Automata Network Simulator Urban Dengue Fever]{Automata Network Simulator Applied to the Epidemiology of Urban Dengue Fever

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 Texas
Hector 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. Introduction
Why 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 Fever
Dengue Fever
What 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 Fever
Dengue Fever
Transmission
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 Fever
Dengue Fever
The 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 Automata
Cellular Automata
What 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 Automata
Cellular Automata
Neighbourhood
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. Model
Model
The 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. Model
Model
Iterative 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. Model
Model
The 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. Model
Model
The 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 Simulator
Automata Network Simulator
Automata 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 Simulator
Automata Network Simulator
Analysis 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 Simulator
Automata Network Simulator
Simulator 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. Conclusions
Conclusions and Future Work
Conclusions 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. Conclusions
Questions??
Questions ???
Hector Cuesta (CeCERA) Automata Network Simulator Urban Dengue Fever September 19, 2011 17 / 17