Global migration dynamics underlie evolution and persistence of human influenza A (H3N2)

Predicting influenza in China from October 1, 2023, to February 5, 2024: A transmission dynamics model based on population migration

Introduction

Since November 2023, influenza has ranked first in reported cases of infectious diseases in China, with the outbreak in both northern and southern provinces exceeding the levels observed during the same period in 2022. This poses a serious health risk to the population. Therefore, short to medium-term influenza predictions are beneficial for epidemic assessment and can reduce the disease burden.

Methods

A transmission dynamics model considering population migration, encompassing susceptible-exposed-infectious-asymptomatic-recovered (SEIAR) was used to predict the dynamics of influenza before the Spring Festival travel rush.

Results

The overall epidemic shows a declining trend, with the peak expected to occur from week 47 in 2023 to week 1 in 2024. The number of cases of A (H3N2) is greater than that of influenza B, and the influenza situation is more severe in the southern provinces compared to the northern ones.

Conclusion

Our method is applicable for short-term and medium-term influenza predictions. As the spring festival travel rush approaches. Therefore, it is advisable to advocate for nonpharmaceutical interventions (NPIs), influenza vaccination, and other measures to reduce healthcare and public health burden.

Local patterns of spread of influenza A H3N2 virus in coastal Kenya over a 1-year period revealed through virus sequence data

The patterns of spread of influenza A viruses in local populations in tropical and sub-tropical regions are unclear due to sparsity of representative spatiotemporal sequence data. We sequenced and analyzed 58 influenza A(H3N2) virus genomes sampled between December 2015 and December 2016 from nine health facilities within the Kilifi Health and Demographic Surveillance System (KHDSS), a predominantly rural region, covering approximately 891 km2 along the Kenyan coastline. The genomes were compared with 1571 contemporaneous global sequences from 75 countries. We observed at least five independent introductions of A(H3N2) viruses into the region during the one-year period, with the importations originating from Africa, Europe, and North America. We also inferred 23 virus location transition events between the nine facilities included in the study. International virus imports into the study area were captured at the facilities of Chasimba, Matsangoni, Mtondia, and Mavueni, while all four exports from the region were captured from the Chasimba facility, all occurring to Africa destinations. A strong spatial clustering of virus strains at all locations was observed associated with local evolution. Our study shows that influenza A(H3N2) virus epidemics in local populations appear to be characterized by limited introductions followed by significant local spread and evolution. Knowledge of the viral lineages that circulate within specific populations in understudied tropical and subtropical regions is required to understand the full diversity and global ecology of influenza viruses and to inform vaccination strategies within these populations.

Antigenic drift and subtype interference shape A(H3N2) epidemic dynamics in the United States

Influenza viruses continually evolve new antigenic variants, through mutations in epitopes of their major surface proteins, hemagglutinin (HA) and neuraminidase (NA). Antigenic drift potentiates the reinfection of previously infected individuals, but the contribution of this process to variability in annual epidemics is not well understood. Here, we link influenza A(H3N2) virus evolution to regional epidemic dynamics in the United States during 1997-2019. We integrate phenotypic measures of HA antigenic drift and sequence-based measures of HA and NA fitness to infer antigenic and genetic distances between viruses circulating in successive seasons. We estimate the magnitude, severity, timing, transmission rate, age-specific patterns, and subtype dominance of each regional outbreak and find that genetic distance based on broad sets of epitope sites is the strongest evolutionary predictor of A(H3N2) virus epidemiology. Increased HA and NA epitope distance between seasons correlates with larger, more intense epidemics, higher transmission, greater A(H3N2) subtype dominance, and a greater proportion of cases in adults relative to children, consistent with increased population susceptibility. Based on random forest models, A(H1N1) incidence impacts A(H3N2) epidemics to a greater extent than viral evolution, suggesting that subtype interference is a major driver of influenza A virus infection ynamics, presumably via heterosubtypic cross-immunity.

The resurgence of influenza A/H3N2 virus in Australia after the relaxation of COVID-19 restrictions during the 2022 season

This study retrospectively analyzed the genetic characteristics of influenza A H3N2 (A/H3N2) viruses circulating in New South Wales (NSW), the Australian state with the highest number of influenza cases in 2022, and explored the phylodynamics of A/H3N2 transmission within Australia during this period. Sequencing was performed on 217 archived specimens, and A/H3N2 evolution and spread within Australia were analyzed using phylogenetic and phylodynamic methods. Hemagglutinin genes of all analyzed NSW viruses belonged to subclade 3C.2a1b.2a.2 and clustered together with the 2022 vaccine strain. Complete genome analysis of NSW viruses revealed highly frequent interclade reassortments between subclades 3C.2a1b.2a.2 and 3C.2a1b.1a. The estimated earliest introduction time of the dominant subgroup 3C.2a1b.2a.2a.1 in Australia was February 22, 2022 (95% highest posterior density: December 19, 2021-March 13, 2022), following the easing of Australian travel restrictions, suggesting a possible international source. Phylogeographic analysis revealed that Victoria drove the transmission of A/H3N2 viruses across the country during this season, while NSW did not have a dominant role in viral dissemination to other regions. This study highlights the importance of continuous surveillance and genomic characterization of influenza viruses in the postpandemic era, which can inform public health decision-making and enable early detection of novel strains with pandemic potential.

Phylogeography and reassortment patterns of human influenza A viruses in sub-Saharan Africa

The role of sub-Saharan Africa in the global spread of influenza viruses remains unclear due to insufficient spatiotemporal sequence data. Here, we analyzed 222 codon-complete sequences of influenza A viruses (IAVs) sampled between 2011 and 2013 from five countries across sub-Saharan Africa (Kenya, Zambia, Mali, Gambia, and South Africa); these genomes were compared with 1209 contemporaneous global genomes using phylogeographical approaches. The spread of influenza in sub-Saharan Africa was characterized by (i) multiple introductions of IAVs into the region over consecutive influenza seasons, with viral importations originating from multiple global geographical regions, some of which persisted in circulation as intra-subtype reassortants for multiple seasons, (ii) virus transfer between sub-Saharan African countries, and (iii) virus export from sub-Saharan Africa to other geographical regions. Despite sparse data from influenza surveillance in sub-Saharan Africa, our findings support the notion that influenza viruses persist as temporally structured migrating metapopulations in which new virus strains can emerge in any geographical region, including in sub-Saharan Africa; these lineages may have been capable of dissemination to other continents through a globally migrating virus population. Further knowledge of the viral lineages that circulate within understudied sub-Saharan Africa regions is required to inform vaccination strategies in those regions.

Commuting-driven competition between transmission chains shapes seasonal influenza virus epidemics in the United States

Despite intensive study, much remains unknown about the dynamics of seasonal influenza virus epidemic establishment and spread in the United States (US) each season. By reconstructing transmission lineages from seasonal influenza virus genomes collected in the US from 2014 to 2023, we show that most epidemics consisted of multiple distinct transmission lineages. Spread of these lineages exhibited strong spatiotemporal hierarchies and lineage size was correlated with timing of lineage establishment in the US. Mechanistic epidemic simulations suggest that mobility-driven competition between lineages determined the extent of individual lineages' geographical spread. Based on phylogeographic analyses and epidemic simulations, lineage-specific movement patterns were dominated by human commuting behavior. These results suggest that given the locations of early-season epidemic sparks, the topology of inter-state human mobility yields repeatable patterns of which influenza viruses will circulate where, but the importance of short-term processes limits predictability of regional and national epidemics.

