University of Arizona
Mel & Enid Zuckerman College of Public Health
Learning Epidemics
Social Network Analysis Dashboard
in partnership with Teya
Baseline
T0 · email_source format
drop or click
Midline
T1 · e-mail corporativo format
drop or click
Endline
T2 · e-mail corporativo format
drop or click
at least Baseline required · midline & endline optional
Baseline 2D
Baseline 3D
Learning Epidemic
Descriptive Stats
Correlation
Random Forest
Node Metrics
Edge List
Load a CSV to see data
3D Force-Directed Network
Three.js · WebGL
Descriptive Statistics
Summary of all variables — source nodes only
Load a CSV to see statistics
Correlation Matrix
Pearson correlations among numeric variables · source nodes only
Load a CSV to see correlations
Random Forest — Feature Importance
Variable importance for predicting centrality · 100 bootstrap trees · MSE variance reduction
Load a CSV to run analysis
Longitudinal Network View
Side-by-side comparison of Baseline · Midline · Endline networks · combined overlay below
T0Baseline
T1Midline
T2Endline
⬡ Combined Network — connections color-coded by wave of first appearance
● Baseline edges ● New in Midline ● New in Endline ● Shared B+M ● Shared all waves
Network Evolution
How key metrics changed across data collection waves · intervention points marked
Metric trajectories over time
● Baseline ● Midline ● Endline ▲ Intervention
Learning Epidemic Modeling — SEIR
Simulation of knowledge diffusion through the organizational network using an adapted SEIR epidemiological framework
Model Parameters
β — Transmission rate (0–1)
0.30
γ — Exposure → Adoption rate (0–1)
0.20
δ — Recovery rate (0–1)
0.10
Initial adopters I₀ (seed)
3
Time horizon (days)
120
Network wave
Organizational R₀
R₀ = β / γ
Peak & Final State
Model Equations
dS/dt = −βS(t)I(t)/N
dE/dt = βS(t)I(t)/N − γE(t)
dI/dt = γE(t) − δI(t)
dR/dt = δI(t)
N = S + E + I + R (constant)
SEIR Compartment Trajectories
S Susceptible E Exposed I Adopting R Recovered
Department-Level R₀ Estimates based on within-department network density