E3SIM: Epidemiological-ecological-evolutionary simulation framework for genomic epidemiology

Infectious disease dynamics are driven by the complex interplay of epidemiological, ecological, and evolutionary processes. Accurately modeling these interactions is crucial for understanding pathogen spread and informing public health strategies. However, existing simulators often fail to capture the dynamic interplay between these processes, resulting in oversimplified models that do not fully reflect real-world complexities in which the pathogen’s genetic evolution dynamically influences disease transmission. We introduce the epidemiological-ecological-evolutionary simulator (e3SIM), an open-source framework that concurrently models the transmission dynamics and molecular evolution of pathogens within a host population while integrating environmental factors. Using an agent-based, discrete-generation, forward-in-time approach, e3SIM incorporates compartmental models, host-population contact networks, and quantitative-trait models for pathogens. This integration allows for realistic simulations of disease spread and pathogen evolution. Key features include a modular and scalable design, flexibility in modeling various epidemiological and population-genetic complexities, incorporation of time-varying environmental factors, and a user-friendly graphical interface. We demonstrate e3SIM’s capabilities through simulations of realistic outbreak scenarios with SARS-CoV-2 and Mycobacterium tuberculosis, illustrating its flexibility for studying the genomic epidemiology of diverse pathogen types.

The ongoing coronavirus disease 2019 (COVID-19) pandemic represents one of the most exigent threats of our lifetime to global public health and economy. As part of the pandemic, from January 10 to March 10, 2020, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) began to spread in Hefei (Anhui Province, China) with a total of 174 confirmed cases of COVID-19. During this period, we were able to gather critical information on the transmission and evolution of pathogens through genomic surveillance. Particularly, the objective of our study was to track putative variants of SARS-CoV-2 circulating in Hefei for the first time and contribute to the global effort toward elucidating the molecular epidemic profile of the virus. Patients who showed symptoms of COVID-19 were routinely tested for SARS-CoV-2 infections via RT-PCR at the First Affiliated Hospital of Anhui Medical University. Whole-genome sequencing was performed on 97 clinical samples collected from 29 confirmed COVID-19 patients. As a result, we identified a local novel single-nucleotide polymorphism site (10,380) harboring a G → T mutation (Gly → Val) in Hefei. Further phylogenetic network analysis with all the sequences of SARS-CoV-2 deposited in GenBank collected in East and Southeast Asia revealed a local subtype of S-type SARS-CoV-2 (a1) harboring a C → T synonymous mutation (Leu) at position 18,060 of ORF1b, likely representing a local SARS-CoV-2 mutation site that is obviously concentrated in Hefei and the Yangtze River Delta region. Moreover, clinical investigation on the inflammatory cytokine profile of the patients suggested that mutations at positions 18,060 (the shared variable site of subtype a1) and 28,253(harboring a C → T synonymous mutation, Phe) were associated with milder immune responses in the patients.

Many of our theories for the generation and maintenance of diversity in nature depend on the existence of specialist biotic interactions which, in host–pathogen systems, also shape cross-species disease emergence. As such, niche breadth evolution, especially in host–parasite systems, remains a central focus in ecology and evolution. The predominant explanation for the existence of specialization in the literature is that niche breadth is constrained by trade-offs, such that a generalist is less fit on any particular environment than a given specialist. This trade-off theory has been used to predict niche breadth (co)evolution in both population genetics and eco-evolutionary models, with the different modelling methods providing separate, complementary insights. However, trade-offs may be far from universal, so population genetics theory has also proposed alternate mechanisms for costly generalism, including mutation accumulation. However, these mechanisms have yet to be integrated into eco-evolutionary models in order to understand how the mechanism of costly generalism alters the biological and ecological circumstances predicted to maintain specialism. In this review, we outline how population genetics and eco-evolutionary models based on trade-offs have provided insights for parasite niche breadth evolution and argue that the population genetics-derived mutation accumulation theory needs to be better integrated into eco-evolutionary theory.