Antigenic drift and subtype interference shape A(H3N2) epidemic dynamics in the United States

Influenza viruses continually evolve new antigenic variants, through mutations in epitopes of their major surface proteins, hemagglutinin (HA) and neuraminidase (NA). Antigenic drift potentiates the reinfection of previously infected individuals, but the contribution of this process to variability in annual epidemics is not well understood. Here we link influenza A(H3N2) virus evolution to regional epidemic dynamics in the United States during 1997-2019. We integrate phenotypic measures of HA antigenic drift and sequence-based measures of HA and NA fitness to infer antigenic and genetic distances between viruses circulating in successive seasons. We estimate the magnitude, severity, timing, transmission rate, age-specific patterns, and subtype dominance of each regional outbreak and find that genetic distance based on broad sets of epitope sites is the strongest evolutionary predictor of A(H3N2) virus epidemiology. Increased HA and NA epitope distance between seasons correlates with larger, more intense epidemics, higher transmission, greater A(H3N2) subtype dominance, and a greater proportion of cases in adults relative to children, consistent with increased population susceptibility. Based on random forest models, A(H1N1) incidence impacts A(H3N2) epidemics to a greater extent than viral evolution, suggesting that subtype interference is a major driver of influenza A virus infection dynamics, presumably via heterosubtypic cross-immunity.

Underdetected dispersal and extensive local transmission drove the 2022 mpox epidemic

The World Health Organization declared mpox a public health emergency of international concern in July 2022. To investigate global mpox transmission and population-level changes associated with controlling spread, we built phylogeographic and phylodynamic models to analyze MPXV genomes from five global regions together with air traffic and epidemiological data. Our models reveal community transmission prior to detection, changes in case reporting throughout the epidemic, and a large degree of transmission heterogeneity. We find that viral introductions played a limited role in prolonging spread after initial dissemination, suggesting that travel bans would have had only a minor impact. We find that mpox transmission in North America began declining before more than 10% of high-risk individuals in the USA had vaccine-induced immunity. Our findings highlight the importance of broader routine specimen screening surveillance for emerging infectious diseases and of joint integration of genomic and epidemiological information for early outbreak control.

MASCOT-Skyline integrates population and migration dynamics to enhance phylogeographic reconstructions

The spread of infectious diseases is shaped by spatial and temporal aspects, such as host population structure or changes in the transmission rate or number of infected individuals over time. These spatiotemporal dynamics are imprinted in the genome of pathogens and can be recovered from those genomes using phylodynamics methods. However, phylodynamic methods typically quantify either the temporal or spatial transmission dynamics, which leads to unclear biases, as one can potentially not be inferred without the other. Here, we address this challenge by introducing a structured coalescent skyline approach, MASCOT-Skyline that allows us to jointly infer spatial and temporal transmission dynamics of infectious diseases using Markov chain Monte Carlo inference. To do so, we model the effective population size dynamics in different locations using a non-parametric function, allowing us to approximate a range of population size dynamics. We show, using a range of different viral outbreak datasets, potential issues with phylogeographic methods. We then use these viral datasets to motivate simulations of outbreaks that illuminate the nature of biases present in the different phylogeographic methods. We show that spatial and temporal dynamics should be modeled jointly even if one seeks to recover just one of the two. Further, we showcase conditions under which we can expect phylogeographic analyses to be biased, particularly different subsampling approaches, as well as provide recommendations of when we can expect them to perform well. We implemented MASCOT-Skyline as part of the open-source software package MASCOT for the Bayesian phylodynamics platform BEAST2.

Local-scale phylodynamics reveal differential community impact of SARS-CoV-2 in a metropolitan US county

SARS-CoV-2 transmission is largely driven by heterogeneous dynamics at a local scale, leaving local health departments to design interventions with limited information. We analyzed SARS-CoV-2 genomes sampled between February 2020 and March 2022 jointly with epidemiological and cell phone mobility data to investigate fine scale spatiotemporal SARS-CoV-2 transmission dynamics in King County, Washington, a diverse, metropolitan US county. We applied an approximate structured coalescent approach to model transmission within and between North King County and South King County alongside the rate of outside introductions into the county. Our phylodynamic analyses reveal that following stay-at-home orders, the epidemic trajectories of North and South King County began to diverge. We find that South King County consistently had more reported and estimated cases, COVID-19 hospitalizations, and longer persistence of local viral transmission when compared to North King County, where viral importations from outside drove a larger proportion of new cases. Using mobility and demographic data, we also find that South King County experienced a more modest and less sustained reduction in mobility following stay-at-home orders than North King County, while also bearing more socioeconomic inequities that might contribute to a disproportionate burden of SARS-CoV-2 transmission. Overall, our findings suggest a role for local-scale phylodynamics in understanding the heterogeneous transmission landscape.

Exploration of the mechanisms of Callicarpa nudiflora Hook. et Arn against influenza A virus (H1N1) infection

BACKGROUND

In our preliminary research on screening traditional Chinese medicine extracts for anti-H1N1 activity, we discovered that the 75 % ethanol extract of Callicarpa nudiflora Hook. & Arn (C. nudiflora) exhibited promising anti-H1N1 infection activity. However, the underlying active components and mechanism of action remain to be elucidated.

AIM OF THE STUDY

This experiment further explores the potential active components and mechanisms of action of C. nudiflora against H1N1.

METHODS

In this study, the composition of the C. nudiflora was determined using UPLC-Q-Orbitrap-MS/MS. The inhibitory effect of C. nudiflora on H1N1 was investigated using a Madin-Darby canine kidney (MDCK) cell model infected with H1N1, and the protective effect of C. nudiflora on H1N1-infected mice was examined using a Balb/c mouse model infected with H1N1. The potential mechanisms of action were demonstrated at the mRNA and protein levels.

RESULTS

A total of 21 compounds were detected in C. nudiflora, which was found to act on the replication stages of H1N1. Moreover, C. nudiflora improved the survival rate of H1N1-infected mice, enhanced the organ index, alleviated the trend of weight loss, reduced lung viral load, mitigated lung tissue damage, and regulated CD4/CD8 and Th1/Th2 immune balance. Molecular mechanism studies revealed that C. nudiflora can regulate the expression of key genes in the toll-like receptor and STAT signaling pathway.

CONCLUSION

C. nudiflora can inhibit H1N1 replication. It also can exert a regulatory effect on the immune response of H1N1-infected mice, and mitigate inflammatory damage by modulating the expression of key genes in the toll-like receptor and STAT signaling pathways, indicating its potential for development as an anti-H1N1 drug.

Early underdetected dissemination across countries followed by extensive local transmission propelled the 2022 mpox epidemic

The World Health Organization declared mpox a public health emergency of international concern in July 2022. To investigate global mpox transmission and population-level changes associated with controlling spread, we built phylogeographic and phylodynamic models to analyze MPXV genomes from five global regions together with air traffic and epidemiological data. Our models reveal community transmission prior to detection, changes in case-reporting throughout the epidemic, and a large degree of transmission heterogeneity. We find that viral introductions played a limited role in prolonging spread after initial dissemination, suggesting that travel bans would have had only a minor impact. We find that mpox transmission in North America began declining before more than 10% of high-risk individuals in the USA had vaccine-induced immunity. Our findings highlight the importance of broader routine specimen screening surveillance for emerging infectious diseases and of joint integration of genomic and epidemiological information for early outbreak control.

