Extracting transmission networks from phylogeographic data for epidemic and endemic diseases: Ebola virus in Sierra Leone, 2009 H1N1 pandemic influenza and polio in Nigeria

Methods Combining Genomic and Epidemiological Data in the Reconstruction of Transmission Trees: A Systematic Review

In order to better understand transmission dynamics and appropriately target control and preventive measures, studies have aimed to identify who-infected-whom in actual outbreaks. Numerous reconstruction methods exist, each with their own assumptions, types of data, and inference strategy. Thus, selecting a method can be difficult. Following PRISMA guidelines, we systematically reviewed the literature for methods combing epidemiological and genomic data in transmission tree reconstruction. We identified 22 methods from the 41 selected articles. We defined three families according to how genomic data was handled: a non-phylogenetic family, a sequential phylogenetic family, and a simultaneous phylogenetic family. We discussed methods according to the data needed as well as the underlying sequence mutation, within-host evolution, transmission, and case observation. In the non-phylogenetic family consisting of eight methods, pairwise genetic distances were estimated. In the phylogenetic families, transmission trees were inferred from phylogenetic trees either simultaneously (nine methods) or sequentially (five methods). While a majority of methods (17/22) modeled the transmission process, few (8/22) took into account imperfect case detection. Within-host evolution was generally (7/8) modeled as a coalescent process. These practical and theoretical considerations were highlighted in order to help select the appropriate method for an outbreak.

Linked surveillance and genetic data uncovers programmatically relevant geographic scale of Guinea worm transmission in Chad

Background

Guinea worm (Dracunculus medinensis) was detected in Chad in 2010 after a supposed ten-year absence, posing a challenge to the global eradication effort. Initiation of a village-based surveillance system in 2012 revealed a substantial number of dogs infected with Guinea worm, raising questions about paratenic hosts and cross-species transmission.

Methodology/principal findings

We coupled genomic and surveillance case data from 2012-2018 to investigate the modes of transmission between dog and human hosts and the geographic connectivity of worms. Eighty-six variants across four genes in the mitochondrial genome identified 41 genetically distinct worm genotypes. Spatiotemporal modeling revealed worms with the same genotype (‘genetically identical’) were within a median range of 18.6 kilometers of each other, but largely within approximately 50 kilometers. Genetically identical worms varied in their degree of spatial clustering, suggesting there may be different factors that favor or constrain transmission. Each worm was surrounded by five to ten genetically distinct worms within a 50 kilometer radius. As expected, we observed a change in the genetic similarity distribution between pairs of worms using variants across the complete mitochondrial genome in an independent population.

Conclusions/significance

In the largest study linking genetic and surveillance data to date of Guinea worm cases in Chad, we show genetic identity and modeling can facilitate the understanding of local transmission. The co-occurrence of genetically non-identical worms in quantitatively identified transmission ranges highlights the necessity for genomic tools to link cases. The improved discrimination between pairs of worms from variants identified across the complete mitochondrial genome suggests that expanding the number of genomic markers could link cases at a finer scale. These results suggest that scaling up genomic surveillance for Guinea worm may provide additional value for programmatic decision-making critical for monitoring cases and intervention efficacy to achieve elimination.

The global eradication effort for Guinea worm disease has dramatically decreased the global burden of the disease and enabled 187 countries to be certified by the World Health Organization to be free of endemic transmission. Despite this progress, several countries continue to have endemic transmission. In Chad, a long absence of reported cases was interrupted with the identification of new Guinea worm cases, prompting a substantial scale up of surveillance and intervention efforts. Here, we study the value of increasing genomic surveillance as a tool for programmatic evaluation of surveillance and intervention efforts in Chad. Linking surveillance and genomic samples, parsimonious spatial models help reveal a consistent geographic clustering of similar genetic sequences across Chad. We also demonstrate that expanding the sequencing can offer better resolution for distinguishing Guinea worm samples. In this retrospective study, we found evidence that scaling up genomic surveillance can be an important monitoring and evaluation tool for the eradication program in Chad.

Can Bayesian phylogeography reconstruct migrations and expansions in linguistic evolution?

