An applied genomic epidemiology handbook.
Welcome to an applied genomic epidemiological handbook
1
An introduction to this resource
1.1
What is this handbook?
1.2
What this handbook is not.
1.3
Who is this handbook written for?
1.4
How should you read this handbook?
2
The value of pathogen genomics in applied epidemiology
2.1
The value of genomic epidemiology for surveillance.
2.2
The value of genomic epidemiology for outbreak response.
2.3
The value of retrospective data
3
Fundamental theory in genomic epidemiology
3.1
The overlapping timescales of pathogen evolution and pathogen transmission.
3.1.1
Viral diversity accumulates over the course of a single individual’s infection.
3.1.2
Stochasticity and selection influence variant frequency of within an infection.
3.1.3
When a transmission event occurs, the within-host viral diversity of the infector is sampled and transmitted to the recipient.
3.1.4
Consensus genomes provide a summary of the within-host diversity.
3.2
Terminology for describing changes in genetic sequences.
3.3
Mutation rates, evolutionary rates, and the molecular clock
3.4
A long note on alignments
3.5
Phylogenetic trees
3.5.1
What is a phylogenetic tree?
3.5.2
Assessing and reading a phylogenetic tree.
3.5.3
Temporally-resolved phylogenetic trees.
3.6
The transmission tree does not equate the phylogenetic tree.
3.7
Why is sequencing better at dismissing links than confirming them?
3.7.1
How many mutations are enough to rule linkage out?
4
Sample selection
4.1
Representative Sampling
4.2
Targeted Sampling
4.3
Contextual data
4.3.1
Contextual data as a backdrop
4.3.2
Contextual data as controls
5
Broad use cases for genomic epidemiology
5.1
Assessing linkage between cases
5.1.1
What kinds of questions fall into this topic?
5.1.2
Fundamental principles
5.1.3
What kind of sampling do you need to answer the question?
5.1.4
What tools or approaches can you use to answer the question?
5.1.5
Caveats, limitations, and ways things go wrong
5.1.6
Relevant case studies
5.2
Exploring relationships between cases of interest and other sequenced infections.
5.2.1
What kinds of questions fall into this topic?
5.2.2
Fundamental principles
5.2.3
What kind of sampling do you need to answer the question?
5.2.4
What tools or approaches can you use to answer the question?
5.2.5
Caveats, limitations, and ways things go wrong
5.2.6
Relevant case studies
5.3
Estimating the start and duration of an outbreak.
5.3.1
What kinds of questions fall into this topic?
5.3.2
Fundamental principles
5.3.3
What kind of sampling do you need to answer the question?
5.3.4
What tools or approaches can you use to answer the question?
5.3.5
Caveats, limitations, and ways things go wrong
5.3.6
Relevant case studies
5.4
Assessing how demographic, exposure, and other epidemiological data relate to a genomically-defined outbreak.
5.4.1
What kinds of questions fall into this topic?
5.4.2
Fundamental principles
5.4.3
What kind of sampling do you need to answer the question?
5.4.4
What tools or approaches can you use to answer the question?
5.4.5
Caveats, limitations, and ways things go wrong
5.4.6
Relevant case studies
6
Case studies
6.1
Are cases of the same Variant of Concern lineage linked?
6.2
Evaluating an intake screening program to prevent introduction of SARS-CoV-2 to prisons.
6.3
Identifying, assigning, and investigating a new SARS-CoV-2 lineage in Lithuania
6.4
Estimating when the Zika Virus epidemic began in Colombia.
7
Tools and methods
7.1
Phylogenetic placements
7.1.1
UShER
7.1.2
Nextclade
7.2
Phylogenetic trees
7.2.1
Nextstrain
7.2.2
IQ-TREE
7.2.3
RAxML
7.2.4
BEAST
7.3
When should I use a phylogenetic tree versus a phylogenetic placement?
7.4
Selecting contextual data for phylogenetic tree analyses
7.5
Notes about node ages in temporally-resolved trees
8
Further concepts
8.1
Recombination
8.2
A deeper dive into molecular clocks
8.2.1
Strict versus relaxed
8.2.2
Reiterating caveats
References
Published with bookdown
An applied genomic epidemiological handbook.
References