Inferring epidemiological links from deep sequencing data: a statistical learning approach for human, animal and plant diseases

Computing Geographical Networks Generated by Air-Mass Movement

As air masses move within the troposphere, they transport a multitude of components including gases and particles such as pollen and microorganisms. These movements generate atmospheric highways that connect geographic areas at distant, local, and global scales that particles can ride depending on their aerodynamic properties and their reaction to environmental conditions. In this article we present an approach and an accompanying web application called tropolink for measuring the extent to which distant locations are potentially connected by air-mass movement. This approach is based on the computation of trajectories of air masses with the HYSPLIT atmospheric transport and dispersion model, and on the computation of connection frequencies, called connectivities, in the purpose of building trajectory-based geographical networks. It is illustrated for different spatial and temporal scales with three case studies related to plant epidemiology. The web application that we designed allows the user to easily perform intensive computation and mobilize massive archived gridded meteorological data to build weighted directed networks. The analysis of such networks allowed us for example, to describe the potential of invasion of a migratory pest beyond its actual distribution. Our approach could also be used to compute geographical networks generated by air-mass movement for diverse application domains, for example, to assess long-term risk of spread from persistent or recurrent sources of pollutants, including wildfire smoke.

Identifying potential significant factors impacting zero-inflated proportion data

Classical supervised methods like linear regression and decision trees are not completely adapted for identifying impacting factors on a response variable corresponding to zero-inflated proportion data (ZIPD) that are dependent, continuous and bounded. In this article we propose a within-block permutation-based methodology to identify factors (discrete or continuous) that are significantly correlated with ZIPD, we propose a performance indicator quantifying the percentage of correlation explained by the subset of significant factors, and we show how to predict the ranks of the response variables conditionally on the observation of these factors. The methodology is illustrated on simulated data and on two real data sets dealing with epidemiology. In the first data set, ZIPD correspond to probabilities of transmission of Influenza between horses. In the second data set, ZIPD correspond to probabilities that geographic entities (eg, states and countries) have the same COVID-19 mortality dynamics.

Graph-Based Reconstruction and Analysis of Disease Transmission Networks Using Viral Genomic Data

Understanding the patterns of viral disease transmissions helps establish public health policies and aids in controlling and ending a disease outbreak. Classical methods for studying disease transmission dynamics that rely on epidemiological data, such as times of sample collection and duration of exposure intervals, struggle to provide desired insight due to limited informativeness of such data. A more precise characterization of disease transmissions may be acquired from sequencing data that reveal genetic distance between viral genomes in patient samples. Indeed, genetic distance between viral strains present in hosts contains valuable information about transmission history, thus motivating the design of methods that rely on genomic data to reconstruct a directed disease transmission network, detect transmission clusters, and identify significant network nodes (e.g., super-spreaders). In this article, we present a novel end-to-end framework for the analysis of viral transmissions utilizing viral genomic (sequencing) data. The proposed framework groups infected hosts into transmission clusters based on the reconstructed viral strains infecting them; the genetic distance between a pair of hosts is calculated using Earth Mover's Distance, and further used to infer transmission direction between the hosts. To quantify the significance of a host in the transmission network, the importance score is calculated by a graph convolutional autoencoder. The viral transmission network is represented by a directed minimum spanning tree utilizing the Edmond's algorithm modified to incorporate constraints on the importance scores of the hosts. The proposed framework outperforms state-of-the-art techniques for the analysis of viral transmission dynamics in several experiments on semiexperimental as well as experimental data.

Evaluating the potential of whole-genome sequencing for tracing transmission routes in experimental infections and natural outbreaks of bovine respiratory syncytial virus