Bayesian phylogeography has been used in historical linguistics to reconstruct homelands and expansions of language families, but the reliability of these reconstructions has remained unclear. We contribute to this discussion with a simulation study where we distinguish two types of spatial processes: migration, where populations or languages leave one place for another, and expansion, where populations or languages gradually expand their territory. We simulate migration and expansion in two scenarios with varying degrees of spatial directional trends and evaluate the performance of state-of-the-art phylogeographic methods. Our results show that these methods fail to reconstruct migrations, but work surprisingly well on expansions, even under severe directional trends. We demonstrate that migrations and expansions have typical phylogenetic and spatial patterns, which in the one case inhibit and in the other facilitate phylogeographic reconstruction. Furthermore, we propose descriptive statistics to identify whether a real sample of languages, their relationship and spatial distribution, better fits a migration or an expansion scenario. Bringing together the results of the simulation study and theoretical arguments, we make recommendations for assessing the adequacy of phylogeographic models to reconstruct the spatial evolution of languages.

Evolutionary dynamics and transmission patterns of Newcastle disease virus in China through Bayesian phylogeographical analysis

The Chinese poultry industry has experienced outbreaks of Newcastle disease (ND) dating back to the 1920s. However, the epidemic has exhibited a downtrend in recent years. In this study, both observational and genetic data [fusion (F) and haemagglutinin-neuraminidase genes (HN)] were analyzed, and phylogeographic analysis based on prevalent genotypes of Newcastle disease virus (NDV) was conducted for better understanding of the evolution and spatiotemporal dynamics of ND in China. In line with the observed trend of epidemic outbreaks, the effective population size of F and HN genes of circulating NDV is no longer growing since 2000, which is supported by 95% highest posterior diversity (HPD) intervals. Phylogeographic analysis indicated that the two eastern coastal provinces, Shandong and Jiangsu were the most relevant hubs for NDV migration, and the geographical regions with active NDV diffusion seemed to be constrained to southern and eastern China. The live poultry trade may play an important role in viral spread. Interestingly, no migration links from wild birds to poultry received Bayes factor support (BF > 3), while the migration links from poultry to wild birds accounted for 64% in all effective migrations. This may indicate that the sporadic cases of ND in wild bird likely spillover events from poultry. These findings contribute to predictive models of NDV transmission, and potentially help in the prevention of future outbreaks.

Review of poliovirus modeling performed from 2000 to 2019 to support global polio eradication

INTRODUCTION

Over the last 20 years (2000-2019) the partners of the Global Polio Eradication Initiative (GPEI) invested in the development and application of mathematical models of poliovirus transmission as well as economics, policy, and risk analyses of polio endgame risk management options, including policies related to poliovirus vaccine use during the polio endgame.

AREAS COVERED

This review provides a historical record of the polio studies published by the three modeling groups that primarily performed the bulk of this work. This review also systematically evaluates the polio transmission and health economic modeling papers published in English in peer-reviewed journals from 2000 to 2019, highlights differences in approaches and methods, shows the geographic coverage of the transmission modeling performed, identified common themes, and discusses instances of similar or conflicting insights or recommendations.

EXPERT OPINION

Polio modeling performed during the last 20 years substantially impacted polio vaccine choices, immunization policies, and the polio eradication pathway. As the polio endgame continues, national preferences for polio vaccine formulations and immunization strategies will likely continue to change. Future modeling will likely provide important insights about their cost-effectiveness and their relative benefits with respect to controlling polio and potentially achieving and maintaining eradication.

Outbreak analytics: a developing data science for informing the response to emerging pathogens

Despite continued efforts to improve health systems worldwide, emerging pathogen epidemics remain a major public health concern. Effective response to such outbreaks relies on timely intervention, ideally informed by all available sources of data. The collection, visualization and analysis of outbreak data are becoming increasingly complex, owing to the diversity in types of data, questions and available methods to address them. Recent advances have led to the rise of outbreak analytics, an emerging data science focused on the technological and methodological aspects of the outbreak data pipeline, from collection to analysis, modelling and reporting to inform outbreak response. In this article, we assess the current state of the field. After laying out the context of outbreak response, we critically review the most common analytics components, their inter-dependencies, data requirements and the type of information they can provide to inform operations in real time. We discuss some challenges and opportunities and conclude on the potential role of outbreak analytics for improving our understanding of, and response to outbreaks of emerging pathogens. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. This theme issue is linked with the earlier issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.