Circulation of influenza and other respiratory viruses during the COVID-19 pandemic in Australia and New Zealand, 2020-2021

Objective

Circulation patterns of influenza and other respiratory viruses have been globally disrupted since the emergence of coronavirus disease (COVID-19) and the introduction of public health and social measures (PHSMs) aimed at reducing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission.

Methods

We reviewed respiratory virus laboratory data, Google mobility data and PHSMs in five geographically diverse regions in Australia and New Zealand. We also described respiratory virus activity from January 2017 to August 2021.

Results

We observed a change in the prevalence of circulating respiratory viruses following the emergence of SARS-CoV-2 in early 2020. Influenza activity levels were very low in all regions, lower than those recorded in 2017-2019, with less than 1% of laboratory samples testing positive for influenza virus. In contrast, rates of human rhinovirus infection were increased. Respiratory syncytial virus (RSV) activity was delayed; however, once it returned, most regions experienced activity levels well above those seen in 2017-2019. The timing of the resurgence in the circulation of both rhinovirus and RSV differed within and between the two countries.

Discussion

The findings of this study suggest that as domestic and international borders are opened up and other COVID-19 PHSMs are lifted, clinicians and public health professionals should be prepared for resurgences in influenza and other respiratory viruses. Recent patterns in RSV activity suggest that these resurgences in non-COVID-19 viruses have the potential to occur out of season and with increased impact.

Bidirectional Movement of Emerging H5N8 Avian Influenza Viruses Between Europe and Asia via Migratory Birds Since Early 2020

Migratory birds play a critical role in the rapid spread of highly pathogenic avian influenza (HPAI) H5N8 virus clade 2.3.4.4 across Eurasia. Elucidating the timing and pattern of virus transmission is essential therefore for understanding the spatial dissemination of these viruses. In this study, we surveyed >27,000 wild birds in China, tracked the year-round migration patterns of 20 bird species across China since 2006, and generated new HPAI H5N8 virus genomic data. Using this new data set, we investigated the seasonal transmission dynamics of HPAI H5N8 viruses across Eurasia. We found that introductions of HPAI H5N8 viruses to different Eurasian regions were associated with the seasonal migration of wild birds. Moreover, we report a backflow of HPAI H5N8 virus lineages from Europe to Asia, suggesting that Europe acts as both a source and a sink in the global HPAI virus transmission network.

Local-Scale phylodynamics reveal differential community impact of SARS-CoV-2 in metropolitan US county

SARS-CoV-2 transmission is largely driven by heterogeneous dynamics at a local scale, leaving local health departments to design interventions with limited information. We analyzed SARS-CoV-2 genomes sampled between February 2020 and March 2022 jointly with epidemiological and cell phone mobility data to investigate fine scale spatiotemporal SARS-CoV-2 transmission dynamics in King County, Washington, a diverse, metropolitan US county. We applied an approximate structured coalescent approach to model transmission within and between North King County and South King County alongside the rate of outside introductions into the county. Our phylodynamic analyses reveal that following stay-at-home orders, the epidemic trajectories of North and South King County began to diverge. We find that South King County consistently had more reported and estimated cases, COVID-19 hospitalizations, and longer persistence of local viral transmission when compared to North King County, where viral importations from outside drove a larger proportion of new cases. Using mobility and demographic data, we also find that South King County experienced a more modest and less sustained reduction in mobility following stay-at-home orders than North King County, while also bearing more socioeconomic inequities that might contribute to a disproportionate burden of SARS-CoV-2 transmission. Overall, our findings suggest a role for local-scale phylodynamics in understanding the heterogeneous transmission landscape.

Discovering recent selection forces shaping the evolution of dengue viruses based on polymorphism data across geographic scales

Incomplete selection makes it challenging to infer selection on genes at short time scales, especially for microorganisms, due to stronger linkage between loci. However, in many cases, the selective force changes with environment, time, or other factors, and it is of great interest to understand selective forces at this level to answer relevant biological questions. We developed a new method that uses the change in d_N /d_S , instead of the absolute value of d_N /d_S , to infer the dominating selective force based on sequence data across geographical scales. If a gene was under positive selection, d_N /d_S was expected to increase through time, whereas if a gene was under negative selection, d_N /d_S was expected to decrease through time. Assuming that the migration rate decreased and the divergence time between samples increased from between-continent, within-continent different-country, to within-country level, d_N /d_S of a gene dominated by positive selection was expected to increase with increasing geographical scales, and the opposite trend was expected in the case of negative selection. Motivated by the McDonald-Kreitman (MK) test, we developed a pairwise MK test to assess the statistical significance of detected trends in d_N /d_S . Application of the method to a global sample of dengue virus genomes identified multiple significant signatures of selection in both the structural and non-structural proteins. Because this method does not require allele frequency estimates and uses synonymous mutations for comparison, it is less prone to sampling error, providing a way to infer selection forces within species using publicly available genomic data from locations over broad geographical scales.

The potential of genomics for infectious disease forecasting

Genomic technologies have led to tremendous gains in understanding how pathogens function, evolve and interact. Pathogen diversity is now measurable at high precision and resolution, in part because over the past decade, sequencing technologies have increased in speed and capacity, at decreased cost. Alongside this, the use of models that can forecast emergence and size of infectious disease outbreaks has risen, highlighted by the coronavirus disease 2019 pandemic but also due to modelling advances that allow for rapid estimates in emerging outbreaks to inform monitoring, coordination and resource deployment. However, genomics studies have remained largely retrospective. While they contain high-resolution views of pathogen diversification and evolution in the context of selection, they are often not aligned with designing interventions. This is a missed opportunity because pathogen diversification is at the core of the most pressing infectious public health challenges, and interventions need to take the mechanisms of virulence and understanding of pathogen diversification into account. In this Perspective, we assess these converging fields, discuss current challenges facing both surveillance specialists and modellers who want to harness genomic data, and propose next steps for integrating longitudinally sampled genomic data with statistical learning and interpretable modelling to make reliable predictions into the future.

Characterization of T cell receptors reactive to HCRTNH2, pHA273-287, and NP17-31 in control and narcolepsy patients

Narcolepsy type 1 (NT1), a disorder caused by hypocretin/orexin (HCRT) cell loss, is associated with human leukocyte antigen (HLA)-DQ0602 (98%) and T cell receptor (TCR) polymorphisms. Increased CD4⁺ T cell reactivity to HCRT, especially DQ0602-presented amidated C-terminal HCRT (HCRT_NH2), has been reported, and homology with pHA_273-287 flu antigens from pandemic 2009 H1N1, an established trigger of the disease, suggests molecular mimicry. In this work, we extended DQ0602 tetramer and dextramer data to 77 cases and 44 controls, replicating our prior finding and testing 709 TCRs in Jurkat 76 T cells for functional activation. We found that fewer TCRs isolated with HCRT_NH2 (∼11%) versus pHA_273-287 or NP_17-31 antigens (∼50%) were activated by their ligand. Single-cell characterization did not reveal phenotype differences in influenza versus HCRT_NH2-reactive T cells, and analysis of TCR CDR3αβ sequences showed TCR clustering by responses to antigens but no cross-peptide class reactivity. Our results do not support the existence of molecular mimicry between HCRT and pHA_273-287 or NP_17-31.

