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{{Short description|Epidemiology method}}
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In the field of [[epidemiology]], '''source attribution''' refers to a category of methods with the objective of reconstructing the [[Transmission (medicine)|transmission]] of an [[Infection|infectious disease]] from a specific source, such as a population, individual, or location.  For example, source attribution methods may be used to trace the origin of a new pathogen that recently [[Zoonosis|crossed from another host species]] into humans, or [[Globalization and disease|from one geographic region to another]].  It may be used to determine the common source of an outbreak of a foodborne infectious disease, such as a [[1854 Broad Street cholera outbreak|contaminated water supply]].  Finally, source attribution may be used to estimate the probability that an infection was transmitted from one specific individual to another, ''i.e.'', "who infected whom".

Source attribution can play an important role in [[public health surveillance]] and  [[Outbreak response|management of infectious disease outbreaks]].  In practice, it tends to be a problem of [[statistical inference]], because transmission events are seldom observed directly and may have occurred in the distant past.  Thus, there is an unavoidable level of uncertainty when reconstructing transmission events from residual evidence, such as the spatial distribution of the disease.  As a result, source attribution models often employ [[Bayesian inference|Bayesian methods]] that can accommodate substantial uncertainty in model parameters.

Molecular source attribution is a subfield of source attribution that uses the molecular characteristics of the pathogen &mdash; most often its nucleic acid [[genome]] &mdash; to reconstruct transmission events.  Many infectious diseases are routinely detected or characterized through [[DNA sequencing|genetic sequencing]], which can be faster than culturing isolates in a [[Public health laboratory|reference laboratory]] and can identify specific strains of the pathogen at substantially higher precision than laboratory assays, such as [[ELISA|antibody-based assays]] or [[Antibiotic sensitivity|drug susceptibility tests]].  On the other hand, analyzing the genetic (or [[Whole genome sequencing|whole genome]]) sequence data requires specialized computational methods to fit models of transmission.  Consequently, molecular source attribution is a highly interdisciplinary area of [[molecular epidemiology]] that incorporates concepts and skills from [[mathematical statistics]] and modeling, [[microbiology]], [[public health]] and [[computational biology]].

There are generally two ways that molecular data are used for source attribution.  First, infections can be categorized into different "subtypes" that each corresponds to a unique molecular variety, or a cluster of similar varieties.  Source attribution can then be inferred from the similarity of subtypes.  Individual infections that belong to the same subtype are more likely to be related [[Transmission (medicine)|epidemiologically]], including direct source-recipient transmission, because they have not substantially evolved away from their common ancestor.  Similarly, we assume the true source population will have frequencies of subtypes that are more similar to the recipient population, relative to other potential sources.  Second, molecular (genetic) sequences from different infections can be directly compared to reconstruct a [[phylogenetic tree]], which represents how they are related by common ancestors.  The resulting phylogeny can approximate the transmission history, and a variety of methods have been developed to adjust for confounding factors.

Due to the associated [[Social stigma|stigma]] and the [[Criminal transmission of HIV|criminalization of transmission]] for specific infectious diseases, molecular source attribution at the level of individuals can be a controversial use of data that was originally collected in a healthcare setting, with potentially severe legal consequences for individuals who become identified as putative sources.  In these contexts, the development and application of molecular source attribution techniques may involve trade-offs between public health responsibilities and individual rights to [[Information privacy|data privacy]].

==Microbial subtyping==

Microbial subtyping or strain typing is the use of laboratory methods to assign microbial samples to subtypes, which are predefined classifications based on distinct characteristics.<ref name="Olive1999">{{cite journal| author=Olive DM, Bean P| title=Principles and applications of methods for DNA-based typing of microbial organisms | journal=J Clin Microbiol | year= 1999 | volume= 37 | issue= 6 | pages= 1661–9 | pmid=10325304 | doi=10.1128/JCM.37.6.1661-1669.1999 | pmc=84917 }}</ref>
The assignment of specimens to subtypes can provide a basis of source attribution, since we assume that a pathogen undergoes minimal change when transmitted to an uninfected host.
Therefore, infections of the same subtype are implied to be epidemiologically related, ''i.e.,'' linked by one or more recent transmission events.
The assumption that the pathogen is unchanged when transmitted is generally reasonable if the rate of evolution for the pathogen is slower than the rate of transmission, such that few mutations are observed on an epidemiological time scale.<ref name="Barco2013">{{cite journal| author=Barco L, Barrucci F, Olsen JE, Ricci A| title=Salmonella source attribution based on microbial subtyping | journal=Int J Food Microbiol | year= 2013 | volume= 163 | issue= 2–3 | pages= 193–203 | pmid=23562696 | doi=10.1016/j.ijfoodmicro.2013.03.005 }}</ref>
For example, suppose host A is infected by a pathogen that we have categorized as subtype 1.
They are more likely to have been infected by host B, who also carries the subtype 1 pathogen, than host C who carries the subtype 2 pathogen ('''Figure 1''').
In other words, transmission from host B is a more parsimonious explanation if there is a relatively small probability that the pathogen population in host C evolved from subtype 1 to subtype 2 after transmission to host A.

[[File:Subtyping.png|thumb|320px|One-to-one versus cluster-based subtyping.
Each set of circles along a line segment represents a molecular sequence from an individual infection.  Circles are coloured to differentiate sequences.  (top) Under one-to-one subtyping, every unique sequence represents a different subtype. This strategy is more common single-locus typing or pathogens with a relatively slow rate of evolution, which limits the variation that accumulates over time.  (bottom) With cluster-based subtyping, multiple sequences are assigned to the same subtype.  This strategy is favoured under multi-locus or whole-genome sequencing or rapidly evolving pathogens, where every sampled infection may have a unique sequence.]]

Today it is more common to use genetic sequencing to characterize the microbial sample at the level of its nucleotide sequence by sequencing the whole genome or proportions thereof.<ref name="Ronholm2016">{{cite journal| author=Ronholm J, Nasheri N, Petronella N, Pagotto F| title=Navigating Microbiological Food Safety in the Era of Whole-Genome Sequencing | journal=Clin Microbiol Rev | year= 2016 | volume= 29 | issue= 4 | pages= 837–57 | pmid=27559074 | doi=10.1128/CMR.00056-16 | pmc=5010751 }}</ref>
However, other molecular methods such as [[Restriction fragment length polymorphism|restriction length fragment polymorphism]]
<ref name="Olive1999" />
have historically played an important role in microbial subtyping before genetic sequencing became an affordable and ubiquitous technology in reference laboratories.
Sequence-based typing methods confer an advantage over other laboratory methods (such as [[Serotype|serotyping]] or [[pulsed-field gel electrophoresis]]
<ref name="Barrett2006">{{cite journal| author=Barrett TJ, Gerner-Smidt P, Swaminathan B| title=Interpretation of pulsed-field gel electrophoresis patterns in foodborne disease investigations and surveillance | journal=Foodborne Pathog Dis | year= 2006 | volume= 3 | issue= 1 | pages= 20–31 | pmid=16602976 | doi=10.1089/fpd.2006.3.20 }}</ref>) 
because there is an enormous number of potential subtypes that can be resolved at the level of the genetic sequence.
Consider the above example again; however, this time host A carries the same infection subtype as many other hosts.
In this case we would have no information to differentiate between these hosts as the potential source of host A's infection.
Our ability to identify potential sources, therefore, depends on having a sufficient number of different subtypes.
However, defining too many subtypes in the population makes it likely that every individual carries a unique subtype, especially for rapidly-evolving pathogens that can accumulate high levels of genetic diversity in a relatively short period of time.
Hence, there exists an intermediate level of subtype resolution that confers the greatest amount of information for source attribution.<ref name="Prosperi2010">{{cite journal| author=Prosperi MC, De Luca A, Di Giambenedetto S, Bracciale L, Fabbiani M, Cauda R | display-authors=etal| title=The threshold bootstrap clustering: a new approach to find families or transmission clusters within molecular quasispecies | journal=PLOS ONE | year= 2010 | volume= 5 | issue= 10 | pages= e13619 | pmid=21049051 | doi=10.1371/journal.pone.0013619 | pmc=2963616 | bibcode=2010PLoSO...513619P| doi-access=free}}</ref>
When source attribution is considered for a pathogen with high diversity, such that most specimens have unique genetic sequences, it is useful to group multiple unique sequences with a [[Molecular source attribution#Genetic clustering|clustering method]].

=== Single and multi-locus typing ===

Before whole-genome sequencing was cost-effective, targeting a specific part of the pathogen genome (a.k.a. single-locus typing) was an important step to facilitate microbial subtyping.
For example, the [[16S ribosomal RNA|ribosomal gene 16S]] is a standard target for identifying bacteria, in part because it is present across all known species and contains a mixture of conserved and variable regions.<ref name="Clarridge2004">{{cite journal| author=Clarridge JE| title=Impact of 16S rRNA gene sequence analysis for identification of bacteria on clinical microbiology and infectious diseases | journal=Clin Microbiol Rev | year= 2004 | volume= 17 | issue= 4 | pages= 840–62, table of contents | pmid=15489351 | doi=10.1128/CMR.17.4.840-862.2004 | pmc=523561 }}</ref>
Within a pathogen species, sequencing targets tended to be selected on the basis of their length, ubiquity and exposure to diversifying selection, which may be dictated by the function of the gene product for expressed regions.
For example, so-called [[Housekeeping gene|"housekeeping"]] or core genes have indispensable biological functions, such as copying genetic material or building proteins.
These genes are often preferred candidates for microbial subtyping because they are less likely to be absent from a given genome.<ref name="Leekitcharoenphon2012">{{cite journal| author=Leekitcharoenphon P, Lukjancenko O, Friis C, Aarestrup FM, Ussery DW| title=Genomic variation in Salmonella enterica core genes for epidemiological typing | journal=BMC Genomics | year= 2012 | volume= 13 | issue=  | pages= 88 | pmid=22409488 | doi=10.1186/1471-2164-13-88 | pmc=3359268 | doi-access=free }}</ref>
Gene presence/absence is particularly relevant for bacteria where genetic material is frequently exchanged through [[horizontal gene transfer]].{{citation needed|date=May 2023}}

Targeting multiple regions ([[Locus (genetics)|loci]]) of the pathogen genome confers greater precision to distinguish between lineages, since the chance to observe [[Synapomorphy and apomorphy|informative genetic differences]] between infections is increased.
This approach is referred to as multi-locus sequence typing (MLST).<ref name="Urwin2003">{{cite journal| author=Urwin R, Maiden MC| title=Multi-locus sequence typing: a tool for global epidemiology | journal=Trends Microbiol | year= 2003 | volume= 11 | issue= 10 | pages= 479–87 | pmid=14557031 | doi=10.1016/j.tim.2003.08.006 }}</ref>
Similar to single-locus typing, MLST requires the selection of specific loci to target for sequencing.
Moreover, for subtyping to be consistent across laboratories a reference database must be maintained that maps sequences from single or multiple loci to a fixed notation of allele numbers or designations.<ref name="Chan2001">{{cite journal| author=Chan MS, Maiden MC, Spratt BG| title=Database-driven multi locus sequence typing (MLST) of bacterial pathogens | journal=Bioinformatics | year= 2001 | volume= 17 | issue= 11 | pages= 1077–83 | pmid=11724739 | doi=10.1093/bioinformatics/17.11.1077 }}</ref>

==== Whole genome sequencing ====

Although single- and multiple-locus subtyping is still predominantly used for [[molecular epidemiology]], ongoing improvements in sequencing technologies and computing power continue to lower the barrier to whole-genome sequencing.
[[Massive parallel sequencing|Next-generation sequencing]] (NGS) technologies provide cost-effective methods to generate whole genome sequences from a given sample by individually amplifying and sequencing templates in parallel using customized technologies such as [[Illumina dye sequencing|sequencing-by-synthesis]].<ref name="Fuller2009">{{cite journal| author=Fuller CW, Middendorf LR, Benner SA, Church GM, Harris T, Huang X | display-authors=etal| title=The challenges of sequencing by synthesis | journal=Nat Biotechnol | year= 2009 | volume= 27 | issue= 11 | pages= 1013–23 | pmid=19898456 | doi=10.1038/nbt.1585 | pmc= | s2cid=205274400 }}</ref>
[[Shotgun sequencing]] applications of NGS generate full-length genome sequences by shearing the nucleic acid extracted from the sample into small fragments that are converted into a sequencing library, and then using a [[De novo sequence assemblers|''de novo'' sequence assembler]] program the genome sequence is reconstituted from the sequence fragments (short reads).<ref name="Kisand2013">{{cite journal| author=Kisand V, Lettieri T| title=Genome sequencing of bacteria: sequencing, de novo assembly and rapid analysis using open source tools | journal=BMC Genomics | year= 2013 | volume= 14 | issue=  | pages= 211 | pmid=23547799 | doi=10.1186/1471-2164-14-211 | pmc=3618134 | doi-access=free }}</ref>
Alternatively, short reads can be mapped to a reference genome sequence that has been [[Burrows–Wheeler transform|converted into an index]] for efficient lookup of exact substring matches.
This approach can be faster than ''de novo'' assembly, but relies on having a reference genome that is sufficiently similar to the genome sequence of the sample.
While NGS makes it feasible to simultaneously generate full-length genome sequences from hundreds of pathogen samples in a single run, it introduces a number of other challenges.
For instance, NGS platforms tend to have higher sequencing error rates than conventional sequencing, and regions of the genome with long stretches of repetitive sequence can be difficult to reassemble.{{citation needed|date=May 2023}}

