Socio-demographic predictors of not having private dental insurance coverage: machine-learning algorithms may help identify the disadvantaged.

BACKGROUND: For accessing dental care in Canada, approximately 62% of the population has employment-based insurance, 6% have some publicly funded coverage, and 32% have to pay out-of pocket. Those with no insurance or public coverage find dental care more unaffordable compared to those with private insurance. To support the development of more comprehensive publicly funded dental care programs, it is important to understand the socio-demographic attributes of all those, who find dental care unaffordable. METHODS: This study is a secondary analysis of the data collected from Ontarians during the latest available cycle of the Canadian Community Health Survey (2017-18), a cross-sectional survey that collects information on health status, health care utilization, and health determinants for the Canadian population. First, bivariate analysis was conducted to determine the characteristics of Ontarians who lack dental insurance. Afterwards, we employed machine learning (ML) to analyze data and identify risk indicators for not having private dental insurance. Specifically, we trained several supervised ML models and utilized Shapley additive explanations (SHAP) to determine the relative feature importance for not having private dental insurance from the best ML model [the gradient boosting (GBM)]. RESULTS: Approximately one-third of Ontarians do not have private insurance coverage for dental care. Individuals with an income below $20,000, those unemployed or working part-time, seniors aged above 70, and those unable to afford to have their own housing are more at risk of not having private dental insurance, leading to financial barriers in accessing dental care. CONCLUSION: In the future, government-funded programs can incorporate these identified risk indicators when determining eligible populations for publicly funded dental programs. Understanding these attributes is critical for developing targeted and effective interventions, ensuring equitable access to dental care for Canadians.

Comparing the transmission potential from sequence and surveillance data of 2009 North American influenza pandemic waves.

Technological advancements in phylodynamic modeling coupled with the accessibility of real-time pathogen genetic data are increasingly important for understanding the infectious disease transmission dynamics. In this study, we compare the transmission potentials of North American influenza A(H1N1)pdm09 derived from sequence data to that derived from surveillance data. The impact of the choice of tree-priors, informative epidemiological priors, and evolutionary parameters on the transmission potential estimation is evaluated. North American Influenza A(H1N1)pdm09 hemagglutinin (HA) gene sequences are analyzed using the coalescent and birth-death tree prior models to estimate the basic reproduction number (R 0 ). Epidemiological priors gathered from published literature are used to simulate the birth-death skyline models. Path-sampling marginal likelihood estimation is conducted to assess model fit. A bibliographic search to gather surveillance-based R 0 values were consistently lower (mean ≤ 1.2) when estimated by coalescent models than by the birth-death models with informative priors on the duration of infectiousness (mean ≥ 1.3 to ≤2.88 days). The user-defined informative priors for use in the birth-death model shift the directionality of epidemiological and evolutionary parameters compared to non-informative estimates. While there was no certain impact of clock rate and tree height on the R 0 estimation, an opposite relationship was observed between coalescent and birth-death tree priors. There was no significant difference (p = 0.46) between the birth-death model and surveillance R 0 estimates. This study concludes that tree-prior methodological differences may have a substantial impact on the transmission potential estimation as well as the evolutionary parameters. The study also reports a consensus between the sequence-based R 0 estimation and surveillance-based R 0 estimates. Altogether, these outcomes shed light on the potential role of phylodynamic modeling to augment existing surveillance and epidemiological activities to better assess and respond to emerging infectious diseases.

Monkeypox Virus Detection in Different Clinical Specimen Types.

A global monkeypox outbreak began in May 2022. Limited data exist on specimen type performance in associated molecular diagnostics. Consequently, a diverse range of specimen sources were collected in the initial weeks of the outbreak in Ontario, Canada. Our clinical evaluation identified skin lesions as the optimal diagnostic specimen source.

Risk factors for severe COVID-19 differ by age for hospitalized adults.

Risk stratification for hospitalized adults with COVID-19 is essential to inform decisions about individual patients and allocation of resources. So far, risk models for severe COVID outcomes have included age but have not been optimized to best serve the needs of either older or younger adults. Models also need to be updated to reflect improvements in COVID-19 treatments. This retrospective study analyzed data from 6906 hospitalized adults with COVID-19 from a community health system across five states in the western United States. Risk models were developed to predict mechanical ventilation illness or death across one to 56 days of hospitalization, using clinical data available within the first hour after either admission with COVID-19 or a first positive SARS-CoV-2 test. For the seven-day interval, models for age ≥ 18 and < 50 years reached AUROC 0.81 (95% CI 0.71-0.91) and models for age ≥ 50 years reached AUROC 0.82 (95% CI 0.77-0.86). Models revealed differences in the statistical significance and relative predictive value of risk factors between older and younger patients including age, BMI, vital signs, and laboratory results. In addition, for hospitalized patients, sex and chronic comorbidities had lower predictive value than vital signs and laboratory results.