Lockdowns exert selection pressure on overdispersion of SARS-CoV-2 variants

The SARS-CoV-2 ancestral strain has caused pronounced superspreading events, reflecting a disease characterized by overdispersion, where about 10% of infected people cause 80% of infections. New variants of the disease have different person-to-person variability in viral load, suggesting for example that the Alpha (B.1.1.7) variant is more infectious but relatively less prone to superspreading. Meanwhile, non-pharmaceutical mitigation of the pandemic has focused on limiting social contacts (lockdowns, regulations on gatherings) and decreasing transmission risk through mask wearing and social distancing. Using a mathematical model, we show that the competitive advantage of disease variants may heavily depend on the restrictions imposed. In particular, we find that lockdowns exert an evolutionary pressure which favours variants with lower levels of overdispersion. Our results suggest that overdispersion is an evolutionarily unstable trait, with a tendency for more homogeneously spreading variants to eventually dominate.

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Novel variants of SARS-CoV-2 appear to be less prone to superspreading.

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A new model shows that it is advantageous for the pathogen to spread homogeneously.

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Interventions exert a selective pressure towards developing homogeneous transmission.

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The results have implications for the assessment of novel variants.

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Adds to understanding of how behaviour and interventions shape pathogen evolution.

Subtype H3N2 Influenza A Viruses: An Unmet Challenge in the Western Pacific

Subtype H3N2 influenza A viruses (A(H3N2)) have been the dominant strain in some countries in the Western Pacific region since the 2009 influenza A(H1N1) pandemic. Vaccination is the most effective way to prevent influenza; however, low vaccine effectiveness has been reported in some influenza seasons, especially for A(H3N2). Antigenic mismatch introduced by egg-adaptation during vaccine production between the vaccine and circulating viral stains is one of the reasons for low vaccine effectiveness. Here we review the extent of this phenomenon, the underlying molecular mechanisms and discuss recent strategies to ameliorate this, including new vaccine platforms that may provide better protection and should be considered to reduce the impact of A(H3N2) in the Western Pacific region.

Circulation patterns of human seasonal Influenza A viruses in Chile before H1N1pdm09 pandemic

Understanding the diversity and circulation dynamics of seasonal influenza viruses is key to public health decision-making. The limited genetic information of pre-pandemic seasonal IAVs in Chile has made it difficult to accurately reconstruct the phylogenetic relationships of these viruses within the country. The objective of this study was to determine the genetic diversity of pre-pandemic human seasonal IAVs in Chile. We sequenced the complete genome of 42 historic IAV obtained between 1996 and 2007. The phylogeny was determined using HA sequences and complemented using other segments. Time-scale phylogenetic analyses revealed that the diversity of pre-pandemic human seasonal IAVs in Chile was influenced by continuous introductions of new A/H1N1 and A/H3N2 lineages and constant viral exchange between Chile and other countries every year. These results provide important knowledge about genetic diversity and evolutionary patterns of pre-pandemic human seasonal IAVs in Chile, which can help design optimal surveillance systems and prevention strategies. However, future studies with current sequences should be conducted.

Characterization of key amino acid substitutions and dynamics of the influenza virus H3N2 hemagglutinin

The annual epidemics of seasonal influenza is partly attributed to the continued virus evolution. It is challenging to evaluate the effect of influenza virus mutations on evading population immunity. In this study, we introduce a novel statistical and computational approach to measure the dynamic molecular determinants underlying epidemics using effective mutations (EMs), and account for the time of waning mutation advantage against herd immunity by measuring the effective mutation periods (EMPs). Extensive analysis is performed on the sequencing and epidemiology data of H3N2 epidemics in ten regions from season to season. We systematically identified 46 EMs in the hemagglutinin (HA) gene, in which the majority were antigenic sites. Eight EMs were located in immunosubdominant stalk domain, an important target for developing broadly reactive antibodies. The EMs might provide timely information on key substitutions for influenza vaccines antigen design. The EMP suggested that major genetic variants of H3N2 circulated in Southeast Asia for an average duration of 4.5 years (SD 2.4) compared to a significantly shorter 2.0 years (SD 1.0) in temperate regions. The proposed method bridges population epidemics and molecular characteristics of infectious diseases, and would find broad applications in various pathogens mutation estimations.

Characterizing the Countrywide Epidemic Spread of Influenza A(H1N1)pdm09 Virus in Kenya between 2009 and 2018

The spatiotemporal patterns of spread of influenza A(H1N1)pdm09 viruses on a countrywide scale are unclear in many tropical/subtropical regions mainly because spatiotemporally representative sequence data are lacking. We isolated, sequenced, and analyzed 383 A(H1N1)pdm09 viral genomes from hospitalized patients between 2009 and 2018 from seven locations across Kenya. Using these genomes and contemporaneously sampled global sequences, we characterized the spread of the virus in Kenya over several seasons using phylodynamic methods. The transmission dynamics of A(H1N1)pdm09 virus in Kenya were characterized by (i) multiple virus introductions into Kenya over the study period, although only a few of those introductions instigated local seasonal epidemics that then established local transmission clusters, (ii) persistence of transmission clusters over several epidemic seasons across the country, (iii) seasonal fluctuations in effective reproduction number (Re) associated with lower number of infections and seasonal fluctuations in relative genetic diversity after an initial rapid increase during the early pandemic phase, which broadly corresponded to epidemic peaks in the northern and southern hemispheres, (iv) high virus genetic diversity with greater frequency of seasonal fluctuations in 2009-2011 and 2018 and low virus genetic diversity with relatively weaker seasonal fluctuations in 2012-2017, and (v) virus spread across Kenya. Considerable influenza virus diversity circulated within Kenya, including persistent viral lineages that were unique to the country, which may have been capable of dissemination to other continents through a globally migrating virus population. Further knowledge of the viral lineages that circulate within understudied low-to-middle-income tropical and subtropical regions is required to understand the full diversity and global ecology of influenza viruses in humans and to inform vaccination strategies within these regions.

The Mechanism behind Influenza Virus Cytokine Storm

Influenza viruses are still a serious threat to human health. Cytokines are essential for cell-to-cell communication and viral clearance in the immune system, but excessive cytokines can cause serious immune pathology. Deaths caused by severe influenza are usually related to cytokine storms. The recent literature has described the mechanism behind the cytokine-storm network and how it can exacerbate host pathological damage. Biological factors such as sex, age, and obesity may cause biological differences between different individuals, which affects cytokine storms induced by the influenza virus. In this review, we summarize the mechanism behind influenza virus cytokine storms and the differences in cytokine storms of different ages and sexes, and in obesity.

Key Factors That Enable the Pandemic Potential of RNA Viruses and Inter-Species Transmission: A Systematic Review

Viruses play a primary role as etiological agents of pandemics worldwide. Although there has been progress in identifying the molecular features of both viruses and hosts, the extent of the impact these and other factors have that contribute to interspecies transmission and their relationship with the emergence of diseases are poorly understood. The objective of this review was to analyze the factors related to the characteristics inherent to RNA viruses accountable for pandemics in the last 20 years which facilitate infection, promote interspecies jump, and assist in the generation of zoonotic infections with pandemic potential. The search resulted in 48 research articles that met the inclusion criteria. Changes adopted by RNA viruses are influenced by environmental and host-related factors, which define their ability to adapt. Population density, host distribution, migration patterns, and the loss of natural habitats, among others, have been associated as factors in the virus-host interaction. This review also included a critical analysis of the Latin American context, considering its diverse and unique social, cultural, and biodiversity characteristics. The scarcity of scientific information is striking, thus, a call to local institutions and governments to invest more resources and efforts to the study of these factors in the region is key.

