An Agent-based Rule Driven Knowledge Model for Epidemic Simulation: A Case Study of Lahore City.

Abstract

In order to mitigate the effects of disease spread, it is necessary to understand the Spatio-temporal dynamics of epidemic spread and response evaluation. For that purpose, an agent-based heterogeneous knowledge model is synthesized using a probabilistic approach that depicts population interactions. The approach can generate a baseline population synthesis for cities such as Lahore, Pakistan where the population is quite dense and agent-based modeling becomes quite complex. An agent-based Spatio-temporal model of airborne disease is analyzed (i.e., influenza) in a metropolitan area. Such diseases result in mass casualties’ events in the populous regions, every year. Using GIS shapefiles, prior knowledge of people activities and IPUMS data, the agent base model is synthesized and an epidemic spread scenario is simulated. The proposed model can be used by healthcare officials for disease spread simulation. The model is helpful when heterogeneity semantics in population bring complexity in computations. It brings different attributes like age, sex and social status under consideration while calculating the probability of being infected as the way of interaction between people is highly depend upon them. This proposed model takes spatial factor into account as the way an infection spreads depending upon spatial distribution of infected and susceptible ones.