Multiple early factors anticipate post-acute COVID-19 sequelae.

Post-acute sequelae of COVID-19 (PASC) represent an emerging global crisis. However, quantifiable risk factors for PASC and their biological associations are poorly resolved. We executed a deep multi-omic, longitudinal investigation of 309 COVID-19 patients from initial diagnosis to convalescence (2-3 months later), integrated with clinical data and patient-reported symptoms. We resolved four PASC-anticipating risk factors at the time of initial COVID-19 diagnosis: type 2 diabetes, SARS-CoV-2 RNAemia, Epstein-Barr virus viremia, and specific auto-antibodies. In patients with gastrointestinal PASC, SARS-CoV-2-specific and CMV-specific CD8+ T cells exhibited unique dynamics during recovery from COVID-19. Analysis of symptom-associated immunological signatures revealed coordinated immunity polarization into four endotypes, exhibiting divergent acute severity and PASC. We find that immunological associations between PASC factors diminish over time, leading to distinct convalescent immune states. Detectability of most PASC factors at COVID-19 diagnosis emphasizes the importance of early disease measurements for understanding emergent chronic conditions and suggests PASC treatment strategies.

Integrated analysis of plasma and single immune cells uncovers metabolic changes in individuals with COVID-19.

A better understanding of the metabolic alterations in immune cells during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection may elucidate the wide diversity of clinical symptoms experienced by individuals with coronavirus disease 2019 (COVID-19). Here, we report the metabolic changes associated with the peripheral immune response of 198 individuals with COVID-19 through an integrated analysis of plasma metabolite and protein levels as well as single-cell multiomics analyses from serial blood draws collected during the first week after clinical diagnosis. We document the emergence of rare but metabolically dominant T cell subpopulations and find that increasing disease severity correlates with a bifurcation of monocytes into two metabolically distinct subsets. This integrated analysis reveals a robust interplay between plasma metabolites and cell-type-specific metabolic reprogramming networks that is associated with disease severity and could predict survival.

Angiotensin-Converting Enzyme (ACE) Inhibitors May Moderate COVID-19 Hyperinflammatory Response: An Observational Study with Deep Immunophenotyping.

Background: Angiotensin-converting enzyme inhibitors (ACEi) and angiotensin-II receptor blockers (ARB), the most commonly prescribed antihypertensive medications, counter renin-angiotensin-aldosterone system (RAAS) activation via induction of angiotensin-converting enzyme 2 (ACE2) expression. Considering that ACE2 is the functional receptor for SARS-CoV-2 entry into host cells, the association of ACEi and ARB with COVID-19 outcomes needs thorough evaluation. Methods: We conducted retrospective analyses using both unmatched and propensity score (PS)-matched cohorts on electronic health records (EHRs) to assess the impact of RAAS inhibitors on the risk of receiving invasive mechanical ventilation (IMV) and 30-day mortality among hospitalized COVID-19 patients. Additionally, we investigated the immune cell gene expression profiles of hospitalized COVID-19 patients with prior use of antihypertensive treatments from an observational prospective cohort. Results: The retrospective analysis revealed that there was no increased risk associated with either ACEi or ARB use. In fact, the use of ACEi showed decreased risk for mortality. Survival analyses using PS-matched cohorts suggested no significant relationship between RAAS inhibitors with a hospital stay and in-hospital mortality compared to non-RAAS medications and patients not on antihypertensive medications. From the analysis of gene expression profiles, we observed a noticeable up-regulation in the expression of 1L1R2 (an anti-inflammatory receptor) and RETN (an immunosuppressive marker) genes in monocytes among prior users of ACE inhibitors. Conclusion: Overall, the findings do not support the discontinuation of ACEi or ARB treatment and suggest that ACEi may moderate the COVID-19 hyperinflammatory response.

