# Phylodynamic approaches to studying avian influenza virus

L. Carnegie, J. Raghwani, G. Fournié, S C Hill

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L. Carnegie, J. Raghwani, G. Fournié, S C Hill. Phylodynamic approaches to studying avian influenza virus. Avian Pathology, 2023, 52 (5), pp.289-308. 10.1080/03079457.2023.2236568. hal-04181701v2

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# Phylodynamic approaches to studying avian influenza virus

L. Carnegie, J. Raghwani, G. Fournié & S. C. Hill

**To cite this article:** L. Carnegie, J. Raghwani, G. Fournié & S. C. Hill (2023) Phylodynamic approaches to studying avian influenza virus, Avian Pathology, 52:5, 289-308, DOI: 10.1080/03079457.2023.2236568

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# Phylodynamic approaches to studying avian influenza virus

L. Carnegie ID a, J. Raghwani ID a, G. Fournié ID a,b,c and S. C. Hill ID a

aDepartment of Pathobiology and Population Sciences, Royal Veterinary College (RVC), Hatfield, UK; bUniversité de Lyon, INRAE, VetAgro Sup, UMR EPIA, Marcy l'Etoile, France; cUniversité Clermont Auvergne, INRAE, VetAgro Sup, UMR EPIA, Saint Genes Chamanelle, France

## ABSTRACT

Avian influenza viruses can cause severe disease in domestic and wild birds and are a pandemic threat. Phylodynamics is the study of how epidemiological, evolutionary, and immunological processes can interact to shape viral phylogenies. This review summarizes how phylodynamic methods have and could contribute to the study of avian influenza viruses. Specifically, we assess how phylodynamics can be used to examine viral spread within and between wild or domestic bird populations at various geographical scales, identify factors associated with virus dispersal, and determine the order and timing of virus lineage movement between geographic regions or poultry production systems. We discuss factors that can complicate the interpretation of phylodynamic results and identify how future methodological developments could contribute to improved control of the virus.

## ARTICLE HISTORY

Received 2 February 2023

Revised 23 June 2023

Accepted 7 July 2023

## KEYWORDS

Phylodynamics; avian influenza virus; poultry; wild birds; epidemiology; phylogeography

## Background

Avian influenza viruses (AIVs) pose a considerable risk to human and animal health (Mostafa et al., 2018; Lycett et al., 2019; Rimi et al., 2019). These viruses belong to the species *Alphainfluenzavirus influenzae* (previously known as Influenza A virus (IAV)) (Lefkowitz et al., 2018; ICTV, 2022), and have single-stranded, negative-sense, and eight-segmented RNA genomes (Seiler et al., 2018; Rimi et al., 2019; Wille & Holmes, 2020). AIVs are sub-typed into “HxNy” based on the antigenicity and genetic diversity of the two surface glycoproteins: haemagglutinin (H1 – H16 in birds) and neuraminidase (N1 – N9 in birds) (Yoon et al., 2014; Blaurock et al., 2020; Verhagen, Eriksson, et al., 2021). Wild aquatic birds, particularly Anseriformes (e.g. geese, ducks) and Charadriiformes (e.g. gulls, shorebirds), are the primary reservoirs of AIVs (Olsen et al., 2006). However, AIVs can spill over to cause sporadic infection or sustained transmission within domestic avian hosts (Mostafa et al., 2018; Lycett et al., 2019). We can categorize viruses as low pathogenic avian influenza viruses (LPAIVs) and highly pathogenic avian influenza viruses (HPAIVs) based on their pathogenicity in chickens and the presence of insertions in the HA cleavage site (More et al., 2017). LPAIVs cause asymptomatic infection or mild disease in domestic birds, thereby harming the poultry industry via decreased egg or meat production, higher vaccination expenses, and trade

restrictions (Busani et al., 2007; Gonzales et al., 2021; Ripa et al., 2021). Only LPAIVs of H5 and H7 subtypes are known to evolve into HPAIVs, a process that involves the insertion of multiple basic amino acids in the HA cleavage site (Rott, 1992; Alexander, 2007). HPAIVs cause severe disease and fatalities in both domestic birds (Verhagen et al., 2014; Nuñez & Ross, 2019) and wild birds, respectively harming the economy and conservation efforts (Kleyheeg et al., 2017; Banyard et al., 2022; Lean et al., 2022). Several AIV strains can also infect humans and other mammalian species (e.g. swine, seals) and, thus, pose a potential pandemic threat (Ren et al., 2016; Nuñez & Ross, 2019; Blagodatski et al., 2021; Agüero et al., 2023; Puryear et al., 2023; Vreman et al., 2023).

Phylodynamics studies how epidemiological, immunological, and evolutionary processes shape viral genetic diversity. Approaches developed within this framework can help recover viral dispersal patterns and evolutionary processes, even when virus genomic data is sampled relatively sparsely from an infected population (Grenfell et al., 2004; Volz et al., 2013; Rife et al., 2017). Time-scaled phylogenies can be inferred using molecular clock models, which quantify the rate of genetic change over time and therefore enable phylogenetic branch lengths to be expressed as units of time rather than as nucleotide substitutions per site (Drummond et al., 2006; Pybus & Rambaut, 2009) (Box 1). RNA viruses,
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including AIVs, typically have exceptionally short generation times, high evolutionary rates, and large population sizes (Duffy, 2018; Wille & Holmes, 2020). Consequently, genetic substitutions in viral genomes often occur on similar time scales as transmission events between hosts. Hence, it is possible to reconstruct outbreak dynamics from time-scaled phylogenies, as they contain a “molecular footprint” of viral spread (Grenfell et al., 2004; Lemey et al., 2009; Pybus & Rambaut, 2009). When genome sampling location is available, we can use phylogenetic techniques to reconstruct the geographical distribution of viral lineages (“phylogeography”), thereby revealing valuable information about viral spread and factors associated with faster or more frequent viral lineage movement events (Lemey et al., 2010; Faria et al., 2011; Gill et al., 2016).

The most common tools for phylogenetic analyses employ a Bayesian Markov Chain Monte Carlo (MCMC) framework to efficiently explore highly complex models involving many different parameters (Drummond & Rambaut, 2007; Bouckaert et al., 2014). A Bayesian framework has several advantages compared to the maximum likelihood or parsimony-based approaches. Firstly, Bayesian approaches allow for the incorporation of multiple sources of data or prior knowledge (e.g. divergence times, substitution rates) (Alfaro & Holder, 2006; Baele et al., 2017; Chakraborty et al., 2021). Perhaps more importantly, such approaches generate posterior distributions of phylogenetic trees, thus allowing uncertainty in parameter estimates to be captured (this has been reviewed extensively elsewhere, (e.g. Faria et al., 2011; Volz et al., 2013; Gill et al., 2016; Rasmussen & Grünwald, 2020; Dellicour et al., 2021). Maximum likelihood (ML)-based methods are more limited in scope of possible analyses, but are often less computationally intensive than the more popular Bayesian approaches (Baele et al., 2018; Sagulenko et al., 2018; Ishikawa et al., 2019). This can be beneficial when dealing with large datasets, limited computed resources, or when faster but lower complexity models are appropriate to inform emergency responses. Such ML phylogenetic methods typically use a single ML tree, enabling faster time-to-answer compared to Bayesian phylogenetic inference.

