Decoding the Fundamental Drivers of Phylodynamic Inference.

Despite its increasing role in the understanding of infectious disease transmission at the applied and theoretical levels, phylodynamics lacks a well-defined notion of ideal data and optimal sampling. We introduce a method to visualize and quantify the relative impact of pathogen genome sequence and sampling times-two fundamental sources of data for phylodynamics under birth-death-sampling models-to understand how each drives phylodynamic inference. Applying our method to simulated data and real-world SARS-CoV-2 and H1N1 Influenza data, we use this insight to elucidate fundamental trade-offs and guidelines for phylodynamic analyses to draw the most from sequence data. Phylodynamics promises to be a staple of future responses to infectious disease threats globally. Continuing research into the inherent requirements and trade-offs of phylodynamic data and inference will help ensure phylodynamic tools are wielded in ever more targeted and efficient ways.

Respiratory syncytial virus surge in 2022 caused by lineages already present before the COVID-19 pandemic.

In 2022, Austria experienced a severe respiratory syncytial virus (RSV) epidemic with an earlier-than-usual start (Weeks 35/2021-45/2022) and increased numbers of pediatric patients in emergency departments. This surge came 2 years after a season with no cases detected as a result of coronavirus disease 2019 nonpharmaceutical interventions. We analyzed epidemiologic patterns and the phylodynamics of RSV based on approximately 30 800 respiratory specimens collected year-round over 10 years from ambulatory and hospitalized patients from 248 locations in Austria. Genomic surveillance and phylogenetic analysis of 186 RSV-A and 187 RSV-B partial glycoprotein sequences collected from 2018 to 2022 revealed that the 2022/2023 surge was driven by RSV-B in contrast to the surge in the 2021/2022 season that was driven by RSV-A. Whole-genome sequencing and phylodynamic analysis indicated that the RSV-B strain GB5.0.6a was the predominant genotype in the 2022/2023 season and emerged in late 2019. The results provide insight into RSV evolution and epidemiology that will be applicable to future monitoring efforts with the advent of novel vaccines and therapeutics.

Model design for nonparametric phylodynamic inference and applications to pathogen surveillance.

Inference of effective population size from genomic data can provide unique information about demographic history and, when applied to pathogen genetic data, can also provide insights into epidemiological dynamics. The combination of nonparametric models for population dynamics with molecular clock models which relate genetic data to time has enabled phylodynamic inference based on large sets of time-stamped genetic sequence data. The methodology for nonparametric inference of effective population size is well-developed in the Bayesian setting, but here we develop a frequentist approach based on nonparametric latent process models of population size dynamics. We appeal to statistical principles based on out-of-sample prediction accuracy in order to optimize parameters that control shape and smoothness of the population size over time. Our methodology is implemented in a new R package entitled mlesky. We demonstrate the flexibility and speed of this approach in a series of simulation experiments and apply the methodology to a dataset of HIV-1 in the USA. We also estimate the impact of non-pharmaceutical interventions for COVID-19 in England using thousands of SARS-CoV-2 sequences. By incorporating a measure of the strength of these interventions over time within the phylodynamic model, we estimate the impact of the first national lockdown in the UK on the epidemic reproduction number.

Phylodynamics of SARS-CoV-2 in France, Europe, and the world in 2020.

Although France was one of the most affected European countries by the COVID-19 pandemic in 2020, the dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) movement within France, but also involving France in Europe and in the world, remain only partially characterized in this timeframe. Here, we analyzed GISAID deposited sequences from January 1 to December 31, 2020 (n = 638,706 sequences at the time of writing). To tackle the challenging number of sequences without the bias of analyzing a single subsample of sequences, we produced 100 subsamples of sequences and related phylogenetic trees from the whole dataset for different geographic scales (worldwide, European countries, and French administrative regions) and time periods (from January 1 to July 25, 2020, and from July 26 to December 31, 2020). We applied a maximum likelihood discrete trait phylogeographic method to date exchange events (i.e., a transition from one location to another one), to estimate the geographic spread of SARS-CoV-2 transmissions and lineages into, from and within France, Europe, and the world. The results unraveled two different patterns of exchange events between the first and second half of 2020. Throughout the year, Europe was systematically associated with most of the intercontinental exchanges. SARS-CoV-2 was mainly introduced into France from North America and Europe (mostly by Italy, Spain, the United Kingdom, Belgium, and Germany) during the first European epidemic wave. During the second wave, exchange events were limited to neighboring countries without strong intercontinental movement, but Russia widely exported the virus into Europe during the summer of 2020. France mostly exported B.1 and B.1.160 lineages, respectively, during the first and second European epidemic waves. At the level of French administrative regions, the Paris area was the main exporter during the first wave. But, for the second epidemic wave, it equally contributed to virus spread with Lyon area, the second most populated urban area after Paris in France. The main circulating lineages were similarly distributed among the French regions. To conclude, by enabling the inclusion of tens of thousands of viral sequences, this original phylodynamic method enabled us to robustly describe SARS-CoV-2 geographic spread through France, Europe, and worldwide in 2020.

