The Role of Human Transportation Networks in Mediating the Genetic Structure of Seasonal Influenza in the United States

Spatial-temporal evolution patterns of influenza incidence in Xinjiang Prefecture from 2014 to 2023 based on GIS

Using GIS technology, this study investigated the spatiotemporal distribution pattern of influenza incidence in Xinjiang from 2014 to 2023 based on influenza surveillance data. The study revealed a noticeable fluctuation trend in influenza incidence rates in Xinjiang, particularly notable spikes observed in 2019 and 2023. The results of the 3-year moving average showed a significant long-term upward trend in influenza incidence rates, confirmed by Theil-Sen method (MAD = 2.202, p < 0.01). Global spatial autocorrelation analysis indicated significant positive spatial autocorrelation in influenza incidence rates from 2016 and from 2018 to 2023 (Moran's I > 0, P < 0.05). Local spatial autocorrelation analysis further revealed clustering patterns in different regions, with high-high clustering and low-high clustering predominating in northern Xinjiang, and low-low clustering predominating in southern Xinjiang. Hotspot analysis indicated a progressive rise in the number of influenza incidence hotspots, primarily concentrated in northern Xinjiang, particularly in Urumqi, Ili Kazakh Autonomous Prefecture, and Hotan Prefecture. Standard deviation ellipse analysis and the trajectory of influenza incidence gravity center migration showed that the transmission range of influenza in Xinjiang has been expanding, with the epidemic center gradually moving northward. The spatiotemporal heterogeneity of influenza incidence in Xinjiang highlights the need for differentiated and precise influenza prevention and control strategies in different regions to address the changing trends in influenza prevalence.

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.

Combining viral genetic and animal mobility network data to unravel peste des petits ruminants transmission dynamics in West Africa

Peste des petits ruminants (PPR) is a deadly viral disease that mainly affects small domestic ruminants. This disease threaten global food security and rural economy but its control is complicated notably because of extensive, poorly monitored animal movements in infected regions. Here we combined the largest PPR virus genetic and animal mobility network data ever collected in a single region to improve our understanding of PPR endemic transmission dynamics in West African countries. Phylogenetic analyses identified the presence of multiple PPRV genetic clades that may be considered as part of different transmission networks evolving in parallel in West Africa. A strong correlation was found between virus genetic distance and network-related distances. Viruses sampled within the same mobility communities are significantly more likely to belong to the same genetic clade. These results provide evidence for the importance of animal mobility in PPR transmission in the region. Some nodes of the network were associated with PPRV sequences belonging to different clades, representing potential "hotspots" for PPR circulation. Our results suggest that combining genetic and mobility network data could help identifying sites that are key for virus entrance and spread in specific areas. Such information could enhance our capacity to develop locally adapted control and surveillance strategies, using among other risk factors, information on animal mobility.

Evaluation of rates of laboratory-confirmed influenza hospitalization in rural and urban census tracts over eight influenza seasons

The burden of influenza in rural areas is largely unstudied. Rural populations may be vulnerable yet isolated from circulating virus. Laboratory-confirmed influenza hospitalizations in rural Colorado census tracts over eight influenza seasons were inconsistently distributed across seasons. Rural rates were, on average, lower than urban rates. Race, ethnicity, poverty, health insurance coverage, and distance from a hospital accounted for rate differences. Our interpretation is: 1) influenza regularly circulates in urban areas and inconsistently spreads to rural areas, 2) demographic and socioeconomic factors drive morbidity in exposed populations, and 3) public health interventions targeting high-risk urban census tracts may be beneficial.

Forecasting influenza in Europe using a metapopulation model incorporating cross-border commuting and air travel

Past work has shown that models incorporating human travel can improve the quality of influenza forecasts. Here, we develop and validate a metapopulation model of twelve European countries, in which international translocation of virus is driven by observed commuting and air travel flows, and use this model to generate influenza forecasts in conjunction with incidence data from the World Health Organization. We find that, although the metapopulation model fits the data well, it offers no improvement over isolated models in forecast quality. We discuss several potential reasons for these results. In particular, we note the need for data that are more comparable from country to country, and offer suggestions as to how surveillance systems might be improved to achieve this goal.