Whole genome sequencing (WGS) can confer a significant advantage for source attribution over single- or multiple-locus subtyping.
Sequencing the entire genome is the maximal extent of multi-locus typing, in that all possible loci are covered.
Having whole genome sequences will tend to make one-to-one subtyping ('''Figure 1''') less useful, since most genomes will be unique by at least one mutation for rapidly evolving pathogens.
Consequently, applications of WGS for source attribution at a population level will likely have to [[Molecular source attribution#Genetic clustering|cluster similar genomes together]].<ref name="Zhang2019">{{cite journal| author=Zhang S, Li S, Gu W, den Bakker H, Boxrud D, Taylor A | display-authors=etal| title=Zoonotic Source Attribution of Salmonella enterica Serotype Typhimurium Using Genomic Surveillance Data, United States | journal=Emerg Infect Dis | year= 2019 | volume= 25 | issue= 1 | pages= 82–91 | pmid=30561314 | doi=10.3201/eid2501.180835 | pmc=6302586 }}</ref>

The breadth of coverage offered by WGS is more advantageous for the epidemiology of bacterial pathogens than viruses.
Bacterial genomes tend to be longer, ranging from [[Genome size|about 10<sup>6</sup> to 10<sup>7</sup> base pairs]], whereas virus genomes [[Virus#Genome size|seldom exceed 10<sup>6</sup>]] base pairs.
In addition, bacteria tend to evolve at a [[Rate of evolution|slower rate]] than viruses, so mutations tend to be distributed more sparsely throughout a bacterial genome.
For example, WGS data revealed differences between isolates of ''Burkholderia pseudomallei'' from Australia and Cambodia that had otherwise appeared to be identical by multi-locus subtyping due to convergent evolution.<ref name="deSmet2014">{{cite journal| author=De Smet B, Sarovich DS, Price EP, Mayo M, Theobald V, Kham C | display-authors=etal| title=Whole-genome sequencing confirms that Burkholderia pseudomallei multilocus sequence types common to both Cambodia and Australia are due to homoplasy | journal=J Clin Microbiol | year= 2015 | volume= 53 | issue= 1 | pages= 323–6 | pmid=25392354 | doi=10.1128/JCM.02574-14 | pmc=4290968 }}</ref>
WGS has also been utilized in several recent studies to resolve transmission networks of ''[[Mycobacterium tuberculosis]]'' in greater detail, because isolates with identical multi-locus subtypes (''e.g.'', [[Variable number tandem repeat|MIRU-VNTR]] profiles targeting 24 loci) were frequently separated by large numbers of nucleotide differences in the full genome sequence, comprising roughly 4.3 million nucleotides encodoing over 4,000 genes.<ref name="Walker2012">{{cite journal| author=Walker TM, Ip CL, Harrell RH, Evans JT, Kapatai G, Dedicoat MJ | display-authors=etal| title=Whole-genome sequencing to delineate Mycobacterium tuberculosis outbreaks: a retrospective observational study | journal=Lancet Infect Dis | year= 2013 | volume= 13 | issue= 2 | pages= 137–46 | pmid=23158499 | doi=10.1016/S1473-3099(12)70277-3 | pmc=3556524 }}</ref><ref name="Gardy2011">{{cite journal| author=Gardy JL, Johnston JC, Ho Sui SJ, Cook VJ, Shah L, Brodkin E | display-authors=etal| title=Whole-genome sequencing and social-network analysis of a tuberculosis outbreak | journal=N Engl J Med | year= 2011 | volume= 364 | issue= 8 | pages= 730–9 | pmid=21345102 | doi=10.1056/NEJMoa1003176 | pmc= | doi-access=free }}</ref>

=== Genetic clustering ===

When applied to genetic sequences, a clustering method is a set of rules for assigning the sequences to a smaller number of clusters such that members of the same cluster are more genetically similar to each other than sequences in other clusters.  Put another way, a clustering method defines a [[Partition of a set|partition]] on the set of genetic sequences using some [[similarity measure]].  Clustering is inherently subjective and there are usually no formal guidelines for setting the clustering criteria.  Consequently, cluster definitions can vary substantially from one study to the next.  In addition, clustering is an intuitive process that can be accomplished by a wide variety of approaches; because of this flexibility, numerous different methods of genetic clustering have been described in the literature.<ref name="Poon2016">{{cite journal| author=Poon AF| title=Impacts and shortcomings of genetic clustering methods for infectious disease outbreaks | journal=Virus Evol | year= 2016 | volume= 2 | issue= 2 | pages= vew031 | pmid=28058111 | doi=10.1093/ve/vew031 | pmc=5210024 }}</ref>

Genetic clustering provides a way of dealing with sequences from rapidly evolving pathogens, or whole genome sequences from pathogens with less divergence.
In either case, there can be an enormous number of distinct genetic sequences in the data set.
If each subtype must correspond to a unique sequence variant, then one could potentially have to track an unwieldy number of microbial subtypes for these pathogens when subtypes are defined on a one-to-one basis ('''Figure 1''').
The number of subtypes can be greatly reduced by expanding the definition of microbial subtypes from individually unique sequence variants to clusters of similar sequences.<ref name="Mullner2009">{{cite journal| author=Mullner P, Jones G, Noble A, Spencer SE, Hathaway S, French NP| title=Source attribution of food-borne zoonoses in New Zealand: a modified Hald model | journal=Risk Anal | year= 2009 | volume= 29 | issue= 7 | pages= 970–84 | pmid=19486473 | doi=10.1111/j.1539-6924.2009.01224.x | pmc= | bibcode=2009RiskA..29..970M | s2cid=44680928 }}</ref>
For example, pairwise distance clustering is a [[Nonparametric statistics|nonparametric]] approach in which clusters are assembled from pairs of sequences that fall within a threshold distance of each other.
The distance between sequences is computed by a [[genetic distance]] measure (a mathematical formula that maps two sequences to a non-negative [[real number]]) that quantifies the evolutionary divergence between the sequences under some model of molecular evolution.

==Frequency-based attribution==

When the potential sources are populations, not individuals, then we are comparing the frequencies of subtypes in the respective populations.
The most likely source population should have a subtype frequency distribution that is the most similar to the reference population.
Methods that employ this approach have been referred to as "frequency-based" or "frequency-matching" models.<ref name="Mughini-Gras2018">{{cite journal| author=Mughini-Gras L, Franz E, van Pelt W| title=New paradigms for Salmonella source attribution based on microbial subtyping | journal=Food Microbiol | year= 2018 | volume= 71 | issue=  | pages= 60–67 | pmid=29366470 | doi=10.1016/j.fm.2017.03.002 | hdl=1874/375921 | hdl-access=free }}</ref>
These subtypes are not necessarily derived from molecular data; for instance, these methods were originally applied to microbial strains defined by non-genetic [[Serotype|antigenic]] or [[Antibiotic#Resistance|resistance]] profiling.
For example, the "Dutch model"
<ref name="VanPelt1999">Van Pelt W, Van de Giessen AW, Van Leeuwen WJ, Wannet W, Henken AM, Evers EG, De Wit MAS, Van Duynhoven WYHP. '''Oorsprong, omvang en kosten van humane salmonellose (Dutch).''' ''Infectieziekten Bulletin'' 1999;10(12):240-243. Available from: [https://www.rivm.nl/documenten/jaargang-10-nummer-12-december-1999 https://www.rivm.nl/documenten/jaargang-10-nummer-12-december-1999]</ref>
was originally developed to estimate the most likely source of a number of [[foodborne illness]]es due to ''[[Salmonella]]'' by comparing the relative frequencies of bacterial subtypes (based on [[phage typing]]) in different commercial livestock populations (including poultry, swine and cattle) through routine surveillance programs.
For a given subtype, the expected number of human cases attributed to each source is proportional to the relative frequencies of that subtype among sources:

:<math>\lambda_{ij}=\frac{p_{ij}}{\sum_{j}p_{ij}}n_i</math>

where <math>p_{ij}</math> is the proportion of (non-human) cases in the <math>j</math>-th source population associated with subtype <math>i</math>, and <math>n_i</math> is the number of cases of subtype <math>i</math> in the recipient (human) population.
For instance, if the frequencies of subtype X among three potential sources was 0.8, 0.5 and 0.1, respectively, then the expected number of cases (out of a total of 100) from the second source is <math>0.5/(0.8+0.5+0.1)\times 100=35.7</math>.
This simple formula is a [[Maximum likelihood estimation|maximum likelihood estimator]] when the total [[force of infection]] from each source into the human population is uniform, ''e.g.'', the sources have equal population sizes.

[[File:Hald model parameters.png|thumb|320px|Summary of Hald model parameters.
Arbitrary numbers are provided for observed quantities, such as the proportion of infections due to subtype 1 in source population 2 (<math>p_{12}</math>).  The marginal effect associated with source population 2 (<math>a_2</math>) is represented by an open rectangle (solid line); while the total size of this source population <math>M_2</math> is observed, <math>a_2</math> must be estimated by regression.  Similarly, the marginal effect associated with subtype 3 (<math>q_3</math>), indicated by a rectangular shaded region) is simultaneously estimated by regression.]]

Subsequently, this model was extended by Hald and colleagues 
<ref name="Hald2007">{{cite journal| author=Hald T, Lo Fo Wong DM, Aarestrup FM| title=The attribution of human infections with antimicrobial resistant Salmonella bacteria in Denmark to sources of animal origin | journal=Foodborne Pathog Dis | year= 2007 | volume= 4 | issue= 3 | pages= 313–26 | pmid=17883315 | doi=10.1089/fpd.2007.0002 }}</ref>
to account for  variation among sources and subtypes using [[Bayesian inference]] methods.
This extension, typically referred to as the Hald model, has become a standard model in source attribution for food-borne illnesses.
The observed numbers of each subtype in the human population was assumed to be a [[Poisson regression|Poisson distributed]] outcome with a mean <math>\lambda_i</math> for the ''i''-th subtype, after adjusting for cases related to travel and outbreaks:

:<math>\lambda_i = \sum_i \lambda_{ij} = \sum_i  q_i M_j a_j p_{ij}</math>

where <math>q_i</math> is the [[Main effect|marginal effect]] of the ''i''-th subtype (''e.g.'', elevated infectiousness of a bacterial variant), <math>M_j</math> is the observed total amount ([[mass]]) of the ''j''-th food source, <math>a_j</math> is the marginal effect of the ''j''-th food source, and <math>p_{ij}</math> is the same observed case proportion as the original "Dutch" model.
This model is visualized in '''Figure 2'''.

=== Bayesian inference ===

The addition of a large number of parameters to the "Dutch" model by Hald and colleagues yielded a more realistic model.  However, it was too complex to solve for exact maximum likelihood estimates, in contrast to the original model.  Many of the parameters could not be directly measured, such as the relative transmission risk associated with a specific food source.  Consequently, Hald and colleagues adopted a [[Bayesian inference|Bayesian]] approach to estimate the model parameters.  A similar approach has also been used to reconstruct the contribution of different environmental and livestock reservoirs of the bacteria  ''[[Campylobacter jejuni]]'' to an outbreak of food poisoning in England,<ref name="Wilson2008">{{cite journal| author=Wilson DJ, Gabriel E, Leatherbarrow AJ, Cheesbrough J, Gee S, Bolton E | display-authors=etal| title=Tracing the source of campylobacteriosis | journal=PLOS Genet | year= 2008 | volume= 4 | issue= 9 | pages= e1000203 | pmid=18818764 | doi=10.1371/journal.pgen.1000203 | pmc=2538567 | doi-access=free}}</ref> 
where the migration of different subtypes among reservoirs was jointly estimated by Bayesian methods.

Although [[Bayesian inference]] is discussed extensively elsewhere, it plays an important role in computationally-intensive methods of source attribution, so we provide a brief description here.
In the context of Bayesian inference every [[parameter]] is described by a [[probability distribution]] that represents our ''[[belief]]'' about its true value.
Thus, the statistical principle that underlies Bayesian inference (''i.e.,'' [[Bayes theorem|Bayes' rule]]) can be expressed in terms of the model parameters (<math>\theta</math>) and the data (<math>D</math>):

:<math>P(\theta \mid D) \propto P(D \mid \theta)P(\theta)</math>

where <math>P(\theta \mid D)</math>, <math>P(D \mid \theta)</math> and <math>P(\theta)</math> are known as the [[Posterior probability|posterior]], [[Sampling distribution|sampling]] ([[Likelihood function|likelihood]]), and [[Prior probability|prior]] distributions, respectively.
A simple way to think about Bayesian inference is that our prior belief about the parameters is "updated" once we have seen the data.
As a result, our posterior belief becomes a compromise between our prior belief and the data.
To update our belief, we need to have a [[sampling distribution]] or model that describes the probability of different outcomes of an experiment.
We also require a prior distribution that represents our belief in a statistical form.
While modern computation allows almost any [[probability distribution]] to be used, the [[Continuous uniform distribution|uniform distribution]] is commonly used because it assigns the same probability to every value within some range.
After incorporating new information from the data, our updated belief about the model parameters is represented by the [[Posterior probability|posterior distribution]].
This use of distributions to represent our belief distinguishes Bayesian inference from maximum likelihood, which results in a single combination of parameter values as a [[Point estimation|point estimate]].