Angiotensin II receptor I auto-antibodies following SARS-CoV-2 infection.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is associated with endothelial activation and coagulopathy, which may be related to pre-existing or infection-induced pro-thrombotic autoantibodies such as those targeting angiotensin II type I receptor (AT1R-Ab). METHODS: We compared prevalence and levels of AT1R-Ab in COVID-19 cases with mild or severe disease to age and sex matched negative controls utilizing multivariate logistic and quantile regression adjusted for comorbidities including hypertension, diabetes, and heart disease. RESULTS: There were trends toward increased prevalence (50% vs. 33%, p = 0.1) and level of AT1R-Ab (median 9.8 vs. 6.1 U/mL, p = 0.06) in all cases versus controls. When considered by COVID-19 disease severity, there was a trend toward increased prevalence of AT1R-Ab (55% vs. 31%, p = 0.07), as well as significantly higher AT1R-Ab levels (median 10.7 vs. 5.9 U/mL, p = 0.03) amongst individuals with mild COVID-19 versus matched controls. In contrast, the prevalence (42% vs. 37%, p = 0.9) and level (both medians 6.7 U/mL, p = 0.9) of AT1R-Ab amongst those with severe COVID-19 did not differ from matched controls. CONCLUSIONS: These findings support an association between COVID-19 and AT1R-Ab, emphasizing that vascular pathology may be present in individuals with mild COVID-19 as well as those with severe disease.

Multi-Omics Resolves a Sharp Disease-State Shift between Mild and Moderate COVID-19.

We present an integrated analysis of the clinical measurements, immune cells, and plasma multi-omics of 139 COVID-19 patients representing all levels of disease severity, from serial blood draws collected during the first week of infection following diagnosis. We identify a major shift between mild and moderate disease, at which point elevated inflammatory signaling is accompanied by the loss of specific classes of metabolites and metabolic processes. Within this stressed plasma environment at moderate disease, multiple unusual immune cell phenotypes emerge and amplify with increasing disease severity. We condensed over 120,000 immune features into a single axis to capture how different immune cell classes coordinate in response to SARS-CoV-2. This immune-response axis independently aligns with the major plasma composition changes, with clinical metrics of blood clotting, and with the sharp transition between mild and moderate disease. This study suggests that moderate disease may provide the most effective setting for therapeutic intervention.

Multiomic Immunophenotyping of COVID-19 Patients Reveals Early Infection Trajectories.

Host immune responses play central roles in controlling SARS-CoV2 infection, yet remain incompletely characterized and understood. Here, we present a comprehensive immune response map spanning 454 proteins and 847 metabolites in plasma integrated with single-cell multi-omic assays of PBMCs in which whole transcriptome, 192 surface proteins, and T and B cell receptor sequence were co-analyzed within the context of clinical measures from 50 COVID19 patient samples. Our study reveals novel cellular subpopulations, such as proliferative exhausted CD8 + and CD4 + T cells, and cytotoxic CD4 + T cells, that may be features of severe COVID-19 infection. We condensed over 1 million immune features into a single immune response axis that independently aligns with many clinical features and is also strongly associated with disease severity. Our study represents an important resource towards understanding the heterogeneous immune responses of COVID-19 patients and may provide key information for informing therapeutic development.

Agricultural and geographic factors shaped the North American 2015 highly pathogenic avian influenza H5N2 outbreak.

The 2014-2015 highly pathogenic avian influenza (HPAI) H5NX outbreak represents the largest and most expensive HPAI outbreak in the United States to date. Despite extensive traditional and molecular epidemiological studies, factors associated with the spread of HPAI among midwestern poultry premises remain unclear. To better understand the dynamics of this outbreak, 182 full genome HPAI H5N2 sequences isolated from commercial layer chicken and turkey production premises were analyzed using evolutionary models able to accommodate epidemiological and geographic information. Epidemiological compartmental models embedded in a phylogenetic framework provided evidence that poultry type acted as a barrier to the transmission of virus among midwestern poultry farms. Furthermore, after initial introduction, the propagation of HPAI cases was self-sustainable within the commercial poultry industries. Discrete trait diffusion models indicated that within state viral transitions occurred more frequently than inter-state transitions. Distance and sample size were very strongly supported as associated with viral transition between county groups (Bayes Factor > 30.0). Together these findings indicate that the different types of midwestern poultry industries were not a single homogenous population, but rather, the outbreak was shaped by poultry industries and geographic factors.

Computational Approaches and Challenges to Developing Universal Influenza Vaccines.