Differences in epidemic spread patterns of norovirus and influenza seasons of Germany: an application of optical flow analysis in epidemiology

This analysis presents data from a new perspective offering key insights into the spread patterns of norovirus and influenza epidemic events. We utilize optic flow analysis to gain an informed overview of a wealth of statistical epidemiological data and identify trends in movement of influenza waves throughout Germany on the NUTS 3 level (413 locations) which maps municipalities on European level. We show that Influenza and norovirus seasonal outbreak events have a highly distinct pattern. We investigate the quantitative statistical properties of the epidemic patterns and find a shifted distribution in the time between influenza and norovirus seasonal peaks of reported infections over one decade. These findings align with key biological features of both pathogens as shown in the course of this analysis.

Impacts of Road Traffic Network and Socioeconomic Factors on the Diffusion of 2009 Pandemic Influenza A (H1N1) in Mainland China

The 2009 pandemic influenza virus caused the majority of the influenza A virus infections in China in 2009. It arrived in several Chinese cities from imported cases and then spread as people travelled domestically by all means of transportation, among which road traffic was the most commonly used for daily commuting. Spatial variation in socioeconomic status not only accelerates migration across regions but also partly induces the differences in epidemic processes and in responses to epidemics across regions. However, the roles of both road travel and socioeconomic factors have not received the attention they deserve. Here, we constructed a national highway network for and between 333 cities in mainland China and extracted epidemiological variables and socioeconomic factors for each city. We calculated classic centrality measures for each city in the network and proposed two new measures (SumRatio and Multicenter Distance). We evaluated the correlation between the centrality measures and epidemiological features and conducted a spatial autoregression to quantify the impacts of road network and socioeconomic factors during the outbreak. The results showed that epidemics had more significant relationships with both our new measures than the classic ones. Higher population density, higher per person income, larger SumRatio and Multicenter Distance, more hospitals and college students, and lower per person GDP were associated with higher cumulative incidence. Higher population density and number of slaughtered pigs were found to advance epidemic arrival time. Higher population density, more colleges and slaughtered pigs, and lower Multicenter Distance were associated with longer epidemic duration. In conclusion, road transport and socioeconomic status had significant impacts and should be considered for the prevention and control of future pandemics.

Ecological and Evolutionary Processes Shaping Viral Genetic Diversity

The contemporary genomic diversity of viruses is a result of the continuous and dynamic interaction of past ecological and evolutionary processes. Thus, genome sequences of viruses can be a valuable source of information about these processes. In this review, we first describe the relevant processes shaping viral genomic variation, with a focus on the role of host–virus coevolution and its potential to give rise to eco-evolutionary feedback loops. We further give a brief overview of available methodology designed to extract information about these processes from genomic data. Short generation times and small genomes make viruses ideal model systems to study the joint effect of complex coevolutionary and eco-evolutionary interactions on genetic evolution. This complexity, together with the diverse array of lifetime and reproductive strategies in viruses ask for extensions of existing inference methods, for example by integrating multiple information sources. Such integration can broaden the applicability of genetic inference methods and thus further improve our understanding of the role viruses play in biological communities.

Causes and Consequences of Spatial Within-Host Viral Spread

The spread of viral pathogens both between and within hosts is inherently a spatial process. While the spatial aspects of viral spread at the epidemiological level have been increasingly well characterized, the spatial aspects of viral spread within infected hosts are still understudied. Here, with a focus on influenza A viruses (IAVs), we first review experimental studies that have shed light on the mechanisms and spatial dynamics of viral spread within hosts. These studies provide strong empirical evidence for highly localized IAV spread within hosts. Since mathematical and computational within-host models have been increasingly used to gain a quantitative understanding of observed viral dynamic patterns, we then review the (relatively few) computational modeling studies that have shed light on possible factors that structure the dynamics of spatial within-host IAV spread. These factors include the dispersal distance of virions, the localization of the immune response, and heterogeneity in host cell phenotypes across the respiratory tract. While informative, we find in these studies a striking absence of theoretical expectations of how spatial dynamics may impact the dynamics of viral populations. To mitigate this, we turn to the extensive ecological and evolutionary literature on range expansions to provide informed theoretical expectations. We find that factors such as the type of density dependence, the frequency of long-distance dispersal, specific life history characteristics, and the extent of spatial heterogeneity are critical factors affecting the speed of population spread and the genetic composition of spatially expanding populations. For each factor that we identified in the theoretical literature, we draw parallels to its analog in viral populations. We end by discussing current knowledge gaps related to the spatial component of within-host IAV spread and the potential for within-host spatial considerations to inform the development of disease control strategies.

