Time Clustered Sampling Can Inflate the Inferred Substitution Rate in Foot-And-Mouth Disease Virus Analyses

Porcine Reproductive and Respiratory Syndrome (PRRSV2) Viral Diversity within a Farrow-to-Wean Farm Cohort Study

Describing PRRSV whole-genome viral diversity data over time within the host and within-farm is crucial for a better understanding of viral evolution and its implications. A cohort study was conducted at one naïve farrow-to-wean farm reporting a PRRSV outbreak. All piglets 3-5 days of age (DOA) born to mass-exposed sows through live virus inoculation with the recently introduced wild-type virus two weeks prior were sampled and followed up at 17-19 DOA. Samples from 127 piglets were individually tested for PRRSV by RT-PCR and 100 sequences were generated using Oxford Nanopore Technologies chemistry. Female piglets had significantly higher median Ct values than males (15.5 vs. 13.7, Kruskal-Wallis p < 0.001) at 3-5 DOA. A 52.8% mortality between sampling points was found, and the odds of dying by 17-19 DOA decreased with every one unit increase in Ct values at 3-5 DOA (OR = 0.76, 95% CI 0.61-0.94, p = 0.01). Although the within-pig percent nucleotide identity was overall high (99.7%) between 3-5 DOA and 17-19 DOA samples, ORFs 4 and 5a showed much lower identities (97.26% and 98.53%, respectively). When looking solely at ORF5, 62% of the sequences were identical to the 3-5 DOA consensus. Ten and eight regions showed increased nucleotide and amino acid genetic diversity, respectively, all found throughout ORFs 2a/2b, 4, 5a/5, 6, and 7.

Identification of diffusion routes of O/EA-3 topotype of foot-and-mouth disease virus in Africa and Western Asia between 1974 and 2019 - a phylogeographic analysis

Foot-and-mouth disease (FMD) affects the livestock industry and socioeconomic sustainability of many African countries. The success of FMD control programs in Africa depends largely on understanding the dynamics of FMD virus (FMDV) spread. In light of the recent outbreaks of FMD that affected the North-Western African countries in 2018 and 2019, we investigated the evolutionary phylodynamics of the causative serotype O viral strains all belonging to the East-Africa 3 topotype (O/EA-3). We analyzed a total of 489 sequences encoding the FMDV VP1 genome region generated from samples collected from 25 African and Western Asian countries between 1974 and 2019. Using Bayesian evolutionary models on genomic and epidemiological data, we inferred the routes of introduction and migration of the FMDV O/EA-3 topotype at the inter-regional scale. We inferred a mean substitution rate of 6.64 × 10^-3  nt/site/year and we predicted that the most recent common ancestor for our panel of samples circulated between February 1967 and November 1973 in Yemen, likely reflecting the epidemiological situation in under sampled cattle-exporting East African countries. Our study also reinforces the role previously described of Sudan and South Sudan as a frequent source of FMDVs spread. In particular, we identified two transboundary routes of O/EA-3 diffusion: the first from Sudan to North-East Africa, and from the latter into Israel and Palestine AT; a second from Sudan to Nigeria, Cameroon, and from there to further into West and North-West Africa. This study highlights the necessity to reinforce surveillance at an inter-regional scale in Africa and Western Asia, in particular along the identified migration routes for the implementation of efficient control measures in the fight against FMD.

Mind your Ps: A probabilistic model to aid the interpretation of molecular epidemiology data

Background

Assessing relatedness of pathogen sequences in clinical samples is a core goal in molecular epidemiology. Tools for Bayesian analysis of phylogeny, such as the BEAST software package, have been typically used in the analysis of sequence/time data in public health. However, they are computationally-, time-, and knowledge-intensive, demanding resources that many laboratories do not have available or cannot allocate frequently.

Methods

To evaluate a faster and simpler alternative method to support the routine interpretation of sequence data for epidemiology, we obtained sequences for two regions in the measles virus genome, N-450 and MF-NCR, from patient samples of genotypes B3, D4 and D8 taken between 2011 and 2017 in the UK and Romania. A mathematical model incorporating time, possible shared ancestry and the Poisson distribution describing the number of expected substitutions at a given time point was developed to exclude epidemiological relatedness between pairs of sequences. The model was validated against the commonly used Bayesian phylogenetic method using an independent dataset collected in 2017–19.

Findings

We demonstrate that our model, using time and sequence information to predict whether two samples may be related within a given time frame, minimises the risk of erroneous exclusion of relatedness. An easy-to-use implementation in the form of a guide and spreadsheet is provided for convenient application.

