bio-epidemiological-genomics-transmission-inference

name	bio-epidemiological-genomics-transmission-inference
description	Infer pathogen transmission networks and identify likely transmission pairs using TransPhylo and outbreak reconstruction algorithms. Estimate who-infected-whom from genomic and epidemiological data. Use when investigating outbreak transmission chains or identifying superspreaders.
tool_type	r
primary_tool	TransPhylo

Transmission Inference

TransPhylo in R

library(TransPhylo)
library(ape)

# Load dated phylogeny (from BEAST/TreeTime)
tree <- read.nexus('dated_tree.nexus')

# Convert to TransPhylo format
ptree <- ptreeFromPhylo(tree, dateLastSample = 2020.5)

# Estimate transmission tree
# Uses MCMC to sample from posterior distribution
res <- inferTTree(
    ptree,
    mcmcIterations = 100000,
    startNeg = 0.1,      # Initial within-host effective population
    startOff.r = 2,      # Initial R0 estimate
    startOff.p = 0.5,    # Initial sampling probability
    startPi = 0.9,       # Initial probability of being sampled
    dateT = 2020.6       # End of outbreak observation
)

# Extract consensus transmission tree
ttree <- extractTTree(res)

# Get transmission pairs
pairs <- ttree$ttree[, c('infector', 'infectee', 'time')]

Prepare Data

def prepare_for_transphylo(dated_tree_file, sample_dates, output_prefix):
    '''Prepare inputs for TransPhylo analysis

    Requirements:
    - Time-scaled phylogeny (from TreeTime or BEAST)
    - Sample collection dates
    - Tips must have matching names

    TransPhylo estimates:
    - Who infected whom
    - Unsampled cases in the transmission chain
    - R0 and generation time
    '''
    from Bio import Phylo
    import pandas as pd

    tree = Phylo.read(dated_tree_file, 'nexus')

    # Verify all tips have dates
    dates_df = pd.read_csv(sample_dates, sep='\t')
    tip_names = {clade.name for clade in tree.get_terminals()}
    dated_names = set(dates_df['name'])

    missing = tip_names - dated_names
    if missing:
        print(f'Warning: {len(missing)} tips without dates: {missing}')

    return {'tree': dated_tree_file, 'dates': sample_dates}

Interpret Results

# Analyze TransPhylo output

# Get median transmission tree
med_tree <- medTTree(res)

# Plot transmission tree
plot(med_tree)

# Get R0 estimate
r0_samples <- res$record[, 'off.r']
cat('R0 estimate:', median(r0_samples), '\n')
cat('95% CI:', quantile(r0_samples, c(0.025, 0.975)), '\n')

# Identify superspreaders
# Count number infected by each case
infections_per_case <- table(med_tree$ttree[, 'infector'])
superspreaders <- names(infections_per_case[infections_per_case > 3]

Python Alternative: outbreaker2 Wrapper

def infer_transmission_simple(distance_matrix, dates, generation_time=5):
    '''Simplified transmission inference

    Uses genomic distance and collection dates to infer likely
    transmission pairs. Less sophisticated than TransPhylo but
    doesn't require dated phylogeny.

    Criteria for transmission pair (A -> B):
    1. A collected before B
    2. Genomic distance consistent with direct transmission
    3. Time difference compatible with generation time
    '''
    import pandas as pd
    import numpy as np

    n = len(dates)
    transmission_pairs = []

    for i in range(n):
        for j in range(n):
            if i == j:
                continue

            time_diff = dates[j] - dates[i]  # Days between collection

            # Potential infector must be sampled first
            if time_diff <= 0:
                continue

            # Check if time difference is compatible
            # Generation time: time between infection of case and infection of secondary
            # Serial interval: time between symptom onset (often used as proxy)
            if time_diff > generation_time * 3:  # Too much time
                continue

            # Check genomic distance
            snp_diff = distance_matrix[i, j]

            # Expected SNPs = rate * time
            # For most pathogens, direct transmission = 0-5 SNP difference
            expected_snps = (time_diff / 365) * 10  # Rough estimate

             snp_diff <= (, expected_snps * ):
                transmission_pairs.append({
                    : i,
                    : j,
                    : snp_diff,
                    : time_diff,
                    :   snp_diff <=   
                })

     pd.DataFrame(transmission_pairs)

Network Visualization

def plot_transmission_network(pairs_df, metadata=None):
    '''Visualize transmission network

    Uses networkx to create directed graph of transmissions.
    '''
    import networkx as nx
    import matplotlib.pyplot as plt

    G = nx.DiGraph()

    for _, row in pairs_df.iterrows():
        G.add_edge(row['infector'], row['infectee'],
                   weight=row.get('confidence', 1))

    # Layout
    pos = nx.spring_layout(G)

    # Draw
    plt.figure(figsize=(12, 8))
    nx.draw(G, pos, with_labels=True, node_color='lightblue',
            node_size=500, arrows=True, arrowsize=20)

    plt.title('Transmission Network')
    return plt.gcf()

Superspreader Analysis

def identify_superspreaders(transmission_pairs, threshold=3):
    '''Identify superspreading events

    Superspreader: Individual who infected many others
    Threshold typically 80/20 rule: 20% of cases cause 80% of transmission

    Common threshold: >3 secondary cases
    '''
    from collections import Counter

    infector_counts = Counter(transmission_pairs['infector'])

    superspreaders = {k: v for k, v in infector_counts.items() if v >= threshold}

    total_transmissions = sum(infector_counts.values())
    ss_transmissions = sum(superspreaders.values())

    print(f'Superspreaders (>{threshold} secondary cases):')
    for ss, count in sorted(superspreaders.items(), key=lambda x: -x[1]):
        print(f'  Case {ss}: {count} secondary infections')

    print(f'\nSuperspreading contribution: {ss_transmissions/total_transmissions:.1%}')

    return superspreaders

Related Skills

• epidemiological-genomics/phylodynamics - Generate dated trees
• epidemiological-genomics/pathogen-typing - Identify outbreak clones
• data-visualization/interactive-visualization - Visualize transmission