Characterising routes of H5N1 and H7N9 spread in China using Bayesian phylogeographical analysis

Avian influenza H5N1 subtype has caused a global public health concern due to its high pathogenicity in poultry and high case fatality rates in humans. The recently emerged H7N9 is a growing pandemic risk due to its sustained high rates of human infections, and recently acquired high pathogenicity in poultry. Here, we used Bayesian phylogeography on 265 H5N1 and 371 H7N9 haemagglutinin sequences isolated from humans, animals and the environment, to identify and compare migration patterns and factors predictive of H5N1 and H7N9 diffusion rates in China. H7N9 diffusion dynamics and predictor contributions differ from H5N1. Key determinants of spatial diffusion included: proximity between locations (for H5N1 and H7N9), and lower rural population densities (H5N1 only). For H7N9, additional predictors included low avian influenza vaccination rates, low percentage of nature reserves and high humidity levels. For both H5N1 and H7N9, we found viral migration rates from Guangdong to Guangxi and Guangdong to Hunan were highly supported transmission routes (Bayes Factor > 30). We show fundamental differences in wide-scale transmission dynamics between H5N1 and H7N9. Importantly, this indicates that avian influenza initiatives designed to control H5N1 may not be sufficient for controlling the H7N9 epidemic. We suggest control and prevention activities to specifically target poultry transportation networks between Central, Pan-Pearl River Delta and South-West regions.

Identifying spatio-temporal dynamics of Ebola in Sierra Leone using virus genomes

Containing the recent West African outbreak of Ebola virus (EBOV) required the deployment of substantial global resources. Despite recent progress in analysing and modelling EBOV epidemiological data, a complete characterization of the spatio-temporal spread of Ebola cases remains a challenge. In this work, we offer a novel perspective on the EBOV epidemic in Sierra Leone that uses individual virus genome sequences to inform population-level, spatial models. Calibrated to phylogenetic linkages of virus genomes, these spatial models provide unique insight into the disease mobility of EBOV in Sierra Leone without the need for human mobility data. Consistent with other investigations, our results show that the spread of EBOV during the beginning and middle portions of the epidemic strongly depended on the size of and distance between populations. Our phylodynamic analysis also revealed a change in model preference towards a spatial model with power-law characteristics in the latter portion of the epidemic, correlated with the timing of major intervention campaigns. More generally, we believe this framework, pairing molecular diagnostics with a dynamic model selection procedure, has the potential to be a powerful forecasting tool along with offering operationally relevant guidance for surveillance and sampling strategies during an epidemic.

Exploiting Genetic Information to Trace Plant Virus Dispersal in Landscapes

During the past decade, knowledge of pathogen life history has greatly benefited from the advent and development of molecular epidemiology. This branch of epidemiology uses information on pathogen variation at the molecular level to gain insights into a pathogen's niche and evolution and to characterize pathogen dispersal within and between host populations. Here, we review molecular epidemiology approaches that have been developed to trace plant virus dispersal in landscapes. In particular, we highlight how virus molecular epidemiology, nourished with powerful sequencing technologies, can provide novel insights at the crossroads between the blooming fields of landscape genetics, phylogeography, and evolutionary epidemiology. We present existing approaches and their limitations and contributions to the understanding of plant virus epidemiology.

Network inference from multimodal data: A review of approaches from infectious disease transmission