How infectious disease spreads both from individual to individual and across a landscape will depend upon many inter-related factors, including the genetic composition of host and pathogen populations, the pathogen transmission rate, host density, population connectivity, and the evolutionary response of both host and pathogen over time. As such, the study of infectious disease straddles a number of fields and approaches. A key advance in the field has come from incorporation of spatial structure into both theoretical and empirical studies of the epidemiology, evolution, and ecology of disease. Researchers taking this approach were recently brought together for a workshop entitled ‘Spatial evolutionary epidemiology’ in Montpellier, France organized by Sebastien Lion and Sylvain Gandon from the Centre d’Ecologie Fonctionnelle et Evolutive (CEFE). The workshop spanned topics from infection genetics in Daphnia, to cooperation and conflict in microbial populations, to the utility of spatial epidemiological models for designing better crop planting strategies, and emphasized the broader need to think about the importance of spatial heterogeneity in order to predict the spread and evolution of pathogens. The recent literature includes a number of studies at the cutting edge of this field. For example, a model based on interactions between bacteria and their bacteriophage parasites by Ben Ashby and collaborators has demonstrated the importance of spatial structure in shaping the breadth of host resistance. They find that hosts and parasites from spatially structured populations should be less constrained by costs associated with ‘generalism’ than those from well-mixed populations, and therefore, that spatial structure is likely to increases the breadth of host resistance/parasite infectivity, especially when this increased breadth carries a significant fitness cost (Ashby et al. 2014). Another potential effect of spatial structure on host-parasite interactions is through changing rates of infection, as opposed to infection range. A recent empirical study of bacteria and phages by Pavitra Roychoudhury and coauthors experimentally evolved phages on spatially structured agar plates for 550 phage generations and found increased phage fitness was associated with a mutation conferring slower phage adsorption rate to bacterial host cells. This is in line with theoretical work suggesting spatial structure may lead to reduced parasite infectivity (e.g., Boots and Sasaki 1999) and builds upon previous empirical work from nonmicrobial systems in support of this theory. Furthermore, the authors develop a system-specific, spatially explicit model to explain how low attachment probability might lead to increased phage fitness through higher plaque density as a result of trade-offs between phage diffusion and adsorption (Roychoudhury et al. 2014). Studying spatial structure in natural populations is of course a more challenging goal, as one loses the ability to control for the type of heterogeneity across which a study is performed. Despite these potential limitations, recent work by Jussi Jousimo, Ayco Tack, and collaborators examined the impact of connectivity among over 4000 populations of the plant, Plantago lanceolata, on resistance to a co-occurring fungal pathogen, Podosphaera plantaginis, over a 12-year period. Using this large spatial dataset, the authors were able to explain the unexpected observation that highly connected populations typically showed lower pathogen prevalence and higher pathogen extinction than more isolated populations. They demonstrate that hosts from highly connected populations are in fact more resistant to the pathogen than those from more isolated populations, most likely as a result of stronger parasite-mediated selection over time (Jousimo et al. 2014). This work highlights the importance of incorporating host and pathogen evolution into models predicting the spread of disease across space, and emphasizes the potential for differential parasite-mediated selection across a host metapopulation. Finally, work by David Rasmussen and colleagues has demonstrated the strength of incorporating spatial structure into phylodynamic (coalescent) approaches to inferring past disease dynamics from genealogies (Rasmussen et al. 2014). Motivated to explain the common discrepancy between phylodynamic inferences and those made from hospital records, the authors apply their new method to a case study of mosquito-borne dengue virus in southern Vietnam. They demonstrate that a spatially structured susceptible-infected-recovered (SIR) model resulted in similar patterns to those seen from the hospitalization data, including large seasonal fluctuations in disease, and that the incorporation of ecological complexity into coalescent models increases the accuracy of inferences to disease demography. Overall, these recent studies demonstrate the added value of incorporating spatial structure into epidemiological, phylodynamic, and evolutionary/ecological models of infectious disease. Although the current body of theory on the topic offers clear predictions for how spatial structure might influence disease evolution and spread, there remains a paucity of empirical and observational studies testing these key ideas. Moving forward, the more we understand about these eco-evolutionary feedbacks, the better we will be able to manage emerging disease in natural, agricultural, and human populations.

The One Health concept underscores the interconnectedness of human, animal, and environmental health, necessitating an integrated, transdisciplinary approach to tackle contemporary health challenges. This perspective paper explores the pivotal role of genomics and bioinformatics in advancing One Health initiatives. By leveraging genomic technologies and bioinformatics tools, researchers can decode complex biological data, enabling comprehensive insights into pathogen evolution, transmission dynamics, and host-pathogen interactions across species and environments (or ecosystems). These insights are crucial for predicting and mitigating zoonotic disease outbreaks, understanding antimicrobial resistance patterns, and developing targeted interventions for health promotion and disease prevention. Furthermore, integrating genomic data with environmental and epidemiological information enhances the precision of public health responses. Here we discuss case studies demonstrating successful applications of genomics and bioinformatics in One Health contexts, such as including data integration, standardization, and ethical considerations in genomic research. By fostering collaboration among geneticists, bioinformaticians, epidemiologists, zoologists, and data scientists, the One Health approach can harness the full potential of genomics and bioinformatics to safeguard global health. This perspective underscores the necessity of continued investment in interdisciplinary education, research infrastructure, and policy frameworks to effectively employ these technologies in the service of a healthier planet.