Shifting patterns of seasonal influenza epidemics

Seasonal waves of influenza display a complex spatiotemporal pattern resulting from the interplay of biological, sociodemographic, and environmental factors. At country level many studies characterized the robust properties of annual epidemics, depicting a typical season. Here we analyzed season-by-season variability, introducing a clustering approach to assess the deviations from typical spreading patterns. The classification is performed on the similarity of temporal configurations of onset and peak times of regional epidemics, based on influenza-like-illness time-series in France from 1984 to 2014. We observed a larger variability in the onset compared to the peak. Two relevant classes of clusters emerge: groups of seasons sharing similar recurrent spreading patterns (clustered seasons) and single seasons displaying unique patterns (monoids). Recurrent patterns exhibit a more pronounced spatial signature than unique patterns. We assessed how seasons shift between these classes from onset to peak depending on epidemiological, environmental, and socio-demographic variables. We found that the spatial dynamics of influenza and its association with commuting, previously observed as a general property of French influenza epidemics, apply only to seasons exhibiting recurrent patterns. The proposed methodology is successful in providing new insights on influenza spread and can be applied to incidence time-series of different countries and different diseases.

Genotyping Toxoplasma gondii with the B1 Gene in Naturally Infected Sheep from an Endemic Region in the Pacific Coast of Mexico

Toxoplasma gondii is a protozoan parasite with a broad ecological valence, which has been detected in a wide range of hosts and landscapes. Although the genus is considered monospecific, in recent years it has been demonstrated to exhibit more genetic variability than previously known. In Mexico, there are few genotyping studies, which suggest that classical, autochthonous, and atypical strains are circulating. The goal of this study was to describe T. gondii genetic diversity in naturally infected sheep from Colima, Mexico. This is a good site to study ecological aspects of this parasite since it is located between the Nearctic and Neotropical ecozones and it includes domestic and wild risks for transmission. We analyzed 305 tissue samples of semicaptive sheep from six coastal and central zones of Colima and border zones of Michoacán. We used an 803 bp amplicon of the B1 gene to genotype T. gondii and seroprevalence was determined by ELISA. Indexes for genetic diversity and genetic differentiation were calculated and compared with reference strains from North America (NA) and South America (SA). Twenty-three tissue samples were positive for the B1 gene by PCR, which were sequenced. Crude prevalence was 24.4%. The genetic analysis showed 16 variable sites along the 803 bp region that grouped all sequences into 13 haplotypes in the phylogenetic tree. Bayesian and haplotype network analysis showed nine new B1-types, of which three were frequent and six had unique alleles. Comparisons among sequence sets revealed that the Mexican population had lower differentiation than SA and an intermediate genetic variability between South America and North America. The B1 gene analysis showed new T. gondii haplotypes in naturally infected sheep; therefore, this marker could be initially used in molecular screening studies to identify potentially virulent genotypes of this parasite using natural host samples directly.

Human mobility and the spatial transmission of influenza in the United States

Seasonal influenza epidemics offer unique opportunities to study the invasion and re-invasion waves of a pathogen in a partially immune population. Detailed patterns of spread remain elusive, however, due to lack of granular disease data. Here we model high-volume city-level medical claims data and human mobility proxies to explore the drivers of influenza spread in the US during 2002–2010. Although the speed and pathways of spread varied across seasons, seven of eight epidemics likely originated in the Southern US. Each epidemic was associated with 1–5 early long-range transmission events, half of which sparked onward transmission. Gravity model estimates indicate a sharp decay in influenza transmission with the distance between infectious and susceptible cities, consistent with spread dominated by work commutes rather than air traffic. Two early-onset seasons associated with antigenic novelty had particularly localized modes of spread, suggesting that novel strains may spread in a more localized fashion than previously anticipated.

