus transmitted by the ”Aedes Aegipty” mosquito. Facts: ARthropod-BOrne viruses (Arbovirues). Typically, people infected with dengue virus are asymptomatic (80%) or only have mild symptoms such as an uncomplicated fever Dengue fever virus (DENV) is an RNA virus of the family Flaviviridae. The incubation period (time between exposure and onset of symptoms) ranges from 3 to 14 days. Hector Cuesta (CeCERA) Automata Network Simulator Urban Dengue Fever September 19, 2011 4 / 17
- 5. Dengue FeverDengue FeverTransmission A human may only become infected by Aedes Aegypti bite and a mosquito only becomes infected by biting an infected human. Facts: Only the female mosquito feeds on blood, this is because they need the protein found in the blood to produce eggs. On average mosquito can lay about 300 eggs during its life span of 14 to 21 days. Dengue can also be transmitted via infected blood products and through organ donation. Hector Cuesta (CeCERA) Automata Network Simulator Urban Dengue Fever September 19, 2011 5 / 17
- 6. Dengue FeverDengue FeverThe problem statement. The reason for this approach is that cellular automata have a signiﬁcant role in epidemic modeling because each individual, or cell, or small region of space ”updates” itself independently (in parallel) allowing for the concurrent development of several epidemic spatial clusters, deﬁning its new state based on the current state of its surrounding cells (locality) and on some shared laws of change [2,5]. Hector Cuesta (CeCERA) Automata Network Simulator Urban Dengue Fever September 19, 2011 6 / 17
- 7. Cellular AutomataCellular AutomataWhat is a Cellular Automata? Discrete model studied in computability theory and mathematics for a non-linear problems. Facts: It consist of an inﬁnite, regular grid of cells, each in one of a ﬁnite number of states. The grid can be any ﬁnite number of dimensions. Each cell is a particular individual. Hector Cuesta (CeCERA) Automata Network Simulator Urban Dengue Fever September 19, 2011 7 / 17
- 8. Cellular AutomataCellular AutomataNeighbourhood The Neighbourhood is a selection of cells relative to some speciﬁed and does not change. Each cell has the same rules for updating based on the values in this neighbourhood. Each time the rules are applied to the whole grid a new generation is produce.Local an Global Neighborhoods, Von Newmnan and Moore Neighborhood Hector Cuesta (CeCERA) Automata Network Simulator Urban Dengue Fever September 19, 2011 8 / 17
- 9. ModelModelThe Epidemic Cellular Automata Model for Dengue The goal of this model is to describe the dynamics of the dengue transmission in a virtual urban environment. Dengue model, which is a human-vector interaction model The schematic model of Dengue spreading representing the stage of the disease for both populations, where thick edges represent the interaction among population(mosquito bite) and the thin ones the internal transmission of states in each model. Hector Cuesta (CeCERA) Automata Network Simulator Urban Dengue Fever September 19, 2011 9 / 17
- 10. ModelModelIterative rules between these two cellular automata The local and global inﬂuences are show in this ﬁgure. The pointed squares represent the mosquitoes aﬀected by the local and global human infective inﬂuence. The same kind of inﬂuence occurring in this bottom-up interactions also occurs for the vector-humans in a top-down sense at each simulation update. Hector Cuesta (CeCERA) Automata Network Simulator Urban Dengue Fever September 19, 2011 10 / 17
- 11. ModelModelThe probability ps of any susceptible individual become infective 1.- Any susceptible individual may become infected with probability ps 2.- An infected individual becomes infective after an average latency time (TE) 3.- Infective individual are removed deterministically from the system(becoming immune) after an infectious period (t > 0), which is considered as a constant for all infected human individuals and inﬁnity to the mosquito population. 4.- Once in the removed class, the individual participate only passively in the spreading of the infection by a period of immunity larger than the complete epidemic process. ps = ΓpG + ΛpL; ΓandΛ are weight’s Hector Cuesta (CeCERA) Automata Network Simulator Urban Dengue Fever September 19, 2011 11 / 17
- 12. ModelModelThe resulting probabilities to each population are as follow Human: ph Nmi (t) pL = 1 − (1 − λm )nIm and pG = Nm Mosquito: pm Nhi (t) pL = 1 − (1 − λh )nIh and pG = Nh p = Susceptible individual in General λ = Susceptible local individual nIh = Number of infected mosquito in Moore neighbourhood Hector Cuesta (CeCERA) Automata Network Simulator Urban Dengue Fever September 19, 2011 12 / 17
- 13. Automata Network SimulatorAutomata Network SimulatorAutomata Network Simulator The main contribution of this paper is to present a software system that incorporates a general probabilistic cellular automata model. Technological Choices: C++ (as a programming language) OpenGL (to create grid animations during simulations) Modules: The speciﬁcation module. The simulation module. The visualization module. The analysis module. Hector Cuesta (CeCERA) Automata Network Simulator Urban Dengue Fever September 19, 2011 13 / 17
- 14. Automata Network SimulatorAutomata Network SimulatorAnalysis module The Status and the Graphic windows of the analysis module. Hector Cuesta (CeCERA) Automata Network Simulator Urban Dengue Fever September 19, 2011 14 / 17
- 15. Automata Network SimulatorAutomata Network SimulatorSimulator module In (a) we see a simulation with the orthogonal view and in (b) the perspective view, where the bottom grid represents the human population while the top grid represents the vector population. Hector Cuesta (CeCERA) Automata Network Simulator Urban Dengue Fever September 19, 2011 15 / 17
- 16. ConclusionsConclusions and Future WorkConclusions and Future Work Help you understand how to the disease spreads and see diﬀerent outcomes. Future Work: Seasonality and Geographical information. A simulation of the Dengue model over Santos city map, a southeast costal Brazilian city which epidemic historical data will also be used in future study Hector Cuesta (CeCERA) Automata Network Simulator Urban Dengue Fever September 19, 2011 16 / 17
- 17. ConclusionsQuestions??Questions ??? Hector Cuesta (CeCERA) Automata Network Simulator Urban Dengue Fever September 19, 2011 17 / 17

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