Interactions among multiple infectious agents are increasingly recognized as a fundamental issue in the understanding of key questions in public health regarding pathogen emergence, maintenance, and evolution. The full description of host-multipathogen systems is, however, challenged by the multiplicity of factors affecting the interaction dynamics and the resulting competition that may occur at different scales, from the within-host scale to the spatial structure and mobility of the host population. Here we study the dynamics of two competing pathogens in a structured host population and assess the impact of the mobility pattern of hosts on the pathogen competition. We model the spatial structure of the host population in terms of a metapopulation network and focus on two strains imported locally in the system and having the same transmission potential but different infectious periods. We find different scenarios leading to competitive success of either one of the strain or to the codominance of both strains in the system. The dominance of the strain characterized by the shorter or longer infectious period depends exclusively on the structure of the population and on the the mobility of hosts across patches. The proposed modeling framework allows the integration of other relevant epidemiological, environmental and demographic factors, opening the path to further mathematical and computational studies of the dynamics of multipathogen systems.

Host individuals and populations often vary in their responses to infection, with direct consequences for pathogen spread and evolution. While considerable work has focused on the mechanisms underlying differences in resistance-the ability to kill pathogens-we know little about the mechanisms underlying tolerance-the ability to minimize fitness losses per unit pathogen. Here, we examine patterns and mechanisms of tolerance between two populations of house finches (Haemorhous [formerly Carpodacus] mexicanus) with different histories with the bacterial pathogen Mycoplasma gallisepticum (MG). After infection in a common environment, we assessed two metrics of pathology, mass loss and eye lesion severity, as proxies for fitness. We calculated tolerance using two methods, one based on pathology and pathogen load at the peak of infection (point tolerance) and the other based on the integrals of these metrics over time (range tolerance). Alabama birds, which have a significantly longer history of exposure to MG, showed more pronounced point tolerance than Arizona birds, while range tolerance did not differ between populations. Alabama birds also displayed lower inflammatory cytokine signaling and lower fever early in infection. These results suggest that differences in inflammatory processes, which can significantly damage host tissues, may contribute to variation in tolerance among house finch individuals and populations. Such variation can affect pathogen spread and evolution in ways not predictable by resistance alone and sheds light on the costs and benefits of inflammation in wild animals.

Whole genome sequencing has revolutionized infectious disease surveillance for tracking and monitoring the spread and evolution of pathogens. However, using a linear reference genome for genomic analyses may introduce biases, especially when studies are conducted on highly variable bacterial genomes of the same species. Pangenome graphs provide an efficient model for representing and analyzing multiple genomes and their variants as a graph structure that includes all types of variations. In this study, we present a practical bioinformatics pipeline that employs the PanGenome Graph Builder and the Variation Graph toolkit to build pangenomes from assembled genomes, align whole genome sequencing data and call variants against a graph reference. The pangenome graph enables the identification of structural variants, rearrangements, and small variants (e.g., single nucleotide polymorphisms and insertions/deletions) simultaneously. We demonstrate that using a pangenome graph, instead of a single linear reference genome, improves mapping rates and variant calling for both simulated and real datasets of the pathogen Neisseria meningitidis. Overall, pangenome graphs offer a promising approach for comparative genomics and comprehensive genetic variation analysis in infectious disease. Moreover, this innovative pipeline, leveraging pangenome graphs, can bridge variant analysis, genome assembly, population genetics, and evolutionary biology, expanding the reach of genomic understanding and applications.

BackgroundPhylogeographic reconstructions serve as a basis to understand the spread and evolution of pathogens. Visualization of these reconstructions often lead to complex graphical representations which are difficult to interpret.ResultWe present EvoLaps, a user-friendly web interface to visualize phylogeographic reconstructions based on the analysis of latitude/longitude coordinates with various clustering levels. EvoLaps also produces transition diagrams that provide concise and easy to interpret summaries of phylogeographic reconstructions.ConclusionThe main contribution of EvoLaps is to assemble known numerical and graphical methods/tools into a user-friendly interface dedicated to the visualization and edition of evolutionary scenarios based on continuous phylogeographic reconstructions. EvoLaps is freely usable at www.evolaps.org.