Molecular epidemiology in the HIV and SARS-CoV-2 pandemics

PURPOSE OF REVIEW

The aim of this review was to compare and contrast the application of molecular epidemiology approaches for the improved management and understanding of the HIV versus SARS-CoV-2 epidemics.

RECENT FINDINGS

Molecular biology approaches, including PCR and whole genome sequencing (WGS), have become powerful tools for epidemiological investigation. PCR approaches form the basis for many high-sensitivity diagnostic tests and can supplement traditional contact tracing and surveillance strategies to define risk networks and transmission patterns. WGS approaches can further define the causative agents of disease, trace the origins of the pathogen, and clarify routes of transmission. When coupled with clinical datasets, such as electronic medical record data, these approaches can investigate co-correlates of disease and pathogenesis. In the ongoing HIV epidemic, these approaches have been effectively deployed to identify treatment gaps, transmission clusters and risk factors, though significant barriers to rapid or real-time implementation remain critical to overcome. Likewise, these approaches have been successful in addressing some questions of SARS-CoV-2 transmission and pathogenesis, but the nature and rapid spread of the virus have posed additional challenges.

SUMMARY

Overall, molecular epidemiology approaches offer unique advantages and challenges that complement traditional epidemiological tools for the improved understanding and management of epidemics.

Characterising the epidemic spread of influenza A/H3N2 within a city through phylogenetics

Infecting large portions of the global population, seasonal influenza is a major burden on societies around the globe. While the global source sink dynamics of the different seasonal influenza viruses have been studied intensively, its local spread remains less clear. In order to improve our understanding of how influenza is transmitted on a city scale, we collected an extremely densely sampled set of influenza sequences alongside patient metadata. To do so, we sequenced influenza viruses isolated from patients of two different hospitals, as well as private practitioners in Basel, Switzerland during the 2016/2017 influenza season. The genetic sequences reveal that repeated introductions into the city drove the influenza season. We then reconstruct how the effective reproduction number changed over the course of the season. While we did not find that transmission dynamics in Basel correlate with humidity or school closures, we did find some evidence that it may positively correlated with temperature. Alongside the genetic sequence data that allows us to see how individual cases are connected, we gathered patient information, such as the age or household status. Zooming into the local transmission outbreaks suggests that the elderly were to a large extent infected within their own transmission network. In the remaining transmission network, our analyses suggest that school-aged children likely play a more central role than pre-school aged children. These patterns will be valuable to plan interventions combating the spread of respiratory diseases within cities given that similar patterns are observed for other influenza seasons and cities.

A large effective population size for established within-host influenza virus infection

Strains of the influenza virus form coherent global populations, yet exist at the level of single infections in individual hosts. The relationship between these scales is a critical topic for understanding viral evolution. Here we investigate the within-host relationship between selection and the stochastic effects of genetic drift, estimating an effective population size of infection Ne for influenza infection. Examining whole-genome sequence data describing a chronic case of influenza B in a severely immunocompromised child we infer an Ne of 2.5 × 107 (95% confidence range 1.0 × 107 to 9.0 × 107) suggesting that genetic drift is of minimal importance during an established influenza infection. Our result, supported by data from influenza A infection, suggests that positive selection during within-host infection is primarily limited by the typically short period of infection. Atypically long infections may have a disproportionate influence upon global patterns of viral evolution.

Infectious Disease Research in the Era of Big Data

Infectious disease research spans scales from the molecular to the global—from specific mechanisms of pathogen drug resistance, virulence, and replication to the movement of people, animals, and pathogens around the world. All of these research areas have been impacted by the recent growth of large-scale data sources and data analytics. Some of these advances rely on data or analytic methods that are common to most biomedical data science, while others leverage the unique nature of infectious disease, namely its communicability. This review outlines major research progress in the past few years and highlights some remaining opportunities, focusing on data or methodological approaches particular to infectious disease.

Modeling analysis revealed the distinct global transmission patterns of influenza A viruses and their influencing factors

Influenza A virus has caused huge damage to human health and poultry production worldwide, but its global transmission patterns and influencing factors remain unclear. Here, by using the Nearest Genetic Distance Approach with genetic sequences data, we reconstructed the global transmission patterns of 4 most common subtypes of influenza A virus (H1N1, H3N2, H5N1, and H7N9) and analyzed associations of transmission velocity of these influenza viruses with environmental factors. We found that the transmission patterns of influenza viruses and their associations with environmental factors were closely related to their host properties. H1N1 and H3N2, which are mainly held by humans, are transmitted between regions at high velocity and over long distances, which may be due to human transportation via airplane; while H5N1 and H7N9, which are mainly carried by animals, are transmitted locally at short distances and at low velocity, which may be facilitated by poultry transportation via railways or high ways. H1N1 and H3N2 spread faster in cold seasons, while H5N1 spread faster in both cold and warm seasons, and H7N9 spread faster in wet seasons. H1N1, H3N2, and H5N1 spread faster in places with both high and low human densities. Our study provided novel insights into the global transmission patterns, processes, and management strategies for influenza under accelerated global change.

Toxigenic Vibrio cholerae evolution and establishment of reservoirs in aquatic ecosystems

The spread of cholera in the midst of an epidemic is largely driven by direct transmission from person to person, although it is well-recognized that Vibrio cholerae is also capable of growth and long-term survival in aquatic ecosystems. While prior studies have shown that aquatic reservoirs are important in the persistence of the disease on the Indian subcontinent, an epidemiological view postulating that locally evolving environmental V. cholerae contributes to outbreaks outside Asia remains debated. The single-source introduction of toxigenic V. cholerae O1 in Haiti, one of the largest outbreaks occurring this century, with 812,586 suspected cases and 9,606 deaths reported through July 2018, provided a unique opportunity to evaluate the role of aquatic reservoirs and assess bacterial transmission dynamics across environmental boundaries. To this end, we investigated the phylogeography of both clinical and aquatic toxigenic V. cholerae O1 isolates and show robust evidence of the establishment of aquatic reservoirs as well as ongoing evolution of V. cholerae isolates from aquatic sites. Novel environmental lineages emerged from sequential population bottlenecks, carrying mutations potentially involved in adaptation to the aquatic ecosystem. Based on such empirical data, we developed a mixed-transmission dynamic model of V. cholerae, where aquatic reservoirs actively contribute to genetic diversification and epidemic emergence, which underscores the complexity of transmission pathways in epidemics and endemic settings and the need for long-term investments in cholera control at both human and environmental levels.

