Inferring Enterovirus D68 Transmission Dynamics from the Genomic Data of Two 2022 North American outbreaks

Inferring Enterovirus D68 Transmission Dynamics from the Genomic Data of Two 2022 North American outbreaks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Inferring Enterovirus D68 Transmission Dynamics from the Genomic Data of Two 2022 North American outbreaks Martin Grunnill, Alireza Eshaghi, Lambodhar Damodaran, Sandeep Nagra, and 17 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4362075/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Enterovirus D68 (EV-D68) has emerged as a significant cause of acute respiratory illness in children globally, notably following its extensive outbreak in North America in 2014. A recent outbreak of EV-D68 was observed in Ontario, Canada, from August to October 2022. Our phylogenetic analysis revealed a notable genetic similarity between the Ontario outbreak and a concurrent outbreak in Maryland, USA. Utilizing Bayesian phylodynamic modeling on whole genome sequences (WGS) from both outbreaks, we determined the median peak time-varying reproduction number (R t ) to be 2.70 (95% HPD 1.76, 4.08) in Ontario and 2.10 (95% HPD 1.41, 3.17) in Maryland. The R t trends in Ontario closely matched those derived via EpiEstim using reported case numbers. Our study also provides new insights into the median infection duration of EV-D68, estimated at 7.94 days (95% HPD 4.55, 12.8) in Ontario and 10.8 days (95% HPD 5.85, 18.6) in Maryland, addressing the gap in the existing literature surrounding EV-D68’s infection period. We observed that the estimated Time since the Most Recent Common Ancestor (TMRCA) and the epidemic's origin coincided with the easing of COVID-19 related social contact restrictions in both areas. This suggests that the relaxation of non-pharmaceutical interventions, initially implemented to control COVID-19, may have inadvertently facilitated the spread of EV-D68. These findings underscore the effectiveness of phylodynamic methods in public health, demonstrating their broad application from local to global scales and underscoring the critical role of pathogen genomic data in enhancing public health surveillance and outbreak characterization. Biological sciences/Microbiology/Virology/Viral epidemiology Biological sciences/Microbiology/Virology/Viral evolution Health sciences/Medical research/Epidemiology Enterovirus-D68 Whole Genome Sequence Data Case Counts Phylodynamics Outbreak Potential Transmission Dynamics Reproduction Number Infection Duration Infection Period Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Phylodynamic methods use pathogen genetic sequences to construct phylogenies to study the diversification of pathogens at different spatio-temporal scales, while inferring key epidemic patterns of transmission between locations 16 – 18 . Hodcroft et al., (2022) 12 reported key aspects of EV-D68 antigenic evolution, showing that age structure within populations has important implications for the diversification of surface proteins and host-specificity of lineages. Other phylodynamic analyses of EV-D68 have studied the relatedness between major epidemics and global circulation 11 , 19 , 20 . Non-polio enteroviruses like EV-D68 are not nationally notifiable infections in North America. As such, case documentation is low which leads to a gap in our understanding of the transmission dynamics of these viruses. For instance, information on the infection period of EV-D68 is limited. Most enterovirus infections are found to shed from the upper respiratory tract over 1 to 3 weeks 21 . A previous epidemiological modelling study of EV-D68 used an infection period of 7 days 22 , which was derived from poliovirus 23 , 24 . Tambyah et al., (2019) 25 described EV-D68 as having 1 to 5 days of incubation period and an infectious period from 1 day before to 5 days post symptom onset but give no reference. Mild symptoms of a median duration of 6 days (range 3 to 10 days) were observed during an EV-D68 outbreak at an elder care facility 26 . In this study, we aim to address knowledge gaps on EV-D68’s transmission dynamics. We investigate the evolutionary history and relatedness of EV-D68 in Ontario and on a global scale, with a specific focus on the 2022 outbreak. We provide a contextual perspective by examining the outbreak’s geographical patterns within the B3 sub-clade of EV-D68 viruses. In particular, we find a high degree of genetic relatedness between samples from the 2022 outbreak, in Ontario, Canada 6 and in Maryland, United States 7 , 9 . We investigate the epidemic transmission (via time-varying reproduction number (R t ) estimation) through phylodynamic modelling of these two outbreaks, while shedding light on the infection duration of EV-D68 viruses. With EV-D68 reporting dates being available for the Ontario outbreak, we compare R t as estimated via phylodynamic methods to more conventional methods (analyzing case incidence data with EpiEstim) 27 . We present epidemiological parameters derived from genome sequences, which can offer actionable information for public health practitioners. Methods Specimen Collection and Whole Genome Sequencing of 2022 EV-D68 in Ontario Specimens submitted to Public Health Ontario Laboratory (PHOL) between July 31 and October 30, 2022, were collected from individuals with respiratory symptoms across various healthcare facilities in Ontario as part of routine care. EV-D68 was identified in 60.1% (n = 238) of randomly selected enterovirus-positive specimens (n = 396), with a predominant presence in nasal or nasopharyngeal samples. The highest number of EV-D68 positive test results was found among children less than 5 years of age. None of these cases presented with AFM. Additionally, whole genome sequencing was conducted on 36.5% (n = 87) of the randomly selected EV-D68 positive specimens 6 . EV-D68 WGSs were retrieved from the National Center for Biotechnology Information (NCBI) from which a consensus sequence was created. The consensus sequence used for designing three primer pairs with an overlap of ~ 600 bp spanning the entire genome ( Table S1 ). Total RNA was extracted using the NucliSENS EMAG system following manufacturer’s instructions (bioMérieux Canada Inc, St-Laurent, Quebec, Canada) and reverse transcribed into cDNA using LunaScript® RT SuperMix Kit (cat# M3010, New England BioLabs, Ipswich, MA, USA). The synthesized cDNAs were used as templates for amplification of 3 long overlapping fragments along the genome. Each PCR reaction of 25 µl included the following: 5 µl of Q5 Hot Start buffer (New England Biolabs, Ipswich, MA), 0.5 µl of 10 mM dNTP, 0.5 µl of Q5 High-Fidelity DNA Polymerase, 1 µL of primer mix (10µM), 2.5µL of template DNA, and 15.5 µL of PCR grade water. The following thermal cycling conditions were used on an ABI SimpliAmp thermocycler: initial denaturation at 98°C for 2 min, followed by 45 cycles at 98°C for 10 seconds and 65°C for 1 min and 72°C for 5 min followed by a final extension at 72°C for 5 min. The presence of each PCR product was confirmed by electrophoresis on 1% agarose gel. Equimolar amounts of each PCR product from the three reactions were pooled and cleaned with AMPure XP beads (0.5 ratio) for Illumina library preparation. Paired-end libraries for the MiniSeq platform were generated using Nextera XT DNA Library Prep Kit (Illumina) and subsequently purified using Agencourt AMPure XP beads (Beckman Coulter). The quality and size of prepared libraries were measured on the Agilent 4200 Tape Station using a High Sensitivity D1000 ScreenTape and reagent (HSD1000). Pooled normalized specimens, at a final