Introduction

A computationally efficient model that cover the three stages of fighting epidemics:
  1. Estimation of Strength
  2. Progression of Spread
  3. Optimization of Containment Strategies
This model is based on Stochastic Cellular Automata with the following rules:
Here’s a flow chart with the procedural rules:
   
Abstract:

Projecting the progression of an epidemic is vital for evaluating and controlling its impact in a population. The objective of this work is to design, implement, and evaluate a computationally efficient, adaptive, and robust methodology for the spatial spread of an infectious disease. This paper covers the three stages of fighting epidemics: estimation of strength, prediction of spread, and analysis of containment strategies. Previous techniques employing complex social networks are based upon a set of partial differential equations, which requires a substantial amount of data and computation time to evaluate. The proposed model uses stochastic cellular automata, representing a discrete form of the SIR (Susceptible-Infected-Recovered) compartment model. It is versatile and yields a quantifiable confidence level.This new methodology was verified using actual surveillance data from the 2009 H1N1 Epidemics published by the Illinois Department of Public Health. It was shown that the model is capable of generating an estimate for the infection rate ß that confirmed the literature. Then, the simulation for Illinois demonstrated it could calculate an expected case number that agreed with the published data quite well (p=0.695). A few simplified cases were presented to demonstrate the ability to simulate external intervention such as quarantine and vaccination. It is envisioned that this model can facilitate the planning and deployment of containment strategies.

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