Early prediction of antigenic transitions for influenza A/H3N2

Influenza A/H3N2 is a rapidly evolving virus which experiences major antigenic transitions every two to eight years. Anticipating the timing and outcome of transitions is critical to developing effective seasonal influenza vaccines. Using a published phylodynamic model of influenza transmission, we identified indicators of future evolutionary success for an emerging antigenic cluster and quantified fundamental trade-offs in our ability to make such predictions. The eventual fate of a new cluster depends on its initial epidemiological growth rate––which is a function of mutational load and population susceptibility to the cluster––along with the variance in growth rate across co-circulating viruses. Logistic regression can predict whether a cluster at 5% relative frequency will eventually succeed with ~80% sensitivity, providing up to eight months advance warning. As a cluster expands, the predictions improve while the lead-time for vaccine development and other interventions decreases. However, attempts to make comparable predictions from 12 years of empirical influenza surveillance data, which are far sparser and more coarse-grained, achieve only 56% sensitivity. By expanding influenza surveillance to obtain more granular estimates of the frequencies of and population-wide susceptibility to emerging viruses, we can better anticipate major antigenic transitions. This provides added incentives for accelerating the vaccine production cycle to reduce the lead time required for strain selection.

The efficacy of annual seasonal influenza vaccines depends on selecting the strain that best matches circulating viruses. This selection takes place 9–12 months prior to the influenza season. To advise this decision, we used an influenza A/H3N2 phylodynamic simulation to explore how reliably and how far in advance can we identify strains that will dominate future influenza seasons? What data should we collect to accelerate and improve the accuracy of such forecasts? And importantly, what is the gap between the theoretical limit of prediction and prediction based on current influenza surveillance? Our results suggest that even with detailed virological information, the tight race between the antigenic turnover dynamics and the vaccine development timeline limits early detection of emerging viruses. Predictions based on current influenza surveillance do not achieve the theoretical limit and thus our results provide impetus for denser sampling and the development of rapid methods for estimating viral fitness.

Divergent evolutionary trajectories of influenza B viruses underlie their contemporaneous epidemic activity

Two influenza B viruses (Victoria and Yamagata) cocirculate in humans and contribute to the estimated 290,000–650,000 annual influenza-attributed deaths. Here, we analysed influenza B genomic data to understand the causes of a recent surge in human influenza B infections. We found that evolution is acting differently on Yamagata and Victoria viruses and that this has led to the cocirculation of a diverse group of influenza B viruses. If this phenomenon continues, we could potentially witness the emergence of 3 or more distinct influenza B viruses that could require their own vaccine component, thereby complicating influenza vaccine formulation and highlighting the urgency of developing universal influenza vaccines.

Influenza B viruses have circulated in humans for over 80 y, causing a significant disease burden. Two antigenically distinct lineages (“B/Victoria/2/87-like” and “B/Yamagata/16/88-like,” termed Victoria and Yamagata) emerged in the 1970s and have cocirculated since 2001. Since 2015 both lineages have shown unusually high levels of epidemic activity, the reasons for which are unclear. By analyzing over 12,000 influenza B virus genomes, we describe the processes enabling the long-term success and recent resurgence of epidemics due to influenza B virus. We show that following prolonged diversification, both lineages underwent selective sweeps across the genome and have subsequently taken alternate evolutionary trajectories to exhibit epidemic dominance, with no reassortment between lineages. Hemagglutinin deletion variants emerged concomitantly in multiple Victoria virus clades and persisted through epistatic mutations and interclade reassortment—a phenomenon previously only observed in the 1970s when Victoria and Yamagata lineages emerged. For Yamagata viruses, antigenic drift of neuraminidase was a major driver of epidemic activity, indicating that neuraminidase-based vaccines and cross-reactivity assays should be employed to monitor and develop robust protection against influenza B morbidity and mortality. Overall, we show that long-term diversification and infrequent selective sweeps, coupled with the reemergence of hemagglutinin deletion variants and antigenic drift of neuraminidase, are factors that contributed to successful circulation of diverse influenza B clades. Further divergence of hemagglutinin variants with poor cross-reactivity could potentially lead to circulation of 3 or more distinct influenza B viruses, further complicating influenza vaccine formulation and highlighting the urgent need for universal influenza vaccines.

Co-circulation and persistence of multiple A/H3N2 influenza variants in China

The spread of influenza A/H3N2 variants possessing the hemagglutinin 121 K mutation and the unexpectedly high incidence of influenza in the 2017–2018 northern hemisphere influenza season have raised serious concerns about the next pandemic. We summarized the national surveillance data of seasonal influenza in China and identified marked differences in influenza epidemics between northern and southern China, particularly the predominating subtype and the presence of an additional summer peak in southern China. Notably, a minor spring peak of influenza caused by a different virus subtype was also observed. We also revealed that the 3C.2a lineage was dominant from the summer of 2015 to the end of the 2015–2016 peak season in China, after which the 3C.2a2 lineage predominated despite the importation and co-circulation of the 121 K variants of 3C.2a1 and 3C.2a3 lineages at the global level. Finally, an analysis based on genetic distances revealed a delay in A/H3N2 vaccine strain update. Overall, our results highlight the complicated circulation pattern of seasonal influenza in China and the necessity for a timely vaccine strain update worldwide.

Advances in Visualization Tools for Phylogenomic and Phylodynamic Studies of Viral Diseases

Genomic and epidemiological monitoring have become an integral part of our response to emerging and ongoing epidemics of viral infectious diseases. Advances in high-throughput sequencing, including portable genomic sequencing at reduced costs and turnaround time, are paralleled by continuing developments in methodology to infer evolutionary histories (dynamics/patterns) and to identify factors driving viral spread in space and time. The traditionally static nature of visualizing phylogenetic trees that represent these evolutionary relationships/processes has also evolved, albeit perhaps at a slower rate. Advanced visualization tools with increased resolution assist in drawing conclusions from phylogenetic estimates and may even have potential to better inform public health and treatment decisions, but the design (and choice of what analyses are shown) is hindered by the complexity of information embedded within current phylogenetic models and the integration of available meta-data. In this review, we discuss visualization challenges for the interpretation and exploration of reconstructed histories of viral epidemics that arose from increasing volumes of sequence data and the wealth of additional data layers that can be integrated. We focus on solutions that address joint temporal and spatial visualization but also consider what the future may bring in terms of visualization and how this may become of value for the coming era of real-time digital pathogen surveillance, where actionable results and adequate intervention strategies need to be obtained within days.

Urbanization and humidity shape the intensity of influenza epidemics in U.S. cities

Influenza epidemics vary in intensity from year to year, driven by climatic conditions and by viral antigenic evolution. However, important spatial variation remains unexplained. Here we show predictable differences in influenza incidence among cities, driven by population size and structure. Weekly incidence data from 603 cities in the United States reveal that epidemics in smaller cities are focused on shorter periods of the influenza season, whereas in larger cities, incidence is more diffuse. Base transmission potential estimated from city-level incidence data is positively correlated with population size and with spatiotemporal organization in population density, indicating a milder response to climate forcing in metropolises. This suggests that urban centers incubate critical chains of transmission outside of peak climatic conditions, altering the spatiotemporal geometry of herd immunity.

Autoimmunity to hypocretin and molecular mimicry to flu in type 1 narcolepsy

This work shows that the amidated terminal ends of the secreted hypocretin (HCRT) peptides (HCRT_NH2) are autoantigens in type 1 narcolepsy, an autoimmune disorder targeting HCRT neurons. The autoimmune process is usually initiated by influenza A flu infections, and a particular piece of the hemagglutinin (HA) flu protein of the pandemic 2009 H1N1 strain was identified as a likely trigger. This HA epitope has homology with HCRT_NH2 and T cells cross-reactive to both epitopes are involved in the autoimmune process by molecular mimicry. Genes associated with narcolepsy mark the particular HLA heterodimer (DQ0602) involved in presentation of these antigens and modulate expression of the specific T cell receptor segments (TRAJ24 and TRBV4-2) involved in T cell receptor recognition of these antigens, suggesting causality.