concentration of 1.2 pM, were loaded onto a MiniSeq High Output Reagent Kit (300-cycles) and sequenced on an Illumina MiniSeq. For sequence analyses, FASTQ files were imported into CLC Genomics Workbench version 8.0.1 (CLC bio, Germantown, MD, USA). Reads were trimmed and mapped to the reference EV-D68 genome NY328 (GenBank: KP745766.1). Sequences were annotated using VAPiD v1.6.7 28 prior to their submission to GenBank. Retrieving publicly available EV-D68 Sequence data To perform phylogenetic analyses, additional EV-D68 sequences were obtained from the NCBI virus database at the end of May 2023. For Whole Genome Sequences (WGS) the following filters were applied to remove sequences: without sample collection date, sequences < 5000 bp in length and sequences where the proportion of nucleotides unassigned was over 0.05. Along with the WGS data produced from the Ontario 2022 outbreak (n = 87), a global dataset of 1134 EV-D68 WGSs was curated. Phylodynamic Analysis of the 2022 EV-D68 Outbreaks in Ontario and Maryland Using Genome Sequence Data Sequence Analysis and Phylogenetic Construction The Nextstrain Augur v22.0.2 29 pipeline was used to align the WGS (n = 1134, length > 5000 bp) data via MAFFT v7.505 30 , build a maximum likelihood (ML) phylogenetic tree via IQTree v2.0 31 , and refine the tree and infer node ancestry via TreeTime v0.10.1 32 . Auspice v2.37.1 29 was used for visualization of Augur outputs. The curated 2022 outbreak WGS datasets from Ontario (ON-2022, n = 87) and Maryland (MD-2022, n = 74) were utilized in building phylodynamic models. TempEst v 1.5.1 33 was employed to check that the temporal signals in ON-2022 and MD-2022 datasets are strong enough to allow phylodynamic analyses. Both ML trees derived from the ON-2022 and the MD-2022 datasets demonstrate a strong association between genetic distances and sampling dates. Genome-based Epidemiological Modelling using Bayesian Phylodynamics Bayesian phylodynamic analyses were performed on the curated ON-2022 (n = 87) and MD-2022 (n = 74) EV-D68 WGS datasets. These datasets were analyzed using BEAST v2.7.5 34 . Birth-Death Skyline Serial (BDSS using BDSKY v1.5.0 35 ) models were fitted to each dataset separately. We used an HKY85 site substitution model with four gamma rate categories to estimate the evolutionary rate and an optimal relaxed molecular clock model 36 that assumes heterogeneous substitution rates across phylogenetic branches, with an initial mean clock rate of 0.003 11 . Considering the possible infection periods put forward for EV-D68 21–23,25,26 , we fitted all BDSS models using a prior for the infection period with a mean of 7 days and a wide standard deviation so as to cover 3 to 21 days ( Figure S1 ). Birth-Death models do not estimate rate of reproductive maturation. Therefore, the model assumes patients immediately become infectious upon infection and remain infectious until being removed, i.e. there is no latent or exposed period 37 . The mean infection period (δ −1 ) was inverted to become the death rate or rate of becoming uninfectious (δ, also called the recovery rate) and converted to years (i.e. δ = 1/7 days = 52 year − 1 ). To produce a gamma distributed prior 38 for δ, we used formulas \(shape=\frac{{mean}^{2}}{variance}=12.018\) and \(scale=\frac{variance}{mean}=4.3269\) with a standard deviation of 15 year −1 . All other parameter priors are listed in Table S2. Three additional independent runs were performed for each model. The performance of these independent runs was evaluated using Tracer v1.7.2 39 , checking the convergence of parameter, posterior, and likelihood values, along with screening individual runs ensuring effective sample sizes (ESS) > 200 40 for all parameters. We repeated the analyses until we obtained at least three model runs meeting the above convergence criteria. Log and tree files were then combined using Log Combiner v2.7.5 40 . The bdskytools package in R (available at: https://github.com/laduplessis/bdskytools ) was then used to produce skyplot figures of R t and KDE plots of epidemic origin from the combined log files. The Python v3.10 package Seaborn v0.12.2 was used to produce box-violin plots of other parameter posterior distributions. Mathematical Analysis of 2022 EV-D68 Outbreak in Ontario Using Case Counts Data The package EpiEstim v 2.2-4 27 in R v4.1.2 was employed to calculate the time varying reproduction number (R t ) from the time-series data of laboratory-confirmed positive cases of EV-D68 in Ontario during 2022. Due to the challenge in obtaining the serial interval for EV-D68, a mean serial interval of 3.7 days with a standard deviation (SD) of 2.6 days, as observed in the related pathogen EV-71 41 , was used for R t estimation via EpiEstim 25 . Considering this a sensitivity analyses was performed on the EpiEstim analyses assuming a mean serial intervals of 2 and 7 days, but keeping the same SD. Statistical Analysis, Code Availability and GenBank Accession Numbers The code developed in this study and the BEAUTi-created xml files are available on GitHub at Grunnill-Duvvuri-co-publications/Transmission-dynamics-inferred-from-Enterovirus-D68-genomic-data-from-2022-North-American-outbreaks (github.com). All genome sequences of the 2022 Ontario EV-D68 outbreak isolates obtained in this study were submitted to GenBank under the accession numbers PP474817 to PP474903. The 2022 Maryland EV-D68 genome sequences were obtained from GenBank (accession numbers OP321139-OP321154, OP389245-OP389246, OP572035-OP572095) 7 . Results Phylogenetics of Ontario 2022 EV-D68 in a global epidemic context The Ontario 2022 EV-D68 isolates cluster with concurrent specimens from Maryland 2022 7 along with a few concurrent sequences from Sweden and France (Fig. 1 ). All these isolates are of the B3 sub-clade and diversify from internal node X in Fig. 1 , which is close to a cluster of US 2018 isolates. The majority of the Ontario 2022 isolates form a sub-grouping with isolates from the Maryland 2022 outbreak and a single Swedish 2022 isolate. This sub-grouping diversifies from internal node Y in Fig. 1 , which is close to Australian 2019 (early) isolates. A single isolate from Ontario 2022, diversifying from the internal node Z as shown in Fig. 1 , forms a sub-grouping with sequences from Maryland 2022, Sweden 2022, France 2021–2022 and other parts of the US in 2021. Additionally, this branch, originating from the internal node that clusters with isolates from a late 2019 to early 2020 outbreak in the Netherlands. Phylodynamic Analysis of the 2022 EV-D68 Outbreaks in Ontario and Maryland Using Genome Sequence Data Inference of Evolutionary Parameters: Substitution Rate and Time since Most Recent Common Ancestor (TMRCA) There are slight differences in the estimated median substitution rates between the BDSS models fitted to different datasets. The dataset ON-2022 has a median estimate of 0.0148 substitutions per site per year (95% Highest Posterior Density (HPD) 0.0112, 0.0185) and the MD-2022 dataset a median estimate of 0.0113 substitutions per site per year (95% HPD 0.00778, 0.0154). There is a higher coefficient of variation (median) of substitution rate for the MD-2022, 0.879 (95% HPD 0.706, 0.11) compared to ON-2022’s 0.655 (95% 0.499, 0.838 HPD) (Fig. 2 A), however the 95% HPD excludes 0 for both datasets, indicating strong support for a relaxed clock model 40 . The median TMRCA estimate for the ON-2022 dataset was February 16, 2022, with 95% HPD interval from December 26, 2021 to March 31, 2022. For the MD-2022 dataset, the median TMRCA estimate was July 11, 2021, with a 95% HPD interval from April 3, 2021 to November 