The traditional design of effective vaccines for rapidly-evolving pathogens, such as influenza A virus, has failed to provide broad spectrum and long-lasting protection. With low cost whole genome sequencing technology and powerful computing capabilities, novel computational approaches have demonstrated the potential to facilitate the design of a universal influenza vaccine. However, few studies have integrated computational optimization in the design and discovery of new vaccines. Understanding the potential of computational vaccine design is necessary before these approaches can be implemented on a broad scale. This review summarizes some promising computational approaches under current development, including computationally optimized broadly reactive antigens with consensus sequences, phylogenetic model-based ancestral sequence reconstruction, and immunomics to compute conserved cross-reactive T-cell epitopes. Interactions between virus-host-environment determine the evolvability of the influenza population. We propose that with the development of novel technologies that allow the integration of data sources such as protein structural modeling, host antibody repertoire analysis and advanced phylodynamic modeling, computational approaches will be crucial for the development of a long-lasting universal influenza vaccine. Taken together, computational approaches are powerful and promising tools for the development of a universal influenza vaccine with durable and broad protection.

Lineage-specific epitope profiles for HPAI H5 pre-pandemic vaccine selection and evaluation.

BACKGROUND: Multiple highly pathogenic avian influenza (HPAI) H5 viruses continue to co-circulate. This has complicated pandemic preparedness and confounded effective vaccine candidate selection and evaluation. OBJECTIVES: In this study, we aimed to predict and map the diversity of CD8+ T-cell epitopes among H5 hemagglutinin (HA) gene lineages to estimate CD8+ T-cell immunity in humans induced by vaccine candidates. METHODS: A dataset consisting of 1125 H5 HA sequences collected between 1996 and 2017 from avian and humans was assembled for phylogenetic and lineage-specific epitope analyses. Conserved epitopes were predicted from WHO-endorsed vaccine candidates and representative clade-defining strains by pairwise comparison with Immune Epitope Database (IEDB). The distribution of predicted epitopes was mapped to each HPAI H5 lineage. We assume that high similarity and conservancy of predicted epitopes from vaccine candidates among all circulating HPAI H5 lineages is correlated with high immunity. RESULTS: A total of 49 conserved CD8+ T-cell epitopes were predicted at 28 different amino acid positions of the HA protein. Mapping these epitopes to the phylogenetic tree allowed us to develop epitope profiles, or "fingerprints," for each HPAI H5 lineage. Vaccine epitope percentage analyses showed some epitope profiles were highly conserved for all H5 isolates and may be valuable for universal vaccine design. However, the positions with low coverage may explain why the vaccine candidates do not always function well. CONCLUSIONS: These findings demonstrate that our analytical approach to evaluate conserved CD8+ T-cell epitope prediction in a phylogenetic framework may provide important insights for computational design of vaccine selection and future epitope-based design.

Global Distribution and Evolutionary History of Enterovirus D68, with Emphasis on the 2014 Outbreak in Ontario, Canada.

Despite its first appearance in 1962, human enterovirus D68 (EV-D68) has been recognized as an emerging respiratory pathogen in the last decade when it caused outbreaks and clusters in several countries including Japan, the Philippines, and the Netherlands. The most recent and largest outbreak of EV-D68 associated with severe respiratory illness took place in North America between August 2014 and January 2015. Between September 1 and October 31 2014, EV-D68 infection was laboratory confirmed among 153/907 (16.9%) persons tested for the virus in Ontario, Canada, using real time RT-PCR and subsequent genotyping by sequencing of partial VP1 gene. In order to understand the evolutionary history of the 2014 North American EV-D68 outbreak, we conducted phylogenetic and phylodynamic analyses using available partial VP1 genes (n = 469) and NCBI available whole genome sequences (WGS) (n = 38). The global EV-D68 phylogenetic tree (n = 469) reconfirms the divergence of three distinct clades A, B, and C from the prototype EV-D68 Fermon strain as previously documented. Two sub-clades (B1 and B2) were identified, with most 2014 EV-D68 outbreak strains belonging to sub-cluster B2b2 (one of the two emerging clusters within sub-clade B2), with two signature substitutions T650A and M700V in BC and DE loops of VP1 gene, respectively. The close homology between WGS of strains from Ontario (n = 2) and USA (n = 21) in the recent EV-D68 outbreak suggests genetic relatedness and also a common source for the outbreak. The time of most recent common ancestor of EV-D68 and the 2014 EV-D68 outbreak strain suggest that the viruses possibly emerged during 1960-1961 and 2012-2013, respectively. We observed lower mean evolutionary rates of global EV-D68 using WGS data than estimated with partial VP1 gene sequences. Based on WGS data, the estimated mean rate of evolution of the EV-D68 B2b cluster was 9.75 × 10-3 substitutions/site/year (95% BCI 4.11 × 10-3 to 16 × 10-3).