Type 1 narcolepsy (T1N) is caused by hypocretin/orexin (HCRT) neuronal loss. Association with the HLA DQB1*06:02/DQA1*01:02 (98% vs. 25%) heterodimer (DQ0602), T cell receptors (TCR) and other immune loci suggest autoimmunity but autoantigens are unknown. Onset is seasonal and associated with influenza A, notably pandemic 2009 H1N1 (pH1N1) infection and vaccination (Pandemrix). Peptides derived from HCRT and influenza A, including pH1N1, were screened for DQ0602 binding and presence of cognate DQ0602 tetramer-peptide–specific CD4⁺ T cells tested in 35 T1N cases and 22 DQ0602 controls. Higher reactivity to influenza pHA_273–287 (pH1N1 specific), PR8 (H1N1 pre-2009 and H2N2)-specific NP_17–31 and C-amidated but not native version of HCRT_54–66 and HCRT_86–97 (HCRT_NH2) were observed in T1N. Single-cell TCR sequencing revealed sharing of CDR3β TRBV4-2-CASSQETQGRNYGYTF in HCRT_NH2 and pHA_273–287-tetramers, suggesting molecular mimicry. This public CDR3β uses TRBV4-2, a segment modulated by T1N-associated SNP rs1008599, suggesting causality. TCR-α/β CDR3 motifs of HCRT_54–66-NH2 and HCRT_86–97-NH2 tetramers were extensively shared: notably public CDR3α, TRAV2-CAVETDSWGKLQF-TRAJ24, that uses TRAJ24, a chain modulated by T1N-associated SNPs rs1154155 and rs1483979. TCR-α/β CDR3 sequences found in pHA_273–287, NP_17–31, and HCRT_NH2 tetramer-positive CD4⁺ cells were also retrieved in single INF-γ–secreting CD4⁺ sorted cells stimulated with Pandemrix, independently confirming these results. Our results provide evidence for autoimmunity and molecular mimicry with flu antigens modulated by genetic components in the pathophysiology of T1N.

Nonannual seasonality of influenza-like illness in a tropical urban setting

BACKGROUND

In temperate and subtropical climates, respiratory diseases exhibit seasonal peaks in winter. In the tropics, with no winter, peak timings are irregular.

METHODS

To obtain a detailed picture of influenza-like illness (ILI) patterns in the tropics, we established an mHealth study in community clinics in Ho Chi Minh City (HCMC). During 2009-2015, clinics reported daily case numbers via SMS, with a subset performing molecular diagnostics for influenza virus. This real-time epidemiology network absorbs 6000 ILI reports annually, one or two orders of magnitude more than typical surveillance systems. A real-time online ILI indicator was developed to inform clinicians of the daily ILI activity in HCMC.

RESULTS

From August 2009 to December 2015, 63 clinics were enrolled and 36 920 SMS reports were received, covering approximately 1.7M outpatient visits. Approximately 10.6% of outpatients met the ILI case definition. ILI activity in HCMC exhibited strong nonannual dynamics with a dominant periodicity of 206 days. This was confirmed by time series decomposition, stepwise regression, and a forecasting exercise showing that median forecasting errors are 30%-40% lower when using a 206-day cycle. In ILI patients from whom nasopharyngeal swabs were taken, 31.2% were positive for influenza. There was no correlation between the ILI time series and the time series of influenza, influenza A, or influenza B (all P > 0.15).

CONCLUSION

This suggests, for the first time, that a nonannual cycle may be an essential driver of respiratory disease dynamics in the tropics. An immunological interference hypothesis is discussed as a potential underlying mechanism.

Frequent Genetic Mismatch between Vaccine Strains and Circulating Seasonal Influenza Viruses, Hong Kong, China, 1996-2012

The World Health Organization selects influenza vaccine compositions biannually to cater to peaks in temperate regions. In tropical and subtropical regions, where influenza seasonality varies and epidemics can occur year-round, the choice of vaccine remains uncertain. Our 17-year molecular epidemiologic survey showed that most influenza A(H3N2) (9/11) and B (6/7) vaccine strains had circulated in East Asia >1 year before inclusion into vaccines. Northern Hemisphere vaccine strains and circulating strains in East Asia were closely matched in 7 (20.6%) of 34 seasons for H3N2 and 5 (14.7%) of 34 seasons for B. Southern Hemisphere vaccines also had a low probability of matching (H3N2, 14.7%; B, 11.1%). Strain drift among seasons was common (H3N2, 41.2%; B, 35.3%), and biannual vaccination strategy (Northern Hemisphere vaccines in November followed by Southern Hemisphere vaccines in May) did not improve matching. East Asia is an important contributor to influenza surveillance but often has mismatch between vaccine and contemporarily circulating strains.

Estimating Vaccine-Driven Selection in Seasonal Influenza

Vaccination could be an evolutionary pressure on seasonal influenza if vaccines reduce the transmission rates of some ("targeted") strains more than others. In theory, more vaccinated populations should have a lower prevalence of targeted strains compared to less vaccinated populations. We tested for vaccine-induced selection in influenza by comparing strain frequencies between more and less vaccinated human populations. We defined strains in three ways: first as influenza types and subtypes, next as lineages of type B, and finally as clades of influenza A/H3N2. We detected spatial differences partially consistent with vaccine use in the frequencies of subtypes and types and between the lineages of influenza B, suggesting that vaccines do not select strongly among all these phylogenetic groups at regional scales. We did detect a significantly greater frequency of an H3N2 clade with known vaccine escape mutations in more vaccinated countries during the 2014⁻2015 season, which is consistent with vaccine-driven selection within the H3N2 subtype. Overall, we find more support for vaccine-driven selection when large differences in vaccine effectiveness suggest a strong effect size. Variation in surveillance practices across countries could obscure signals of selection, especially when strain-specific differences in vaccine effectiveness are small. Further examination of the influenza vaccine's evolutionary effects would benefit from improvements in epidemiological surveillance and reporting.

Centennial review of influenza in Taiwan

The history of influenza in Taiwan can be traced up to the 1918 H1N1 Spanish flu pandemic, followed by several others including the 1957 H2N2, 1968 H3N2, and the 2009 new H1N1. A couple of avian influenza viruses of H5N1 and H7N9 also posed threats to the general public in Taiwan in the two recent decades. Nevertheless, two seasonal influenza A viruses and two lineages of influenza B viruses continue causing annual endemics one after the other, or appearing simultaneously. Their interplay provided interesting evolutionary trajectories for these viruses, allowing us to computationally model their global migrations together with the data collected elsewhere from different geographical locations. An island-wide laboratory-based surveillance network was also established since 2000 for systematically collecting and managing the disease and molecular epidemiology. Experiences learned from this network helped in encountering and managing newly emerging infectious diseases, including the 2003 SARS and 2009 H1N1 outbreaks.