Bovine respiratory syncytial virus (BRSV) is a major cause of respiratory disease in cattle. Genomic sequencing can resolve phylogenetic relationships between virus populations, which can be used to infer transmission routes and potentially inform the design of biosecurity measures. Sequencing of short (<2000 nt) segments of the 15 000-nt BRSV genome has revealed geographic and temporal clustering of BRSV populations, but insufficient variation to distinguish viruses collected from herds infected close together in space and time. This study investigated the potential for whole-genome sequencing to reveal sufficient genomic variation for inferring transmission routes between herds. Next-generation sequencing (NGS) data were generated from experimental infections and from natural outbreaks in Jämtland and Uppsala counties in Sweden. Sufficient depth of coverage for analysis of consensus and sub-consensus sequence diversity was obtained from 47 to 20 samples respectively. Few (range: 0-6 polymorphisms across the six experiments) consensus-level polymorphisms were observed along experimental transmissions. A much higher level of diversity (146 polymorphic sites) was found among the consensus sequences from the outbreak samples. The majority (144/146) of polymorphisms were between rather than within counties, suggesting that consensus whole-genome sequences show insufficient spatial resolution for inferring direct transmission routes, but might allow identification of outbreak sources at the regional scale. By contrast, within-sample diversity was generally higher in the experimental than the outbreak samples. Analyses to infer known (experimental) and suspected (outbreak) transmission links from within-sample diversity data were uninformative. In conclusion, analysis of the whole-genome sequence of BRSV from experimental samples discriminated between circulating isolates from distant areas, but insufficient diversity was observed between closely related isolates to aid local transmission route inference.

Characterizing viral within-host diversity in fast and non-equilibrium demo-genetic dynamics

High-throughput sequencing has opened the route for a deep assessment of within-host genetic diversity that can be used, e.g., to characterize microbial communities and to infer transmission links in infectious disease outbreaks. The performance of such characterizations and inferences cannot be analytically assessed in general and are often grounded on computer-intensive evaluations. Then, being able to simulate within-host genetic diversity across time under various demo-genetic assumptions is paramount to assess the performance of the approaches of interest. In this context, we built an original model that can be simulated to investigate the temporal evolution of genotypes and their frequencies under various demo-genetic assumptions. The model describes the growth and the mutation of genotypes at the nucleotide resolution conditional on an overall within-host viral kinetics, and can be tuned to generate fast non-equilibrium demo-genetic dynamics. We ran simulations of this model and computed classic diversity indices to characterize the temporal variation of within-host genetic diversity (from high-throughput amplicon sequences) of virus populations under three demographic kinetic models of viral infection. Our results highlight how demographic (viral load) and genetic (mutation, selection, or drift) factors drive variations in within-host diversity during the course of an infection. In particular, we observed a non-monotonic relationship between pathogen population size and genetic diversity, and a reduction of the impact of mutation on diversity when a non-specific host immune response is activated. The large variation in the diversity patterns generated in our simulations suggests that the underlying model provides a flexible basis to produce very diverse demo-genetic scenarios and test, for instance, methods for the inference of transmission links during outbreaks.

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.

Population genomics of Bacillus anthracis from an anthrax hyperendemic area reveals transmission processes across spatial scales and unexpected within-host diversity

Genomic sequencing has revolutionized our understanding of bacterial disease epidemiology, but remains underutilized for zoonotic pathogens in remote endemic settings. Anthrax, caused by the spore-forming bacterium Bacillus anthracis, remains a threat to human and animal health and rural livelihoods in low- and middle-income countries. While the global genomic diversity of B. anthracis has been well-characterized, there is limited information on how its populations are genetically structured at the scale at which transmission occurs, critical for understanding the pathogen's evolution and transmission dynamics. Using a uniquely rich dataset, we quantified genome-wide SNPs among 73 B. anthracis isolates derived from 33 livestock carcasses sampled over 1 year throughout the Ngorongoro Conservation Area, Tanzania, a region hyperendemic for anthrax. Genome-wide SNPs distinguished 22 unique B. anthracis genotypes (i.e. SNP profiles) within the study area. However, phylogeographical structure was lacking, as identical SNP profiles were found throughout the study area, likely the result of the long and variable periods of spore dormancy and long-distance livestock movements. Significantly, divergent genotypes were obtained from spatio-temporally linked cases and even individual carcasses. The high number of SNPs distinguishing isolates from the same host is unlikely to have arisen during infection, as supported by our simulation models. This points to an unexpectedly wide transmission bottleneck for B. anthracis, with an inoculum comprising multiple variants being the norm. Our work highlights that inferring transmission patterns of B. anthracis from genomic data will require analytical approaches that account for extended and variable environmental persistence, as well as co-infection.