3, 2021. (Fig. 2 B). Inference of Epidemiological Parameters: Infection Period, Epidemic Origin and Time-varying Reproduction number (R t ) The estimated median duration of the infection period (in days) were 7.94, with a 95% HPD interval ranging from 4.55 to 12.8 for the Ontario outbreak, and 10.8 with 95% HPD 5.85, 18.6 for the Maryland outbreak (Fig. 3 A). The median epidemic origin estimate was February 7, 2022 (95% HPD interval, December 21, 2021, April 20, 2022) for the Ontario EV-D68 outbreak (dataset, ON-2022). The Maryland EV-D68 outbreak (MD-2022 dataset) has a median epidemic origin estimate, June 8, 2021 (95% HPD interval, March 9, 2021, September 14, 2021) (Fig. 3 B). It is interesting to note that the estimates of TMRCA (Fig. 2 A and Fig. 2 B ) of both epidemics overlapped with these origin estimates. BDSS models estimate a rise in the time-varying reproduction number (R t ) leading to a plateaued peak occurring in the summer of 2022 (Fig. 3 C), which models fitted to the Ontario dataset (ON-2022) having a higher and later but shorted peak in R t compared to models fitted to the Maryland dataset (MD-2022). The median R t values for these peaks are 2.70 with 95% HPD 1.76, 4.08 for the Ontario outbreak and 2.10 with 95% HPD 1.41, 3.17 for the Maryland outbreak. Mathematical Analysis of 2022 EV-D68 Outbreak in Ontario Using Case Counts Data Figure 4 shows that R t estimated via case counts has a comparable peak to the R t estimates from the phylodynamic (BDSKY) analysis of genome sequence data. Likewise, both methodologies estimate a decline in R t from early September 2022. R t estimates produced by BDSKY and EpiEstim are more comparable when the higher serial interval (mean 7, SD = 2.6) was used for the EpiEstim based analyses. It should be noted that the case counts are a small samples size, and no genome sequence data were obtained from clinical cases beyond October 6, 2022 (Fig. 4 D ) . Discussion The phylogenetic and phylodynamic analyses reported here have demonstrated key features of two concurrent North American EV-D68 outbreaks and EV-D68’s epidemiology more widely. Through phylogenetic approaches we have demonstrated the epidemiological connection between the 2022 EV-D68 outbreaks in Maryland and Ontario. Our use of phylodynamic methods have aided in narrowing down the plausible window for EV-D68’s infection period. Furthermore, we have illustrated the practicality of phylodynamic methods in deriving R t and epidemic origin. Notably, our epidemic origin estimates for the 2022 EV-D68 outbreaks coincide with the removal of restriction aimed at curtailing the spread of COVID-19. Cross-border disease dynamics between North American countries have been studied for other viral pathogen such as SARS-CoV-2, mumps virus and West Nile viruses 42 – 44 . In our analysis we demonstrate close genetic proximity between viruses circulating in Maryland and Ontario in 2022, where isolates captured from each outbreak cluster within the same sub-clade, B3, and share recent common ancestry. These viruses belong to the B3 clade of EV-D68 lineages which have previously been shown to play an important role in outbreaks in the region 13 , 45 . The genetic proximity of these isolates indicates that significant epidemiological connections exist between these regions. Using phylodynamic modelling we were able to estimate important epidemiological parameters: the infection period and the time-varying reproduction number (R t ). Our models showed highest support for a duration of infection of 7.94 days (95% HPD 4.55, 12.8 days) for the ON-2022 dataset (n = 87) and 10.8 days (95% HPD 5.85, 18.6 days) for the MD-2022 dataset. Prior estimates of the infection period are lacking, however, respiratory viral shedding for enteroviruses has been documented to be between 1–3 weeks 21 . Specifically, the infection period for the EV-D68 ranges from 6–10 days 25 , and a symptomatic period from 3–10 days 26 . More recently Nguyen-Tran et al., (2023) 8 found that the EV-D68 genome could be detected in the upper respiratory tract for a median of 12 days post symptom onset (7–15 days). Nguyen-Tran et al., (2023) 8 point out that these RNA detection period should only be seen as an upper limit for infectious period. Given that infectious period (unlike infection period) does not include the latency period, Nguyen-Tran et al.,’s (2023) 8 findings are concurrent with our infection period of 7.94–10.8 days. The concurrence between our findings and Nguyen-Tran et al.,’s (2023) 8 brings important specificity, which is clinically relevant in the management of patients, given the previously suggested broad range in infection periods for EV-D68. Our time-varying reproduction number (R t ) estimates derived through phylodynamic methods produced similar values compared to deriving R t through case count data and the serial interval (Fig. 4 ). Of particular note, greater concordance was seen when case count derived R t values used a higher serial interval (mean = 7 days, SD = 2.6). Our mid-point serial interval for EV-D68 (mean = 3.6 days, SD = 2.6) was based on EV-71 41 , but given our estimate of the EV-D68 infection period (7.94–10.8 days) and an upper limit for the infectious period of 12 days 8 , EV-D68’s serial interval is likely to be closer to the higher value used in our sensitivity analysis. Previous epidemiological estimates of R t from EV-D68 outbreaks (across several US states 2014-17) range between 0.5-1.6 22 . We find that our estimates of the median R t (Fig. 4 ) were just over 1 in non-epidemic periods and 2.70 (95% HPD 1.76, 4.08) in Ontario and 2.10 (95% HPD 1.41, 3.17) in Maryland during the respective peak epidemic periods. A build up in the susceptible population due to reduced contacts over 2020–2021 may have led to the increased R t values observed in Ontario and Maryland 2022, compared to estimates from several US states over 2014-17, a pre-pandemic period 22 . More generally, our EV-D68 BDSKY derived R t estimates are consistent with other respiratory pathogens particularly other enteroviruses 24 , 46 – 49 . As with Park et al., (2021) 22 , we found delays in the increase of R t were associated with outbreaks occurring farther north within North America. The WGS-based substitution rates reported here, 0.0148 substitution per site per year (95% HPD 0.0112, 0.0185) for ON-2022 and 0.0113 substitution per site per year (95% HPD 0.0078, 0.0154) for MD-2022, are substantially higher than reported previously, 0.003 substitution per site per year 11 . The 38 WGS used in the analysis Eshaghi et al., (2017) 11 come from 14 different countries, span 1960–2014 and therefore come from different EV-D68 clades, whereas, the WGS being used in our analyses are from one regional outbreak of a single sub-clade, B3. Time varying evolutionary metrics have been observed before, with faster rates observed when samples are drawn from shorter time periods 50 – 52 . Ghafari et al., (2022) 53 demonstrated that during the SARS-CoV-2 and pH1N1 influenza pandemics this time varying evolutionary rate could be attributed to a short-term buildup of mildly deleterious mutations, that were eradicated over a longer term through purifying selection. This process of incomplete purifying selection may be the reason for the discrepancy between the EV-D68 substitution rates reported here and earlier 11 . The estimated 2022 EV-D68 epidemic origin and TMRCA statistics from BDSS models for both regions coincide with the periods when measures to reduce social contact, known as Non-Pharmaceutical Interventions (NPIs), were relaxed. Specifically, in Ontario, Canada, the period was from