Interpretation

The proposed model only requires a previously calculated substitution rate for the locus and pathogen of interest. It allows for an informed but quick decision on the likelihood of relatedness between two samples within a time frame, without the need for phylogenetic reconstruction, thus facilitating rapid epidemiological interpretation of sequence data.

Funding

This work was funded by the United Kingdom Health Security Agency (UKHSA). The World Health Organization European Regional Office funded Aurora Fernández-García and Mihaela Lazar training visits to UKHSA.

Phylogeographical and cross-species transmission dynamics of SAT1 and SAT2 foot-and-mouth disease virus in Eastern Africa

Understanding the dynamics of foot-and-mouth disease virus (FMDV), an endemic and economically constraining disease, is critical in designing control programmes in Africa. This study investigates the evolutionary epidemiology of SAT1 and SAT2 FMDV in Eastern Africa, as well as between cattle and wild African buffalo. Bayesian phylodynamic models were used to analyse SAT1 and SAT2 VP1 gene segments collected between 1975 and 2016, focusing on the SAT1 and SAT2 viruses currently circulating in Eastern Africa. The root state posterior probabilities inferred from our analyses suggest Zimbabwe as the ancestral location for SAT1 currently circulating in Eastern Africa (p = 0.67). For the SAT2 clade, Kenya is inferred to be the ancestral location for introduction of the virus into other countries in Eastern Africa (p = 0.72). Salient (Bayes factor >10) viral dispersal routes were inferred from Tanzania to Kenya, and from Kenya to Uganda for SAT1 and SAT2, respectively. Results suggest that cattle are the source of the SAT1 and SAT2 clades currently circulating in Eastern Africa. In addition, our results suggest that the majority of SAT1 and SAT2 in livestock come from other livestock rather than wildlife, with limited evidence that buffalo serve as reservoirs for cattle. Insights from the present study highlight the role of cattle movements and anthropogenic activities in shaping the evolutionary history of SAT1 and SAT2 in Eastern Africa. While the results may be affected by inherent limitations of imperfect surveillance, our analysis elucidates the dynamics between host species in this region, which is key to guiding disease intervention activities.

Simultaneous inference of phylogenetic and transmission trees in infectious disease outbreaks

Whole-genome sequencing of pathogens from host samples becomes more and more routine during infectious disease outbreaks. These data provide information on possible transmission events which can be used for further epidemiologic analyses, such as identification of risk factors for infectivity and transmission. However, the relationship between transmission events and sequence data is obscured by uncertainty arising from four largely unobserved processes: transmission, case observation, within-host pathogen dynamics and mutation. To properly resolve transmission events, these processes need to be taken into account. Recent years have seen much progress in theory and method development, but existing applications make simplifying assumptions that often break up the dependency between the four processes, or are tailored to specific datasets with matching model assumptions and code. To obtain a method with wider applicability, we have developed a novel approach to reconstruct transmission trees with sequence data. Our approach combines elementary models for transmission, case observation, within-host pathogen dynamics, and mutation, under the assumption that the outbreak is over and all cases have been observed. We use Bayesian inference with MCMC for which we have designed novel proposal steps to efficiently traverse the posterior distribution, taking account of all unobserved processes at once. This allows for efficient sampling of transmission trees from the posterior distribution, and robust estimation of consensus transmission trees. We implemented the proposed method in a new R package phybreak. The method performs well in tests of both new and published simulated data. We apply the model to five datasets on densely sampled infectious disease outbreaks, covering a wide range of epidemiological settings. Using only sampling times and sequences as data, our analyses confirmed the original results or improved on them: the more realistic infection times place more confidence in the inferred transmission trees.

It is becoming easier and cheaper to obtain (whole genome) sequences of pathogen samples during outbreaks of infectious diseases. If all hosts during an outbreak are sampled, and these samples are sequenced, the small differences between the sequences (single nucleotide polymorphisms, SNPs) give information on the transmission tree, i.e. who infected whom, and when. However, correctly inferring this tree is not straightforward, because SNPs arise from unobserved processes including infection events, as well as pathogen growth and mutation within the hosts. Several methods have been developed in recent years, but often for specific applications or with limiting assumptions, so that they are not easily applied to new settings and datasets. We have developed a new model and method to infer transmission trees without putting prior limiting constraints on the order of unobserved events. The method is easily accessible in an R package implementation. We show that the method performs well on new and previously published simulated data. We illustrate applicability to a wide range of infectious diseases and settings by analysing five published datasets on densely sampled infectious disease outbreaks, confirming or improving the original results.