The importance of vaccinated individuals to population-level evolution of pathogens.

Virus evolution shapes the epidemiological patterns of infectious disease, particularly via evasion of population immunity. At the individual level, host immunity itself may drive viral evolution towards antigenic escape. Using compartmental SIR-style models with imperfect vaccination, we allow the probability of immune escape to differ in vaccinated and unvaccinated hosts. As the relative contribution to selection in these different hosts varies, the overall effect of vaccination on the antigenic escape pressure at the population level changes. We find that this relative contribution to escape is important for understanding the effects of vaccination on the escape pressure and we draw out some fairly general patterns. If vaccinated hosts do not contribute much more than unvaccinated hosts to the escape pressure, then increasing vaccination always reduces the overall escape pressure. In contrast, if vaccinated hosts contribute significantly more than unvaccinated hosts to the population level escape pressure, then the escape pressure is maximised for intermediate vaccination levels. Past studies find only that the escape pressure is maximal for intermediate levels with fixed extreme assumptions about this relative contribution. Here we show that this result does not hold across the range of plausible assumptions for the relative contribution to escape from vaccinated and unvaccinated hosts. We also find that these results depend on the vaccine efficacy against transmission, particularly through the partial protection against infection. This work highlights the potential value of understanding better how the contribution to antigenic escape pressure depends on individual host immunity.

The Omicron Lineages BA.1 and BA.2 (Betacoronavirus SARS-CoV-2) Have Repeatedly Entered Brazil through a Single Dispersal Hub.

Brazil currently ranks second in absolute deaths by COVID-19, even though most of its population has completed the vaccination protocol. With the introduction of Omicron in late 2021, the number of COVID-19 cases soared once again in the country. We investigated in this work how lineages BA.1 and BA.2 entered and spread in the country by sequencing 2173 new SARS-CoV-2 genomes collected between October 2021 and April 2022 and analyzing them in addition to more than 18,000 publicly available sequences with phylodynamic methods. We registered that Omicron was present in Brazil as early as 16 November 2021 and by January 2022 was already more than 99% of samples. More importantly, we detected that Omicron has been mostly imported through the state of São Paulo, which in turn dispersed the lineages to other states and regions of Brazil. This knowledge can be used to implement more efficient non-pharmaceutical interventions against the introduction of new SARS-CoV variants focused on surveillance of airports and ground transportation.

Deciphering the Tangible Spatio-Temporal Spread of a 25-Year Tuberculosis Outbreak Boosted by Social Determinants.

Outbreak strains of Mycobacterium tuberculosis are promising candidates as targets in the search for intrinsic determinants of transmissibility, as they are responsible for many cases with sustained transmission; however, the use of low-resolution typing methods and restricted geographical investigations represent flaws in assessing the success of long-lived outbreak strains. We can now address the nature of outbreak strains by combining large genomic data sets and phylodynamic approaches. We retrospectively sequenced the whole genome of representative samples assigned to an outbreak circulating in the Canary Islands (the GC strain) since 1993, which accounts for ~20% of local tuberculosis cases. We selected a panel of specific single nucleotide polymorphism (SNP) markers for an in-silico search for additional outbreak-related sequences within publicly available tuberculosis genomic data. Using this information, we inferred the origin, spread, and epidemiological parameters of the GC strain. Our approach allowed us to accurately trace the historical and more recent dispersion of the GC strain. We provide evidence of a highly successful nature within the Canarian archipelago but limited expansion abroad. Estimation of epidemiological parameters from genomic data disagree with a distinctive biology of the GC strain. With the increasing availability of genomic data allowing for the accurate inference of strain spread and critical epidemiological parameters, we can now revisit the link between Mycobacterium tuberculosis genotypes and transmission, as is routinely carried out for SARS-CoV-2 variants of concern. We demonstrate that social determinants rather than intrinsically higher bacterial transmissibility better explain the success of the GC strain. Importantly, our approach can be used to trace and characterize strains of interest worldwide. IMPORTANCE Infectious disease outbreaks represent a significant problem for public health. Tracing outbreak expansion and understanding the main factors behind emergence and persistence remain critical to effective disease control. Our study allows researchers and public health authorities to use Whole-Genome Sequencing-based methods to trace outbreaks, and shows how available epidemiological information helps to evaluate the factors underpinning outbreak persistence. Taking advantage of all the freely available information placed in public repositories, researchers can accurately establish the expansion of an outbreak beyond original boundaries, and determine the potential risk of a strain to inform health authorities which, in turn, can define target strategies to mitigate expansion and persistence. Finally, we show the need to evaluate strain transmissibility in different geographic contexts to unequivocally associate spread to local or pathogenic factors, an important lesson taken from genomic surveillance of SARS-CoV-2.