Population structure and drug resistance patterns of emerging non-PCV-13 Streptococcus pneumoniae serotypes 22F, 15A, and 8 isolated from adults in Ontario, Canada.

The introduction of pneumococcal conjugate vaccines has led to the emergence of non-vaccine serotypes, which contributed to invasive pneumococcal disease in Canada and worldwide. A significant increase in the prevalence of non-13-valent pneumococcal conjugate vaccine (PCV-13)-included serotypes 22F, 15A, and 8 was observed from 2009 to 2013 in Ontario (all p values<0.01). In this study, whole genome sequencing was conducted on the 25 isolates of serotype 22F, seven of 15A and 10 of 8 to investigate the population structure and antibiotic resistance. All seven serotype 15A isolates were found to be multidrug resistant. From whole genome analysis, we observed recombination events among serotypes 22F, 15A and 8 populations. Serotype 22F (ST433) has emerged into two sub-populations, with 28% (7/25) exhibiting recombination events, and five also acquiring macrolide resistance as a result of recombination. This study enhances the knowledge on the molecular evolution of emerging non-PCV-13 vaccine serotype 22F, including acquisition of resistance genes through recombination events. It underpins the importance of whole genome sequencing in studying Streptococcus pneumoniae population structures and dynamics, and its utility in molecular surveillance.

Genetic diversity and evolutionary insights of respiratory syncytial virus A ON1 genotype: global and local transmission dynamics.

Human respiratory syncytial virus (RSV) A ON1 genotype, first detected in 2010 in Ontario, Canada, has been documented in 21 countries to date. This study investigated persistence and transmission dynamics of ON1 by grouping 406 randomly selected RSV-positive specimens submitted to Public Health Ontario from August 2011 to August 2012; RSV-A-positive specimens were genotyped. We identified 370 RSV-A (181 NA1, 135 NA2, 51 ON1 3 GA5) and 36 RSV-B positive specimens. We aligned time-stamped second hypervariable region (330 bp) of G-gene sequence data (global, n = 483; and Ontario, n = 60) to evaluate transmission dynamics. Global data suggests that the most recent common ancestor of ON1 emerged during the 2008-2009 season. Mean evolutionary rate of the global ON1 was 4.10 × 10(-3) substitutions/site/year (95% BCI 3.1-5.0 × 10(-3)), not significantly different to that of Ontario ON1. The estimated mean reproductive number (R0 = ∼ 1.01) from global and Ontario sequences showed no significant difference and implies stability among global RSV-A ON1. This study suggests that local epidemics exhibit similar underlying evolutionary and epidemiological dynamics to that of the persistent global RSV-A ON1 population. These findings underscore the importance of continual molecular surveillance of RSV in order to gain a better understanding of epidemics.

Human metapneumovirus prevalence and molecular epidemiology in respiratory outbreaks in Ontario, Canada.

Human metapneumovirus (hMPV) has been identified previously as a cause of respiratory outbreaks in adults, including the elderly. The objective of this study was to document respiratory outbreaks that were caused by hMPV in Ontario, Canada and to identify the various circulating genotypes during April 2009-February 2012. The majority of the outbreaks that were part of this study were in adults (>65 years). Total nucleic acid extraction was done on 123 residual anonymized clinical specimens from 51 different respiratory outbreaks. Specimens were subjected to PCR amplification and Sanger sequencing targeting the F and G genes of hMPV. Phylogenetic analysis was performed to identify genotypes. HMPV accounted for 195 (8.5%) of 2,292 respiratory outbreaks. Genotype A2b was most prevalent, detected in 28 (54.9%) of 51 typed hMPV-positive outbreaks. The genotype A2b2 that was described recently was also identified. In earlier reports, subtype A1 was reported in Canada which was absent in the specimens typed in this study. This shift in genotype may be significant in terms of disease severity, and for any future vaccine considerations. Regular testing for hMPV should be done as part of outbreak investigation.

The human immune system recognizes neopeptides derived from mitochondrial DNA deletions.