The underlying mechanisms dictating the spatial spread of seasonal influenza remain poorly understood, in part because of the lack of spatially resolved disease data to quantify patterns of spread. In this paper, we address this issue by analyzing fine-grain insurance claims data on influenza-like-illnesses over eight seasons in ~300 locations throughout the United States. Using statistical methods, we found that seven of eight epidemics likely originated in the Southern US, that influenza spatial transmission is dominated by local traffic between cities, and that seasons marked by novel influenza virus circulation had a particularly radial, localized spatial structure. These findings are in stark contrast to prevailing theories of influenza spatial transmission that suggest that transmission is favored in low humidity environments and that spread is a dominated by air traffic between populous hubs.

Virus evolution and transmission in an ever more connected world

The frequency and global impact of infectious disease outbreaks, particularly those caused by emerging viruses, demonstrate the need for a better understanding of how spatial ecology and pathogen evolution jointly shape epidemic dynamics. Advances in computational techniques and the increasing availability of genetic and geospatial data are helping to address this problem, particularly when both information sources are combined. Here, we review research at the intersection of evolutionary biology, human geography and epidemiology that is working towards an integrated view of spatial incidence, host mobility and viral genetic diversity. We first discuss how empirical studies have combined viral spatial and genetic data, focusing particularly on the contribution of evolutionary analyses to epidemiology and disease control. Second, we explore the interplay between virus evolution and global dispersal in more depth for two pathogens: human influenza A virus and chikungunya virus. We discuss the opportunities for future research arising from new analyses of human transportation and trade networks, as well as the associated challenges in accessing and sharing relevant spatial and genetic data.

Genomic Analysis of the Emergence, Evolution, and Spread of Human Respiratory RNA Viruses

The emergence and reemergence of rapidly evolving RNA viruses-particularly those responsible for respiratory diseases, such as influenza viruses and coronaviruses-pose a significant threat to global health, including the potential of major pandemics. Importantly, recent advances in high-throughput genome sequencing enable researchers to reveal the genomic diversity of these viral pathogens at much lower cost and with much greater precision than they could before. In particular, the genome sequence data generated allow inferences to be made on the molecular basis of viral emergence, evolution, and spread in human populations in real time. In this review, we introduce recent computational methods that analyze viral genomic data, particularly in combination with metadata such as sampling time, geographic location, and virulence. We then outline the insights these analyses have provided into the fundamental patterns and processes of evolution and emergence in human respiratory RNA viruses, as well as the major challenges in such genomic analyses.

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

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

Influence of Average Income on Epidemics of Seasonal Influenza

Understanding the local factors influencing the transmission of communicable diseases is important to minimize social damage. The aim of this study was to investigate local factors influencing seasonal influenza epidemics in Aomori prefecture consisting of 6 regions, i.e., Seihoku, Chunan, and Tosei on the west side, and Sanpachi, Kamikita, and Shimokita on the east side. Four indices (epidemic onset, duration, scale, and steepness of epidemic curves) were defined, and their correlations with regional characteristics and meteorological factors were investigated. Data for influenza seasons from 2006-2007 to 2014-2015 were collected. The 2009-2010 season was excluded because of the pandemic of A (H1N1)pdm09. Average income was strongly correlated with epidemic onset, duration, and scale. The ratio of children aged ≤5 years to the total population was strongly correlated with epidemic duration and scale. Low temperature in January showed moderate correlation with epidemic duration and scale. Cluster analysis showed that 2 isolated regions, Seihoku and Chunan, belonged to the same cluster in the 4 indices of epidemic curves, and other 2 relatively urbanized regions formed another cluster in 3 of the 4 indices. This study highlights important local factors that influence seasonal influenza epidemics and may help in implementation of preventive measures.