Hald and colleagues used [[Uniform distribution (continuous)|uniform]] prior distributions for many of their parameters to express the prior belief that the true value fell within a [[Probability density function|continuous range]] with specific upper and lower limits.
They constrained some parameters to take the same numerical value as others.
For example, the effects of domestic and imported supplies of the same food source were linked in this manner.
This assumption expressed a strong belief that a given food source carried the same transmission risk irrespective of its origin, and simplified the model so that it was more feasible to fit the data.
Other parameters were set to a fixed [[Reference range|reference value]] to further simplify the model.

Hald and colleagues employed a [[Poisson distribution|Poisson model]] to describe the probability of observing the number (<math>Y</math>) of rare transmission events that occur at a rate <math>\lambda</math>.  As described above, the rate of cases due to a specific bacterial subtype was the sum of transmission rates across all potential sources.  The Hald model was more realistic than the "Dutch" model because it allowed transmission rates to vary between subtypes and food sources.  However, it was not feasible to directly measure these different rates &mdash; these parameters needed to be estimated from the data.

==Comparative methods==

Instead of comparing the frequencies of subtypes to reconstruct the transmission of pathogens between populations, many source attribution methods compare the pathogen sequences at the level of individual hosts.
One way of comparing sequences is to calculate some measure of genetic distance or similarity, a concept that we introduced earlier on the topic of [[Molecular source attribution#Genetic clustering|pooling sequences into composite subtypes]].
For example, infections that are grouped into clusters are assumed to be related through one or more recent and rapid transmission events.
Short genetic distances imply that limited time has passed for mutations to accumulate in lineages descending from their common ancestor.
Consequently, these clusters are often referred to as "transmission clusters".
Other studies have used genetic distances that exceed some threshold to rule out host individuals as potential sources of transmission.<ref name="Ypma2011">{{cite journal| author=Ypma RJ, Bataille AM, Stegeman A, Koch G, Wallinga J, van Ballegooijen WM| title=Unravelling transmission trees of infectious diseases by combining genetic and epidemiological data | journal=Proc Biol Sci | year= 2012 | volume= 279 | issue= 1728 | pages= 444–50 | pmid=21733899 | doi=10.1098/rspb.2011.0913 | pmc=3234549 }}</ref><ref name="Walker2012"/>
Although this application of clustering is related to source attribution, it is not possible to infer the direction of transmission solely from the genetic distance between infections.
Furthermore, the genetic distance separating infections is not solely determined by the rate of transmission; for example, they are strongly influenced by how infections are sampled from the population.<ref name="Volz2012a">{{cite journal| author=Volz EM, Koopman JS, Ward MJ, Brown AL, Frost SD| title=Simple epidemiological dynamics explain phylogenetic clustering of HIV from patients with recent infection | journal=PLOS Comput Biol | year= 2012 | volume= 8 | issue= 6 | pages= e1002552 | pmid=22761556 | doi=10.1371/journal.pcbi.1002552 | pmc=3386305 | bibcode=2012PLSCB...8E2552V | doi-access=free }}</ref><ref name="Poon2016" />

Sequences can also be compared in the context of their shared evolutionary history.
A [[Phylogenetic Tree|phylogenetic tree]] or phylogeny is a hypothesis about the common ancestry of species or populations.  In the context of [[molecular epidemiology]], phylogenies are used to relate infections in different hosts and are usually reconstructed from genetic sequences of each pathogen population.  To reconstruct the phylogeny, the sequences must cover the same parts of the pathogen genome; for example, sequences that represent multiple copies of the same gene from different infections.  It is this residual similarity ([[Sequence homology|homology]]) between diverging populations that implies recent common ancestry.  A molecular phylogeny comprises "tips" or "leaves" that represent different genetic sequences that are connected by branches to a series of common ancestors that eventually converge to a "root".  The composition of the ancestral sequence at the root, the order of branching events, and the relative amount of change along each branch are all quantities that must be extrapolated from the observed sequences at the tips.
There are multiple approaches to [[Computational phylogenetics|reconstruct a phylogenetic tree]] from genetic sequence variation.<ref name="YangRannala">{{cite journal| author=Yang Z, Rannala B| title=Molecular phylogenetics: principles and practice | journal=Nat Rev Genet | year= 2012 | volume= 13 | issue= 5 | pages= 303–14 | pmid=22456349 | doi=10.1038/nrg3186 | pmc= | s2cid=1313627 }}</ref>
For example, [[Distance matrices in phylogeny|distance-based methods]] use a [[hierarchical clustering]] method to build up a tree based on the observed genetic distances.

=== Phylogenetic uncertainty ===

A common simplifying assumption in phylogenetic investigations is that the phylogenetic tree reconstructed from the data is the "true" tree &mdash; that is, an accurate representation of the common ancestry relating the sampled infections.
For instance, a single tree is often used as the input for comparative methods to detect the signature of [[Ka/Ks ratio|natural selection in protein-coding sequences]].
On the other hand, if the phylogeny is handled as an uncertain estimate derived from the data (including the sequence alignment), then the analysis becomes a [[Bayesian network|hierarchical model]] in which the problem of phylogenetic reconstruction is nested within the problem of estimating the other model parameters that are [[Conditional probability|conditional]] on the phylogeny ('''Figure 3''').
Sampling both the phylogeny and other model parameters from their joint posterior distribution using methods such as [[Markov chain Monte Carlo]] (MCMC) should confer more accurate parameter estimates.
However, the greatly expanded model space also makes it more difficult for MCMC samples to [[Markov chain Monte Carlo#Convergence|converge]] to the posterior distribution.
Such hierarchical methods are often implemented in the software package [https://www.beast2.org/ BEAST2]
<ref name="BEAST2">{{cite journal| author=Bouckaert R, Heled J, Kühnert D, Vaughan T, Wu CH, Xie D | display-authors=etal| title=BEAST 2: a software platform for Bayesian evolutionary analysis | journal=PLOS Comput Biol | year= 2014 | volume= 10 | issue= 4 | pages= e1003537 | pmid=24722319 | doi=10.1371/journal.pcbi.1003537 | pmc=3985171 | bibcode=2014PLSCB..10E3537B| doi-access=free}}</ref>
(Bayesian Evolutionary Analysis by Sampling Trees), which provides generic routines for MCMC sampling from tree space, and calculates the likelihood of a time-scaled phylogenetic tree given sequence data and sample collection dates.

There are a number of sources of phylogenetic uncertainty.
For instance, the common ancestry of lineages can be difficult to reconstruct if there has been little to no evolution along the respective branches.
This can occur when the rate of evolution is substantially slower than the time scale of transmission, such that mutations are unlikely to accumulate between the start of one infection and its transmission to the next host (''i.e.'', the generation time).
It can also arise when existing divergence is not captured due to incomplete sequencing of the respective genomes.
Furthermore, reconstructing the common ancestry of lineages is progressively more uncertain as we move deeper into the tree, forcing us to extrapolate the ancestral states at greater distances from the observed data.

====Alignment====
Reconstructing phylogenies from molecular sequences generally requires a [[multiple sequence alignment]], a table in which homologous residues in different sequences occupy the same position.
Although alignments are often treated as observed data known without ambiguity, the process of aligning sequences is also uncertain and can become more difficult with the rapid accumulation of sequence [[Indel|insertions and deletions]] among diverging pathogen lineages.
While there are Bayesian methods that address uncertainty in alignment by joint sampling of the alignment along with the phylogeny,<ref name="Redelings2005">{{cite journal| author=Redelings BD, Suchard MA| title=Joint Bayesian estimation of alignment and phylogeny | journal=Syst Biol | year= 2005 | volume= 54 | issue= 3 | pages= 401–18 | pmid=16012107 | doi=10.1080/10635150590947041 | pmc= | doi-access=free }}</ref> 
this approach is computationally complex and is seldom used in the context of source attribution.
Furthermore, sequences are themselves uncertain estimates of the genetic composition of individual pathogens or infecting populations, and [[DNA sequencing#Next-generation methods|next-generation sequencing]] technologies tend to have substantially higher error rates than conventional [[Sanger sequencing]],<ref name="vanDijk2018">{{cite journal| author=van Dijk EL, Jaszczyszyn Y, Naquin D, Thermes C| title=The Third Revolution in Sequencing Technology | journal=Trends Genet | year= 2018 | volume= 34 | issue= 9 | pages= 666–681 | pmid=29941292 | doi=10.1016/j.tig.2018.05.008 | pmc= | s2cid=49408925 }}</ref> 
and analysis pipelines must be carefully validated to reduce the effects of sample cross-contamination and adapter contamination.

====Recombination====
[[Genetic recombination]] is the exchange of genetic material between individual genomes.
For pathogens, recombination can occur when a cell is infected by multiple copies of the pathogen.
If some hosts were infected multiple times by two or more divergent variants from different sources (''i.e.,'' [[superinfection]]), then recombination can produce mosaic genomes that complicate the reconstruction of an accurate phylogeny.<ref name="Posada2002">{{cite journal| author=Posada D, Crandall KA| title=The effect of recombination on the accuracy of phylogeny estimation | journal=J Mol Evol | year= 2002 | volume= 54 | issue= 3 | pages= 396–402 | pmid=11847565 | doi=10.1007/s00239-001-0034-9 | pmc= | bibcode=2002JMolE..54..396P | s2cid=10214942 }}</ref>
In other words, different segments of a recombinant genome may be related to other genomes through discordant phylogenies in such a way that cannot be accurately represented by a single tree.
In practice, it is common to screen for recombinant sequences and discard them before reconstructing a phylogeny from an alignment that is assumed to be free of recombination.<ref name="Rose2018">{{cite journal| author=Rose R, Lamers SL, Massaccesi G, Osburn W, Ray SC, Thomas DL | display-authors=etal| title=Complex patterns of Hepatitis-C virus longitudinal clustering in a high-risk population | journal=Infect Genet Evol | year= 2018 | volume= 58 | issue=  | pages= 77–82 | pmid=29253674 | doi=10.1016/j.meegid.2017.12.015 | pmc=5807204 | bibcode=2018InfGE..58...77R}}</ref>

=== Inferring transmission history from the phylogeny ===

The basic premise in applying phylogenetics to source attribution is that the shape of the phylogenetic tree approximates the transmission history,<ref name="Leitner1996">{{cite journal| author=Leitner T, Escanilla D, Franzén C, Uhlén M, Albert J| title=Accurate reconstruction of a known HIV-1 transmission history by phylogenetic tree analysis | journal=Proc Natl Acad Sci U S A | year= 1996 | volume= 93 | issue= 20 | pages= 10864–9 | pmid=8855273 | doi=10.1073/pnas.93.20.10864 | pmc=38248 | bibcode=1996PNAS...9310864L | doi-access=free }}</ref> 
which can also be represented by a tree where each split into two branches represents the transmission of an infection from one host to another.
In conjunction with reconstructing the transmission tree from other sources of information, such as [[contact tracing]], reconstructing a phylogenetic tree can serve as a useful, additional information source especially when genetic sequences are already available.
Because of the visual and conceptual similarity between phylogenetic and transmission trees, it is a common assumption that the branching points (splits) of the phylogeny represent transmission events.
However, this assumption will often be inaccurate.
A transmission event may have occurred at any point along the two branches that separate one sampled infection from the other in the virus phylogeny ('''Figure 3A''').
The transmission tree only constrains the shape of the phylogenetic tree.
Thus, even if we can reconstruct the phylogenetic tree without error, there are several reasons why it will not be an accurate representation of the transmission tree, including incomplete sampling, pathogen evolution within hosts, and secondary infection of the same host.

[[File:Transmission tree issues.png|thumb|350px|Some challenges equating phylogeny with transmission history. 
The solid lines represent the phylogenetic relationship between infections that have been sampled from two or three different hosts.  Shaded regions correspond to the location of virus lineages in different hosts, as indicated by colour.  The transmission of a lineage from one host to another is represented by a gap between shaded regions and highlighted with a red arrow. (A) Although hosts 1 and 2 are closely related, the phylogeny does not indicate whether the infection was transmitted from host 2 to 1 (as shown), or vice versa.  The transmission event may be located anywhere along the two branches connecting the hosts. (B) An infection may have been transmitted through any number of unsampled hosts before reaching the host that was sampled.  (C) An unsampled host may be the source of infections transmitted to both hosts 1 and 2. (D) If pathogens establish a large diverse population within each host, the branches of the phylogeny may occur in a different order than the transmission history; as shown, hosts 1 and 3 are more closely related in the transmission history, but not in the phylogeny. ]]

==== Incomplete sampling ====

Equating the phylogenetic tree with the transmission history implicitly assumes that genetic sequences have been obtained from every infected host in the epidemic.  In practice, only a fraction of infected hosts are represented in the sequence data.  The existence of an unknown and inevitably substantial number of unsampled infected hosts is a major challenge for source attribution.  Even if the phylogenetic tree indicates that two infections are most closely related than any other sampled infection, one cannot rule out the existence of one or more unsampled hosts whom are intermediate links in the "transmission chain" separating the known hosts ('''Figure 3B''').  Similarly, an unsampled infection may have been the source population for both observed infections at the tips of the tree ('''Figure 3C''').  By itself, the phylogenetic tree does not explicitly discriminate among these alternative transmission scenarios.

