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Preventing Smallpox Epidemics Using a Computational Model

Explore how vaccinating a certain percentage of the population can effectively prevent smallpox epidemics using a computational model. Learn about the stages and spread of the disease in a simulated social network setting.

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Preventing Smallpox Epidemics Using a Computational Model

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  1. Preventing Smallpox Epidemics Using a Computational Model By Chintan Hossain and Hiren Patel

  2. Facts About Smallpox • Symptoms occur in stages • Highly contagious (causes epidemics) • Fatal in 30% cases • There is a vaccine - Death may occur

  3. GOAL (Objective) • Prevent smallpox epidemics via. vaccination. • Vaccinate as few as possible because: 1. Minimize reactions 2. Reduce cost HYPOTHESIS: Vaccinating certain percentage of the population may be sufficient to prevent a smallpox epidemic.

  4. Normal (Susceptible) Immune (or vaccinated) Incubation First Stage Early Symptoms Late Symptoms Death Normal (Susceptible) Contraction Vaccination Incubation 14 days First Stage 0.1% chance / day 3 days Early Symptoms Death 0.5% chance / day 9 days 3.0% chance / day Late Symptoms Recovery 9 days Immune \ Vaccinated Stages of Smallpox

  5. Cliques Represent: Families Workplaces School Our Model: Social Networks

  6. Our Society Generator Algorithm • Use random numbers to pick a family size. • Generate a clique of that size. • Repeat to create more families. • Use a similar technique to generate schools and workplaces. • Schools and workplaces connect existing vertices, not new vertices.

  7. Incubation Spread DEAD  Our Model Comes Alive! Normal (Susceptible) • MARKOV GRAPH + SOCIETY NETWORK SIMULATION • Advance time 1 day • Spread Disease • Advance Stages • Death Infected Stage Vaccinated / Immune  Death   EARLY FIRST LATE

  8. Procedure • Run the society generator • Vaccinate k% of people with most friends (vertices with the greatest degree) • Control: k = 0% • Variable: Vary percent, k, vaccinated • Randomly, infect one person. • Run simulation, and observe results (percent infect and length of epidemic)

  9. OUR PROGRAM

  10. Results • Epidemics intensify, reach a peak, and then vanish • Vaccination reduces intensity and speed.

  11. Results (cont…) • Vaccinating more people decreases the % infected • The % infected becomes small if over 50% are vaccinated.

  12. Conclusion • Vaccinating 50% of the population effectively prevents epidemics. • Vaccinating less than 50% may not prevent an epidemic, but it reduces the severity and speed of the epidemic. • This model can be used for other diseases by changing the Markov Graph.

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