Epidemiology Through Cellular AutomataCase Study: Avian Influenza in Indonesia

Hokky Situngkir (hokky@elka.ee.itb.ac.id)

by

joa@deinfo.ufrpe.br

xiscanoe.org

Abstract

This paper performs the utilization of cellular automata computational analysis as the dynamic model of spatial epidemiology. Here, explored elementary aspects of cellular automata and its application in analyzing contagious disease, in this case avian influenza disease in Indonesia. Computational model is built and map-based simulation is performed using several simplified data of such transportation through sea in Indonesia, and its accordance with poultries in Indonesia, with initial condition of notified avian influenza infected area in Indonesia. The initial places are Pekalongan, West Java, East Java, and several regions in Sumatera. The result of simulation is showing the spreading-rate of influenza and in simple way and describing possible preventive action through isolation of infected areas as a major step of preventing pandemic.

Cenário do estudo de caso: gripe aviária em thailand, cambodia, vietnam

Preto: dead victimHachurado: epidemiological area

Realidade!

aims...

The paper aims to give description on how epidemiology has also been enriched with one of computational complexity tools, the cellular automata – as a spatial and discrete model of dynamical system - in this case the spreading of contagious disease.

cellular automata on epidemics - cells

we recognize 3 states of automata, i.e.:

1. Susceptible condition that is a condition where a population agent has not yet infected, but has certain probabilistic potential to be infected by the disease.

2. Infected condition that is a condition when an agent infected.

3. Recovery condition that is a condition when the disease disappears from the agent; be it recover or die.

cellular automata on epidemics - grid

we also represent population computational parameters, they are:

1. Neighborhood relation (interconnectedness of one agent with other agent) spatially or in network (agent spatially does not stay aside, but it has close inter-relation, such as transportation, etc).

2. Probability for someone to be infected and one’s capability to recover or die from the disease.

3. Phases as the result of disease infection and probability of recover person and to be re-infected.

a doença na realidade...

a doença modelada no artigo...

α

β

Ύ

α < β > ΎThis relation is made by facts that animals in one poultry area are easier to infect each other relativeto infection in human and by transportation of infected animals.

presuposições do modelo...

• Until the paper is written, there has not been any report of infection through inter-human contact, nor infection to human through infected poultry food consumption. It attacks human through direct contact with living infected poultry or animals. [...] From this we may conclude that the very risky places in the case of avian influenza disease epidemiology are:

1. Poultry areas2. Trading center of living poultry3. Transportation area of living poultry distribution

o modelo...

[...] we can say that this model is focusing in virus diffusion occurs in society. The epidemic model with cellular

automata as explained in Rhodes & Anderson (1998) ?? basically inspires this model.

Dynamics in a lattice epidemic modelhttp://www.comp.nus.edu.sg/~cs6211/papers/rhodes.pdf

células...

To give spatial description, we performed discrete Indonesia territorial map by grids. Based on the data of poultry area issued by the government, Dirjen Bina Produksi Peternakan (2003), basically we can see that each area in those grids owns folk poultry area that possibly infected and also living poultry market throughout the archipelago – this is assumed for thesake of simplicity. On contact probability between islands, we use naval map PELNI (Loud, 2003) showing major port locations in Indonesia. From here we can pick some major ports that are possibly used as poultry transportation route.

a política de vizinhança é clássica: von Neumann Neighborhood

as iterações...

conclusões...• From the simulation carried out and model constructed, it is obvious

that avian influenza epidemic case is an interesting and unique case, regarding to the indirect route of virus to infect human – yet frantically and rapidly attack animals that are close to human and social system where human live. The simulation also showed epidemiology discourse should be put more on priority than curative

action since the spreading rate is very high. Isolation of poultry and animal domestication area, monitoring market that trading living animals and distribution of poultry that is high-risk on bringing the virus needs to be major priority. In addition, educational training and self-help suggestion should not be the center or focus to repress the virus diffusion.