Network Modeling with SIS for Malaria Training Participants Project Description This project introduces students to the intersection of network analysis and epidemic modeling through a hands-on exploration of disease transmission dynamics. Using a dataset of participants from various countries. Herein, participants learn to construct social networks based on connection patterns and simulate disease spread using a Susceptible-Infected-Susceptible (SIS) model. The project demonstrates how network structure influences epidemic outcomes by modeling connections between individuals from the same countries, with Kenya serving as a hub connecting participants from different nations. Participants explore key epidemiological concepts, including transmission rates (β=0.4), recovery rates (γ=0.1), and intervention strategies, such as mask-wearing and identification of superspreaders. Through practical R programming, students gain experience with network visualization, epidemic simulation, and quantitative analysis of public health interventions. The project emphasizes real-world applications by examining scenarios such as the impact of protective measures introduced mid-outbreak and the role of highly connected individuals in accelerating disease spread. This project aims to bridge theoretical epidemiology with computational methods, preparing students to understand and model infectious disease dynamics in networked populations—a critical skill for modern public health analysis.
OmondiG/Malaria_Training_Network_Modelling
Folders and files
| Name | Name | Last commit date | ||
|---|---|---|---|---|