Recent trending decreases in the cost and time required to generate and analyse virus genetic data have led to rapid innovation within the field of phylogenetics and a subsequent increase in popularity (Gill et al., 2016; Rife et al., 2017; Grubaugh et al., 2019; Cardona-Ospina et al., 2021). This review highlights how phylogenetic methods have and may continue to aid the study of AIV spatiotemporal dispersal. We first explore recent studies that use phylogenetics to infer AIV dispersal dynamics within or between wild birds and domestic poultry populations at various geographical scales and discuss factors that can

complicate the generation of reliable conclusions. Specifically, we discuss the inference of factors associated with AIV transmission, the order and timing of transmission and lineage dispersal events during an outbreak, and how viral lineages can move between different regions and sectors of poultry production systems. We then consider future challenges and opportunities for using phylogenetic approaches within AIV research.

## Wild bird populations

### Tracing viral incursions

Phylodynamics has helped identify global movements of AIV lineages in wild bird populations (Baele et al., 2018; Zhang et al., 2023). Many studies have used time-calibrated phylogenies (Box 1) to estimate the sampling time and location of unobserved viral ancestors, and hence reconstruct the timings and origins of viral (particularly HPAIV) incursions into different wild bird populations (e.g. Lee et al., 2018; Hill et al., 2019; Zhang, Fan et al., 2020; Beerens et al., 2021; Liang, Krog, et al., 2021; Xie et al., 2022). For instance, one study showed that HPAIV H5N8 infections in wild birds present in the Netherlands in late 2020 were likely introductions from wild birds in Egypt and not elsewhere in Europe, as was anticipated based on proximity (Beerens et al., 2021). This finding demonstrates how phylogenetic approaches can provide information on AIV spread that might not be detectable using standard epidemiological analyses based on reported cases.

Complications in interpreting where virus lineages originate can arise in instances where gene segments from multiple genetically diverse AIVs reassort during co-infection (Araujo et al., 2018; Wille & Holmes, 2020; Verhagen, Fouchier et al., 2021). Gene segments acquired from each different parent virus typically require analysis using separate phylogenies to capture the different evolutionary histories of each parent lineage (Lu et al., 2014). Accordingly, reassortant sequences are sometimes represented using a phylogenetic network instead of a single tree (Frost et al., 2015; Stolz et al., 2022) or, more commonly, removed, with only one gene segment (usually haemagglutinin (HA) (Heaton et al., 2013)) selected for analyses. The latter approach limits our understanding of AIV diffusion (Lu et al., 2014; Parvin et al., 2014; Frost et al., 2015; Venkatesh et al., 2018). Although reassortant sequences could perhaps be better accommodated in AIV phylogenetic analyses using a similar approach to that detailed in Müller et al., (2020), which was used to explicitly infer reassortment rates in different human influenza virus lineages, the complexity of this method likely prevents it being used widely (Müller et al., 2020).
## Geographic structuring of genetic diversity

Phylodynamic methods have been used to investigate whether AIV lineages found in wild birds are structured according to avian flyways and host ecology (Hurt et al., 2014; Araujo et al., 2018; Mine et al., 2019; Sharshov et al., 2019; Verhagen et al., 2020; Zhang et al., 2023). Several studies indicate that avian hosts within geographically isolated regions sometimes harbour AIV segment lineages that are seemingly relatively distinct from other sampled lineages circulating globally (Hansbro et al., 2010; Hurt et al., 2014; Araujo et al., 2018; Wille et al., 2022). For instance, one study detected an H11N2 lineage in Adélie penguins (*Pygoscelis adeliae*) in Antarctica in 2013 that likely diverged approximately between the 1960s-

1980s from the most closely related AIV sequences worldwide (Hurt et al., 2014). However, it is difficult to determine whether these findings represent true lineage geographic isolation or if lineages only appear geographically structured due to a scarcity of samples from nearby locations that may nevertheless be epidemiologically linked (e.g. southern Chile, Argentina). Other analyses reveal that AIV lineages can be shared between distant regions (Bahl et al., 2009; zu Dohna et al., 2009; Mine et al., 2019; Sharshov et al., 2019; Verhagen et al., 2020; Caliendo et al., 2022). For instance, recent discrete phylogeographic analyses (Box 1) based on the neuraminidase (NA) indicated likely intercontinental AIV dispersal between North America and Eurasia (Mine et al., 2019).

### Box 1.

**Viral phylogeography** is the reconstruction of viral movement between geographic locations using virus genetic sequences. Phylogeographic approaches can be categorized based on whether the modelled locations are discrete (e.g. country, administrative district) or continuous (geographic coordinates) (Lemey et al., 2009; Faria et al., 2011).

Discrete trait analyses are sometimes used out of necessity, for instance, when precise geographic sampling coordinates are unavailable (De Maio et al., 2015; Hill et al., 2015; Lycett et al., 2019). However, these approaches are usefully applied when sequences cluster naturally by geographic location because viral movement is affected by geographical (e.g. oceans, mountains) and/or political (e.g. borders) barriers (Alkhamis et al., 2015; De Maio et al., 2015; Zhang, Chen et al., 2020). Several software packages (e.g. TreeTime (Sagulenko et al., 2018), PastML (Ishikawa et al., 2019)) have been developed that enable reconstruction of ancestral characters within a maximum-likelihood framework. The most commonly used software packages for phylodynamic inference, BEAST (Drummond & Rambaut, 2007) and BEAST2 (Bouckaert et al., 2014), rely on a Bayesian inference framework and offer several different approaches for phylogeography using discrete traits. We summarize different maximum likelihood and Bayesian approaches below.

**Maximum likelihood ancestral character reconstruction:** Maximum likelihood approaches can also be used to estimate the most likely states of discrete traits (e.g. which country or host species) at internal nodes of phylogenies (Cunningham et al., 1998; Schmidt & von Haeseler, 2009; Hadfield et al., 2018; Sagulenko et al., 2018; Ishikawa et al., 2019). These approaches focus on first estimating the most likely phylogeny given the data (i.e. the maximum likelihood tree), and then estimating the most likely history of discrete states at each node (Cunningham et al., 1998; Schmidt & von Haeseler, 2009; Sagulenko et al., 2018; Ishikawa et al., 2019). Branch length or evolutionary time is accounted for, such that geographic movements are more likely to occur on longer branches. This is in contrast to Bayesian approaches implemented in BEAST and BEAST2, which estimate the history of discrete traits for each tree in the posterior tree distribution and hence can more fully account for phylogenetic uncertainty but as a result can be significantly slower (Faria et al., 2011; Volz et al., 2013; Gill et al., 2016; Dellicour et al., 2021).

