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Research Article Open access CC BY 4.0

Simulations of Infectious Disease Propagation

William J. B. Oldham Jr.

Asian Journal of Research in Infectious Diseases · pp. 33–44 · Published 7 Jul 2020

10.9734/ajrid/2020/v4i230144

Abstract

Introduction and Objectives: The results of two simulations of the propagation of an infectious disease are presented. The objective of this research is to track the propagation of an infectious disease as a function of particle density and time. The results are given as a percentage of the population that is infected as a function of time. Methods: The method here is to use computer simulation on a particle basis to track the progress of the infection. An uninfected particle becomes infected if it is closer than the critical distance to an infected particle. The movement of the particles is force driven in the first simulation while in the second each particle executes a random walk. In the second simulation the infection rates are given for different amounts of protection in the population. Results and Discussion: These simulations show the entire population is at risk if proper measures are not taken early. For 400 particles the infection rate is 100% after approximately 100,000 iterations.  We give the results from one dual simulation in which protection was afforded for a significant part of the population and carried out until all of the unprotected were infected.  In the second part the protection was lifted to see how fast the total population was infected. For the cases of 50% protected it took 400,000 iterations to infect the unprotected particles. After the restrictions were lifted it took 140,000 to infect the other half. The simulations here are particle based which has the advantage of seeing individual particle involvement. Conclusion: The propagation of the disease can be fast and depends on particle density. Protection is vital to containing the disease. Restrictions must be lifted carefully and slowly or the total population is again at risk.

Simulation infectious disease immunity propagation restrictions

Cited by 1

A Study of Self-Organization in Small Systems with Simple Dynamics

William J. B. Oldham, James Rejcek · Physical Science International Journal · 2020

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