Networks inference problems are commonly found in multiple biomedical subfields such as genomics, metagenomics, neuroscience, and epidemiology. Networks are useful for representing a wide range of complex interactions ranging from those between molecular biomarkers, neurons, and microbial communities, to those found in human or animal populations. Recent technological advances have resulted in an increasing amount of healthcare data in multiple modalities, increasing the preponderance of network inference problems. Multi-domain data can now be used to improve the robustness and reliability of recovered networks from unimodal data. For infectious diseases in particular, there is a body of knowledge that has been focused on combining multiple pieces of linked information. Combining or analyzing disparate modalities in concert has demonstrated greater insight into disease transmission than could be obtained from any single modality in isolation. This has been particularly helpful in understanding incidence and transmission at early stages of infections that have pandemic potential. Novel pieces of linked information in the form of spatial, temporal, and other covariates including high-throughput sequence data, clinical visits, social network information, pharmaceutical prescriptions, and clinical symptoms (reported as free-text data) also encourage further investigation of these methods. The purpose of this review is to provide an in-depth analysis of multimodal infectious disease transmission network inference methods with a specific focus on Bayesian inference. We focus on analytical Bayesian inference-based methods as this enables recovering multiple parameters simultaneously, for example, not just the disease transmission network, but also parameters of epidemic dynamics. Our review studies their assumptions, key inference parameters and limitations, and ultimately provides insights about improving future network inference methods in multiple applications.

Vaccination via Chloroplast Genetics: Affordable Protein Drugs for the Prevention and Treatment of Inherited or Infectious Human Diseases

Plastid-made biopharmaceuticals treat major metabolic or genetic disorders, including Alzheimer's, diabetes, hypertension, hemophilia, and retinopathy. Booster vaccines made in chloroplasts prevent global infectious diseases, such as tuberculosis, malaria, cholera, and polio, and biological threats, such as anthrax and plague. Recent advances in this field include commercial-scale production of human therapeutic proteins in FDA-approved cGMP facilities, development of tags to deliver protein drugs to targeted human cells or tissues, methods to deliver precise doses, and long-term stability of protein drugs at ambient temperature, maintaining their efficacy. Codon optimization utilizing valuable information from sequenced chloroplast genomes enhanced expression of eukaryotic human or viral genes in chloroplasts and offered unique insights into translation in chloroplasts. Support from major biopharmaceutical companies, development of hydroponic production systems, and evaluation by regulatory agencies, including the CDC, FDA, and USDA, augur well for advancing this novel concept to the clinic and revolutionizing affordable healthcare.

The 2014 Ebola virus outbreak in West Africa highlights no evidence of rapid evolution or adaptation to humans

Following its immergence in December 2013, the recent Zaire Ebola virus (EBOV) outbreak in West Africa has spread and persisted for more than two years, making it the largest EBOV epidemic in both scale and geographical region to date. In this study, a total of 726 glycoprotein (GP) gene sequences of the EBOV full-length genome obtained from West Africa from the 2014 outbreak, combined with 30 from earlier outbreaks between 1976 and 2008 were used to investigate the genetic divergence, evolutionary history, population dynamics, and selection pressure of EBOV among distinct epidemic waves. Results from our dataset showed that no non-synonymous substitutions occurred on the GP gene coding sequences of EBOV that were likely to have affected protein structure or function in any way. Furthermore, the significantly different dN/dS ratios observed between the 2014 West African outbreak and earlier outbreaks were more likely due to the confounding presence of segregating polymorphisms. Our results highlight no robust evidence that the 2014 EBOV outbreak is fast-evolving and adapting to humans. Therefore, the unprecedented nature of the 2014 EBOV outbreak might be more likely related to non-virological elements, such as environmental and sociological factors.

Spatial Dynamics and High Risk Transmission Pathways of Poliovirus in Nigeria 2001-2013

The polio eradication programme in Nigeria has been successful in reducing incidence to just six confirmed cases in 2014 and zero to date in 2015, but prediction and management of future outbreaks remains a concern. A Poisson mixed effects model was used to describe poliovirus spread between January 2001 and November 2013, incorporating the strength of connectivity between districts (local government areas, LGAs) as estimated by three models of human mobility: simple distance, gravity and radiation models. Potential explanatory variables associated with the case numbers in each LGA were investigated and the model fit was tested by simulation. Spatial connectivity, the number of non-immune children under five years old, and season were associated with the incidence of poliomyelitis in an LGA (all P < 0.001). The best-fitting spatial model was the radiation model, outperforming the simple distance and gravity models (likelihood ratio test P < 0.05), under which the number of people estimated to move from an infected LGA to an uninfected LGA was strongly associated with the incidence of poliomyelitis in that LGA. We inferred transmission networks between LGAs based on this model and found these to be highly local, largely restricted to neighbouring LGAs (e.g. 67.7% of secondary spread from Kano was expected to occur within 10 km). The remaining secondary spread occurred along routes of high population movement. Poliovirus transmission in Nigeria is predominantly localised, occurring between spatially contiguous areas. Outbreak response should be guided by knowledge of high-probability pathways to ensure vulnerable children are protected.