Mapping the Evolutionary Potential of RNA Viruses

The deterministic force of natural selection and stochastic influence of drift shape RNA virus evolution. New deep-sequencing and microfluidics technologies allow us to quantify the effect of mutations and trace the evolution of viral populations with single-genome and single-nucleotide resolution. Such experiments can reveal the topography of the genotype-fitness landscapes that shape the path of viral evolution. By combining historical analyses, like phylogenetic approaches, with high-throughput and high-resolution evolutionary experiments, we can observe parallel patterns of evolution that drive important phenotypic transitions. These developments provide a framework for quantifying and anticipating potential evolutionary events. Here, we examine emerging technologies that can map the selective landscapes of viruses, focusing on their application to pathogenic viruses. We identify areas where these technologies can bolster our ability to study the evolution of viruses and to anticipate and possibly intervene in evolutionary events and prevent viral disease.

The Structured Coalescent and Its Approximations

Phylogeographic methods can help reveal the movement of genes between populations of organisms. This has been widely done to quantify pathogen movement between different host populations, the migration history of humans, and the geographic spread of languages or gene flow between species using the location or state of samples alongside sequence data. Phylogenies therefore offer insights into migration processes not available from classic epidemiological or occurrence data alone. Phylogeographic methods have however several known shortcomings. In particular, one of the most widely used methods treats migration the same as mutation, and therefore does not incorporate information about population demography. This may lead to severe biases in estimated migration rates for data sets where sampling is biased across populations. The structured coalescent on the other hand allows us to coherently model the migration and coalescent process, but current implementations struggle with complex data sets due to the need to infer ancestral migration histories. Thus, approximations to the structured coalescent, which integrate over all ancestral migration histories, have been developed. However, the validity and robustness of these approximations remain unclear. We present an exact numerical solution to the structured coalescent that does not require the inference of migration histories. Although this solution is computationally unfeasible for large data sets, it clarifies the assumptions of previously developed approximate methods and allows us to provide an improved approximation to the structured coalescent. We have implemented these methods in BEAST2, and we show how these methods compare under different scenarios.

Landscape attributes governing local transmission of an endemic zoonosis: Rabies virus in domestic dogs

Landscape heterogeneity plays an important role in disease spread and persistence, but quantifying landscape influences and their scale dependence is challenging. Studies have focused on how environmental features or global transport networks influence pathogen invasion and spread, but their influence on local transmission dynamics that underpin the persistence of endemic diseases remains unexplored. Bayesian phylogeographic frameworks that incorporate spatial heterogeneities are promising tools for analysing linked epidemiological, environmental and genetic data. Here, we extend these methodological approaches to decipher the relative contribution and scale-dependent effects of landscape influences on the transmission of endemic rabies virus in Serengeti district, Tanzania (area ~4,900 km2 ). Utilizing detailed epidemiological data and 152 complete viral genomes collected between 2004 and 2013, we show that the localized presence of dogs but not their density is the most important determinant of diffusion, implying that culling will be ineffective for rabies control. Rivers and roads acted as barriers and facilitators to viral spread, respectively, and vaccination impeded diffusion despite variable annual coverage. Notably, we found that landscape effects were scale-dependent: rivers were barriers and roads facilitators on larger scales, whereas the distribution of dogs was important for rabies dispersal across multiple scales. This nuanced understanding of the spatial processes that underpin rabies transmission can be exploited for targeted control at the scale where it will have the greatest impact. Moreover, this research demonstrates how current phylogeographic frameworks can be adapted to improve our understanding of endemic disease dynamics at different spatial scales.

In-depth phylodynamics, evolutionary analysis and in silico predictions of universal epitopes of Influenza A subtypes and Influenza B viruses

This study applied High-Performance Computing to explore the high-resolution phylodynamics and the evolutionary dynamics of Influenza viruses (IVs) A and B and their subtypes in-depth to identify peptide-based candidates for broad-spectrum vaccine targets. For this purpose, we collected all the available Hemagglutinin (HA) and Neuraminidase (NA) nucleotide and amino acid sequences (more than 100,000) of IVs isolated from all the reservoirs and intermediate hosts species, from all geographic ranges and from different isolation sources, covering a period of almost one century of sampling years. We highlight that despite the constant changes in Influenza evolutionary dynamics over time, which are responsible for the generation of novel strains, our study identified the presence of highly conserved peptides distributed in all the HA and NA found in H1-H18 and N1-N11 IAV subtypes and IBVs. Additionally, predictions through computational methods showed that these peptides could have a strong affinity to bind to HLA-A∗02:01/HLA-DRB1∗01:01 major histocompatibility complex (MHC) class I and II molecules, therefore acting as a double ligand. Moreover, epitope prediction in antigens from pathogens responsible for secondary bacterial infection was also studied. These findings show that the regions mapped here may potentially be explored as universal epitope-based candidates to develop therapies leading to a broader response against the infection induced by all circulating IAVs, IBVs and Influenza-associated bacterial infections.

Explaining the geographical origins of seasonal influenza A (H3N2)

Most antigenically novel and evolutionarily successful strains of seasonal influenza A (H3N2) originate in East, South and Southeast Asia. To understand this pattern, we simulated the ecological and evolutionary dynamics of influenza in a host metapopulation representing the temperate north, tropics and temperate south. Although seasonality and air traffic are frequently used to explain global migratory patterns of influenza, we find that other factors may have a comparable or greater impact. Notably, a region's basic reproductive number (R0) strongly affects the antigenic evolution of its viral population and the probability that its strains will spread and fix globally: a 17-28% higher R0 in one region can explain the observed patterns. Seasonality, in contrast, increases the probability that a tropical (less seasonal) population will export evolutionarily successful strains but alone does not predict that these strains will be antigenically advanced. The relative sizes of different host populations, their birth and death rates, and the region in which H3N2 first appears affect influenza's phylogeography in different but relatively minor ways. These results suggest general principles that dictate the spatial dynamics of antigenically evolving pathogens and offer predictions for how changes in human ecology might affect influenza evolution.

On the effective depth of viral sequence data

Genome sequence data are of great value in describing evolutionary processes in viral populations. However, in such studies, the extent to which data accurately describes the viral population is a matter of importance. Multiple factors may influence the accuracy of a dataset, including the quantity and nature of the sample collected, and the subsequent steps in viral processing. To investigate this phenomenon, we sequenced replica datasets spanning a range of viruses, and in which the point at which samples were split was different in each case, from a dataset in which independent samples were collected from a single patient to another in which all processing steps up to sequencing were applied to a single sample before splitting the sample and sequencing each replicate. We conclude that neither a high read depth nor a high template number in a sample guarantee the precision of a dataset. Measures of consistency calculated from within a single biological sample may also be insufficient; distortion of the composition of a population by the experimental procedure or genuine within-host diversity between samples may each affect the results. Where it is possible, data from replicate samples should be collected to validate the consistency of short-read sequence data.

In Silico Vaccine Strain Prediction for Human Influenza Viruses

Vaccines preventing seasonal influenza infections save many lives every year; however, due to rapid viral evolution, they have to be updated frequently to remain effective. To identify appropriate vaccine strains, the World Health Organization (WHO) operates a global program that continually generates and interprets surveillance data. Over the past decade, sophisticated computational techniques, drawing from multiple theoretical disciplines, have been developed that predict viral lineages rising to predominance, assess their suitability as vaccine strains, link genetic to antigenic alterations, as well as integrate and visualize genetic, epidemiological, structural, and antigenic data. These could form the basis of an objective and reproducible vaccine strain-selection procedure utilizing the complex, large-scale data types from surveillance. To this end, computational techniques should already be incorporated into the vaccine-selection process in an independent, parallel track, and their performance continuously evaluated.