January 31, 2022 to March 14, 2022, coinciding with the decline of the Omicron COVID-19 wave 54 . Meanwhile, the Maryland data corresponded with the phase of winding down several NPIs aimed at curtailing the spread of COVID-19, from early February 2021 to August 13, 2021 55–57 . NPIs aimed at controlling COVID-19 transmission have also significantly reduced influenza cases, virtually eliminated respiratory syncytial virus (RSV) hospitalization and diminish detectable circulation of several enteroviruses 58 – 61 . Therefore, it is possible that the coinciding of our epidemic origin and TMRCA estimates with the lifting of NPIs demonstrates the suppressing effect of NPIs on EV-D68 transmission. However, Fig. 1 depicts 2022 EV-D68 Maryland and Ontario sequences interspersed with each other and sequences from Sweden. This pattern suggests that the 2022 EV-D68 outbreaks in Ontario and Maryland may be the result of several independent introductions into their respective populations, and not a single introduction. This would mean that our R t estimates are more likely to be for EV-D68 outbreaks in regions greater than Ontario or Maryland the further back in time the estimate is. Likewise, this may mean that our TMRCA and origin estimates are for EV-D68 outbreaks occurring over a much wider region than Ontario or Maryland. This study has limitations that should be addressed in further research efforts. For instance, the above caveats over R t , TMRCA and origin estimates have, in part, come about through sampling in acute healthcare settings during an ongoing transmission within the wider community (Fig. 4 C). It is important that sampling efforts are broader and capture more localities nationally, as well as broadly in North America, if not globally. As seen in the wider phylogenetic analysis (Fig. 1 ) there are long branches across the phylogeny which may indicate prolonged periods of within-host evolution, missed infections or un-sampled diversity. Thus, active surveillance is critical in identifying major source and sink populations for the EV-D68 virus, directing intervention efforts effectively. In addition to sampling biases, it is important that clinical observation studies of positive cases are conducted to validate the in-silico estimates of infection period for EV-D68 viruses to robustly model epidemiological dynamics further. Future study of EV-D68 in a phylodynamic framework will not only be bolstered by wider sampling efforts but will also be aided by the inclusion of secondary metadata to study the importance of different host traits on viral evolution and diffusion. If metadata pertaining to severity of infection, age, and travel history of a patient is available phylodynamic methods can be used to determine the importance of traits in the diffusion process and potentially identify host characteristics that can inform control measures 62 , 63 . In summary, this study underscores the importance of pathogen genome surveillance combined with phylodynamics in complementing conventional epidemiological approaches within public health investigations. Declarations Author contributions Conceptualization: M.G and V.R.D., methodology: M.G., A.E., S.N., A.P., A.G., C.C., V.R.D., formal analysis: M.G., A.E., P.B., and A.G, funding acquisition: J.W. and V.R.D, investigation: M.G., and A.E., supervision: V.R.D, validation: L.D., A.G., L.D.P., V.R.D, visualization: M.G., writing - original draft preparation: M.G., writing – review & editing: A.E., L.D., S.N., A.G., T.B., S.C., A.P., S.I., P.B., L.D.P., C.L.M., C.C., S.M., M.H, J.B., H.H.M., J.B.G., S.N.P., J.W., and V.R.D. Acknowledgements We acknowledge the authors, originating and submitting laboratories of the sequences from NCBI Database on which this research is based. MG and JW were supported by the NSERC-PHAC Emerging Infectious Disease Modeling Initiative Mathematics for Public Health. MG received The Emerging and Pandemic Infections Consortium (EPIC) 2023 researcher mobility award to participate in the ‘Taming the Beast” workshop at Squamish, British Columbia, Canada. Conflict of Interest JBG is a paid consultant scientific editor for GIDEON Informatics, Inc., which is unrelated to the current work. Other authors have no conflicts of interest to disclose. Ethics Statement This project has received ethics review clearance from Public Health Ontario’s Ethics Review Board. References Schieble, J. H., Fox, V. L. & Lennette, E. H. A probable new human picornavirus associated with respiratory disease. American Journal of Epidemiology 85, 297–310 (1967). Levy, A. et al. Enterovirus D68 disease and molecular epidemiology in Australia. Journal of Clinical Virology 69, 117–121 (2015). Messacar, K. et al. 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Bayesian Inference of Evolutionary Histories under Time-Dependent Substitution Rates. Molecular Biology and Evolution 36, 1793–1803 (2019). Ghafari, M. et al. Purifying Selection Determines the Short-Term Time Dependency of Evolutionary Rates in SARS-CoV-2 and pH1N1 Influenza. Molecular Biology and Evolution 39, 1–8 (2022). Government of Ontario. Ontario Outlines Steps to Cautiously and Gradually Ease Public Health Measures. NEWS RELEASE https://news.ontario.ca/en/release/1001451/ontario-outlines-steps-to-cautiously-and-gradually-ease-public-health-measures (2022). Neives, R., O’Brien, M., Shipley, J., Green, K. & Laping, S. Summary of State-Specific Government Response to COVID-19 in the US 2020/2021 - MARYLAND . 1–15 (2023). Ng, G., Fulginiti, J. & Lucas, T. 2021 Timeline: Coronavirus in Maryland. WBalTV https://www.wbaltv.com/article/covid-19-in-maryland-2021-timeline/35169408 (2022). Lucas, T., Young, B. & Ng, G. 2022 Timeline: Coronavirus in Maryland. WBalTV https://www.wbaltv.com/article/covid-19-maryland-2022-timeline/38665369 (2022). Baker, R. E. et al. The impact of COVID-19 nonpharmaceutical interventions on the future dynamics of endemic infections. Proceedings of the National Academy of Sciences of the United States of America 117, 30547–30553 (2020). Feng, L. et al. Impact of COVID-19 outbreaks and interventions on influenza in China and the United States. Nature Communications 12, 1–8 (2021). Van Brusselen, D. et al. Bronchiolitis in COVID-19 times: a nearly absent disease? European Journal of Pediatrics 180, 1969–1973 (2021). Forero, E. L. et al. Changes in enterovirus epidemiology after easing of lockdown measures. Journal of Clinical Virology 169, 105617 (2023). Lemey, P., Rambaut, A., Drummond, A. J. & Suchard, M. A. Bayesian phylogeography finds its roots. PLoS Computational Biology 5, e1000520 (2009). Lemey, P. et al. Accommodating individual travel history and unsampled diversity in Bayesian phylogeographic inference of SARS-CoV-2. Nature Communications 11, 1–14 (2020). Additional Declarations Competing interest reported. JBG is a paid consultant scientific editor for GIDEON Informatics, Inc., which is unrelated to the current work. HMM serves on the advisory board of Seegene, an advisor for BD Diagnostics and has research collaborations with DiaSorin and Hologic, none of these affiliations are related to the current work. Other authors have no conflicts of interest to disclose. Supplementary Files Grunnilletal.EVD68manuscriptsupplementarymaterials.