With an increasing body of evidence that SARS-CoV-2 is an airborne pathogen, droplet character formed during speech, coughs, and sneezes are important. Larger droplets tend to fall faster and are less prone to drive the airborne transmission pathway. Alternatively, small droplets (aerosols) can remain suspended for long time periods. The small size of SARS-CoV-2 enables it to be encapsulated in these aerosols, thereby increasing the pathogen’s ability to be transmitted via airborne paths. Droplet formation during human respiratory events relates to airspeed (speech, cough, sneeze), fluid properties of the saliva/mucus, and the fluid content itself. In this work, we study the fluidic drivers (fluid properties and content) and their influence on factors relating to transmissibility. We explore the relationship between saliva fluid properties and droplet airborne transmission paths. Interestingly, the natural human response appears to potentially work with these drivers to mitigate pathogen transmission. In this work, the saliva is varied using two approaches: (1) modifying the saliva with colloids that increase the viscosity/surface tension, and (2) stimulating the saliva content to increased/decreased levels. Through modern experimental and numerical flow diagnostic methods, the character, content, and exposure to droplets and aerosols are all evaluated. The results indicate that altering the saliva properties can significantly impact the droplet size distribution, the formation of aerosols, the trajectory of the bulk of the droplet plume, and the exposure (or transmissibility) to droplets. High-fidelity numerical methods used and verify that increased droplet size character enhances droplet fallout. In the context of natural saliva response, we find previous studies indicating natural human responses of increased saliva viscosity from stress and reduced saliva content from either stress or illness. These responses both favorably correspond to reduced transmissibility. Such a finding also relates to potential control methods, hence, we compared results to a surgical mask. In general, we find that saliva alteration can produce fewer and larger droplets with less content and aerosols. Such results indicate a novel approach to alter SARS-CoV-2’s transmission path and may act as a way to control the COVID-19 pandemic, as well as influenza and the common cold.

Recent outbreaks of novel infectious diseases (e.g. SARS, influenza H1N1) have highlighted the threat of cross-species pathogen transmission. When first introduced to a population, a pathogen is often poorly adapted to its new host and must evolve in order to escape extinction. Theoretical arguments and empirical studies have suggested various factors to explain why some pathogens emerge and others do not, including host contact structure, pathogen adaptive pathways and mutation rates. Using a multi-type branching process, we model the spread of an introduced pathogen evolving through several strains. Extending previous models, we use a network-based approach to separate host contact patterns from pathogen transmissibility. We also allow for arbitrary adaptive pathways. These generalizations lead to novel predictions regarding the impact of hypothesized risk factors. Pathogen fitness depends on the host population in which it circulates, and the ‘riskiest’ contact distribution and adaptive pathway depend on initial transmissibility. Emergence probability is sensitive to mutation probabilities and number of adaptive steps required, with the possibility of large adaptive steps (e.g. simultaneous point mutations or recombination) having a dramatic effect. In most situations, increasing overall mutation probability increases the risk of emergence; however, notable exceptions arise when deleterious mutations are available.

Sexual development is vital for the transmission, genetic hybridization, and population evolution of apicomplexan pathogens, which include several clinically relevant parasites, such as Plasmodium, Eimeria, and Toxoplasma gondii. Previous studies have demonstrated different morphological characteristics and division patterns between asexual and sexual stages of the parasites. However, the primary regulation is poorly understood. A transition from the asexual to the sexual stage is supposedly triggered/accompanied by rewiring of gene expression and controlled by transcription factors and chromatin modulators. Herein, we discovered a tachyzoite-specific transcriptional factor AP2XII-1, which represses the presexual development in the asexual tachyzoite stage of T. gondii. Conditional knockdown of AP2XII-1 perturbs tachyzoite proliferation by endodyogeny and drives a transition to a morphologically and transcriptionally distinct merozoite stage. The results also suggest a hierarchical transcriptional regulation of sexual development by AP2 factors and provide a path to culturing merozoites and controlling inter-host transmission of T. gondii.