Mutations in mitochondrial (mt) DNA accumulate with age and can result in the generation of neopeptides. Immune surveillance of such neopeptides may allow suboptimal mitochondria to be eliminated, thereby avoiding mt-related diseases, but may also contribute to autoimmunity in susceptible individuals. To date, the direct recognition of neo-mtpeptides by the adaptive immune system has not been demonstrated. In this study we used bioinformatics approaches to predict MHC binding of neopeptides identified from known deletions in mtDNA. Six such peptides were confirmed experimentally to bind to HLA-A*02. Pre-existing human CD4(+) and CD8(+) T cells from healthy donors were shown to recognize and respond to these neopeptides. One remarkably promiscuous immunodominant peptide (P9) could be presented by diverse MHC molecules to CD4(+) and/or CD8(+) T cells from 75% of the healthy donors tested. The common soil microbe, Bacillus pumilus, encodes a 9-mer that differs by one amino acid from P9. Similarly, the ATP synthase F0 subunit 6 from normal human mitochondria encodes a 9-mer with a single amino acid difference from P9 with 89% homology to P9. T cells expanded from human PBMCs using the B. pumilus or self-mt peptide bound to P9/HLA-A2 tetramers, arguing for cross-reactivity between T cells with specificity for self and foreign homologs of the altered mt peptide. These findings provide proof of principal that the immune system can recognize peptides arising from spontaneous somatic mutations and that such responses might be primed by foreign peptides and/or be cross-reactive with self.

Preexisting CD4+ T-cell immunity in human population to avian influenza H7N9 virus: whole proteome-wide immunoinformatics analyses.

In 2013, a novel avian influenza H7N9 virus was identified in human in China. The antigenically distinct H7N9 surface glycoproteins raised concerns about lack of cross-protective neutralizing antibodies. Epitope-specific preexisting T-cell immunity was one of the protective mechanisms in pandemic 2009 H1N1 even in the absence of cross-protective antibodies. Hence, the assessment of preexisting CD4+ T-cell immunity to conserved epitopes shared between H7N9 and human influenza A viruses (IAV) is critical. A comparative whole proteome-wide immunoinformatics analysis was performed to predict the CD4+ T-cell epitopes that are commonly conserved within the proteome of H7N9 in reference to IAV subtypes (H1N1, H2N2, and H3N2). The CD4+ T-cell epitopes that are commonly conserved (∼ 556) were further screened against the Immune Epitope Database (IEDB) to validate their immunogenic potential. This analysis revealed that 45.5% (253 of 556) epitopes are experimentally proven to induce CD4+ T-cell memory responses. In addition, we also found that 23.3% of CD4+ T-cell epitopes have ≥ 90% of sequence homology with experimentally defined CD8+ T-cell epitopes. We also conducted the population coverage analysis across different ethnicities using commonly conserved CD4+ T-cell epitopes and corresponding HLA-DRB1 alleles. Interestingly, the indigenous populations from Canada, United States, Mexico and Australia exhibited low coverage (28.65% to 45.62%) when compared with other ethnicities (57.77% to 94.84%). In summary, the present analysis demonstrate an evidence on the likely presence of preexisting T-cell immunity in human population and also shed light to understand the potential risk of H7N9 virus among indigenous populations, given their high susceptibility during previous pandemic influenza events. This information is crucial for public health policy, in targeting priority groups for immunization programs.

Estimated effects of projected climate change on the basic reproductive number of the Lyme disease vector Ixodes scapularis.

BACKGROUND: The extent to which climate change may affect human health by increasing risk from vector-borne diseases has been under considerable debate. OBJECTIVES: We quantified potential effects of future climate change on the basic reproduction number (R0) of the tick vector of Lyme disease, Ixodes scapularis, and explored their importance for Lyme disease risk, and for vector-borne diseases in general. METHODS: We applied observed temperature data for North America and projected temperatures using regional climate models to drive an I. scapularis population model to hindcast recent, and project future, effects of climate warming on R0. Modeled R0 increases were compared with R0 ranges for pathogens and parasites associated with variations in key ecological and epidemiological factors (obtained by literature review) to assess their epidemiological importance. RESULTS: R0 for I. scapularis in North America increased during the years 1971-2010 in spatio-temporal patterns consistent with observations. Increased temperatures due to projected climate change increased R0 by factors (2-5 times in Canada and 1.5-2 times in the United States), comparable to observed ranges of R0 for pathogens and parasites due to variations in strains, geographic locations, epidemics, host and vector densities, and control efforts. CONCLUSIONS: Climate warming may have co-driven the emergence of Lyme disease in northeastern North America, and in the future may drive substantial disease spread into new geographic regions and increase tick-borne disease risk where climate is currently suitable. Our findings highlight the potential for climate change to have profound effects on vectors and vector-borne diseases, and the need to refocus efforts to understand these effects.