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 Jun, 2024 Reviews received at journal 25 May, 2024 Reviews received at journal 12 May, 2024 Reviewers agreed at journal 09 May, 2024 Reviewers agreed at journal 09 May, 2024 Reviewers agreed at journal 08 May, 2024 Reviewers invited by journal 07 May, 2024 Editor assigned by journal 07 May, 2024 Submission checks completed at journal 07 May, 2024 First submitted to journal 03 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Duvvuri","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYBACgwMMzGCGPYQvIcfAwEOMlgQGBsMGiBZj4rUAGWCQ2EBIi2R772GDnz/sGAxu5JhJ8+6xSO9nP3vscQGDnTxOLT3nkhN7EpIhWnieSeTO7MlLN57BkAx1KRYtM3KMD/AkMAO1pCUb8xyQyN1wAKSX4QAjLi388m+MD/5JqIdrSTc4/wasxR6nFgke42SehMNALckHHwO1JEBcyHAgEZcWNp4cY2OZtOM8hj2PDz6cc0DCcOaMN+bGMwySk3FqYT9jLPnGplrOnj2x4cCbA3Xy/Pw5Zo8LKuxscWmBAZS4YAOGBgH1GFYzk6hhFIyCUTAKhjcAAIV3UZmOT4Y5AAAAAElFTkSuQmCC","orcid":"","institution":"Public Health Ontario","correspondingAuthor":true,"prefix":"","firstName":"Venkata","middleName":"R.","lastName":"Duvvuri","suffix":""}],"badges":[],"createdAt":"2024-05-03 05:38:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4362075/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4362075/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56548620,"identity":"5016ddbd-77af-4b40-b4d5-972583018965","added_by":"auto","created_at":"2024-05-15 15:42:36","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":101192,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEV-D68 WGS based phylogenetic tree using Nextstrain\u003c/strong\u003e\u003csup\u003e29\u003c/sup\u003e.\u003cstrong\u003e \u003c/strong\u003eThe\u003cstrong\u003e \u003c/strong\u003eexert zooms on the 2022 EV-D68 outbreaks of Ontario, Canada and Maryland\u003csup\u003e7,9\u003c/sup\u003e. X, Y and Z are internal nodes of the tree referenced in the main text.\u003c/p\u003e","description":"","filename":"Fig1Tree.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4362075/v1/4dadec411462eb54b6efb69f.jpg"},{"id":56548621,"identity":"69af7568-d04b-4f8e-8a00-38d02581f502","added_by":"auto","created_at":"2024-05-15 15:42:36","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":178405,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of posterior estimates of evolutionary parameters from 3 convergent runs of the best supported BDSS models fitted to different datasets of EV-D68 samples. \u003c/strong\u003eA: Box-Violin plots of posterior estimates of mean and the coefficient of variation for substitution rate (per site per year). B: Kernel Density Estimate (KDE) plots of posterior estimates for TMRCA. The grey patches denote easing of COVID-19 restrictions in Maryland\u003csup\u003e55–57\u003c/sup\u003e on the left and Ontario\u003csup\u003e54\u003c/sup\u003e on the right. ON-2022 dataset contains all WGS sequences collected from Ontario 2022 EV-D68 cases. MD-2022 dataset contains WGS sequences collected from Maryland 2022 EV-D68 cases.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4362075/v1/e0a20bf602abd70ddf048b20.jpg"},{"id":56548622,"identity":"d9ff5cf3-cbc9-4bb8-b017-0e66df3b31cb","added_by":"auto","created_at":"2024-05-15 15:42:36","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":207106,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of posterior estimates of epidemiological parameters from 3 convergent runs of the best supported BDSS models fitted to different datasets of EV-D68 samples. A. \u003c/strong\u003eBox-Violin plots of posterior estimates of Infection period (Rates of Becoming non-infectious). \u003cstrong\u003eB.\u003c/strong\u003e Kernel density estimate of estimated Epidemic Origin. \u003cstrong\u003eC\u003c/strong\u003e \u003cstrong\u003etop\u003c/strong\u003e: R\u003csub\u003et\u003c/sub\u003e estimates \u0026amp; 95% HPD intervals for ON-2022. \u003cstrong\u003eC bottom\u003c/strong\u003e: R\u003csub\u003et\u003c/sub\u003e estimates \u0026amp; 95% HPD intervals for MD-2022. The grey patches in \u003cstrong\u003eB\u003c/strong\u003e and \u003cstrong\u003eC\u003c/strong\u003e denote easing of COVID-19 restrictions in Maryland\u003csup\u003e55–57\u003c/sup\u003e on the left and Ontario\u003csup\u003e54\u003c/sup\u003e on the right.. ON-2022 dataset contains all WGS sequences collected from Ontario 2022 EV-D68 cases. MD-2022 dataset contains WGS sequences collected from Maryland 2022 EV-D68 cases.\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4362075/v1/2ccfef4a97d17cc4e3b729e4.jpg"},{"id":56548623,"identity":"198a0dd3-63f8-494f-9832-e31846cf5450","added_by":"auto","created_at":"2024-05-15 15:42:36","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":238059,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of Time-varying Reproduction Number (R\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e) estimation from BDSKY (using genome sequence data) and EpiEstim (using case counts) methods, using Ontario 2022 EV-D68 data. Subplots A, B and C\u003c/strong\u003e: Effective reproductive number estimated via three convergent runs of the best supported BDSS models fitted to the ON-2022 EV-D68 WGSs (blue) compared to estimation via EpiEstim case counts. All serial intervals (SI) used in EpiEstim method’s had an standard deviation (SD) of 2.6, The \u003cstrong\u003esubplot D\u003c/strong\u003e depicts Ontario EV-D68 case counts used in effective reproductive number estimation, only sequenced cases in green were used in the BDSKY based method. Note the BDSKY estimate of R\u003csub\u003et\u003c/sub\u003e goes back until February 2022 (\u003cstrong\u003eFigure 3C\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4362075/v1/f6292be50687d0166520ade8.jpg"},{"id":57504782,"identity":"60197991-1336-46ea-a05e-9531d569ac8b","added_by":"auto","created_at":"2024-05-31 14:52:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1636237,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4362075/v1/8097c886-d932-44fa-9fd8-e9af13fc4ea2.pdf"},{"id":56548625,"identity":"a7391adb-361b-4617-8546-02e587980835","added_by":"auto","created_at":"2024-05-15 15:42:37","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":1207740,"visible":true,"origin":"","legend":"","description":"","filename":"Grunnilletal.EVD68manuscriptsupplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-4362075/v1/4a6c3f58a82a82b931295cb0.docx"}],"financialInterests":"Competing interest reported. JBG is a paid consultant scientific editor for GIDEON Informatics, Inc., which is unrelated to the current work. HMM serves on the advisory board of Seegene, an advisor for BD Diagnostics and has research collaborations with DiaSorin and Hologic, none of these affiliations are related to the current work. Other authors have no conflicts of interest to disclose.","formattedTitle":"Inferring Enterovirus D68 Transmission Dynamics from the Genomic Data of Two 2022 North American outbreaks","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003c/p\u003e \u003cp\u003ePhylodynamic methods use pathogen genetic sequences to construct phylogenies to study the diversification of pathogens at different spatio-temporal scales, while inferring key epidemic patterns of transmission between locations\u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Hodcroft et al., (2022)\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e reported key aspects of EV-D68 antigenic evolution, showing that age structure within populations has important implications for the diversification of surface proteins and host-specificity of lineages. Other phylodynamic analyses of EV-D68 have studied the relatedness between major epidemics and global circulation\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNon-polio enteroviruses like EV-D68 are not nationally notifiable infections in North America. As such, case documentation is low which leads to a gap in our understanding of the transmission dynamics of these viruses. For instance, information on the infection period of EV-D68 is limited. Most enterovirus infections are found to shed from the upper