==== Evolution within hosts ====

The shape of the phylogenetic tree may diverge from the underlying transmission history because of the evolution of diverse populations of the pathogen within each host.  Individual copies of the pathogen genome that are transmitted to the next host are, by definition, no longer in the source population.  A split exists in the phylogenetic tree that represents the common ancestor between the transmitted lineages and the other lineages that have remained and persisted in the source population.  If we follow both sets of lineages back in time, the time of the transmission event is the ''most recent'' possible time that they could converge to a common ancestor.  Put another way, this event represents one extreme of a continuous range where the common ancestor is located further back in time.

This process is often modelled by [[John Kingman|Kingman's]] [[Coalescent theory|coalescent]],<ref name="Kingman1982">{{cite journal |last1=Kingman |first1=J.F.C. |title=The coalescent |journal=Stochastic Processes and Their Applications |date=September 1982 |volume=13 |issue=3 |pages=235–248 |doi=10.1016/0304-4149(82)90011-4 |doi-access=free }}</ref> which describes the number of generations we expect to follow randomly selected lineages back in time until we encounter a common ancestor.
The expected time until two lineages converge to a common ancestor, known as a coalescence event, is proportional to the [[effective population size]], which determines the number of possible ancestors.
Put another way, two randomly selected people in a large city are less likely to have a great-grandparent in common than two people in a small rural community.

Longer coalescence times in a large, diverse within-host pathogen populations are a significant challenge for source attribution, because it uncouples the virus phylogeny from the transmission tree.
For example, if a host has transmitted their infection to two others, then there can be as many as three sets of lineages whose ancestry can be traced in the source population in that host ('''Figure 3D''').
As a result, there is some chance that the branching order in the virus phylogeny implies a different order of transmission events if we interpret the phylogeny as equivalent to a transmission tree.
For example, in '''Figure 3D''' hosts 1 and 3 are more closely related in the transmission history, but not in the phylogeny.

==== Clearance and secondary infection ====

Many infections can be spontaneously cleared by the host's immune system.  If a host that has cleared a previously diagnosed infection becomes re-infected from another source, then it is possible for the same host to be represented by different infections in the phylogenetic and transmission trees, respectively.  In addition, some individuals may become infected from multiple different sources.  For example, roughly one-third of infections by [[Hepacivirus C|hepatitis C virus]] are spontaneously cleared within the first six months of infection.<ref name="Thomas2009">{{cite journal| author=Thomas DL, Thio CL, Martin MP, Qi Y, Ge D, O'Huigin C | display-authors=etal| title=Genetic variation in IL28B and spontaneous clearance of hepatitis C virus | journal=Nature | year= 2009 | volume= 461 | issue= 7265 | pages= 798–801 | pmid=19759533 | doi=10.1038/nature08463 | pmc=3172006 | bibcode=2009Natur.461..798T}}</ref>  
This previous exposure, however, does not confer immunity to re-infection by the same virus.<ref name="Bowen2005">{{cite journal| author=Bowen DG, Walker CM| title=Adaptive immune responses in acute and chronic hepatitis C virus infection | journal=Nature | year= 2005 | volume= 436 | issue= 7053 | pages= 946–52 | pmid=16107834 | doi=10.1038/nature04079 | pmc= | bibcode=2005Natur.436..946B | s2cid=14374858 }}</ref>  
In addition, [[Coinfection|co-infection]] by multiple strains of hepatitis C virus that persist simultaneously within the same host can occur relatively frequently in populations with a high rate of transmission, such as people who inject drugs [[Needle sharing|using shared equipment]] (ranging from 14% to 39%).<ref name="Cunningham2015">{{cite journal| author=Cunningham EB, Applegate TL, Lloyd AR, Dore GJ, Grebely J| title=Mixed HCV infection and reinfection in people who inject drugs--impact on therapy | journal=Nat Rev Gastroenterol Hepatol | year= 2015 | volume= 12 | issue= 4 | pages= 218–30 | pmid=25782091 | doi=10.1038/nrgastro.2015.36 | pmc= | s2cid=19381093 | doi-access=free }}</ref>  
The persistence of strains from additional exposures may be missed by [[Sanger sequencing|conventional genetic sequencing]] techniques if they are present at a low frequencies within the host, necessitating the use of [[DNA sequencing#Next-generation methods|"next-generation" sequencing]] technologies.  For these reasons, the epidemiological linkage of hepatitis C virus infections through genetic similarity may be a transient phenomenon, leading some investigators to recommend using multiple virus sequences sampled from different time points of each infection for molecular epidemiology applications.<ref name="Rose2018"/>

===Ancestral host-state reconstruction===

[[Ancestral reconstruction]] is the application of a model of evolution to a phylogenetic tree to reconstruct [[Character evolution|character states]], such as nucleotide sequences or [[phenotype]]s, at the different ancestral nodes of the tree down to the root.<ref name="Joy2016">{{cite journal| author=Joy JB, Liang RH, McCloskey RM, Nguyen T, Poon AF| title=Ancestral Reconstruction | journal=PLOS Comput Biol | year= 2016 | volume= 12 | issue= 7 | pages= e1004763 | pmid=27404731 | doi=10.1371/journal.pcbi.1004763 | pmc=4942178 | bibcode=2016PLSCB..12E4763J | doi-access=free }}</ref>
In the context of source attribution, ancestral reconstruction is frequently used to estimate the geographic location of pathogen lineages as they are carried from one region to another by their hosts.<ref name="Dellicour2021">{{cite journal| author=Dellicour S, Durkin K, Hong SL, Vanmechelen B, Martí-Carreras J, Gill MS | display-authors=etal| title=A Phylodynamic Workflow to Rapidly Gain Insights into the Dispersal History and Dynamics of SARS-CoV-2 Lineages | journal=Mol Biol Evol | year= 2021 | volume= 38 | issue= 4 | pages= 1608–1613 | pmid=33316043 | doi=10.1093/molbev/msaa284 | pmc=7665608 }}</ref>
Drawing this analogy between character evolution and the spatial migration of individuals or populations is known as  [[phylogeography]],<ref name="Lemey2009">{{cite journal| author=Lemey P, Rambaut A, Drummond AJ, Suchard MA| title=Bayesian phylogeography finds its roots | journal=PLOS Comput Biol | year= 2009 | volume= 5 | issue= 9 | pages= e1000520 | pmid=19779555 | doi=10.1371/journal.pcbi.1000520 | pmc=2740835 | bibcode=2009PLSCB...5E0520L | doi-access=free }}</ref> 
where the geographic location of an ancestral population is reconstructed from the current locations of its sampled descendants under some model of migration.

Migration models generally fall into two categories of discrete-state and continuous-state models.
Discrete-state or island migration models assume that a given lineage is in one of a finite number of locations, and that it changes location at a constant rate over time according to a [[Markov chain#Continuous-time Markov chain|continuous-time Markov process]], analogous to the [[Substitution model|models used for molecular evolution]].
Ancestral reconstruction with a discrete-state migration model has also been utilized to reconstruct the early spread of HIV-1 in association with development of transport networks and increasing population density in central Africa.<ref name="Faria2014">{{cite journal| author=Faria NR, Rambaut A, Suchard MA, Baele G, Bedford T, Ward MJ | display-authors=etal| title=HIV epidemiology. The early spread and epidemic ignition of HIV-1 in human populations | journal=Science | year= 2014 | volume= 346 | issue= 6205 | pages= 56–61 | pmid=25278604 | doi=10.1126/science.1256739 | pmc=4254776 | bibcode=2014Sci...346...56F}}</ref>
Discrete models can also be applied to the population-level source attribution of zoonotic transmissions by reconstructing different host species as ancestral character states.
For example, a discrete trait model of evolution was used to reconstruct the ancestral host species in a phylogeny relating [[Staphylococcus aureus]] specimens from humans and domesticated animals.<ref name="Spoor2013">{{cite journal| author=Spoor LE, McAdam PR, Weinert LA, Rambaut A, Hasman H, Aarestrup FM | display-authors=etal| title=Livestock origin for a human pandemic clone of community-associated methicillin-resistant Staphylococcus aureus | journal=mBio | year= 2013 | volume= 4 | issue= 4 | pages=  | pmid=23943757 | doi=10.1128/mBio.00356-13 | pmc=3747577 }}</ref>
Similarly, Faria and colleagues
<ref name="Faria2013">{{cite journal| author=Faria NR, Suchard MA, Rambaut A, Streicker DG, Lemey P| title=Simultaneously reconstructing viral cross-species transmission history and identifying the underlying constraints | journal=Philos Trans R Soc Lond B Biol Sci | year= 2013 | volume= 368 | issue= 1614 | pages= 20120196 | pmid=23382420 | doi=10.1098/rstb.2012.0196 | pmc=3678322 }}</ref>
analyzed the cross-species transmission of [[rabies virus]] as a discrete diffusion process along the virus phylogeny, with rates influenced by the evolutionary relatedness and geographic range overlap of the respective host species.

Continuous-state migration models are more similar to models of [[Brownian motion]] in that a lineage may occupy any point within a defined space.
Although continuous models can be more realistic than discrete migration models, they may also be more challenging to fit to data.
Taken literally, a continuous model requires precise [[Geopositioning|geolocation]] data for every infection sampled from the population.
In many applications, however, these metadata are not available; for example, some studies approximate the true spatial distribution of sampled infections by the [[centroid]]s of their respective regions.<ref name="Pybus2012">{{cite journal| author=Pybus OG, Suchard MA, Lemey P, Bernardin FJ, Rambaut A, Crawford FW | display-authors=etal| title=Unifying the spatial epidemiology and molecular evolution of emerging epidemics | journal=Proc Natl Acad Sci U S A | year= 2012 | volume= 109 | issue= 37 | pages= 15066–71 | pmid=22927414 | doi=10.1073/pnas.1206598109 | pmc=3443149 | bibcode=2012PNAS..10915066P| doi-access=free}}</ref>
This can become problematic if the regions vary substantially in area, and host populations are seldom uniformly distributed within regions.

===Paraphyly===

Paraphyly is a term that originates from the study of [[cladistics]], an evolutionary approach to [[systematics]] that groups organisms on the basis of their common ancestry. A group of infections is [[Paraphyly|paraphyletic]] if the group includes the most recent common ancestor, but does not include all its descendants. In other words, one group is ''nested'' within an ancestral group.  For example, [[bird]]s are descended from a common ancestor that in turn shares a common ancestor with all [[reptile]]s; thus, birds are nested within the phylogeny of reptiles, making the latter a paraphyletic group.  Thus, paraphyly is evidence of evolutionary precedence: the ancestor of all birds was a reptile.  In the context of source attribution, paraphyly can be used as evidence that one infection preceded another.  It does not provide evidence that the infection was directly transmitted from one individual to another, in part because of [[#Incomplete sampling|incomplete sampling]].

The application of paraphyly for source attribution requires that the phylogenetic tree relates multiple copies of the pathogen from both the putative source and recipient hosts.  To elaborate, phylogenetic trees relating different infections are often reconstructed from population-based sequences (direct sequencing of the PCR amplification product), where each sequence represents the consensus of the individual pathogen genomes sampled from the infected host.  If copies of the pathogen genome are sequenced individually by [[Dilution cloning|limiting dilution protocols]] or [[Massive parallel sequencing|next-generation sequencing]], then one can reconstruct a tree that represents the [[genealogy]] of individual pathogen lineages, rather than the phylogeny of pathogen populations.

If sequences from host A form a [[Monophyly|monophyletic clade]] (in which members are the complete set of descendants from a common ancestor) that has a nested paraphyletic clade of sequences from host B, then the tree is consistent with the direction of transmission having originated from host A.<ref name="RomeroSeverson2016" />  
Directionality does not imply that host A directly transmitted their infection to host B, because the pathogen may have been transmitted through an unknown number of intermediate unsampled hosts before establishing an infection in host B.

==== Node support ====
The statistical confidence in directionality of transmission from a given tree is usually quantified by the support value associated with the node that is ancestral to the nested monophyletic clade.  The support of node ''X'' is the estimated probability that if we repeated the phylogenetic reconstruction on an equivalent data set, the new  tree would contain exactly the same clade consisting exclusively of all descendants of node ''X'' in the original tree.  In other words, it quantifies the reproducibility of that node given the data.  It should not be interpreted as the probability that the clade below node ''X'' appears in the "true" tree.<ref name="Lemoine2018">{{cite journal| author=Lemoine F, Domelevo Entfellner JB, Wilkinson E, Correia D, Dávila Felipe M, De Oliveira T | display-authors=etal| title=Renewing Felsenstein's phylogenetic bootstrap in the era of big data | journal=Nature | year= 2018 | volume= 556 | issue= 7702 | pages= 452–456 | pmid=29670290 | doi=10.1038/s41586-018-0043-0 | pmc=6030568 | bibcode=2018Natur.556..452L}}</ref>  
There are generally three approaches to estimating node support:

1. ''Bootstrapping.''  [[Joseph Felsenstein|Felsenstein]] adapted the concept of [[Bootstrapping (statistics)|nonparametric bootstrapping]] to the problem of phylogenetic reconstruction by maximum likelihood.<ref name="Felsenstein1985">{{cite journal| author=Felsenstein J| title=Confidence Limits on Phylogenies: An Approach Using the Bootstrap | journal=Evolution | year= 1985 | volume= 39 | issue= 4 | pages= 783–791 | pmid=28561359 | doi=10.1111/j.1558-5646.1985.tb00420.x | pmc= | s2cid=24057516 | doi-access=free }}</ref>  
Bootstrapping provides a way to characterize the [[Sampling error|sampling variation]] associated with the data without having to collect additional, equivalent samples.  To start, one generates a new data set by sampling an equivalent number of nucleotide or amino acid positions [[Simple random sample|at random with replacement]] from the [[multiple sequence alignment]] – this new data set is referred to as a "bootstrap sample".  A tree is reconstructed from the bootstrap sample using the same method as the original tree.  Since we are sampling sets of homologous characters (columns) from the alignment, the information on the evolutionary history contained at that position is intact.  We record the presence or absence of clades from the original tree in the new tree, and then repeat the entire process until a target number of replicate trees have been processed.  The frequency at which a given clade is observed in the bootstrap sample of trees quantifies the reproducibility of that node in the original tree.