**Discrete trait analysis:** In one common approach (often known as "discrete trait analysis", whilst being only one of several approaches for phylogeographic inference using discrete traits), phylogenetic branch locations are estimated using continuous-time Markov chains, i.e. modelled as moving instantaneously at specific rates between a fixed number of discrete locations (Lemey et al., 2009; Faria et al., 2011). (Figure 1A). A frequently applied extension of this model known as **Bayesian stochastic search variable selection (BSSVS)** attempts to limit the number of possible recovered transitions between pairs of locations to those that adequately explain the phylogenetic diffusion process (Lemey et al., 2009). Biased sampling can affect the statistical inference of discrete trait analyses as the relative sampling intensities of different discrete locations or traits affect the estimates of viral movements (De Maio et al., 2015; Layan et al., 2023).

**Structured coalescent approaches:** Structured coalescent approaches, including those implemented in MASCOT (Müller et al., 2018) and BASTA (De Maio et al., 2015) within BEAST 2, explicitly model ancestry within and movement between discrete subpopulations, known as "demes" (Figure 1A) (Vaughan et al., 2014; De Maio et al., 2015; Müller et al., 2018). These methods can be less susceptible to sampling bias than classical coalescent models in certain instances (De Maio et al., 2015; Müller et al., 2018; Layan et al., 2023). However, structured coalescent phylogeographic models are typically more computationally demanding than discrete trait phylogeographic models, and thus are mostly applied to small numbers of demes (De Maio et al., 2015; Müller et al., 2018; Layan et al., 2023). Additionally, although population size can vary between demes, structured coalescent models assume that virus population size in each deme remains constant, which may be less appropriate for investigating AIV lineage expansions in naïve host populations or AIVs with strong seasonality in transmission (De Maio et al., 2015; Layan et al., 2023). Both discrete trait analyses and structured coalescent approaches can be used to model virus movement between other discrete traits (Faria et al., 2011; De Maio et al., 2015), such as host species (Figure 1B) (Ren et al., 2016; Hicks et al., 2020). Furthermore, generalized linear model (GLM) extensions of these methods (Lemey et al., 2012, 2014; Faria et al., 2013; Müller et al., 2019) can be used to evaluate covariates of virus movement, such as avian host density at each location or geographic distance between pairs of locations.

**Multitype birth-death models:** Like structured coalescent approaches, multitype birth-death (MTBD) models (e.g. as implemented in the BEAST 2 package bdmm (Kühnert et al., 2016)) are able to explicitly model transmission within structured populations, and represent an extension of the birth-death models described in Box 2. MTBD models involve the estimation of time-varying "birth" rates (transmission rate), "death" rates (rate of becoming non-infectious or death) for each of several discrete subpopulations, and "per-lineage migration rate" (changes in the subpopulation of an individual due to migration) (FitzJohn, 2012; Stadler & Bonhoeffer, 2013; Kühnert et al., 2016; Barido-Sottani et al., 2020). Unlike the structured coalescent approaches, which assume a constant population size within demes, the MTBD allows for population size to change through time (Stadler et al., 2015; Seidel et al., 2020).

**Continuous phylogeography:** In the continuous phylogeographic model, virus lineage movement is modelled between geographical coordinates (e.g. latitude and longitude) (Figure 1C & D) (Lemey et al., 2009, 2010). Virus lineage movement is most commonly modelled using a relaxed random walk model (Lemey et al., 2010), which allows the rate of viral dispersal to vary across the phylogeny. To evaluate spatiotemporal covariates associated with virus dispersal route or velocity, we can conduct post-hoc analysis using the R package "seraphim" (Dellicour et al., 2016).
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<figure><img src="image_17.png" /><figcaption>**Figure 1.** Conceptual representation of common phylogeographic methods. A: Time-calibrated phylogeny estimated with a phylogeographic model with discrete traits. The inferred locations of the ancestral internal nodes (squares) are estimated from the set of discrete locations predefined at the tips (i.e. locations of sampled sequences; circles). B: Discrete trait (e.g. location or host species) analysis. Here, arrows indicate statistically significant virus lineage transitions between bird types. Arrow thickness corresponds to the inferred viral flow rate. C: Time-calibrated phylogeny estimated with continuous phylogeographic inference. The inferred geographic coordinates of the internal nodes (i.e. unsampled virus ancestor) can differ from the geographical sampling coordinates of the sequences at the tips. Both internal nodes and tips are coloured by location. D: Continuous phylogeographic reconstruction using information contained in estimated phylogenies such as C, in which the dots represent the internal and external nodes of the time-scaled phylogeny, coloured according to time. The curvature direction of the lines between dots indicates the inferred direction of viral movement. Coloured polygons represent the statistical uncertainty of the inferred internal node locations, which is derived from a posterior tree distribution.</figcaption></figure>

Sampling biases (i.e. disproportionate sampling of virus genomes compared to true infection prevalence) can strongly impact phylogeographic analyses, with under-sampled locations more likely to be inferred

as sinks when using discrete trait approaches (De Maio *et al.*, 2015; Kalkauskas *et al.*, 2021; Layan *et al.*, 2023). The international spread of AIV has been predominately inferred from virus genetic data
collected in North America and northern Europe (Hill
et al., 2022). Wild bird sampling remains relatively
limited in several areas of Central and South America,
Africa, the Middle East, and polar regions, which may
result in these regions being overestimated as sink
locations for virus lineages (Hurt et al., 2014; Araujo
et al., 2018; Fusaro et al., 2019; Naguib et al., 2019;
Kalonda et al., 2020; Hill et al., 2021; Lo et al.,
2022). Accordingly, phylodynamic investigations of
the global spread of AIV would benefit from increased
AIV genomic surveillance in undersampled locations
and host species.

Migratory flyways and mixing zones

Over 50 billion birds are estimated to migrate annually, with most flyways linking breeding grounds at high latitudes and over-wintering sites at low latitudes (Boere et al., 2006; Bahl et al., 2013). Several different phylodynamic studies using discrete trait analyses (Box 1) report that AIV lineage movement rates are greater within flyways than between flyways in Asia (Central Asian, East Asian) (Tian, Zhou, et al., 2015) and in North America (Central, Mississippi, Atlantic) (Scotch et al., 2014; Fries et al., 2015; Li et al., 2018). Continuous phylogeographic analyses (Box 1), in which virus lineage movement is modelled as a diffusion process between geographical coordinates (Lemey et al., 2009, 2010), demonstrated that H5N1 movement throughout Asia and Russia (1996–2011) correlates with the geographical extent of known flyways (Trovão et al., 2015). While a crude simplification to capture avian movement trends, “flyways” appear a crucial facilitator of long-distance AIV spread in wild birds.