respiratory tract over 1 to 3 weeks\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. A previous epidemiological modelling study of EV-D68 used an infection period of 7 days\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, which was derived from poliovirus\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Tambyah et al., (2019)\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e described EV-D68 as having 1 to 5 days of incubation period and an infectious period from 1 day before to 5 days post symptom onset but give no reference. Mild symptoms of a median duration of 6 days (range 3 to 10 days) were observed during an EV-D68 outbreak at an elder care facility\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, we aim to address knowledge gaps on EV-D68\u0026rsquo;s transmission dynamics. We investigate the evolutionary history and relatedness of EV-D68 in Ontario and on a global scale, with a specific focus on the 2022 outbreak. We provide a contextual perspective by examining the outbreak\u0026rsquo;s geographical patterns within the B3 sub-clade of EV-D68 viruses. In particular, we find a high degree of genetic relatedness between samples from the 2022 outbreak, in Ontario, Canada\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e and in Maryland, United States\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. We investigate the epidemic transmission (via time-varying reproduction number (R\u003csub\u003et\u003c/sub\u003e) estimation) through phylodynamic modelling of these two outbreaks, while shedding light on the infection duration of EV-D68 viruses. With EV-D68 reporting dates being available for the Ontario outbreak, we compare R\u003csub\u003et\u003c/sub\u003e as estimated via phylodynamic methods to more conventional methods (analyzing case incidence data with EpiEstim)\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. We present epidemiological parameters derived from genome sequences, which can offer actionable information for public health practitioners.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSpecimen Collection and Whole Genome Sequencing of 2022 EV-D68 in Ontario\u003c/h2\u003e \u003cp\u003eSpecimens submitted to Public Health Ontario Laboratory (PHOL) between July 31 and October 30, 2022, were collected from individuals with respiratory symptoms across various healthcare facilities in Ontario as part of routine care. EV-D68 was identified in 60.1% (n\u0026thinsp;=\u0026thinsp;238) of randomly selected enterovirus-positive specimens (n\u0026thinsp;=\u0026thinsp;396), with a predominant presence in nasal or nasopharyngeal samples. The highest number of EV-D68 positive test results was found among children less than 5 years of age. None of these cases presented with AFM. Additionally, whole genome sequencing was conducted on 36.5% (n\u0026thinsp;=\u0026thinsp;87) of the randomly selected EV-D68 positive specimens \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEV-D68 WGSs were retrieved from the National Center for Biotechnology Information (NCBI) from which a consensus sequence was created. The consensus sequence used for designing three primer pairs with an overlap of ~\u0026thinsp;600 bp spanning the entire genome (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Total RNA was extracted using the NucliSENS EMAG system following manufacturer\u0026rsquo;s instructions (bioM\u0026eacute;rieux Canada Inc, St-Laurent, Quebec, Canada) and reverse transcribed into cDNA using LunaScript\u0026reg; RT SuperMix Kit (cat# M3010, New England BioLabs, Ipswich, MA, USA). The synthesized cDNAs were used as templates for amplification of 3 long overlapping fragments along the genome. Each PCR reaction of 25 \u0026micro;l included the following: 5 \u0026micro;l of Q5 Hot Start buffer (New England Biolabs, Ipswich, MA), 0.5 \u0026micro;l of 10 mM dNTP, 0.5 \u0026micro;l of Q5 High-Fidelity DNA Polymerase, 1 \u0026micro;L of primer mix (10\u0026micro;M), 2.5\u0026micro;L of template DNA, and 15.5 \u0026micro;L of PCR grade water. The following thermal cycling conditions were used on an ABI SimpliAmp thermocycler: initial denaturation at 98\u0026deg;C for 2 min, followed by 45 cycles at 98\u0026deg;C for 10 seconds and 65\u0026deg;C for 1 min and 72\u0026deg;C for 5 min followed by a final extension at 72\u0026deg;C for 5 min. The presence of each PCR product was confirmed by electrophoresis on 1% agarose gel. Equimolar amounts of each PCR product from the three reactions were pooled and cleaned with AMPure XP beads (0.5 ratio) for Illumina library preparation. Paired-end libraries for the MiniSeq platform were generated using Nextera XT DNA Library Prep Kit (Illumina) and subsequently purified using Agencourt AMPure XP beads (Beckman Coulter). The quality and size of prepared libraries were measured on the Agilent 4200 Tape Station using a High Sensitivity D1000 ScreenTape and reagent (HSD1000). Pooled normalized specimens, at a final concentration of 1.2 pM, were loaded onto a MiniSeq High Output Reagent Kit (300-cycles) and sequenced on an Illumina MiniSeq.\u0026nbsp;For sequence analyses, FASTQ files were imported into CLC Genomics Workbench version 8.0.1 (CLC bio, Germantown, MD, USA). Reads were trimmed and mapped to the reference EV-D68 genome NY328 (GenBank: KP745766.1). Sequences were annotated using VAPiD v1.6.7\u003csup\u003e28\u003c/sup\u003e prior to their submission to GenBank.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eRetrieving publicly available EV-D68 Sequence data\u003c/h2\u003e \u003cp\u003eTo perform phylogenetic analyses, additional EV-D68 sequences were obtained from the NCBI virus database at the end of May 2023. For Whole Genome Sequences (WGS) the following filters were applied to remove sequences: without sample collection date, sequences\u0026thinsp;\u0026lt;\u0026thinsp;5000 bp in length and sequences where the proportion of nucleotides unassigned was over 0.05. Along with the WGS data produced from the Ontario 2022 outbreak (n\u0026thinsp;=\u0026thinsp;87), a global dataset of 1134 EV-D68 WGSs was curated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePhylodynamic Analysis of the 2022 EV-D68 Outbreaks in Ontario and Maryland Using Genome Sequence Data\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eSequence Analysis and Phylogenetic Construction\u003c/h2\u003e \u003cp\u003eThe Nextstrain Augur v22.0.2\u003csup\u003e29\u003c/sup\u003e pipeline was used to align the WGS (n\u0026thinsp;=\u0026thinsp;1134, length\u0026thinsp;\u0026gt;\u0026thinsp;5000 bp) data via MAFFT v7.505\u003csup\u003e30\u003c/sup\u003e, build a maximum likelihood (ML) phylogenetic tree via IQTree v2.0\u003csup\u003e31\u003c/sup\u003e, and refine the tree and infer node ancestry via TreeTime v0.10.1\u003csup\u003e32\u003c/sup\u003e. Auspice v2.37.1\u003csup\u003e29\u003c/sup\u003e was used for visualization of Augur outputs. The curated 2022 outbreak WGS datasets from Ontario (ON-2022, n\u0026thinsp;=\u0026thinsp;87) and Maryland (MD-2022, n\u0026thinsp;=\u0026thinsp;74) were utilized in building phylodynamic models. TempEst v 1.5.1\u003csup\u003e33\u003c/sup\u003e was employed to check that the temporal signals in ON-2022 and MD-2022 datasets are strong enough to allow phylodynamic analyses. Both ML trees derived from the ON-2022 and the MD-2022 datasets demonstrate a strong association between genetic distances and sampling dates.