A cellular automaton framework for infectious disease spread simulation

Abstract

In this paper, a cellular automaton framework for processing the spatiotemporal spread of infectious diseases is presented. The developed environment simulates and visualizes how infectious diseases might spread, and hence provides a powerful instrument for health care organizations to generate disease prevention and contingency plans. In this study, the outbreak of an avian flu like virus was modeled in the state of Tyrol, and various scenarios such as quarantine, effect of different medications on viral spread and changes of social behavior were simulated.The proposed framework is implemented using the programming language Java. The set up of the simulation environment requires specification of the disease parameters and the geographical information using a population density colored map, enriched with demographic data.The results of the numerical simulations and the analysis of the computed parameters will be used to get a deeper understanding of how the disease spreading mechanisms work, and how to protect the population from contracting the disease. Strategies for optimization of medical treatment and vaccination regimens will also be investigated using our cellular automaton framework.In this study, six different scenarios were simulated. It showed that geographical barriers may help to slow down the spread of an infectious disease, however, when an aggressive and deadly communicable disease spreads, only quarantine and controlled medical treatment are able to stop the outbreak, if at all.

Fig. (1)

population density of state Tyrol. The used colors (from white to red) for the population densities specify the density steps from 0, 200, 400, 600, 800 and 1000 inhabitants per square kilometer. The color black was used to describe the non-state area.

Fig. (2)

The scenarios A-F and An, Bn are listed from the left upper to the right lower graphic. The blue color (Ind) is used for the population, red color (S) depicts the susceptible individuals, yellow (I) is used to visualize the infected individuals and the green color (R) was taken to depict the removed or temporarily immune individuals. For more information see text.

Fig. (3)

In the left image the changes in population over the time are depicted. The right image shows the death cases caused by the communicable disease aggregated per month.

Fig. (4)

The colors were used as following: green describes individuals in the state susceptible (S), gentle-pink marks individuals as in state infective (I) and dark blue marks the individuals as to be in state recovered (R). The first graphic depicts the simulation after 20 days. Although the simulation has started in the capital of Tyrol, in Innsbruck, after 20 days there are some outbreaks in the west of the state as well as in the east, which results form the fact that individuals are moving from cell to cell. Furthermore, unknown activities, which are denoted as spontaneous infection, are responsible for these characteristics. The second image shows a snapshot 80 days later at the time point 100 days after the infection started. It is clearly visible that in the capital where the disease spread started most people are in state recovered. Individual who did not die from the disease do have a temporary immunity. More than 50% of the individuals are in state infected. The last image shows a snapshot at time point 200 days after outbreak where individuals may get infected again. This represents the second outbreak wave with smaller amplitude.

Fig. (5)

Individuals in one cell of the automaton. During executing the state transition function individuals exposed to the germ can be infected using a stochastic function. For more information see text.

Fig. (6)

The finite state automaton working in each individual. For more information see text. (Arrow from R to M)