Several studies indicate that AIV transmission
between birds during congregation at breeding
grounds or staging areas where multiple flyways
overlap (“mixing zones”) may shape the subsequent
global dissemination of AIV lineages (Ramey et al.,
2010; Gerloff et al., 2013; Huang et al., 2014; Lee
et al., 2015; Venkatesh et al., 2018; Mine et al.,
2019; Gass et al., 2023). For example, Mine et al.
(2019) determined that AIVs sampled in mixing
zones in Mongolia and Siberia from wild birds that
use different flyways were often phylogenetically
intermixed. The study used the Bayesian tip-associ-
ation significance testing (BaTS) software, which
implement了 post-hoc statistical tests on time-cali-
brated trees to determine if sequences significantly
cluster by a trait, such as sampling location. This
approach enables rapid assessment of whether viral
genetic diversity at tree tips is geographically struc-
tured, but, unlike discrete or continuous phylogeo-
graphic approaches, cannot be used to estimate full
histories of virus lineage movement. The above-

mentioned phylogenetic clustering patterns reported
in Mine *et al.* (2019) are consistent with birds sharing
AIVs in mixing zones before dissemination along
different flyways, hence enabling long-distance diffu-
sion of viral lineages between multiple continents or
regions (Mine *et al.*, 2019). Consequently, the
authors hypothesized that cross-flyway viral diffusion
may have contributed to the simultaneous outbreaks
of H5N6 HPAIVs in East Asia and Europe in 2017–
2018 (Mine *et al.*, 2019). Similar findings have been
observed in mixing zones in the Nile Delta and the
Republic of Georgia (Gerloff *et al.*, 2013; Venkatesh
*et al.*, 2018).

Several phylodynamic studies have shown high AIV genetic diversity in some mixing zones (Ramey et al., 2010; Gerloff et al., 2013; Venkatesh et al., 2018, 2020; Mine et al., 2019). High-density congregation of wild birds associated with different flyways at mixing zones may provide ideal conditions for AIV reassortment by increasing the probability of co-infections with diverse genotypes and subtypes (Venkatesh et al., 2018; Mine et al., 2019). However, further research is required to robustly determine if reassortant AIV genotypes exist at higher frequencies in mixing zones than in non-mixing zones. While phylodynamic approaches have been valuable in demonstrating the role of flyways and mixing zones in structuring AIV dispersal and reassortment patterns, several challenges remain. The scarcity of both wild bird migration data and wild bird sampling in some regions (particularly low- and middle-income countries) limits our ability to fully characterize how migratory flyways impact AIV dispersal patterns globally (Takekawa et al., 2010; Palm et al., 2015; Tian, Zhou, et al., 2015; Fusaro et al., 2019; Mine et al., 2019; Yong et al., 2021; Zhang et al., 2023). Secondly, using environmental and flyway data averaged over months or years limits our ability to understand the impacts of changes in weather or environmental conditions on wild bird-mediated AIV dispersal patterns (Kirby et al., 2008; Vandegrift et al., 2010; Iwamura et al., 2013; Bahl et al., 2016; Sullivan et al., 2018).

Roles of different host taxa

Several studies employing phylodynamic methods
demonstrate how movement variation between
wild birds from different taxonomic orders might
impact AIV dispersal patterns (Ramey et al., 2010;
Miller et al., 2011; Wille et al., 2011; Hall et al.,
2013; Hill et al., 2022; Gass et al., 2023). In the bor-
eal and temperate territories of the Northern Hemi-
sphere, migratory Charadriiformes (e.g. shorebirds,
gulls) are more abundant and undertake long-dis-
tance migrations across oceans more frequently
than migratory Anseriformes (e.g. ducks, geese)
<figure><img src="image_18.png" /></figure>

(Ramey et al., 2010; Miller et al., 2011; Wille et al., 2011; Hall et al., 2013; Hill et al., 2022; Gass et al., 2023). Consequently, it has been suggested that Charadriiformes may facilitate intercontinental AIV transmission in these areas more than Anseriformes (Cappelle et al., 2012; Gaidet et al., 2012; Gaidet, 2016; Rimondi et al., 2018; Hoye et al., 2021; Wille et al., 2023). Phylodynamic support for this hypothesis exists in the seemingly more frequent detection of inter-hemispheric reassortment events between American and Eurasian lineages associated with Charadriiformes compared to Anseriformes (Bahl et al., 2009; Ramey et al., 2010; Wille et al., 2011; Van Borm et al., 2012; Hall et al., 2013; Lang et al., 2016). Conversely, in areas such as the tropical regions of West Africa, there is a high abundance of long-distance migratory Anseriformes but a low abundance of Charadriiformes migrants. There, Anseriformes are instead thought to be the primary drivers of virus movement between regions and continents (Cappelle et al., 2012; Gaidet, 2016).

Discrete trait analyses (Box 1) have been used to explore how wild bird species with contrasting migration behaviours may differently influence AIV spread (Hill et al., 2020, 2022). For instance, one study suggested that in Egypt and adjacent Black Sea-Mediterranean countries, local migrant species (common shelduck (*Tadorna tadorna*)) generally contributed to local AIV amplification, while longer distance migrants (northern shoveler (*Spatula clypeata*)) and northern pintail (*Anas acuta*)) carried AIV lineages over longer distances (Hill et al., 2020). Careful analyses and interpretation of results regarding the contribution of different species to AIV dispersal is critical because virus genome sequences are often biased towards hunted species (e.g. game birds) or those that are easily detected when they die (e.g. larger inland birds such as mute swans (*Cygnus olor*)) (Runstadler et al., 2013; Lebarbenchon et al., 2015; Bahl et al., 2016; Beerens et al., 2021; McBride et al., 2021; Hill et al., 2022). The existence of within-species variation in movement ecology, both between juveniles and adults (van Dijk et al., 2014) and between resident and migratory populations from the same species (Lisovski et al., 2018), further complicates generalization of host species traits associated with long-distance AIV spread.

## Between wild and domestic birds

### Frequency and timing of movement

AIVs are regularly transmitted between wild and domestic birds (Fusaro et al., 2011; Lebarbenchon & Stallknecht, 2011; Bahl et al., 2016; Nuñez & Ross, 2019).

