1 / 12

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.

jacquelinelearned
Télécharger la présentation

Preventing Smallpox Epidemics Using a Computational Model

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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.

More Related