Non-parametric bootstrapping is a time-consuming process that scales linearly with the number of replicates, since every bootstrap sample is processed by the same method as the original tree, and post-processing steps are required to enumerate clades.  The precision of estimating the node support values increases with the number of bootstrap replicates.  For instance, it is not possible to obtain a node support of 99% if fewer than 100 bootstrap samples have been processed.  Consequently, it is now more common to use faster approximate methods to estimate the support values associated with different nodes of the tree (for instance, see approximate likelihood-ratio testing below).

2. ''Bayesian sampling.''  Instead of using bootstrapping to resample the data, one can quantify node support by examining the uncertainty in reconstructing the phylogeny from the given data.  Bayesian sampling methods such as Markov chain Monte Carlo (see [[#Markov_chain_Monte_Carlo|Hald model]]) are designed to generate a random sample of parameters from the posterior distribution given the model and data.  In this case, the tree is a collection of parameters.  A Bayesian estimate of node support can be extracted from this sample of trees by counting the number of trees in which the monophyletic clade that descends from that specific node appears.<ref name="Larget1999">Larget B, Simon DL. Markov chain Monte Carlo algorithms for the Bayesian analysis of phylogenetic trees. Molecular Biology and Evolution. 1999 Jun 1;16(6):750-9.</ref>  
Bayesian sampling is computationally demanding because the space of all possible trees is enormous, making convergence difficult or not feasible to attain for large data sets.<ref name="Whidden2015">{{cite journal| author=Whidden C, Matsen FA| title=Quantifying MCMC exploration of phylogenetic tree space | journal=Syst Biol | year= 2015 | volume= 64 | issue= 3 | pages= 472–91 | pmid=25631175 | doi=10.1093/sysbio/syv006 | pmc=4395846 }}</ref>

3. ''Approximate likelihood-ratio testing.''  Unlike Bayesian sampling, this method is performed on a single estimate of the tree based on maximum [[Maximum likelihood estimation|likelihood]], where the likelihood is the probability of the observed data given the tree and model of evolution.  The [[Likelihood-ratio test|likelihood ratio test]] (LRT) is a method for selecting between two models or hypotheses, where the ratio of their likelihoods is a test statistic that is mapped to a null distribution to assess statistical significance.  In this application, the alternative hypothesis is that a branch in the reconstructed tree has a length of zero, which would imply that the descendant clade cannot be distinguished from its background.<ref name="Anisimova2006">{{cite journal| author=Anisimova M, Gascuel O| title=Approximate likelihood-ratio test for branches: A fast, accurate, and powerful alternative | journal=Syst Biol | year= 2006 | volume= 55 | issue= 4 | pages= 539–52 | pmid=16785212 | doi=10.1080/10635150600755453 | pmc= | s2cid=5000756 | doi-access=free }}</ref>  
This makes the LRT a localized analysis: it evaluates the support of a node when the rest of the tree is assumed to be true.  On the other hand, this narrow scope makes the approximate LRT method computationally efficient in comparison to Bayesian sampling and bootstrap sampling.  In addition to the LRT method, there are several other methods for fast approximation of bootstrap support and this remains an active area of research.<ref name="Simon2020">{{cite journal| author=Simon C| title=An Evolving View of Phylogenetic Support | journal=Syst Biol | year= 2022 | volume= 71 | issue= 4 | pages= 921–928 | pmid=32915964 | doi=10.1093/sysbio/syaa068 | pmc= | doi-access=free }}</ref>

==== Background sequences ====

The interpretation of monophyletic and paraphyletic clades is contingent on whether a sufficient number of infections have been sampled from the host population.  Sequences from one host can only become paraphyletic relative to sequences from a second host if the tree contains additional sequences from at least one other host in the population.  As noted above, there may be unsampled host individuals in a "transmission chain" connecting the putative source to the recipient host ('''Figure 3B''').  The incorporation of background sequences from additional hosts in the population is similar to the problem of rooting a phylogeny using an [[Outgroup (cladistics)|outgroup]], where the root represents the earliest point in time in the tree.  The location of this "root" in the section of the tree relating the sequences from the two hosts determines which host is interpreted to be the potential source.

There are no formal guidelines for selecting background sequences.  Typically, one incorporates sequences that were collected in the same geographic region as the two hosts under investigation.  These local sequences are sometimes augmented with additional sequences that are retrieved from public databases based on their genetic similarity (''e.g.'', [[BLAST (biotechnology)|BLAST]]), which were not necessarily collected from the same region.  Generally, the background data comprise consensus (bulk) sequences where each host is represented by a single sequence, unlike the putative source and recipient hosts from whom multiple clonal sequences have been sampled.  Because clonal sequencing is more labor-intensive, such data are usually not available to use as background sequences.  The incorporation of different types of sequences (clonal and bulk) into the same phylogeny may bias the interpretation of results, because it is not possible for sequences to be nested within the consensus sequence from a single background host.

===Phylodynamic methods===

In general, [[Viral phylodynamics|phylodynamics]] is a subdiscipline of [[molecular epidemiology]] and phylogenetics that concerns the reconstruction of epidemiological processes, such as the rapid expansion of an [[epidemic]] or the emergence of [[herd immunity]] in the host population, from the shape of the phylogenetic tree relating infections sampled from the population.<ref name="Volz2013">{{cite journal| author=Volz EM, Koelle K, Bedford T| title=Viral phylodynamics | journal=PLOS Comput Biol | year= 2013 | volume= 9 | issue= 3 | pages= e1002947 | pmid=23555203 | doi=10.1371/journal.pcbi.1002947 | pmc=3605911 | bibcode=2013PLSCB...9E2947V | doi-access=free }}</ref>  
A phylodynamic method uses tree shape as the primary data source to parameterize models representing the biological processes that influenced the evolutionary relationships among the observed infections.  This process should not be confused with fitting models of evolution (such as a [[Models of DNA evolution|nucleotide substitution model]] or [[molecular clock]] model) to reconstruct the shape of the tree from the observed characteristics of related populations (infections), which originates from the field of [[phylogenetics]].  The relatively rapid evolution of [[Viral phylodynamics|viruses]] and [[Bacterial phylodynamics|bacteria]] makes it feasible to reconstruct the recent dynamics of an epidemic from the shape of the phylogeny reconstructed from infections sampled in the present.

The use of phylodynamic methods for source attribution involve reconstruction of the transmission tree, which cannot be directly observed, from its residual effect on the shape of the phylogenetic tree.  Although there are established methods for reconstructing phylogenetic trees from the genetic divergence among pathogen populations sampled from different host individuals, there are several reasons why the phylogeny may be a poor approximation of the transmission tree ('''Figure 3''').  In this context, phylodynamic methods attempt to reconcile the discordance between the phylogeny and the transmission tree by modeling one or more of the processes responsible for this discordance, and fitting these models to the data ('''Figure 4''').

[[File:Bayesian phylodynamic model components.png|thumb|400px|Components of a phylodynamic source attribution analysis.
This diagram summarizes the structure of a phylodynamic analysis as a [[Bayesian network|hierarchical Bayesian model]].  Rectangular nodes represent data sources (sequence alignment, sample collection times), and nodes with rounded corners represent parameter estimates (fitted models) that can in turn be used as data inputs for a subsequent model.  Each arrow represents a model inference step that generates samples from the posterior distribution defined by the data and the model.  The model associated with each step is represented by a circular node; each model comprises a number of [[Prior probability|prior beliefs]].  This schematic displays multiple model nodes to emphasize the existence of other models with different assumptions and prior beliefs that are not necessarily evaluated on the data.]]

Given the complexity of phylodynamic models, these methods predominantly use [[#Bayesian_inference|Bayesian inference]] to sample transmission trees from the posterior distribution, where the transmission tree is an explicit model of "who infected whom".  Although these methods can estimate the probability of a direct transmission from one individual to another, this probability is conditional on how well the model (selected from a number of possible models) approximates reality.
Below we describe models that have been implemented to incorporate, but not eliminate, the additional uncertainty caused by the various assumptions required when using the phylogenetic tree as an approximation of the transmission history.

==== Demographic and transmission models ====

A basic simplifying assumption is that every infection in the epidemic is represented by at least one genetic sequence in the data set
<ref name="Ypma2013">{{cite journal| author=Ypma RJ, van Ballegooijen WM, Wallinga J| title=Relating phylogenetic trees to transmission trees of infectious disease outbreaks | journal=Genetics | year= 2013 | volume= 195 | issue= 3 | pages= 1055–62 | pmid=24037268 | doi=10.1534/genetics.113.154856 | pmc=3813836 }}</ref>
<ref name="Didelot2014">{{cite journal| author=Didelot X, Gardy J, Colijn C| title=Bayesian inference of infectious disease transmission from whole-genome sequence data | journal=Mol Biol Evol | year= 2014 | volume= 31 | issue= 7 | pages= 1869–79 | pmid=24714079 | doi=10.1093/molbev/msu121 | pmc=4069612 }}</ref>
<ref name="Worby2016">{{cite journal| author=Worby CJ, O'Neill PD, Kypraios T, Robotham JV, De Angelis D, Cartwright EJ | display-authors=etal| title=Reconstructing transmission trees for communicable diseases using densely sampled genetic data | journal=Ann Appl Stat | year= 2016 | volume= 10 | issue= 1 | pages= 395–417 | pmid=27042253 | doi=10.1214/15-aoas898 | pmc=4817375 }}</ref>
(complete sampling).  Although complete sampling may be feasible in circumstances such as an outbreak of disease transmission among farms in a defined geographic region,<ref name="Cottam2008">{{cite journal| author=Cottam EM, Thébaud G, Wadsworth J, Gloster J, Mansley L, Paton DJ | display-authors=etal| title=Integrating genetic and epidemiological data to determine transmission pathways of foot-and-mouth disease virus | journal=Proc Biol Sci | year= 2008 | volume= 275 | issue= 1637 | pages= 887–95 | pmid=18230598 | doi=10.1098/rspb.2007.1442 | pmc=2599933 }}</ref> 
it is generally not possible to rule out unsampled sources in other contexts.  This is especially true for infectious diseases that are stigmatized and/or associated with marginalized populations,<ref name="Weiss2006">{{cite journal| author=Weiss MG, Ramakrishna J, Somma D| title=Health-related stigma: rethinking concepts and interventions | journal=Psychol Health Med | year= 2006 | volume= 11 | issue= 3 | pages= 277–87 | pmid=17130065 | doi=10.1080/13548500600595053 | pmc= | s2cid=23053532 }}</ref> 
that have a long asymptomatic period,<ref name="Blenkinsop2022">{{cite journal| author=Blenkinsop A, Monod M, van Sighem A, Pantazis N, Bezemer D, Op de Coul E | display-authors=etal| title=Estimating the potential to prevent locally acquired HIV infections in a UNAIDS Fast-Track City, Amsterdam | journal=eLife | year= 2022 | volume= 11 | issue=  | pages=  | pmid=35920649 | doi=10.7554/eLife.76487 | pmc=9545569 | doi-access=free}}</ref> 
or in the context of a generalized epidemic where disease prevalence may substantially exceed the local capacity for sample collection and genetic sequencing.

Several methods attempt to address the presence of unsampled hosts by modeling the growth of the epidemic over time, which predicts the total number of infected hosts at any given time.  Put another way, the probability that an infection was transmitted from an unsampled source is determined in part by the total size of the infected population at the time of transmission.  These models of epidemic growth are sometimes referred to as demographic models because some are derived from [[Population dynamics|population growth models]] such as the exponential and logistic growth models.  Alternatively, the number of infections can be modeled by a [[Compartmental models in epidemiology|compartmental model]] that describes the rate that individual hosts switch from susceptible to infected states, and can be extended to incorporate additional states such as [[Immune response|recovery from infection]] or [[Infection#Classification|different stages of infection]].<ref name="VolzFrost">{{cite journal| author=Volz EM, Frost SD| title=Inferring the source of transmission with phylogenetic data | journal=PLOS Comput Biol | year= 2013 | volume= 9 | issue= 12 | pages= e1003397 | pmid=24367249 | doi=10.1371/journal.pcbi.1003397 | pmc=3868546 | bibcode=2013PLSCB...9E3397V | doi-access=free }}</ref><ref name="Didelot2014"/>  
An important distinction between population growth and compartmental models is that the number of uninfected susceptible hosts is tracked explicitly in the latter.