Precision Tracing of Household Dengue Spread Using Inter- and Intra-Host Viral Variation Data, Kamphaeng Phet, Thailand

Dengue control approaches are best informed by granular spatial epidemiology of these viruses, yet reconstruction of inter- and intra-household transmissions is limited when analyzing case count, serologic, or genomic consensus sequence data. To determine viral spread on a finer spatial scale, we extended phylogenomic discrete trait analyses to reconstructions of house-to-house transmissions within a prospective cluster study in Kamphaeng Phet, Thailand. For additional resolution and transmission confirmation, we mapped dengue intra-host single nucleotide variants on the taxa of these time-scaled phylogenies. This approach confirmed 19 household transmissions and revealed that dengue disperses an average of 70 m per day between households in these communities. We describe an evolutionary biology framework for the resolution of dengue transmissions that cannot be differentiated based on epidemiologic and consensus genome data alone. This framework can be used as a public health tool to inform control approaches and enable precise tracing of dengue transmissions.

How mechanistic modelling supports decision making for the control of enzootic infectious diseases

Controlling enzootic diseases, which generate a large cumulative burden and are often unregulated, is needed for sustainable farming, competitive agri-food chains, and veterinary public health. We discuss the benefits and challenges of mechanistic epidemiological modelling for livestock enzootics, with particular emphasis on the need for interdisciplinary approaches. We focus on issues arising when modelling pathogen spread at various scales (from farm to the region) to better assess disease control and propose targeted options. We discuss in particular the inclusion of farmers' strategic decision-making, the integration of within-host scale to refine intervention targeting, and the need to ground models on data.

Detection, forecasting and control of infectious disease epidemics: modelling outbreaks in humans, animals and plants

The 1918 influenza pandemic is one of the most devastating infectious disease epidemics on record, having caused approximately 50 million deaths worldwide. Control measures, including prohibiting non-essential gatherings as well as closing cinemas and music halls, were applied with varying success and limited knowledge of transmission dynamics. One hundred years later, following developments in the field of mathematical epidemiology, models are increasingly used to guide decision-making and devise appropriate interventions that mitigate the impacts of epidemics. Epidemiological models have been used as decision-making tools during outbreaks in human, animal and plant populations. However, as the subject has developed, human, animal and plant disease modelling have diverged. Approaches have been developed independently for pathogens of each host type, often despite similarities between the models used in these complementary fields. With the increased importance of a One Health approach that unifies human, animal and plant health, we argue that more inter-disciplinary collaboration would enhance each of the related disciplines. This pair of theme issues presents research articles written by human, animal and plant disease modellers. In this introductory article, we compare the questions pertinent to, and approaches used by, epidemiological modellers of human, animal and plant pathogens, and summarize the articles in these theme issues. We encourage future collaboration that transcends disciplinary boundaries and links the closely related areas of human, animal and plant disease epidemic modelling. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'. This issue is linked with the subsequent theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'.

High-throughput sequencing (HTS) for the analysis of viral populations

The development of High-Throughput Sequencing (HTS) technologies is having a major impact on the genomic analysis of viral populations. Current HTS platforms can capture nucleic acid variation across millions of genes for both selected amplicons and full viral genomes. HTS has already facilitated the discovery of new viruses, hinted new taxonomic classifications and provided a deeper and broader understanding of their diversity, population and genetic structure. Hence, HTS has already replaced standard Sanger sequencing in basic and applied research fields, but the next step is its implementation as a routine technology for the analysis of viruses in clinical settings. The most likely application of this implementation will be the analysis of viral genomics, because the huge population sizes, high mutation rates and very fast replacement of viral populations have demonstrated the limited information obtained with Sanger technology. In this review, we describe new technologies and provide guidelines for the high-throughput sequencing and genetic and evolutionary analyses of viral populations and metaviromes, including software applications. With the development of new HTS technologies, new and refurbished molecular and bioinformatic tools are also constantly being developed to process and integrate HTS data. These allow assembling viral genomes and inferring viral population diversity and dynamics. Finally, we also present several applications of these approaches to the analysis of viral clinical samples including transmission clusters and outbreak characterization.