1 / 11

Multiple Dynamic Network Simulations with Ugandan Data

Multiple Dynamic Network Simulations with Ugandan Data. Monisha Sharma Kipruto Kirwa Cristina Metzger Sarah Roberts. Network Characteristics. num.females <- 372 num.males <- 361 nw <- network.initialize(num.females + num.males,

yoko
Download Presentation

Multiple Dynamic Network Simulations with Ugandan Data

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. Multiple Dynamic Network Simulations with Ugandan Data Monisha Sharma KiprutoKirwa Cristina Metzger Sarah Roberts

  2. Network Characteristics num.females <- 372 num.males <- 361 nw <- network.initialize(num.females + num.males, bipartite = num.females, directed = F) nw %v% 'sex' <- c(rep(1, num.females), rep(2, num.males)) deg.dist.females <- c(0.2, 0.786, 0.011, 0.003) deg.dist.males <- c(0.328, 0.524, 0.127, 0.021)

  3. Vital Dynamics & Disease i.prev<-0.1 i.rand=TRUE trans.rate <- c(rep(0.2055, 3), rep(0.0088, 100), rep(0.0614, 9), rep(0, 10)) trans.rate<-trans.rate*4 trans.rate.m2<-trans.rate/2 act.rate<-1 b.rate=0.0066 ds.rate=0.0025

  4. Prevalence at time 100 = 15.8% Prevalence at time 250 = 20.5%

  5. Prevalence at time 100 = 15.9% Prevalence at time 250 = 17.1%

  6. Prevalence at time 100 = 14.2% Prevalence at time 250 = 12.5%

  7. Prevalence at time 100 = 11.5% Prevalence at time 250 = 0%

More Related