Tracing the First Days of the SARS-CoV-2 Pandemic in Greece and the Role of the First Imported Group of Travelers.

The first SARS-CoV-2 case in Greece was confirmed on February 26, 2020, and since then, multiple strains have circulated the country, leading to regional and country-wide outbreaks. Our aim is to enlighten the events that took place during the first days of the SARS-CoV-2 pandemic in Greece, focusing on the role of the first imported group of travelers. We used whole-genome SARS-CoV-2 sequences obtained from the infected travelers of the group as well as Greece-derived and globally subsampled sequences and applied dedicated phylogenetics and phylodynamics tools as well as in-house-developed bioinformatics pipelines. Our analyses reveal the genetic variants circulating in Greece during the first days of the pandemic and the role of the group's imported strains in the course of the first pandemic wave in Greece. The strain that dominated in Greece throughout the first wave, bearing the D614G mutation, was primarily imported from a certain group of travelers, while molecular and clinical data suggest that the infection of the travelers occurred in Egypt. Founder effects early in the pandemic are important for the success of certain strains, as those arriving early, several times, and to diverse locations lead to the formation of large transmission clusters that can be estimated using molecular epidemiology approaches and can be a useful surveillance tool for the prioritization of nonpharmaceutical interventions and combating present and future outbreaks. IMPORTANCE The strain that dominated in Greece during the first pandemic wave was primarily imported from a group of returning travelers in February 2020, while molecular and clinical data suggest that the origin of the transmission was Egypt. The observed molecular transmission clusters reflect the transmission dynamics of this particular strain bearing the D614G mutation while highlighting the necessity of their use as a surveillance tool for the prioritization of nonpharmaceutical interventions and combating present and future outbreaks.

Investigation of cross-regional spread and evolution of equine influenza H3N8 at US and global scales using Bayesian phylogeography based on balanced subsampling.

Equine influenza virus (EIV) is a highly contagious pathogen of equids, and a well-known burden in global equine health. EIV H3N8 variants seasonally emerged and resulted in EIV outbreaks in the United States and worldwide. The present study evaluated the pattern of cross-regional EIV H3N8 spread and evolutionary characteristics at US and global scales using Bayesian phylogeography with balanced subsampling based on regional horse population size. A total of 297 haemagglutinin (HA) sequences of global EIV H3N8 were collected from 1963 to 2019 and subsampled to global subset (n = 67), raw US sequences (n = 100) and US subset (n = 44) datasets. Discrete trait phylogeography analysis was used to estimate the transmission history of EIV using four global and US genome datasets. The North American lineage was the major source of globally dominant EIV variants and spread to other global regions. The US EIV strains generally spread from the southern and midwestern regions to other regions. The EIV H3N8 accumulated approximately three nucleotide substitutions per year in the HA gene under heterogeneous local positive selection. Our findings will guide better decision making of target intervention strategies of EIV H3N8 infection and provide the better scheme of genomic surveillance in the United States and global equine health.

Statistical Challenges in Tracking the Evolution of SARS-CoV-2.

Genomic surveillance of SARS-CoV-2 has been instrumental in tracking the spread and evolution of the virus during the pandemic. The availability of SARS-CoV-2 molecular sequences isolated from infected individuals, coupled with phylodynamic methods, have provided insights into the origin of the virus, its evolutionary rate, the timing of introductions, the patterns of transmission, and the rise of novel variants that have spread through populations. Despite enormous global efforts of governments, laboratories, and researchers to collect and sequence molecular data, many challenges remain in analyzing and interpreting the data collected. Here, we describe the models and methods currently used to monitor the spread of SARS-CoV-2, discuss long-standing and new statistical challenges, and propose a method for tracking the rise of novel variants during the epidemic.