The relative frequency of viral cross-species transmission events can be estimated using phylodynamic tools such as Markov jumps counting in combination with discrete trait phylogeography (Minin & Suchard, 2008a, b; Faria et al., 2011) (Box 1). Markov jump counting approaches enable the counting of the expected number of transitions between modelled discrete traits, such as country or host species, along phylogenetic branches (Minin & Suchard, 2008a, b; Faria et al., 2011). For example, Markov jumps counting analyses were used to infer that North America, east and southeast Asia are hotspots for cross-species transmission between wild and domestic birds (Ren et al., 2016). Likewise, one large study of a globally sampled H9 AIV dataset used both discrete trait analyses and Markov jump counting to identify spatial asymmetry in the geographical areas where wild-to-domestic and/or domestic-to-wild viral transmission most often occurred (Bahl et al., 2016). These methods must be used cautiously because Markov jumps analyses are very sensitive to disproportionate sampling from each group relative to true virus prevalence (Layan et al., 2023). This is particularly problematic for AIV, where genome sequences are almost always more frequently available from poultry relative to wild birds (Bahl et al., 2016; Ren et al., 2016; Yang, Xie et al., 2019).

The relative rates of transmission between domestic and wild bird populations can also be investigated using structured coalescent approaches and multitype birth-death models, which may be less sensitive biased sampling (Box 1) (De Maio et al., 2015; Grear et al., 2017; Yang, Müller, et al., 2019; Guinat et al., 2022). Such methods can allow the transition rates between populations or demes to vary depending on the direction or estimate a single rate regardless of the directionality (De Maio et al., 2015; Kühnert et al., 2016; Müller et al., 2018; Barido-Sottani et al., 2020). One study of a 2014 HPAIV outbreak in North American domestic poultry, caused by wild bird lineage spill-over, inferred that minimal viral movement had occurred between wild and domestic birds over the subsequent course of the domestic outbreak (Grear et al., 2017). Furthermore, estimates of basic reproductive number ($R_0$; the expected number of susceptible individuals in a naïve population infected by one infected host – see Box 2) indicated that the poultry outbreak size was stable ($R_0 \approx 1$) (Grear et al., 2017). Taken together, these findings suggested that the poultry outbreak was largely self-sustaining (Grear et al., 2017).

Several studies indicate that AIV transmission between wild and domestic birds can vary seasonally (Alarcon et al., 2018; Ferenczi et al., 2021; Gonzales et al., 2021; Zhang et al., 2021; Liang, Nissen, et al., 2021). Recurrent temporal peaks in cross-species transmission events can be
investigated using phylodynamic tools that infer
population demographic history (Frost & Volz,
2010; Karcher et al., 2020) (Box 2). For example,
one study observed that the effective population
size of clade 2.3.4.4b H5N8 viruses in Chinese
poultry, as estimated by a skyride coalescent
model (Gill et al., 2013) (Box 2), increased during
winter 2020/21, as previously observed for H5

lineages/clades during winter 2013/14 and 2016/
17 (Zhang et al., 2021). Seasonal increases in
viral effective population size and other phyloge-
netic evidence, such as estimated dates of incur-
sion of new lineages, were interpreted as
consistent with immigrating wild birds recurrently
introducing new H5 viruses into domestic poultry
(Zhang et al., 2021).

**Box 2.**

**Past population dynamics** can be estimated through phylodynamic inference by analysing how virus **effective population size (N)e** changes over time. Briefly, the effective population size represents the size of an idealized viral outbreak (i.e. one without selection or population structure) that experiences the same level of genetic drift as the studied population (Magiorkinis et al., 2013). N<sub>e</sub> estimates are affected by transmission rates and therefore almost always cannot be scaled directly to the number of infected individuals (Frost & Volz, 2010). Furthermore, uneven sampling strategies such as focused sampling during transmission peaks can bias the estimation of effective population size (Karcher et al., 2020; Parag et al., 2020; Cappello & Palacios, 2022). Nevertheless, estimates of N<sub>e</sub> can provide important information about the viral outbreak dynamics, including capturing seasonality or the possible efficacy of interventions in reducing subsequent outbreak size (Frost & Volz, 2010; Rife et al., 2017; Drummond et al., 2005) (Figure 2A).

Methods that are commonly employed to estimate population size trajectories over an epidemic include the **Bayesian skyline** (Drummond *et al.*, 2005), the **skyride** (Minin *et al.*, 2008), the **birth–death skyline** (Stadler *et al.*, 2013), and the **skygrid** (Gill *et al.*, 2013).

Several approaches estimate population size history alongside other tree parameters based on the principle that when the population size is small, sampled viruses are more likely to share a common ancestor in the very recent past, and therefore lineages coalesce (join) faster (Drummond et al., 2005). Therefore, a faster rate of branch coalescence suggests a comparatively smaller population at that time point (Drummond et al., 2005). The **Bayesian skyline model** requires pre-determination of the number of points at which effective population size can change, generating estimates that are summaries of multiple step-wise changes (Drummond et al., 2005). The **skyride model** does not require predefined points, and introduces a method of temporal smoothing based on the assumption that N<sub>e</sub> is correlated across successive coalescent intervals (Minin et al., 2008). The **skygrid model** modifies and extends the skyride model by allowing the N<sub>e</sub> trajectory to change at specific time-points pre-specified by the user (Gill et al., 2013). A further extension of the skygrid model implements a generalized linear model (GLM) to test how time-varying covariates, such as monthly temperature, are associated with temporal changes in N<sub>e</sub> in a method known as "skygrid-GLM" (Gill et al., 2016).

<figure><img src="image_19.png" /></figure>

**Figure 2.** Exemplar outputs of phylodynamic methods used to infer past demographic parameters. A: Effective population size (N<sub>e</sub>) over time: the solid line is the posterior median estimate for N<sub>e</sub>, and the surrounding ribbon indicates the 95% Bayesian credibility interval. The bold vertical dashed line represents the median estimate of the time of the root. Thinner vertical lines indicate the lower (left) and highest (right) values of the 95% highest posterior density interval for the root age. B: Effective reproduction number (R<sub>e</sub>) over time: the solid line is the posterior median estimate for R<sub>e</sub>, and the surrounding ribbon indicates the 95% Bayesian highest posterior density interval. The horizontal dashed line indicates R<sub>e</sub> of 1.
<figure><img src="image_20.png" /></figure>

The **birth–death skyline** (Figure 2) provides an alternative method to these coalescent-based approaches (e.g. Bayesian skyline, skyride, and skygrid) (Stadler *et al.*, 2013). While in the Bayesian skyline approach only the effective population size changes over time, the birth–death skyline model infers temporal changes in transmission, death/recovery, and sampling rates at discrete intervals alongside variation in population size (Stadler *et al.*, 2013). Birth–death models infer a forward-in-time process, starting with the common ancestor of all sampled infections, followed by bifurcating new lineages representing observed and unobserved transmission events, thus generating a tree (Stadler *et al.*, 2013). In contrast, the coalescent is modelled backwards-in-time starting from the sampled sequences in the present time until the most recent common ancestor of the sample in the past, with the internal nodes representing the merging or coalescing of two lineages into their most recent common ancestor (i.e. coalescent event) (Drummond *et al.*, 2005; Frost & Volz, 2010).