A phylodynamic analysis attempts to parameterize the growth model by using the phylogeny as either a direct proxy of the transmission tree, or to account for the discordance between these trees due to within-host diversity using a population genetic model, such as the [[Molecular source attribution#Evolution within hosts|coalescent]] ('''Figure 4''').
Bayesian methods make it feasible to supplement this task with other data sources, such as the reported case incidence and/or prevalence over time.<ref name="Magiorkinis2013">{{cite journal| author=Magiorkinis G, Sypsa V, Magiorkinis E, Paraskevis D, Katsoulidou A, Belshaw R | display-authors=etal| title=Integrating phylodynamics and epidemiology to estimate transmission diversity in viral epidemics | journal=PLOS Comput Biol | year= 2013 | volume= 9 | issue= 1 | pages= e1002876 | pmid=23382662 | doi=10.1371/journal.pcbi.1002876 | pmc=3561042 | bibcode=2013PLSCB...9E2876M| doi-access=free}}</ref>
The transmission process can be mapped to the size of the infected population using either a [[Coalescent theory|coalescent]] (reverse-time) model or a forward-time model such as [[Birth–death process|birth-death]] or [[branching process]]es.

Thus, the coalescent model has two different applications in phylodynamics.
First, it can be used to address the confounding effect of diverse pathogen populations within hosts, by explicitly modeling the common ancestry of individual pathogens.<ref name="Kingman1982" />
Second, the coalescent can be adapted to model the spread of infections back in time,<ref name="Volz2012">{{cite journal| author=Volz EM| title=Complex population dynamics and the coalescent under neutrality | journal=Genetics | year= 2012 | volume= 190 | issue= 1 | pages= 187–201 | pmid=22042576 | doi=10.1534/genetics.111.134627 | pmc=3249372 }}</ref> 
drawing an analogy between the common ancestry of individuals within hosts and the transmission of infections among hosts.
This parallel has also been explored by phylodynamic models based on the structured coalescent,<ref name="deMaio2017" /> 
where the population can be partitioned into two or more subpopulations ([[Deme (biology)|demes]]).
Each deme represents an infected host individual.
Due to limited migration of pathogen lineages between demes, two pathogen lineages sampled at random are more likely to share a recent common ancestor if they belong to the same deme.

[[Birth–death process|Birth-death models]] describe the proliferation of infections forward in time, where a "birth" event represents the transmission of an infection to an uninfected susceptible host, and a "death" event can represent either the diagnosis and treatment of an infection, or its spontaneous clearance by the host.<ref name="Stadler2009">{{cite journal| author=Stadler T| title=On incomplete sampling under birth-death models and connections to the sampling-based coalescent | journal=J Theor Biol | year= 2009 | volume= 261 | issue= 1 | pages= 58–66 | pmid=19631666 | doi=10.1016/j.jtbi.2009.07.018 | pmc= | bibcode=2009JThBi.261...58S }}</ref>  
This class of models was originally formulated to describe the proliferation of species through speciation and extinction.<ref name="Nee1994">{{cite journal| author=Nee S, May RM, Harvey PH| title=The reconstructed evolutionary process | journal=Philos Trans R Soc Lond B Biol Sci | year= 1994 | volume= 344 | issue= 1309 | pages= 305–11 | pmid=7938201 | doi=10.1098/rstb.1994.0068 }}</ref>  
Similarly, branching processes model the growth of an epidemic forward in time where the number of transmissions from each infected host ("offspring") is described by a [[Probability distribution#Discrete probability distribution|discrete probability distribution]] over non-negative integers, such as the [[negative binomial distribution]].<ref name="Didelot2017">{{cite journal| author=Didelot X, Fraser C, Gardy J, Colijn C| title=Genomic Infectious Disease Epidemiology in Partially Sampled and Ongoing Outbreaks | journal=Mol Biol Evol | year= 2017 | volume= 34 | issue= 4 | pages= 997–1007 | pmid=28100788 | doi=10.1093/molbev/msw275 | pmc=5850352 }}</ref>  
Branching process models tend to use the simplifying assumption that this offspring distribution remains constant over time, making this class of models more appropriate for the initial stage of an epidemic where most of the population is uninfected.

==== Within-host diversity ====

As noted [[Molecular source attribution#Evolution within hosts|above]], the diversification of pathogen populations within each host results in a discordance between the shapes of the pathogen phylogeny and the transmission tree.  [[Viral phylodynamics|Phylodynamic]] methods that treat the phylogeny as equivalent to the transmission tree assume implicitly that the population within each host is small enough to be approximated by a single lineage.<ref name="Cottam2008" /><ref name="Volz2013"/><ref name="Jombart2014">{{cite journal| author=Jombart T, Cori A, Didelot X, Cauchemez S, Fraser C, Ferguson N| title=Bayesian reconstruction of disease outbreaks by combining epidemiologic and genomic data | journal=PLOS Comput Biol | year= 2014 | volume= 10 | issue= 1 | pages= e1003457 | pmid=24465202 | doi=10.1371/journal.pcbi.1003457 | pmc=3900386 | bibcode=2014PLSCB..10E3457J | doi-access=free }}</ref>  
If the within-host population is diverse, then a transmission event will tend to underestimate the time since two lineages split from their common ancestor ('''Figure 3A'''); this phenomenon is analogous to the [[incomplete lineage sorting]] affecting gene trees relative to the species tree.<ref name="Maddison2006">{{cite journal| author=Maddison WP, Knowles LL| title=Inferring phylogeny despite incomplete lineage sorting | journal=Syst Biol | year= 2006 | volume= 55 | issue= 1 | pages= 21–30 | pmid=16507521 | doi=10.1080/10635150500354928 | pmc= | s2cid=13453831 | doi-access=free }}</ref>  
The resulting discordance between the phylogenetic and transmission trees makes it more difficult to reconstruct the latter from the observed data.  Moreover, the effect of within-host diversity becomes even greater if there are incomplete [[Founder effect|transmission bottlenecks]] &mdash; where a new infection is established by more than one lineage transmitted from the source population &mdash; because the common ancestor of pathogen lineages may be located in previous hosts further back in time.<ref name="deMaio2017">{{cite journal| author=De Maio N, Wu CH, Wilson DJ| title=SCOTTI: Efficient Reconstruction of Transmission within Outbreaks with the Structured Coalescent | journal=PLOS Comput Biol | year= 2016 | volume= 12 | issue= 9 | pages= e1005130 | pmid=27681228 | doi=10.1371/journal.pcbi.1005130 | pmc=5040440 | arxiv=1603.01994 | bibcode=2016PLSCB..12E5130D | doi-access=free }}</ref>

==Controversies==

Source attribution is an inherently controversial application of molecular epidemiology because it identifies a specific population or individual as being responsible for the onward transmission of an infectious disease.
Because molecular source attribution increasingly requires the specialized and computationally-intensive analysis of complex data, the underlying model assumptions and level of uncertainty in these analyses are often not made accessible to principal stakeholders, including the key affected populations and community advocates.

===Molecular forensics and HIV-1 transmission===

Outside of a public health context, the concept of source attribution has significant legal and ethical implications for people living with HIV to potentially become prosecuted for transmitting their infection to another person.  The transmission of HIV-1 without disclosing one's infection status is a [[Criminal transmission of HIV|criminally prosecutable offense]] in many countries,<ref>{{cite web | url=https://www.hivjustice.net/global-hiv-criminalisation-database/ | title=Overview }}</ref> 
including [[Criminal transmission of HIV in the United States|the United States]].  For example, defendants in HIV transmission cases in Canada have been charged with [[aggravated sexual assault]], with a "maximum penalty of life imprisonment and mandatory lifetime registration as a sex offender".<ref>{{cite web | url=http://www.aidslaw.ca/site/the-criminalization-of-hiv-non-disclosure-in-canada-report/?lang=en | title=The Criminalization of HIV Non-Disclosure in Canada: Current Status and the Need for Change – HIV Legal Network }}</ref>  
Molecular source attribution methods have been utilized as forensic evidence in such criminal cases.

====Forensic applications of phylogenetic clustering====

One of the earliest and well-known examples of an HIV-1 transmission case was the investigation of the so-called [[Kimberly Bergalis|"Florida dentist"]], where an HIV-positive dentist was accused of transmitting his infection to a patient.
Although [[Molecular source attribution#Genetic clustering|genetic clustering]] &mdash; specifically, clustering in the context of a phylogeny &mdash; was applied to these data to demonstrate that HIV-1 particles sampled from the dentist were genetically similar to those sampled from the patient,<ref name="Ou1992">{{cite journal| author=Ou CY, Ciesielski CA, Myers G, Bandea CI, Luo CC, Korber BT | display-authors=etal| title=Molecular epidemiology of HIV transmission in a dental practice | journal=Science | year= 1992 | volume= 256 | issue= 5060 | pages= 1165–71 | pmid=1589796 | doi=10.1126/science.256.5060.1165 | pmc= | bibcode=1992Sci...256.1165O| s2cid=21603554 }}</ref> 
clustering alone is not sufficient for source attribution.
Clusters can only provide evidence that infections are unlikely to be epidemiologically linked because they are too dissimilar relative to other infections in the population.<ref name="Bernard2007">{{cite journal| author=Bernard EJ, Azad Y, Vandamme AM, Weait M, Geretti AM| title=HIV forensics: pitfalls and acceptable standards in the use of phylogenetic analysis as evidence in criminal investigations of HIV transmission | journal=HIV Med | year= 2007 | volume= 8 | issue= 6 | pages= 382–7 | pmid=17661846 | doi=10.1111/j.1468-1293.2007.00486.x | pmc= | s2cid=38883310 | doi-access=free }}</ref>
For example, similar phylogenetic methods were used in a subsequent case to demonstrate that the HIV-1 sequence obtained from the patient was far more similar to the sequence from their sexual partner than the sequence from a third party under investigation.<ref name="Jaffe1994">{{cite journal| author=Jaffe HW, McCurdy JM, Kalish ML, Liberti T, Metellus G, Bowman BH | display-authors=etal| title=Lack of HIV transmission in the practice of a dentist with AIDS | journal=Ann Intern Med | year= 1994 | volume= 121 | issue= 11 | pages= 855–9 | pmid=7978698 | doi=10.7326/0003-4819-121-11-199412010-00005 | pmc= | s2cid=45511717 }}</ref>

Clustering provides no information on the directionality of transmission (''e.g.'', whether the infection was transmitted from individual A to individual B, or from B to A; '''Figure 3'''), nor can it rule out the possibility that one or more other unknown persons (from whom no virus sequences have been obtained) were involved in the transmission history.
Despite these known limitations of clustering, statements on the genetic similarity of infections continue to appear in court cases.<ref>[https://www.yukoncourts.ca/sites/default/files/documents/en/r_v_ngeruka_2015_yktc_10.pdf R. v. Ngeruka, 2015 YKTC 10]</ref>
On the other hand, clustering can have population-level benefits by enabling public health agencies to rapidly detect elevated rates of transmission in a population, and thereby optimize the allocation of prevention efforts.<ref name="Poon2016RT">{{cite journal| author=Poon AF, Gustafson R, Daly P, Zerr L, Demlow SE, Wong J | display-authors=etal| title=Near real-time monitoring of HIV transmission hotspots from routine HIV genotyping: an implementation case study | journal=Lancet HIV | year= 2016 | volume= 3 | issue= 5 | pages= e231-8 | pmid=27126490 | doi=10.1016/S2352-3018(16)00046-1 | pmc=4853759 }}</ref>
The expansion of public health applications of clustering
<ref name="Oster2018">{{cite journal| author=Oster AM, France AM, Panneer N, Bañez Ocfemia MC, Campbell E, Dasgupta S | display-authors=etal| title=Identifying Clusters of Recent and Rapid HIV Transmission Through Analysis of Molecular Surveillance Data | journal=J Acquir Immune Defic Syndr | year= 2018 | volume= 79 | issue= 5 | pages= 543–550 | pmid=30222659 | doi=10.1097/QAI.0000000000001856 | pmc=6231979 }}</ref>
has raised concerns among people living with HIV that this use of personal health data might also expose them to a greater risk of criminal prosecution for transmission.<ref name="Schrairer2017">{{cite journal| author=Schairer C, Mehta SR, Vinterbo SA, Hoenigl M, Kalichman M, Little S| title=Perceptions of molecular epidemiology studies of HIV among stakeholders | journal=J Public Health Res | year= 2017 | volume= 6 | issue= 3 | pages= 992 | pmid=29291190 | doi=10.4081/jphr.2017.992 | pmc=5736996 }}</ref>
<ref name="McClelland2019">{{cite journal |last1=McClelland |first1=Alexander |last2=Guta |first2=Adrian |last3=Gagnon |first3=Marilou |title=The rise of molecular HIV surveillance: implications on consent and criminalization |journal=Critical Public Health |date=7 August 2020 |volume=30 |issue=4 |pages=487–493 |doi=10.1080/09581596.2019.1582755 |s2cid=150472059 }}</ref>

==== Forensic applications of paraphyly methods ====

[[File:Louisiana HIV phylogeny.png|thumb|300px|Reproduction of phylogenetic tree from Metzker study. 
This unrooted tree was reconstructed by maximum likelihood from published HIV-1 RT sequences from Metzker ''et al.''
<ref name="Metzker2002">{{cite journal| author=Metzker ML, Mindell DP, Liu XM, Ptak RG, Gibbs RA, Hillis DM| title=Molecular evidence of HIV-1 transmission in a criminal case | journal=Proc Natl Acad Sci U S A | year= 2002 | volume= 99 | issue= 22 | pages= 14292–7 | pmid=12388776 | doi=10.1073/pnas.222522599 | pmc=137877 | bibcode=2002PNAS...9914292M | doi-access=free }}</ref>
and supplemented with additional sequences from Genbank.  Tips representing sequences are coloured by source (see legend), and branches are coloured by bootstrap support (darker
shades indicate higher support).  The branch (labelled '*') separating sequences from both patients P and V from the "background" sequences, including the original LA (Louisiana) control sequences from the study, had a support of 95%.  The branch (labelled '**') cited by the study as evidence that sequences from patient V were nested within a paraphyletic group of sequences from patient P had a support of 100%.]]
[[#Paraphyly|Source attribution methods based on paraphyly]] have been used in the prosecution of individuals for HIV-1 transmission.  One of the earliest examples was published in 2002, where a physician was accused of intentionally injecting blood from one patient (''P'') who was HIV-1 positive into another patient (''V'') who had previous been in a relationship with the physician.<ref name="Metzker2002" />  
This study used maximum likelihood methods to reconstruct a phylogenetic tree relating HIV-1 sequences from both patients.  Paraphyly of sequences from ''P'' implying either direct or indirect transmission to ''V'' was reported for the phylogeny reconstructed from RT sequences ('''Figure 5''').  However, a second tree reconstructed from the more diverse HIV-1 envelope (''env'') sequences from the same group was inconclusive on the direction of transmission - only that the ''env'' sequences from patients ''P'' and ''V'' clustered respectively into two monophyletic groups that were jointly distinct from the background.