Identifying SARS-CoV-2 regional introductions and transmission clusters in real time.

The unprecedented severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) global sequencing effort has suffered from an analytical bottleneck. Many existing methods for phylogenetic analysis are designed for sparse, static datasets and are too computationally expensive to apply to densely sampled, rapidly expanding datasets when results are needed immediately to inform public health action. For example, public health is often concerned with identifying clusters of closely related samples, but the sheer scale of the data prevents manual inspection and the current computational models are often too expensive in time and resources. Even when results are available, intuitive data exploration tools are of critical importance to effective public health interpretation and action. To help address this need, we present a phylogenetic heuristic that quickly and efficiently identifies newly introduced strains in a region, resulting in clusters of infected individuals, and their putative geographic origins. We show that this approach performs well on simulated data and yields results largely congruent with more sophisticated Bayesian phylogeographic modeling approaches. We also introduce Cluster-Tracker (https://clustertracker.gi.ucsc.edu/), a novel interactive web-based tool to facilitate effective and intuitive SARS-CoV-2 geographic data exploration and visualization across the USA. Cluster-Tracker is updated daily and automatically identifies and highlights groups of closely related SARS-CoV-2 infections resulting from the transmission of the virus between two geographic areas by travelers, streamlining public health tracking of local viral diversity and emerging infection clusters. The site is open-source and designed to be easily configured to analyze any chosen region, making it a useful resource globally. The combination of these open-source tools will empower detailed investigations of the geographic origins and spread of SARS-CoV-2 and other densely sampled pathogens.

Genomic epidemiology of seasonal influenza circulation in China during prolonged border closure from 2020 to 2021.

China experienced a resurgence of seasonal influenza activity throughout 2021 despite intermittent control measures and prolonged international border closure. We show genomic evidence for multiple A(H3N2), A(H1N1), and B/Victoria transmission lineages circulating over 3 years, with the 2021 resurgence mainly driven by two B/Victoria clades. Phylodynamic analysis revealed unsampled ancestry prior to widespread outbreaks in December 2020, showing that influenza lineages can circulate cryptically under non-pharmaceutical interventions enacted against COVID-19. Novel haemagglutinin gene mutations and altered age profiles of infected individuals were observed, and Jiangxi province was identified as a major source for nationwide outbreaks. Following major holiday periods, fluctuations in the effective reproduction number were observed, underscoring the importance of influenza vaccination prior to holiday periods or travel. Extensive heterogeneity in seasonal influenza circulation patterns in China determined by historical strain circulation indicates that a better understanding of demographic patterns is needed for improving effective controls.

Variational Phylodynamic Inference Using Pandemic-scale Data.

The ongoing global pandemic has sharply increased the amount of data available to researchers in epidemiology and public health. Unfortunately, few existing analysis tools are capable of exploiting all of the information contained in a pandemic-scale data set, resulting in missed opportunities for improved surveillance and contact tracing. In this paper, we develop the variational Bayesian skyline (VBSKY), a method for fitting Bayesian phylodynamic models to very large pathogen genetic data sets. By combining recent advances in phylodynamic modeling, scalable Bayesian inference and differentiable programming, along with a few tailored heuristics, VBSKY is capable of analyzing thousands of genomes in a few minutes, providing accurate estimates of epidemiologically relevant quantities such as the effective reproduction number and overall sampling effort through time. We illustrate the utility of our method by performing a rapid analysis of a large number of SARS-CoV-2 genomes, and demonstrate that the resulting estimates closely track those derived from alternative sources of public health data.

Epidemiological inference from pathogen genomes: A review of phylodynamic models and applications.

Phylodynamics requires an interdisciplinary understanding of phylogenetics, epidemiology, and statistical inference. It has also experienced more intense application than ever before amid the SARS-CoV-2 pandemic. In light of this, we present a review of phylodynamic models beginning with foundational models and assumptions. Our target audience is public health researchers, epidemiologists, and biologists seeking a working knowledge of the links between epidemiology, evolutionary models, and resulting epidemiological inference. We discuss the assumptions linking evolutionary models of pathogen population size to epidemiological models of the infected population size. We then describe statistical inference for phylodynamic models and list how output parameters can be rearranged for epidemiological interpretation. We go on to cover more sophisticated models and finish by highlighting future directions.

Comparative phylodynamics reveals the evolutionary history of SARS-CoV-2 emerging variants in the Arabian Peninsula.

Emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants continue to be responsible for an unprecedented worldwide public health and economic catastrophe. Accurate understanding and comparison of global and regional evolutionary epidemiology of novel SARS-CoV-2 variants are critical to guide current and future interventions. Here, we utilized a Bayesian phylodynamic pipeline to trace and compare the evolutionary dynamics, spatiotemporal origins, and spread of five variants (Alpha, Beta, Delta, Kappa, and Eta) across the Arabian Peninsula. We found variant-specific signatures of evolution and spread that are likely linked to air travel and disease control interventions in the region. Alpha, Beta, and Delta variants went through sequential periods of growth and decline, whereas we inferred inconclusive population growth patterns for the Kappa and Eta variants due to their sporadic introductions in the region. Non-pharmaceutical interventions imposed between mid-2020 and early 2021 likely played a role in reducing the epidemic progression of the Beta and the Alpha variants. In comparison, the combination of the non-pharmaceutical interventions and the rapid rollout of vaccination might have shaped Delta variant dynamics. We found that the Alpha and Beta variants were frequently introduced into the Arab peninsula between mid-2020 and early 2021 from Europe and Africa, respectively, whereas the Delta variant was frequently introduced between early 2021 and mid-2021 from East Asia. For these three variants, we also revealed significant and intense dispersal routes between the Arab region and Africa, Europe, Asia, and Oceania. In contrast, the restricted spread and stable effective population size of the Kappa and the Eta variants suggest that they no longer need to be targeted in genomic surveillance activities in the region. In contrast, the evolutionary characteristics of the Alpha, Beta, and Delta variants confirm the dominance of these variants in the recent outbreaks. Our study highlights the urgent need to establish regional molecular surveillance programs to ensure effective decision making related to the allocation of intervention activities targeted toward the most relevant variants.

Reconstruction of the Evolutionary Origin, Phylodynamics, and Phylogeography of the Porcine Circovirus Type 3.

Porcine circovirus type 3 (PCV3) is a newly identified virus associated with porcine dermatitis and nephropathy syndrome (PDNS) and multisystemic inflammatory responses in pigs. Recent studies suggests that PCV3 originated from bat circoviruses; however, the origin time, mode of spread, and geographic distribution of PCV3 remain unclear. In this study, the evolutionary origin, phylodynamics, and phylogeography of PCV3 were reconstructed based on the available complete genome sequences. PCV3 showed a closer relationship with bird circovirus than with bat circovirus, but their common ancestor was bat circovirus, indicating that birds may be intermediate hosts for the spread of circoviruses in pigs. Using the BEAST and phylogenetic analyses, three different clades of PCV3 (PCV3a, PCV3b, and PCV3c) were identified, with PCV3a being the most prevalent PCV3 clade. Further studies indicated that the earliest origin of PCV3 can be traced back to 1907.53-1923.44, with a substitution rate of 3.104 × 10-4 to 6.8524 × 10-4 substitution/site/year. A phylogeographic analysis highlighted Malaysia as the earliest location of the original PCV3, which migrated to Asia, America, and Europe. Overall, this study provides novel insights into the evolutionary origin, spread mode, and geographic distribution of PCV3, which will facilitate the prevention and control of PCV3 epidemics in the future.

Phylogenetic and Spatiotemporal Analyses of the Complete Genome Sequences of Avian Coronavirus Infectious Bronchitis Virus in China During 1985-2020: Revealing Coexistence of Multiple Transmission Chains and the Origin of LX4-Type Virus.

Infectious bronchitis (IB) virus (IBV) causes considerable economic losses to poultry production. The data on transmission dynamics of IBV in China are limited. The complete genome sequences of 212 IBV isolates in China during 1985-2020 were analyzed as well as the characteristics of the phylogenetic tree, recombination events, dN/dS ratios, temporal dynamics, and phylogeographic relationships. The LX4 type (GI-19) was found to have the highest dN/dS ratios and has been the most dominant genotype since 1999, and the Taiwan-I type (GI-7) and New type (GVI-1) showed an increasing trend. A total of 59 recombinants were identified, multiple recombination events between the field and vaccine strains were found in 24 isolates, and the 4/91-type (GI-13) isolates were found to be more prone to being involved in the recombination. Bayesian phylogeographic analyses indicated that the Chinese IBVs originated from Liaoning province in the early 1900s. The LX4-type viruses were traced back to Liaoning province in the late 1950s and had multiple transmission routes in China and two major transmission routes in the world. Viral phylogeography identified three spread regions for IBVs (including LX4 type) in China: Northeastern China (Heilongjiang, Liaoning, and Jilin), north and central China (Beijing, Hebei, Shanxi, Shandong, and Jiangsu), and Southern China (Guangxi and Guangdong). Shandong has been the epidemiological center of IBVs (including LX4 type) in China. Overall, our study highlighted the reasons why the LX4-type viruses had become the dominant genotype and its origin and transmission routes, providing more targeted strategies for the prevention and control of IB in China.