Compared to coalescent approaches, birth–death skyline models can more easily explicitly infer the number of expected secondary infections caused by an infected individual (Figure 2B), either as a time-varying average (R<sub>e</sub>: **effective reproduction number**), or at the start of an outbreak when all individuals are considered as a completely susceptible population (R<sub>0</sub>: **basic reproduction number**) (Stadler *et al.*, 2012, 2013; Volz *et al.*, 2013). Changes in effective reproduction number can reflect depletion of the susceptible population as well as control interventions, with R<sub>e</sub> > 1 indicating a growing epidemic, R<sub>e</sub> < 1 a declining epidemic, and R<sub>e</sub> ≈ 1 that the outbreak is stable (Stadler *et al.*, 2013; Domingo, 2020; Gostic *et al.*, 2021).

## Factors and settings associated with viral movement

To identify factors associated with higher rates of cross-species AIV transmission or lineage dispersal, phylodynamic analyses previously relied primarily on *post-hoc* literature searches for relevant events that occurred concurrently with reconstructed viral movements (Vijaykrishna *et al.*, 2013). For example, one study suggested that higher rainfall might increase H10 subtype AIV spillover from wild aquatic birds to poultry in Australia because inferred spill-over events occurred more frequently during periods of increased rainfall in the region (Vijaykrishna *et al.*, 2013). More recently, the incorporation of GLMs into discrete trait analyses (frequently referred to as “DTA-GLMs”) (Box 1) (Lemey *et al.*, 2012, 2014; Gill *et al.*, 2016) has enabled hypotheses to be formally tested during the phylogenetic estimation process (Beard *et al.*, 2014). For instance, a DTA-GLM showed that H5N1 spill-over from wild birds to poultry in Egypt tended to occur in locations with a higher density of birds and humans, higher elevation, and several meteorological variables (Magee *et al.*, 2015).

To implement effective infection control, it is necessary to understand the settings in which AIVs move between wild and domestic birds. AIV outbreaks frequently occur in Asian wetlands or rice fields where free-grazing ducks are reared at high density and in close contact with wild birds, suggesting that these areas may support transmission between wild and domestic birds (Hulse-Post et al., 2005; Martin et al., 2011; Cappelle et al., 2014; Prosser et al., 2016; Sullivan et al., 2018). Several phylodynamic analyses have supported this epidemiological finding, for example, indicating that the spread and maintenance of H5N8 lineages in the Republic of Korea were positively associated with regions of high wild waterfowl immigration and domestic duck density (Hill et al., 2015). Low precision of metadata on sampled locations of virus sequences often limits analyses to considering predictors at district-level scales, making it harder to account for local heterogeneity in ecological suitability for wild birds or farming intensity (Hill et al., 2015; Bahl et al., 2016; Hill et al., 2020).

Furthermore, these methods have not been frequently applied in regions such as Africa and the Middle East where AIV genomic surveillance is limited (Naguib *et al.*, 2019; Ayala *et al.*, 2020; Kalonda *et al.*, 2020).

## Between avian and mammalian species

HPAIVs and LPAIVs can occasionally infect humans and therefore are a public health threat (Webster *et al.*, 1992; Li *et al.*, 2019; Lycett *et al.*, 2019). Epidemiological studies suggest that most infected patients had recent exposure to live poultry or had visited live-bird markets, rather than exposure to wild birds or non-avian species (Zhou *et al.*, 2013; Yu *et al.*, 2014; Yang *et al.*, 2017; Li *et al.*, 2019; Oliver *et al.*, 2022). Phylogenetic analyses of virus genomes from humans and birds have been frequently used to support that poultry were the likely origin (Liu *et al.*, 2013; Joseph *et al.*, 2017; Yang *et al.*, 2017; Zhang *et al.*, 2021). Sporadic AIV transmission from birds to mammals can, in theory, select for variants that have increased transmissibility in mammals (Balzli *et al.*, 2016; Bourret, 2018; Nuñez & Ross, 2019; Zhao *et al.*, 2019) and therefore potentially increase pandemic risk (Nelson *et al.*, 2012; Bourret, 2018; Bravo-Vasquez *et al.*, 2020). Phylogenetic analyses have identified multiple AIV lineages associated with cross-species transmission from birds to swine or other mammals (Bodewes *et al.*, 2016; Ramey *et al.*, 2017; Zhao *et al.*, 2019; Chauhan & Gordon, 2021; Rijks *et al.*, 2021). These transmission events sometimes result in sustained transmission, particularly within swine populations (Nelson *et al.*, 2015; Bourret, 2018). For example, a phylodynamic study used time-calibrated phylogenies to determine that H10N7 viruses that caused significant mortality in harbour seals (*Phoca vitulina*) in 2014 in Europe were closely related to various avian-origin H10N7 viruses detected in wild birds in the Netherlands (Bodewes *et al.*, 2015, 2016). More recently, H5Nx clade 2.3.4.4b viruses in red foxes and wild birds in Europe (the Netherlands) were found to be closely genetically related (Rijks *et al.*, 2021). Our understanding of the frequency of spill-over to, and dissemination within, mammalian
species is limited by the sparseness of virus genomic surveillance in these taxa (Runstadler *et al.*, 2013; Bod-ewes *et al.*, 2016; Ren *et al.*, 2016).

## Domestic poultry populations

### HPAIV origins and diversification

HPAIVs pose a severe problem for the poultry industry, and understanding their origins could help predict and prevent future outbreaks (Nuñez & Ross, 2019; Beerens *et al.*, 2021). Time-calibrated phylogenies of HA show that multiple H5 and H7 HPAIV genotypes have independently evolved from ancestral LPAIV viruses, potentially facilitated by high poultry density and contact rates within intensive farming systems (Monne *et al.*, 2014; Dhingra *et al.*, 2018; Seekings *et al.*, 2018; Escalera-Zamudio *et al.*, 2020). Phylogeographic investigations have tracked the geographic origins of multiple HPAIV genotypes, and in rare cases, even suggested a possible source farm (Monne *et al.*, 2014; Dhingra *et al.*, 2018; Seekings *et al.*, 2018).

### Geographic spread within poultry populations

Discrete trait analyses (Box 1) have frequently been employed to reconstruct AIV spatial spread within domestic poultry populations (Jin *et al.*, 2014; Bahl *et al.*, 2016; Zhang *et al.*, 2020; Harvey *et al.*, 2021). For example, southern China was shown to be an epi-centre for the national spread of H5N6 HPAIV in poultry from 2013 to 2017 (Zhang *et al.*, 2020) and was identified as a potential source of a national wave of H9N2 infections in domestic birds (Jin *et al.*, 2014).

