Ebola Superspreading Analysis
Oluwatobi Olatunbosun
2025-07-22
1. Load Libraries
library(epicontacts)
library(fitdistrplus)
library(superspreading)
library(tidyverse)
library(outbreaks)2. Load and Prepare Ebola Data
# Retrieve Ebola simulated line list and contacts data
ebola_linelist <- outbreaks::ebola_sim_clean$linelist
ebola_contact <- outbreaks::ebola_sim_clean$contacts
# Create an epicontacts object
ebola_epicontacts <- epicontacts::make_epicontacts(
linelist = ebola_linelist,
contacts = ebola_contact,
directed = TRUE
)3. Visualize the Contact Network
# Plot the contact network
plot(ebola_epicontacts)4. Analyze Secondary Cases (Out-Degree)
# Get secondary case counts (number of people each case infected)
secondary_cases <- epicontacts::get_degree(
x = ebola_epicontacts,
type = "out",
only_linelist = TRUE
)
# Print secondary cases
secondary_cases## d1fafd 53371b f5c3d8 6c286a 0f58c4 49731d f9149b 881bd4 e66fa4 20b688 40ae5f
## 1 0 2 0 1 0 3 0 0 3 1
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## 3 1 1 0 0 2 0 0 0 0 0
## 8689b7 6285c9 865581 5fe599 79ad06 9d4019 c43190 900021 102363 11f8ea dce5cc
## 0 0 0 0 0 0 0 0 1 5 0
## d4ff47 b8812a 4b38b7 f96aa5 893f25 be99c8 c29eb5 bc9ace 49d786 e7ee34 c4e706
## 0 0 1 0 1 0 1 1 1 0 1
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## 1 0 1 3 0 0 0 0 1 0 0
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## 0 0 0 0 0 2 2 0 0 1 0
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## 0 0 0 0 0 0 0 0 0 05. Plot Degree Distribution
# Histogram of secondary cases
hist(secondary_cases,
breaks = 30,
main = "Degree Distribution of Secondary Cases",
xlab = "Number of Secondary Cases",
ylab = "Frequency",
col = "lightblue",
border = "black")
6. Fit a Negative Binomial Distribution
# Fit negative binomial to secondary cases
fitnb <- fitdistrplus::fitdist(secondary_cases, "nbinom")
print(fitnb)## Fitting of the distribution ' nbinom ' by maximum likelihood
## Parameters:
## estimate Std. Error
## size 0.8539443 0.072505326
## mu 0.3675993 0.009497097The negative binomial distribution is characterized by two parameters: -
size(k): Dispersion parameter -mu: Mean number of secondary casesExample estimates might look like: - size = 0.854 - mu = 0.368
7. Superspreading Risk Assessment
# Estimate probability of clusters from backward tracing
superspreading::proportion_cluster_size(
R = fitnb$estimate["mu"],
k = fitnb$estimate["size"],
cluster_size = c(5, 10, 15)
)## R k prop_5 prop_10 prop_15
## 1 0.3675993 0.8539443 2.64% 0.03% 0%8. Interpretation
Using the estimated parameters (R ≈ 0.368, k ≈ 0.854), the chance of a backward-traced case causing: - ≥5 secondary cases: ~2.6% - ≥10 or ≥15 secondary cases: near 0%
📌 Implication:
Backward tracing likely provides limited added value for containing Ebola transmission in this scenario, as the superspreading potential is low.