Evolutionary history and introduction of SARS-CoV-2 Alpha VOC/B.1.1.7 in Pakistan through international travelers.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants continue to emerge, and their identification is important for the public health response to coronavirus disease 2019 (COVID-19). Genomic sequencing provides robust information but may not always be accessible, and therefore, mutation-based polymerase chain reaction (PCR) approaches can be used for rapid identification of known variants. International travelers arriving in Karachi between December 2020 and February 2021 were tested for SARS-CoV-2 by PCR. A subset of positive samples was tested for S-gene target failure (SGTF) on TaqPathTM COVID-19 (Thermo Fisher Scientific) and for mutations using the GSD NovaType SARS-CoV-2 (Eurofins Technologies) assays. Sequencing was conducted on the MinION platform (Oxford Nanopore Technologies). Bayesian phylogeographic inference was performed integrating the patients' travel history information. Of the thirty-five COVID-19 cases screened, thirteen had isolates with SGTF. The travelers transmitted infection to sixty-eight contact cases. The B.1.1.7 lineage was confirmed through sequencing and PCR. The phylogenetic analysis of sequence data available for six cases included four B.1.1.7 strains and one B.1.36 and B.1.1.212 lineage isolate. Phylogeographic modeling estimated at least three independent B.1.1.7 introductions into Karachi, Pakistan, originating from the UK. B.1.1.212 and B.1.36 were inferred to be introduced either from the UK or the travelers' layover location. We report the introduction of SARS-CoV-2 B.1.1.7 and other lineages in Pakistan by international travelers arriving via different flight routes. This highlights SARS-CoV-2 transmission through travel, importance of testing, and quarantine post-travel to prevent transmission of new strains, as well as recording detailed patients' metadata. Such results help inform policies on restricting travel from destinations where new highly transmissible variants have emerged.

Phylodynamic analysis and spike protein mutations in porcine deltacoronavirus with a new variant introduction in Taiwan.

Porcine deltacoronavirus (PDCoV) is a highly transmissible intestinal pathogen that causes mild to severe clinical symptoms, such as anorexia, vomiting, and watery diarrhea in pigs. By comparing the genetic sequences of the spike glycoprotein between historical and current Taiwanese PDCoV strains, we identified a novel PDCoV variant that displaced the PDCoV responsible for the 2015 epidemic. This PDCoV variant belongs to a young population within the US lineage, and infected pigs carry high concentrations of the virus. It also has several critical point mutations and an amino acid insertion at position 52 that may enhance the affinity between the B-cell epitopes located in the N-terminal domain with its complementarity regions, consequently facilitating binding or penetration between the fusion peptide and cellular membrane. Furthermore, viral protein structure prediction demonstrated that these amino acid changes may change the ability of the virus to bind to the receptor, which may consequently alter virus infectivity. Our results hence suggest the emergence of new PDCoV strains in Taiwan with the potential for greater transmission and pathogenesis.

Infectious disease phylodynamics with occurrence data

1. Phylodynamic models use pathogen genome sequence data to infer epidemiological dynamics. With the increasing genomic surveillance of pathogens, especially during the SARS-CoV-2 pandemic, new practical questions about their use are emerging. 2. One such question focuses on the inclusion of un-sequenced case occurrence data alongside sequenced data to improve phylodynamic analyses. This approach can be particularly valuable if sequencing efforts vary over time. 3. Using simulations, we demonstrate that birth-death phylodynamic models can employ occurrence data to eliminate bias in estimates of the basic reproductive number due to misspecification of the sampling process. In contrast, the coalescent exponential model is robust to such sampling biases, but in the absence of a sampling model it cannot exploit occurrence data. Subsequent analysis of the SARS-CoV-2 epidemic in the northwest USA supports these results. 4. We conclude that occurrence data are a valuable source of information in combination with birth-death models. These data should be used to bolster phylodynamic analyses of infectious diseases and other rapidly spreading species in the future.