Some studies have employed structured coalescent models (Box 1) to reconstruct AIV spatial spread in domestic poultry (Yang *et al.*, 2019; Hicks *et al.*, 2020). Such analyses have shown that viral diffusion tended to occur within, rather than between, North American states during an H5N2 outbreak (Hicks *et al.*, 2020). However, as previously described, sampling bias or the presence of unsampled locations (Box 1) can be problematic for discrete trait phylogeographic models, and results must be interpreted with caution given regional differences in AIV genomic surveillance capacity (De Maio *et al.*, 2015; Layan *et al.*, 2023). LPAIV surveillance is particularly challenging because infections rarely cause severe disease in poultry and can easily be missed (Hurt *et al.*, 2014; Parvin *et al.*, 2020). Although HPAIV is likely easier to detect due to its higher pathogenicity, in some countries farmers may avoid reporting HPAIV cases for fear that birds will be culled without financial compensation (Chattopadhyay *et al.*, 2018; Parvin *et al.*, 2020; Moyen *et al.*, 2021; Ripa *et al.*, 2021).

Phylogeographic analysis can be performed using continuous models (Box 1) in which the spread of viruses is modelled using geographical coordinates (Lemey *et al.*, 2009, 2010). Continuous phylogeographic analyses can be preferable to discrete analyses when it is beneficial to understand virus spread in both sampled and intermediate unsampled locations (Box 1) (Lemey *et al.*, 2009, 2010). Continuous phylogeographic analysis indicated that the HPAIV H5N1 movement in Java in 2003 was characterized by short-range dispersal events interspersed with occasional long-range movements (Lam *et al.*, 2012). Similar approaches were also used to show that short-distance viral movement was more common than long-distance movements during Italy's 2016-17 HPAI H5N8 epidemic, with the first outbreak wave generally restricted to the north-eastern areas of the country (Harvey *et al.*, 2021). Biased sampling between geographical locations can result in a failure to determine the true origin of the outbreak and in the underestimation of viral diffusion rates into an oversampled region from an undersampled area when using continuous phylogeographic methods (Hill *et al.*, 2021; Kalkauskas *et al.*, 2021). The incorporation of sequence-free samples from affected yet unsampled areas may somewhat alleviate the effect of sampling bias in continuous phylogeographic analyses (as proposed in Kalkauskas *et al.* (2021)). However, this approach requires a prior understanding of the spatial distribution of outbreaks.

### Drivers of dispersal

Understanding which species or breeds are most important for maintaining AIV within poultry systems can allow for the preferential targeting of surveillance and control efforts toward certain host types (Hill *et al.*, 2015; Barman *et al.*, 2017; Hicks *et al.*, 2020; Youk *et al.*, 2020; Harvey *et al.*, 2021). A study using Markov jump and reward analysis showed that, in live bird markets in the Republic of Korea, the transmission rate of H9N2 from domestic ducks to chickens was higher than the rate in the opposite direction (Youk *et al.*, 2020). Similarly, structured coalescent approaches (Box 1) demonstrated that the H5N2 viral transmission rates from layer chicken to turkey populations were higher than the reverse during a 2014-2015 outbreak in North America (Hicks *et al.*, 2020).

Phylogeographic analyses helped identify factors associated with AIV spread in domestic birds (Yang, Müller, *et al.*, 2019; Dellicour, Lemey, *et al.*, 2020; Dellicour, Lequime, *et al.*, 2020; Hicks *et al.*, 2020), thus highlighting which components of the production system may be most affected in future outbreaks. DTA-GLMs (Box 1) are commonly used for this purpose and have identified several economic and agricultural factors significantly associated with AIV dispersal in
<figure><img src="image_21.png" /></figure>

domestic birds in China (such as poultry population
density, freight transportation, the number of markets
selling poultry or poultry products, and high human
density) (Lu et al., 2017). When both precise location
and detailed geographical environmental data are
available, we can also investigate factors associated
with viral dispersal in a continuous phylogeographic
analysis using the R package “seraphim” (Dellicour
et al., 2016) (Box 1). For example, this approach ident-
ified weak support for the association of several spatial
factors (e.g. human, chicken and duck population
densities, inaccessibility, savanna) with heterogeneity
in lineage dispersal velocity of H5N1 in the Mekong
region (Cambodia, Laos, Thailand, Vietnam) (Delli-
cour, Lemey, et al., 2020).

The impact of commercial poultry movement net-
works on AIV dissemination in domestic birds rep-
resents an important, but particularly challenging,
area of research (Lu et al., 2017; Yang et al., 2020;
Moyen et al., 2021). Poultry trading records are
difficult to obtain on a large scale, especially in low-
and middle-income countries where poultry pro-
duction systems can be highly complex and dynamic,
and where these data are not already routinely col-
lected (Yang et al., 2020; Moyen et al., 2021). As
such, most phylogeographic studies have used proxies
for poultry trade networks (Lu et al., 2017; Yang et al.,
2020). For example, one study used DTA-GLMs (Box
1) to show that a proxy network of poultry transpor-
tation in China, as determined by a gravity model
built from metrics of domestic poultry production
and egg production, was positively associated with
the large-scale movement of three AIV subtypes
(H5N1, H7N9, and H5N6) (Yang et al., 2020). Greater
availability of accurate poultry trade data would likely
allow for more rigorous assessment of the impact of
poultry movements on AIV dispersal (Parvin et al.,
2020; Yang et al., 2020; Moyen et al., 2021).

Across borders

Although AIV dispersal in domestic poultry typically
occurs within countries, several phylodynamic studies
have investigated lineage movements across borders
(Melville & Shortridge, 2006; Yang, Müller et al.,
2019; Yang, Xie et al., 2019). The occurrence of
long-distance trade-facilitated AIV movements was
highlighted by a 2004 report that linked the 1500 km
spread of HPAIV H5N1 from Lanzhou (Gansu Pro-
vince, Northwest China) to Lhasa (Tibetan Auton-
omous Region) to the transport of domestic birds
(Melville & Shortridge, 2006). More recently, studies
have begun to explore larger-scale factors that can
drive interprovincial or international virus movement
(Yang, Müller et al., 2019; Yang, Xie, et al., 2019).
Through a GLM extension of a structured coalescent
model (Box 1), one study identified annual levels of

international live poultry trade between countries
and national poultry production as predictors of
cross-border H9N2 virus movement in domestic
Asian birds (Yang, Chowdury et al., 2019). Illegal
trade may also drive the international spread of AIV.
However, phylodynamic investigations are limited in
their ability to investigate the impact of illegal bird
trade on the global dispersal of AIV, as this predictor
cannot be easily quantified (Tian, Zhou, et al., 2015;
Yang, Müller, et al., 2019; Yang, Xie, et al., 2019).