Although shrews are one of the largest groups of mammals, little is known about their role in the evolution and transmission of viral pathogens, including coronaviruses (CoVs). We captured 266 Asian house shrews (Suncus murinus) in Jiangxi and Zhejiang Provinces, China, during 2013 to 2015. CoV RNA was detected in 24 Asian house shrews, with an overall prevalence of 9.02%. Complete viral genome sequences were successfully recovered from the RNA-positive samples. The newly discovered shrew CoV fell into four lineages reflecting their geographic origins, indicative of largely allopatric evolution. Notably, these viruses were most closely related to alphacoronaviruses but sufficiently divergent that they should be considered a novel member of the genus Alphacoronavirus, which we denote Wénchéng shrew virus (WESV). Phylogenetic analysis revealed that WESV was a highly divergent member of the alphacoronaviruses and, more dramatically, that the S gene of WESV fell in a cluster that was genetically distinct from that of known coronaviruses. The divergent position of WESV suggests that coronaviruses have a long association with Asian house shrews. In addition, the genome of WESV contains a distinct NS7 gene that exhibits no sequence similarity to genes of any known viruses. Together, these data suggest that shrews are natural reservoirs for coronaviruses and may have played an important and long-term role in CoV evolution.IMPORTANCE The subfamily Coronavirinae contains several notorious human and animal pathogens, including severe acute respiratory syndrome coronavirus, Middle East respiratory syndrome coronavirus, and porcine epidemic diarrhea virus. Because of their genetic diversity and phylogenetic relationships, it has been proposed that the alphacoronaviruses likely have their ultimate ancestry in the viruses residing in bats. Here, we describe a novel alphacoronavirus (Wénchéng shrew virus [WESV]) that was sampled from Asian house shrews in China. Notably, WESV is a highly divergent member of the alphacoronaviruses and possesses an S gene that is genetically distinct from those of all known coronaviruses. In addition, the genome of WESV contains a distinct NS7 gene that exhibits no sequence similarity to those of any known viruses. Together, these data suggest that shrews are important and longstanding hosts for coronaviruses that merit additional research and surveillance.

SummaryStudies on bacterial plant diseases have thus far been focused on the single bacterial species causing the disease, with very little attention given to the many other microorganisms present in the microbiome. This study intends to use pathobiome analysis of the rice foot rot disease, caused by Dickeya zeae, as a case study to investigate the effects of this bacterial pathogen to the total resident microbiome and to highlight possible interactions between the pathogen and the members of the community involved in the disease process. The microbiome of asymptomatic and the pathobiome of foot‐rot symptomatic field‐grown rice plants over two growing periods and belonging to two rice cultivars were determined via 16S rRNA gene amplicon sequencing. Results showed that the presence of D. zeae is associated with an alteration of the resident bacterial community in terms of species composition, abundance and richness, leading to the formation of microbial consortia linked to the disease state. Several bacterial species were significantly co‐presented with the pathogen in the two growing periods suggesting that they could be involved in the disease process. Besides, culture‐dependent isolation and in planta inoculation studies of a bacterial member of the pathobiome, identified as positive correlated with the pathogen in our in silico analysis, indicated that it benefits from the presence of D. zeae. A similar microbiome/pathobiome experiment was also performed in a symptomatically different rice disease evidencing that not all plant diseases have the same consequence/relationship with the plant microbiome. This study moves away from a pathogen‐focused stance and goes towards a more ecological perception considering the effect of the entire microbial community which could be involved in the pathogenesis, persistence, transmission and evolution of plant pathogens.

In a One Health perspective general wildlife health surveillance (GWHS) gains importance worldwide, as pathogen transmission among wildlife, domestic animals and humans raises health, conservation and economic concerns. However, GWHS programs operate in the face of legal, geographical, financial, or administrative challenges. The present study uses a multi-tiered approach to understand the current characteristics, strengths and gaps of a European GWHS that operates in a fragmented legislative and multi-stakeholder environment. The aim is to support the implementation or improvement of other GWHS systems by managers, surveillance experts, and administrations. To assess the current state of wildlife health investigations and trends within the GWHS, we retrospectively analyzed 20 years of wildlife diagnostic data to explore alterations in annual case numbers, diagnosed diseases, and submitter types, conducted an online survey and phone interviews with official field partners (hunting administrators, game wardens and hunters) to assess their case submission criteria as well as their needs for post-mortem investigations, and performed in-house time estimations of post-mortem investigations to conduct a time-per-task analysis. Firstly, we found that infectious disease dynamics, the level of public awareness for specific diseases, research activities and increasing population sizes of in depth-monitored protected species, together with biogeographical and political boundaries all impacted case numbers and can present unexpected challenges to a GWHS. Secondly, we found that even a seemingly comprehensive GWHS can feature pronounced information gaps, with underrepresentation of common or easily recognizable diseases, blind spots in non-hunted species and only a fraction of discovered carcasses being submitted. Thirdly, we found that substantial amounts of wildlife health data may be available at local hunting administrations or disease specialist centers, but outside the reach of the GWHS and its processes. In conclusion, we recommend that fragmented and federalist GWHS programs like the one addressed require a central, consistent and accessible collection of wildlife health data. Also, considering the growing role of citizen observers in environmental research, we recommend using online reporting systems to harness decentrally available information and fill wildlife health information gaps.