The use of paraphyly for source attribution was stimulated with the onset of [[DNA sequencing#Next-generation methods|next-generation sequencing]], which made it more cost-effective to rapidly sequence large numbers of individual viruses from multiple host individuals.  More recent work
<ref name="RomeroSeverson2016">{{cite journal| author=Romero-Severson EO, Bulla I, Leitner T| title=Phylogenetically resolving epidemiologic linkage | journal=Proc Natl Acad Sci U S A | year= 2016 | volume= 113 | issue= 10 | pages= 2690–5 | pmid=26903617 | doi=10.1073/pnas.1522930113 | pmc=4791024 | bibcode=2016PNAS..113.2690R | doi-access=free }}</ref>
has also developed a formalized framework for interpreting the distribution of sequences in the phylogeny as being consistent with a direction of transmission.  Several studies have since applied this framework to re-analyze or develop forensic evidence for HIV transmission cases in Serbia,<ref name="Siljic2017">{{cite journal| author=Siljic M, Salemovic D, Cirkovic V, Pesic-Pavlovic I, Ranin J, Todorovic M | display-authors=etal| title=Forensic application of phylogenetic analyses - Exploration of suspected HIV-1 transmission case | journal=Forensic Sci Int Genet | year= 2017 | volume= 27 | issue=  | pages= 100–105 | pmid=28024238 | doi=10.1016/j.fsigen.2016.12.006 }}</ref> 
Taiwan,<ref name="Li2020">{{cite journal| author=Li WY, Huang SW, Wang SF, Liu HF, Chou CH, Wu SJ | display-authors=etal| title=Source identification of HIV-1 transmission in three lawsuits Using Ultra-Deep pyrosequencing and phylogenetic analysis | journal=J Microbiol Immunol Infect | year= 2021 | volume= 54 | issue= 4 | pages= 596–605 | pmid=32067946 | doi=10.1016/j.jmii.2019.12.002 | pmc= | s2cid=211158135| doi-access=free }}</ref> 
China
<ref name="Wu2019">{{cite journal| author=Wu J, Hu Z, Yao H, Wang H, Lei Y, Zhong P | display-authors=etal| title=The inference of HIV-1 transmission direction between HIV-1 positive couples based on the sequences of HIV-1 quasi-species | journal=BMC Infect Dis | year= 2019 | volume= 19 | issue= 1 | pages= 566 | pmid=31253127 | doi=10.1186/s12879-019-4163-4 | pmc=6599307 | doi-access=free}}</ref>
and Portugal
<ref name="Romero-Severson2017">{{cite journal| author=Romero-Severson EO, Bulla I, Hengartner N, Bártolo I, Abecasis A, Azevedo-Pereira JM | display-authors=etal| title=Donor-Recipient Identification in Para- and Poly-phyletic Trees Under Alternative HIV-1 Transmission Hypotheses Using Approximate Bayesian Computation | journal=Genetics | year= 2017 | volume= 207 | issue= 3 | pages= 1089–1101 | pmid=28912340 | doi=10.1534/genetics.117.300284 | pmc=5676238 }}</ref>
The growing number of such studies has led to controversy on the ethical and legal implications of this type of phylogenetic analysis for HIV-1.<ref name="Taylor2020">{{cite journal|last1=Taylor|first1=B.|last2=Sapién|first2=H.|year=2020|title=Determining the direction of HIV transmission: Benefits and potential harms of taking phylogenetic analysis one step further|journal=Clinical Infectious Diseases|volume=72 |issue=1 |pages=38–40 |doi=10.1093/cid/ciz1248|pmid=31922539 }}</ref>

The accuracy of classifying a group of sequences in a phylogeny into monophyletic or paraphyletic groups is highly contingent on the [[Molecular source attribution#Phylogenetic uncertainty|accuracy of tree reconstruction]].
As described above (see [[#Paraphyly|Paraphyly]]), our statistical confidence of a specific clade in the tree is quantified by the estimated probability that the same clade would be obtained if the tree reconstruction was repeated on an equivalent data set.
This support value is not the probability that the clade appears in the "true" tree because this quantity is conditional on the data at hand - however, it is often misinterpreted this way.<ref name="BerryGascuel">{{cite journal|last1=Berry|first1=V|last2= Gascuel|first2=O.|year=1996|title=On the interpretation of bootstrap trees: appropriate threshold of clade selection and induced gain.|journal= Molecular Biology and Evolution|volume=13|number=7|pages=999–1011|doi=10.1093/molbev/13.7.999|doi-access=free}}</ref>
If the branch separating a nested monophyletic clade of sequences from host A from the paraphyletic group of sequences from host B has a low support value, then the conventional procedure would be to remove that branch from the tree.
This would have the result of collapsing the monophyletic and paraphyletic clades so that the tree is inconclusive about either direction of transmission.
However, this procedure has not been consistently used in source attribution investigations.
For example, the trees displayed in the 2020 study in Taiwan
<ref name="Li2020" />
do not support transmission from the defendant to the plaintiff when branches with low support (<80%) are collapsed.
Moreover, the result can vary with the region of the virus genome targeted for sequencing.<ref name="Todesco2019">{{cite journal| author=Todesco E, Wirden M, Calin R, Simon A, Sayon S, Barin F | display-authors=etal| title=Caution is needed in interpreting HIV transmission chains by ultradeep sequencing | journal=AIDS | year= 2019 | volume= 33 | issue= 4 | pages= 691–699 | pmid=30585843 | doi=10.1097/QAD.0000000000002105 | pmc= | s2cid=58641592 | url=https://researchonline.lshtm.ac.uk/id/eprint/4650862/1/Todesco-etal-2018-Caution-is-Needed-in-Interpreting-HIV-Transmission-Chains.pdf}}</ref>

The use of paraphyly to infer the direction of transmission was recently evaluated on a prospective cohort of HIV serodiscordant couples (where one partner was HIV positive at the start of the study).<ref name="Rose2019">{{cite journal| author=Rose R, Hall M, Redd AD, Lamers S, Barbier AE, Porcella SF | display-authors=etal| title=Phylogenetic Methods Inconsistently Predict the Direction of HIV Transmission Among Heterosexual Pairs in the HPTN 052 Cohort | journal=J Infect Dis | year= 2019 | volume= 220 | issue= 9 | pages= 1406–1413 | pmid=30590741 | doi=10.1093/infdis/jiy734 | pmc=6761953 }}</ref>
Applying the paraphyly method to next-generation sequence data generated from samples obtained from 33 pairs where the HIV negative partner became infected over the course of the study, the authors found that the direction of transmission was incorrectly reconstructed in about 13% to 21% of cases, depending on which sequences were analyzed.
However, a follow-up study involving many of the same authors
<ref name="Zhang2020">{{cite journal| author=Zhang Y, Wymant C, Laeyendecker O, Grabowski MK, Hall M, Hudelson S | display-authors=etal| title=Evaluation of Phylogenetic Methods for Inferring the Direction of Human Immunodeficiency Virus (HIV) Transmission: HIV Prevention Trials Network (HPTN) 052 | journal=Clin Infect Dis | year= 2021 | volume= 72 | issue= 1 | pages= 30–37 | pmid=31922537 | doi=10.1093/cid/ciz1247 | pmc=7823077 }}</ref>
used a more comprehensive sequencing method to cover the full virus genome in depth from all host individuals, lowering the percentage of misclassified cases to 3.1%.

==== Forensic applications of phylodynamics ====

A common feature of both clustering and paraphyly methods is that neither approach explicitly tests the hypothesis that an infection was directly transmitted from a specific source population or individual to the recipient.
[[Viral phylodynamics|Phylodynamic]] methods attempt to overcome the discordance between the pathogen phylogeny and the underlying transmission history by modeling the processes that contribute to this discordance, such as the evolution of pathogen populations within each host.
The development of phylodynamic methods for source attribution has been a rapidly expanding area, with a large number of published studies and associated software released since 2014 (see [[#Software|Software]]).
Because these methods have tended to be applied to other infectious diseases including influenza A virus,<ref name="Jombart2010">{{cite journal| author=Jombart T, Eggo RM, Dodd PJ, Balloux F| title=Reconstructing disease outbreaks from genetic data: a graph approach | journal=Heredity (Edinb) | year= 2011 | volume= 106 | issue= 2 | pages= 383–90 | pmid=20551981 | doi=10.1038/hdy.2010.78 | pmc=3183872 }}</ref> 
foot-and-mouth disease virus
<ref name="Morelli2012">{{cite journal| author=Morelli MJ, Thébaud G, Chadœuf J, King DP, Haydon DT, Soubeyrand S| title=A Bayesian inference framework to reconstruct transmission trees using epidemiological and genetic data | journal=PLOS Comput Biol | year= 2012 | volume= 8 | issue= 11 | pages= e1002768 | pmid=23166481 | doi=10.1371/journal.pcbi.1002768 | pmc=3499255 | bibcode=2012PLSCB...8E2768M | doi-access=free }}</ref>
and ''Mycobacterium tuberculosis'',<ref name="Didelot2014" /> 
they have so far avoided the ethical issues of stigma and criminalization associated with HIV-1.
However, applications of phylodynamic source attribution to HIV-1 have begun to appear in the literature.
For example, in a study based in Alberta, Canada,<ref name="Mak2020">{{Cite journal|title=Evaluation of A Phylogenetic Pipeline to Examine Transmission Networks in A Canadian HIV Cohort|first1=Lauren|last1=Mak|first2=Deshan|last2=Perera|first3=Raynell|last3=Lang|first4=Pathum|last4=Kossinna|first5=Jingni|last5=He|first6=M. John|last6=Gill|first7=Quan|last7=Long|first8=Guido|last8=van Marle|date=February 26, 2020|journal=Microorganisms|volume=8|issue=2|pages=196|doi=10.3390/microorganisms8020196|pmid=32023939|pmc=7074708|doi-access=free }}</ref> the investigators used a phylodynamic method (TransPhylo <ref name="Didelot2017" />) to reconstruct transmission events among patients receiving treatment at their clinic from HIV-1 sequence data.
Although the program TransPhylo attempts, by default, to estimate the proportion of infections that are unsampled, the investigators fixed this proportion to 1%.
By so doing, their analysis carried the unrealistic assumption that nearly every person living with HIV-1 in their regional epidemic (comprising at least 1,800 people) was represented in their data set of 139 sequences.