Using genomics data to reconstruct transmission trees during disease outbreaks

Genetic sequence data from pathogens present a novel means to investigate the spread of infectious disease between infected hosts or infected premises, complementing traditional contact-tracing approaches, and much recent work has gone into developing methods for this purpose. The objective is to recover the epidemic transmission tree, which identifies who infected whom. This paper reviews the various approaches that have been taken. The first step is to define a measure of difference between sequences, which must be done while taking into account such factors as recombination and convergent evolution. Three broad categories of method exist, of increasing complexity: those that assume no withinhost genetic diversity or mutation, those that assume no within-host diversity but allow mutation, and those that allow both. Until recently, the assumption was usually made that every host in the epidemic could be identified, but this is now being relaxed, and some methods are intended for sparsely sampled data, concentrating on the identification of pairs of sequences that are likely to be the result of direct transmission rather than inferring the complete transmission tree. Many of the procedures described here are available to researchers as free software.

Genetic diversity, seasonality and transmission network of human metapneumovirus: identification of a unique sub-lineage of the fusion and attachment genes

Human metapneumovirus (HMPV) is an important viral respiratory pathogen worldwide. Current knowledge regarding the genetic diversity, seasonality and transmission dynamics of HMPV among adults and children living in tropical climate remains limited. HMPV prevailed at 2.2% (n = 86/3,935) among individuals presented with acute respiratory tract infections in Kuala Lumpur, Malaysia between 2012 and 2014. Seasonal peaks were observed during the northeast monsoon season (November-April) and correlated with higher relative humidity and number of rainy days (P < 0.05). Phylogenetic analysis of the fusion and attachment genes identified the co-circulation of three known HMPV sub-lineages, A2b and B1 (30.2% each, 26/86) and B2 (20.9%, 18/86), with genotype shift from sub-lineage B1 to A2b observed in 2013. Interestingly, a previously unrecognized sub-lineage of A2 was identified in 18.6% (16/86) of the population. Using a custom script for network construction based on the TN93 pairwise genetic distance, we identified up to nine HMPV transmission clusters circulating as multiple sub-epidemics. Although no apparent major outbreak was observed, the increased frequency of transmission clusters (dyads) during seasonal peaks suggests the potential roles of transmission clusters in driving the spread of HMPV. Our findings provide essential information for therapeutic research, prevention strategies, and disease outbreak monitoring of HMPV.

Plant-made oral vaccines against human infectious diseases-Are we there yet?

Although the plant-made vaccine field started three decades ago with the promise of developing low-cost vaccines to prevent infectious disease outbreaks and epidemics around the globe, this goal has not yet been achieved. Plants offer several major advantages in vaccine generation, including low-cost production by eliminating expensive fermentation and purification systems, sterile delivery and cold storage/transportation. Most importantly, oral vaccination using plant-made antigens confers both mucosal (IgA) and systemic (IgG) immunity. Studies in the past 5 years have made significant progress in expressing vaccine antigens in edible leaves (especially lettuce), processing leaves or seeds through lyophilization and achieving antigen stability and efficacy after prolonged storage at ambient temperatures. Bioencapsulation of antigens in plant cells protects them from the digestive system; the fusion of antigens to transmucosal carriers enhances efficiency of their delivery to the immune system and facilitates successful development of plant vaccines as oral boosters. However, the lack of oral priming approaches diminishes these advantages because purified antigens, cold storage/transportation and limited shelf life are still major challenges for priming with adjuvants and for antigen delivery by injection. Yet another challenge is the risk of inducing tolerance without priming the host immune system. Therefore, mechanistic aspects of these two opposing processes (antibody production or suppression) are discussed in this review. In addition, we summarize recent progress made in oral delivery of vaccine antigens expressed in plant cells via the chloroplast or nuclear genomes and potential challenges in achieving immunity against infectious diseases using cold-chain-free vaccine delivery approaches.