5.5. Evaluating control efforts

In addition to helping guide the design of novel
measures aimed at tackling AIV spread, phylodynamic
analyses have been used to evaluate the efficacy of pre-
viously implemented control measures (Lee et al.,
2014; Tian, Cui, et al., 2015; Nickbakhsh et al., 2016;
Kwon et al., 2020; Chakraborty et al., 2022). By incor-
porating time-varying predictors into a GLM exten-
sion of a structured coalescent phylogeographic
model (Box 1), one study showed that duck culling
in France likely reduced the spread of HPAIV H5N8
between French administrative divisions (Chakra-
borty et al., 2022). Several studies have also employed
phylodynamic methods that infer past population
demographic dynamics to explore the effectiveness
of control measures (Lee et al., 2014; Tian, Cui,
et al., 2015; Kwon et al., 2020). For example, Kwon
et al. (2020) argued that an observed fall in H5N1
virus effective population size in Bangladesh over
3 years was due to reduced virus prevalence in dom-
estic birds following the concurrent introduction of
wide-scale vaccination, although alternative expla-
nations are possible (Kwon et al., 2020). Likewise,
one study used estimates of effective reproduction
numbers (Box 2) to infer that HPAI-targeted control
measures (e.g. culling of infected flocks, pre-emptive
culling of neighbouring flocks) introduced in Italy in
2000 successfully slowed the epidemic growth of a
novel HPAI outbreak but not that of its LPAI progeni-
tor lineage (Nickbakhsh et al., 2016).

Outlook

Whilst this review highlights where phylodynamic
analyses have contributed significantly to our under-
standing of AIV over the last few decades, we identify
several areas that can aid future progress of the field.
First, we can benefit from the wide range of new
approaches developed and extended during the
COVID-19 pandemic. For example, methods that
enable accessible, “real-time” and easily scalable
incorporation of sequences into viral phylogenies
(e.g. Nextstrain (Hadfield et al., 2018)) could be
used more extensively to support surveillance, and
methods that incorporate host travel history within
phylogeographic analyses could be explored to
accommodate bird migration histories (Lemey
et al., 2020).

Despite many recent methodological advances, for-
mally integrating different data types within phylody-
namic analyses remains a key challenge (Frost et al.,
2015; Baele et al., 2017). Approaches that can better
integrate temporally varying environmental and demo-
graphic data alongside genetic sequences would be
extremely valuable to study how bird movements
drive AIV lineage spread. Ideally, such models would
be developed to handle a broad array of new data
types, including bird tracking data from mobile global
positioning systems (e.g. GPS-3G-Bluetooth technol-
ogies developed to investigate bird spatial behaviour
(Yu et al., 2022)), satellite imagery of high-risk locations
for AIV transmission between wild and domestic
species, and poultry trade data. In locations where it
is difficult for authorities to access trading records com-
prehensively, purpose-built apps could facilitate collec-
tion of more complete poultry trade network data for
use in such new models (Ravindran, 2021; Grubaugh
et al., 2019; Lycett et al., 2019; Cardona-Ospina et al.,
2021). The development of methods that effectively
accommodate reassortment in phylodynamic analyses
in a user-friendly fashion would help better understand
AIV spread (Frost et al., 2015; Lycett et al., 2019; Ver-
hagen, Fouchier, et al., 2021).

It is essential to improve AIV genome sequence
and metadata availability (Kalkauskas et al., 2021;
Layan et al., 2023). Missing metadata (such as date
of collection, host type or species, precise location
for wild birds, or production context for domestic
poultry) reduces the value of virus genomes within
phylodynamics. Likewise, as discussed throughout
this review, sampling biases increase the risk of draw-
ing incorrect conclusions from phylodynamic ana-
lyses. It is therefore important to grow capacity for
AIV genomic monitoring in currently under-rep-
resented countries and sampling from understudied
species.

Finally, tackling challenges related to feasibility and
resource-intensity is essential for enhancing phylody-
namic studies of AIVs. At present, and particularly
when dealing with large genomic datasets, a significant
level of technical expertise and robust computing
infrastructure is often required to formulate appropri-
ate phylodynamic models, execute them, and interpret
their results (Duchene et al., 2018; Sagulenko et al.,
2018; Attwood et al., 2022). As such, the continued
development of faster tools (e.g. recently introduced
ML methods (Sagulenko et al., 2018; Ishikawa et al.,
2019)) and the advancement of computational
packages that allow existing tools to make more effec-
tive use of available computer hardware (e.g. BEAGLE
(Ayres et al., 2012)) are critical, especially in more
resource-constrained environments and where results

are intended to inform emergency responses (Suchard
& Rambaut, 2009; Baele et al., 2019). The use of cloud
computing in phylodynamic analyses, which was criti-
cal for handling the unprecedentedly large genomic
datasets produced the COVID-19 pandemic, could
be beneficial in helping researchers handle increased
numbers of genome sequences. Greater investment
in appropriate training and computational infrastruc-
ture in many lower- and middle-income countries
would improve global accessibility of phylodynamic
approaches (Rife et al., 2017; Hill et al., 2021; Attwood
et al., 2022).

Conclusions

AIVs can severely harm domestic and wild birds, and
their effective control in birds can help protect the
health of humans and other mammalian species.
Phylodynamic approaches can be insightful in recon-
structing the spatiotemporal dispersal of AIVs, with
models capable of analysing viral diffusion within
and between different host populations and locations.
However, limitations in phylodynamic models exist
when key metadata are missing, virus genomic
sampling is uneven, and for analysing reassortant
viruses. Addressing these challenges will be important
to further fulfil the potential of phylodynamic analyses
to improve human and animal health.

Disclosure Statement

No potential conflict of interest was reported by the authors.

Funding

L.C. was supported by a Biotechnology and Biological
Sciences Research Council (BBSRC) London Interdisciplin-
ary Biosciences Consortium (LIDo) studentship [grant
number BB/T008709/1]. S.C.H. is supported by a Sir
Henry Wellcome Postdoctoral Fellowship from the Well-
come Trust [220414/Z/20/Z] (https://wellcome.org/). GF
and JR were supported by the UKRI GCRF One Health
Poultry Hub (BB/S011269/1), one of twelve interdisciplinary
research hubs funded under the UK government's Grand
Challenge Research Fund Interdisciplinary Research Hub
initiative. For the purpose of open access, the author has
applied a CC BY public copyright license to any Author
Accepted Manuscript version arising from this submission.

Additional Information

Author contributions: L.C. wrote the manuscript. S.C.H.,
G.F., and J.R, edited the manuscript and supervised the work.

ORCID

L. Carnegie https://orcid.org/0000-0001-5810-7271

J. Raghwani https://orcid.org/0000-0001-8538-4995

G. Fournié http://orcid.org/0000-0002-6998-1201

S. C. Hill http://orcid.org/0000-0002-2995-2596
<figure><img src="image_22.png" /></figure>

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