===2010 cholera outbreak in Haiti===

In the aftermath of [[2010 Haiti earthquake|a magnitude 7.0 earthquake]] that struck Haiti in 2010, there was a large-scale outbreak of [[cholera]], a gastrointestinal infection caused by the bacterium ''[[Vibrio cholerae]]''.
Nearly 800,000 Haitians became infected and nearly 10,000 died in one of the most significant outbreaks of cholera in modern history.
Initial microbial subtyping using [[pulsed-field gel electrophoresis]] indicated that the outbreak was most genetically similar to cholera strains sampled in South Asia.<ref name="CDCcholera">{{cite web | url=https://www.cdc.gov/media/pressrel/2010/r101101.html | title=CDC Online Newsroom - Press Release - Laboratory Test Results of Cholera Outbreak Strain in Haiti Announced }}</ref>
In order to more comprehensively map the plausible source of infection, cholera strains from Southern Asia and South America were compared to the strains sampled from the Haitian outbreak.
Whole genome sequences taken from cases in Haiti shared more sites in common with the sequences taken from South Asia (''i.e.'', Nepal and Bangladesh) than those in geographic areas more immediate to Haiti.<ref name="2010cholera">{{cite journal| author=Orata FD, Keim PS, Boucher Y| title=The 2010 cholera outbreak in Haiti: how science solved a controversy | journal=PLOS Pathog | year= 2014 | volume= 10 | issue= 4 | pages= e1003967 | pmid=24699938 | doi=10.1371/journal.ppat.1003967 | pmc=3974815 | doi-access=free }}</ref>
Direct comparisons were also made between the cholera strains taken from three Nepalese soldiers and three Haitian locals, which were nearly identical in genome sequence, forming a phylogenetic cluster.<ref name="Hendriksen2011">{{cite journal| author=Hendriksen RS, Price LB, Schupp JM, Gillece JD, Kaas RS, Engelthaler DM | display-authors=etal| title=Population genetics of Vibrio cholerae from Nepal in 2010: evidence on the origin of the Haitian outbreak | journal=mBio | year= 2011 | volume= 2 | issue= 4 | pages= e00157-11 | pmid=21862630 | doi=10.1128/mBio.00157-11 | pmc=3163938 }}</ref>
Based on the evidence gathered by phylogenetic source attribution studies, the role of Nepalese soldiers who were part of the United Nations Stabilization Mission to Haiti (MINUSTAH) in this outbreak was officially recognized by the United Nations in 2016.<ref name="2016nytUN">{{cite news | url=https://www.nytimes.com/2016/08/18/world/americas/united-nations-haiti-cholera.html | title=U.N. Admits Role in Cholera Epidemic in Haiti | work=The New York Times | date=18 August 2016 | last1=Katz | first1=Jonathan M. }}</ref>

===2019/2020 novel coronavirus outbreak===

In December 2019, an [[2019–20 coronavirus outbreak|outbreak of 27 cases]] of viral pneumonia was reported in association with a seafood market in Wuhan, China.  Known respiratory viruses including influenza A virus, respiratory syncytial virus and SARS coronavirus were soon ruled out by laboratory testing.  On January 10, 2020, the genome sequence of the novel coronavirus, most closely related to bat SARS-coronaviruses, was released into the public domain.  Despite unprecedented quarantine measures, the virus (eventually named [[Severe acute respiratory syndrome coronavirus 2|SARS-CoV-2]]) spread to other countries including the United States, with global prevalence exceeding 556 million confirmed cases as of July 15, 2022.<ref>WHO Coronavirus (COVID-19) Dashboard, https://covid19.who.int/</ref>

This outbreak spurred an unprecedented level of epidemiological and genomic data sharing and real-time analysis, which was often communicated by social media prior to peer review.
Much of this knowledge translation was mediated through the open-source project [https://nextstrain.org/ Nextstrain]
<ref name="nextstrain">{{cite journal| author=Hadfield J, Megill C, Bell SM, Huddleston J, Potter B, Callender C | display-authors=etal| title=Nextstrain: real-time tracking of pathogen evolution | journal=Bioinformatics | year= 2018 | volume= 34 | issue= 23 | pages= 4121–4123 | pmid=29790939 | doi=10.1093/bioinformatics/bty407 | pmc=6247931 }}</ref>
that performs phylogenetic analyses on pathogen sequence data as they become available on public and access-restricted databases, and uses the results to update web documents in real time.
On March 4, 2020, Nextstrain developers released a phylogeny in which a SARS-CoV-2 genome that was isolated from a German patient occupied an ancestral position relative to a monophyletic clade of sequences sampled from Europe and Mexico.
Users of the [[Twitter]] social media platform soon commented on the related post from Nextstrain that onward transmission from the German individual seemed to have "led directly to some fraction of the widespread outbreak circulating in Europe today".
<ref>{{cite tweet|user=trvrb|number=1235105849841872897|title= Incredibly, it appears that this cluster containing Germany/BavPat1/2020 is the direct ancestor of these later viruses and thus led directly to some fraction of the widespread outbreak circulating in Europe today. 5/7}}</ref>
These comments were soon followed by criticism from other users that attributing the outbreak in Europe to the German patient as the source individual was drawing conclusions about the directionality of transmission from an incompletely sampled tree.<ref name="Worobey2020">{{cite journal| author=Worobey M, Pekar J, Larsen BB, Nelson MI, Hill V, Joy JB | display-authors=etal| title=The emergence of SARS-CoV-2 in Europe and North America | journal=Science | year= 2020 | volume= 370 | issue= 6516 | pages= 564–570 | pmid=32912998 | doi=10.1126/science.abc8169 | pmc=7810038 }}</ref>
In other words, the tree was reconstructed from a highly incomplete sample of cases from the ongoing outbreak, and the addition of other sequences had a substantial probability of modifying the inferred relationship between the German sequence and the clade in question.
Nevertheless, the interpretation attributing the European outbreak to a German source propagated through social media, causing some users to call on Germany to apologize.<ref name="Kupferschmidt">Kai Kupferschmidt (2020) Mutations can reveal how the coronavirus moves—but they're easy to overinterpret. ''Science Magazine'', doi:10.1126/science.abb6526</ref>

==Software==

There are numerous computational tools for source attribution that have been published, particularly for [[#Phylogenetic_methods|phylodynamic methods]].
'''Table 1''' provides a non-exhaustive listing of some of the software in the public domain.
Several of these programs are implemented within the Bayesian software package BEAST,<ref name="BEAST2" /> 
including SCOTTI, BadTrIP, and beastlier.
This listing does not include [[#Phylogenetic_clustering|clustering methods]], which are not designed for the purpose of source attribution, but may be used to develop microbial subtype definitions &mdash; clustering methods have previously been reviewed in molecular epidemiology literature
<ref name="Hassan2017">{{cite journal| author=Hassan AS, Pybus OG, Sanders EJ, Albert J, Esbjörnsson J| title=Defining HIV-1 transmission clusters based on sequence data | journal=AIDS | year= 2017 | volume= 31 | issue= 9 | pages= 1211–1222 | pmid=28353537 | doi=10.1097/QAD.0000000000001470 | pmc=5482559 }}</ref>
<ref name="Poon2016" />

{| class="wikitable sortable"
|+'''Table 1. Summary of source attribution software packages in the public domain.'''
|-
! Name !! Ref !! Method !! Input !! [[Software license|Software License]]
|-
| [http://www.repidemicsconsortium.org/outbreaker2/ outbreaker2] || <ref name="Campbell2018">{{cite journal| author=Campbell F, Didelot X, Fitzjohn R, Ferguson N, Cori A, Jombart T| title=outbreaker2: a modular platform for outbreak reconstruction | journal=BMC Bioinformatics | year= 2018 | volume= 19 | issue= Suppl 11 | pages= 363 | pmid=30343663 | doi=10.1186/s12859-018-2330-z | pmc=6196407 | doi-access=free }}</ref> || Phylodynamic, Bayesian || Sampling dates, contacts, genetic sequences || [https://github.com/reconhub/outbreaker2/blob/master/CONDUCT.md Contributor Code of Conduct]
|-
| [https://bitbucket.org/nicofmay/scotti/src/master/ SCOTTI] || <ref name="deMaio2017" /> || Phylodynamic, Bayesian (MCMC, structural coalescent) ||Sampling dates, genetic sequences|| [https://www.gnu.org/licenses/gpl-3.0.en.html GNU General Public License v3.0]
|-
| [https://www.rdocumentation.org/packages/adegenet/versions/2.0.1/topics/seqTrack seqTrack] || <ref name="Jombart2010" /> || Genetic distance clustering (directed graph, maximum parsimony) || Collection dates, genetic distances || [https://www.gnu.org/licenses/gpl-3.0.en.html GNU General Public License v3.0]
|-
| [https://cran.r-project.org/web/packages/sourceR/index.html sourceR] || <ref name="sourceR">{{cite journal| author=Miller P, Marshall J, French N, Jewell C| title=sourceR: Classification and source attribution of infectious agents among heterogeneous populations | journal=PLOS Comput Biol | year= 2017 | volume= 13 | issue= 5 | pages= e1005564 | pmid=28558033 | doi=10.1371/journal.pcbi.1005564 | pmc=5473572 | arxiv=1702.07422 | bibcode=2017PLSCB..13E5564M | doi-access=free }}</ref> || Hald model, Bayesian MCMC ||Sampling dates, sampling locations, genetic sequences  || [https://www.gnu.org/licenses/gpl-3.0.en.html GNU General Public License v3.0]
|-
| [https://github.com/xavierdidelot/TransPhylo TransPhylo] || <ref name="Didelot2017" /> || Phylodynamic, Bayesian (MCMC, branching process) ||Sampling dates, genetic sequences|| [https://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html GNU General Public License v2.0]
|-
| [https://github.com/BDI-pathogens/phyloscanner PhyloScanner] || <ref name="phyloscanner">{{cite journal| author=Wymant C, Hall M, Ratmann O, Bonsall D, Golubchik T, de Cesare M | display-authors=etal| title=PHYLOSCANNER: Inferring Transmission from Within- and Between-Host Pathogen Genetic Diversity | journal=Mol Biol Evol | year= 2018 | volume= 35 | issue= 3 | pages= 719–733 | pmid=29186559 | doi=10.1093/molbev/msx304 | pmc=5850600 }}</ref> || Ancestral reconstruction (maximum parsimony) || Short read alignment ([[Binary Alignment Map|BAM format]]) || [https://www.gnu.org/licenses/gpl-3.0.en.html GNU General Public License v3.0]
|-
| [https://github.com/skumsp/QUENTIN QUENTIN] || <ref name="Skums2018">{{cite journal| author=Skums P, Zelikovsky A, Singh R, Gussler W, Dimitrova Z, Knyazev S | display-authors=etal| title=QUENTIN: reconstruction of disease transmissions from viral quasispecies genomic data | journal=Bioinformatics | year= 2018 | volume= 34 | issue= 1 | pages= 163–170 | pmid=29304222 | doi=10.1093/bioinformatics/btx402 | pmc=6355096 }}</ref> || Phylodynamic, Bayesian (MCMC) || Genetic sequences ||  [https://www.gnu.org/licenses/gpl-3.0.en.html GNU General Public License v3.0]
|-
| [https://github.com/kingaa/genpomp/ genPomp] || <ref name="genPomp">{{cite journal| author=Smith RA, Ionides EL, King AA| title=Infectious Disease Dynamics Inferred from Genetic Data via Sequential Monte Carlo | journal=Mol Biol Evol | year= 2017 | volume= 34 | issue= 8 | pages= 2065–2084 | pmid=28402447 | doi=10.1093/molbev/msx124 | pmc=5815633 }}</ref> || Phylodynamic, Bayesian (sequential Monte Carlo) || Sampling dates, genetic sequences ||[https://www.gnu.org/licenses/gpl-3.0.en.html GNU General Public License v3.0]
|-
| [https://bitbucket.org/nicofmay/badtrip/src/master/ BadTrIP] || <ref name="BadTrIP">{{cite journal| author=De Maio N, Worby CJ, Wilson DJ, Stoesser N| title=Bayesian reconstruction of transmission within outbreaks using genomic variants | journal=PLOS Comput Biol | year= 2018 | volume= 14 | issue= 4 | pages= e1006117 | pmid=29668677 | doi=10.1371/journal.pcbi.1006117 | pmc=5927459 | bibcode=2018PLSCB..14E6117D | doi-access=free }}</ref> || Phylodynamic, Bayesian (MCMC) || Sampling dates, genetic sequences, host infectious interval || [https://www.gnu.org/licenses/gpl-3.0.en.html GNU General Public License v3.0]
|-
| [https://cran.r-project.org/web/packages/phybreak/vignettes/phybreak_intro.html phybreak] || <ref name="phybreak">{{cite journal| author=Klinkenberg D, Backer JA, Didelot X, Colijn C, Wallinga J| title=Simultaneous inference of phylogenetic and transmission trees in infectious disease outbreaks | journal=PLOS Comput Biol | year= 2017 | volume= 13 | issue= 5 | pages= e1005495 | pmid=28545083 | doi=10.1371/journal.pcbi.1005495 | pmc=5436636 | bibcode=2017PLSCB..13E5495K | doi-access=free }}</ref> || Phylodynamic, Bayesian (MCMC) ||Sampling dates, genetic sequences|| [https://www.gnu.org/licenses/gpl-3.0.en.html GNU General Public License v3.0]
|-
| [https://github.com/xavierdidelot/TransPairs TransPairs] || <ref name="TransPairs">{{cite journal| author=Eldholm V, Rieux A, Monteserin J, Lopez JM, Palmero D, Lopez B | display-authors=etal| title=Impact of HIV co-infection on the evolution and transmission of multidrug-resistant tuberculosis | journal=eLife | year= 2016 | volume= 5 | issue=  | pages=  | pmid=27502557 | doi=10.7554/eLife.16644 | pmc=4978521 | doi-access=free}}</ref> || Phylodynamic, maximum-likelihood (optimum branching) || Phylogeny || [https://www.gnu.org/licenses/gpl-3.0.en.html GNU General Public License v3.0]
|-
| [https://github.com/mdhall272/beastlier beastlier] || <ref name="Hall">{{cite journal| author=Hall M, Woolhouse M, Rambaut A| title=Epidemic Reconstruction in a Phylogenetics Framework: Transmission Trees as Partitions of the Node Set | journal=PLOS Comput Biol | year= 2015 | volume= 11 | issue= 12 | pages= e1004613 | pmid=26717515 | doi=10.1371/journal.pcbi.1004613 | pmc=4701012 | arxiv=1406.0428 | bibcode=2015PLSCB..11E4613H | doi-access=free }}</ref> || Phylodynamic, Bayesian || Sequence alignment, non-infectious dates, sample collection dates || None specified
|}

== Sources ==
{{Free-content attribution
|title= "Molecular source attribution"
|author = Chao E, Chato C, Vender R, Olabode AS, Ferreira RC, Poon AFY (2022)
|publisher = ''[[PLOS Computational Biology]]'', {{doi|10.1371/journal.pcbi.1010649}}
|documentURL= https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010649
|license= [https://creativecommons.org/licenses/by/4.0/ CC BY 4.0]
|howto= no
}}

== References ==
{{reflist}}

[[Category:Epidemiology]]
[[Category:Computational biology]]