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eGenome-based Epidemiological Modelling using Bayesian Phylodynamics\u003c/h2\u003e \u003cp\u003eBayesian phylodynamic analyses were performed on the curated ON-2022 (n\u0026thinsp;=\u0026thinsp;87) and MD-2022 (n\u0026thinsp;=\u0026thinsp;74) EV-D68 WGS datasets. These datasets were analyzed using BEAST v2.7.5\u003csup\u003e34\u003c/sup\u003e. Birth-Death Skyline Serial (BDSS using BDSKY v1.5.0\u003csup\u003e35\u003c/sup\u003e) models were fitted to each dataset separately. We used an HKY85 site substitution model with four gamma rate categories to estimate the evolutionary rate and an optimal relaxed molecular clock model\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e that assumes heterogeneous substitution rates across phylogenetic branches, with an initial mean clock rate of 0.003\u003csup\u003e11\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eConsidering the possible infection periods put forward for EV-D68\u003csup\u003e21\u0026ndash;23,25,26\u003c/sup\u003e, we fitted all BDSS models using a prior for the infection period with a mean of 7 days and a wide standard deviation so as to cover 3 to 21 days (\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Birth-Death models do not estimate rate of reproductive maturation. Therefore, the model assumes patients immediately become infectious upon infection and remain infectious until being removed, i.e. there is no latent or exposed period\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. The mean infection period (δ\u003csup\u003e\u0026minus;1\u003c/sup\u003e) was inverted to become the death rate or rate of becoming uninfectious (δ, also called the recovery rate) and converted to years (i.e. δ\u0026thinsp;=\u0026thinsp;1/7 days\u0026thinsp;=\u0026thinsp;52 year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). To produce a gamma distributed prior\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e for δ, we used formulas \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(shape=\\frac{{mean}^{2}}{variance}=12.018\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(scale=\\frac{variance}{mean}=4.3269\\)\u003c/span\u003e\u003c/span\u003e with a standard deviation of 15 year\u003csup\u003e\u0026minus;1\u003c/sup\u003e. All other parameter priors are listed in \u003cb\u003eTable S2.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThree additional independent runs were performed for each model. The performance of these independent runs was evaluated using Tracer v1.7.2\u003csup\u003e39\u003c/sup\u003e, checking the convergence of parameter, posterior, and likelihood values, along with screening individual runs ensuring effective sample sizes (ESS)\u0026thinsp;\u0026gt;\u0026thinsp;200\u003csup\u003e40\u003c/sup\u003e for all parameters. We repeated the analyses until we obtained at least three model runs meeting the above convergence criteria. Log and tree files were then combined using Log Combiner v2.7.5 \u003csup\u003e40\u003c/sup\u003e. The bdskytools package in R (available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/laduplessis/bdskytools\u003c/span\u003e\u003cspan address=\"https://github.com/laduplessis/bdskytools\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was then used to produce skyplot figures of R\u003csub\u003et\u003c/sub\u003e and KDE plots of epidemic origin from the combined log files. The Python v3.10 package Seaborn v0.12.2 was used to produce box-violin plots of other parameter posterior distributions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMathematical Analysis of 2022 EV-D68 Outbreak in Ontario Using Case Counts Data\u003c/h2\u003e \u003cp\u003eThe package EpiEstim v 2.2-4 \u003csup\u003e27\u003c/sup\u003e in R v4.1.2 was employed to calculate the time varying reproduction number (R\u003csub\u003et\u003c/sub\u003e) from the time-series data of laboratory-confirmed positive cases of EV-D68 in Ontario during 2022. Due to the challenge in obtaining the serial interval for EV-D68, a mean serial interval of 3.7 days with a standard deviation (SD) of 2.6 days, as observed in the related pathogen EV-71\u003csup\u003e41\u003c/sup\u003e, was used for R\u003csub\u003et\u003c/sub\u003e estimation via EpiEstim\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Considering this a sensitivity analyses was performed on the EpiEstim analyses assuming a mean serial intervals of 2 and 7 days, but keeping the same SD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis, Code Availability and GenBank Accession Numbers\u003c/h2\u003e \u003cp\u003eThe code developed in this study and the BEAUTi-created xml files are available on GitHub at Grunnill-Duvvuri-co-publications/Transmission-dynamics-inferred-from-Enterovirus-D68-genomic-data-from-2022-North-American-outbreaks (github.com). All genome sequences of the 2022 Ontario EV-D68 outbreak isolates obtained in this study were submitted to GenBank under the accession numbers PP474817 to PP474903. The 2022 Maryland EV-D68 genome sequences were obtained from GenBank (accession numbers OP321139-OP321154, OP389245-OP389246, OP572035-OP572095)\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePhylogenetics of Ontario 2022 EV-D68 in a global epidemic context\u003c/h2\u003e \u003cp\u003eThe Ontario 2022 EV-D68 isolates cluster with concurrent specimens from Maryland 2022 \u003csup\u003e7\u003c/sup\u003e along with a few concurrent sequences from Sweden and France (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All these isolates are of the B3 sub-clade and diversify from internal node \u003cb\u003eX in\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which is close to a cluster of US 2018 isolates. The majority of the Ontario 2022 isolates form a sub-grouping with isolates from the Maryland 2022 outbreak and a single Swedish 2022 isolate. This sub-grouping diversifies from internal node \u003cb\u003eY in\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which is close to Australian 2019 (early) isolates. A single isolate from Ontario 2022, diversifying from the internal node \u003cb\u003eZ as shown in\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, forms a sub-grouping with sequences from Maryland 2022, Sweden 2022, France 2021\u0026ndash;2022 and other parts of the US in 2021. Additionally, this branch, originating from the internal node that clusters with isolates from a late 2019 to early 2020 outbreak in the Netherlands.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePhylodynamic Analysis of the 2022 EV-D68 Outbreaks in Ontario and Maryland Using Genome Sequence Data\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003eInference of Evolutionary Parameters: Substitution Rate and Time since Most Recent Common Ancestor (TMRCA)\u003c/h2\u003e \u003cp\u003eThere are slight differences in the estimated median substitution rates between the BDSS models fitted to different datasets. The dataset ON-2022 has a median estimate of 0.0148 substitutions per site per year (95% Highest Posterior Density (HPD) 0.0112, 0.0185) and the MD-2022 dataset a median estimate of 0.0113 substitutions per site per year (95% HPD 0.00778, 0.0154). There is a higher coefficient of variation (median) of substitution rate for the MD-2022, 0.879 (95% HPD 0.706, 0.11) compared to ON-2022\u0026rsquo;s 0.655 (95% 0.499, 0.838 HPD) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), however the 95% HPD excludes 0 for both datasets, indicating strong support for a relaxed clock model\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. The median TMRCA estimate for the ON-2022 dataset was February 16, 2022, with 95% HPD interval from December 26, 2021 to March 31, 2022. For the MD-2022 dataset, the median TMRCA estimate was July 11, 2021, with a 95% HPD interval from April 3, 2021 to November 3, 2021. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eInference of Epidemiological Parameters: Infection Period, Epidemic Origin and Time-varying Reproduction number (R\u003csub\u003et\u003c/sub\u003e)\u003c/h2\u003e \u003cp\u003eThe estimated median duration of the infection period (in days) were 7.94, with a 95% HPD interval ranging from 4.55 to 12.8 for the Ontario outbreak, and 10.8 with 95% HPD 5.85, 18.6 for the Maryland outbreak (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe median epidemic origin estimate was February 7, 2022 (95% HPD interval, December 21, 2021, April 20, 2022) for the Ontario EV-D68 outbreak (dataset, ON-2022). The Maryland EV-D68 outbreak (MD-2022 dataset) has a median epidemic origin estimate, June 8, 2021 (95% HPD interval, March 9, 2021, September 14, 2021) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). It is interesting to note that the estimates of TMRCA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e of both epidemics overlapped with these origin estimates.\u003c/p\u003e \u003cp\u003eBDSS models estimate a rise in the time-varying reproduction number (R\u003csub\u003et\u003c/sub\u003e) leading to a plateaued peak occurring in the summer of 2022 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), which models fitted to the Ontario dataset (ON-2022) having a higher and later but shorted peak in R\u003csub\u003et\u003c/sub\u003e compared to models fitted to the Maryland dataset (MD-2022). The median R\u003csub\u003et\u003c/sub\u003e values for these peaks are 2.70 with 95% HPD 1.76, 4.08 for the Ontario outbreak and 2.10 with 95% HPD 1.41, 3.17 for the Maryland outbreak.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMathematical Analysis of 2022 EV-D68 Outbreak in Ontario Using Case Counts Data\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows that R\u003csub\u003et\u003c/sub\u003e estimated via case counts has a comparable peak to the R\u003csub\u003et\u003c/sub\u003e estimates from the phylodynamic (BDSKY) analysis of genome sequence data. Likewise, both methodologies estimate a decline in R\u003csub\u003et\u003c/sub\u003e from early September 2022. R\u003csub\u003et\u003c/sub\u003e estimates produced by BDSKY and EpiEstim are more comparable when the higher serial interval (mean 7, SD\u0026thinsp;=\u0026thinsp;2.6) was used for the EpiEstim based analyses. It should be noted that the case counts are a small samples size, and no genome sequence data were obtained from clinical cases beyond October 6, 2022 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe phylogenetic and phylodynamic analyses reported here have demonstrated key features of two concurrent North American EV-D68 outbreaks and EV-D68\u0026rsquo;s epidemiology more widely. Through phylogenetic approaches we have demonstrated the epidemiological connection between the 2022 EV-D68 outbreaks in Maryland and Ontario. Our use of phylodynamic methods have aided in narrowing down the plausible window for EV-D68\u0026rsquo;s infection period. Furthermore, we have illustrated the practicality of phylodynamic methods in deriving R\u003csub\u003et\u003c/sub\u003e and epidemic origin. Notably, our epidemic origin estimates for the 2022 EV-D68 outbreaks coincide with the removal of restriction aimed at curtailing the spread of COVID-19.\u003c/p\u003e \u003cp\u003eCross-border disease dynamics between North American countries have been studied for other viral pathogen such as SARS-CoV-2, mumps virus and West Nile viruses\u003csup\u003e\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. In our analysis we demonstrate close genetic proximity between viruses circulating in Maryland and Ontario in 2022, where isolates captured from each outbreak cluster within the same sub-clade, B3, and share recent common ancestry. These viruses belong to the B3 clade of EV-D68 lineages which have previously been shown to play an important role in outbreaks in the region\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. The genetic proximity of these isolates indicates that significant epidemiological connections exist between these regions.\u003c/p\u003e \u003cp\u003eUsing phylodynamic modelling we were able to estimate important epidemiological parameters: the infection period and the time-varying reproduction number (R\u003csub\u003et\u003c/sub\u003e). Our models showed highest support for a duration of infection of 7.94 days (95% HPD 4.55, 12.8 days) for the ON-2022 dataset (n\u0026thinsp;=\u0026thinsp;87) and 10.8 days (95% HPD 5.85, 18.6 days) for the MD-2022 dataset. Prior estimates of the infection period are lacking, however, respiratory viral shedding for enteroviruses has been documented to be between 1\u0026ndash;3 weeks\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Specifically, the infection period for the EV-D68 ranges from 6\u0026ndash;10 days\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, and a symptomatic period from 3\u0026ndash;10 days\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. More recently Nguyen-Tran et al., (2023)\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e found that the EV-D68 genome could be detected in the upper respiratory tract for a median of 12 days post symptom onset (7\u0026ndash;15 days). Nguyen-Tran et al., (2023)\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e point out that these RNA detection period should only be seen as an upper limit for infectious period. Given that infectious period (unlike infection period) does not include the latency period, Nguyen-Tran et al.,\u0026rsquo;s (2023)\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e findings are concurrent with our infection period of 7.94\u0026ndash;10.8 days. The concurrence between our findings and Nguyen-Tran et al.,\u0026rsquo;s (2023)\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e brings important specificity, which is clinically relevant in the management of patients, given the previously suggested broad range in infection periods for EV-D68.\u003c/p\u003e \u003cp\u003eOur time-varying reproduction number (R\u003csub\u003et\u003c/sub\u003e) estimates derived through phylodynamic methods produced similar values compared to deriving R\u003csub\u003et\u003c/sub\u003e through case count data and the serial interval (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Of particular note, greater concordance was seen when case count derived R\u003csub\u003et\u003c/sub\u003e values used a higher serial interval (mean\u0026thinsp;=\u0026thinsp;7 days, SD\u0026thinsp;=\u0026thinsp;2.6). Our mid-point serial interval for EV-D68 (mean\u0026thinsp;=\u0026thinsp;3.6 days, SD\u0026thinsp;=\u0026thinsp;2.6) was based on EV-71\u003csup\u003e41\u003c/sup\u003e, but given our estimate of the EV-D68 infection period (7.94\u0026ndash;10.8 days) and an upper limit for the infectious period of 12 days\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, EV-D68\u0026rsquo;s serial interval is likely to be closer to the higher value used in our sensitivity analysis. Previous epidemiological estimates of R\u003csub\u003et\u003c/sub\u003e from EV-D68 outbreaks (across several US states 2014-17) range between 0.5-1.6\u003csup\u003e22\u003c/sup\u003e. We find that our estimates of the median R\u003csub\u003et\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) were just over 1 in non-epidemic periods and 2.70 (95% HPD 1.76, 4.08) in Ontario and 2.10 (95% HPD 1.41, 3.17) in Maryland during the respective peak epidemic periods. A build up in the susceptible population due to reduced contacts over 2020\u0026ndash;2021 may have led to the increased R\u003csub\u003et\u003c/sub\u003e values observed in Ontario and Maryland 2022, compared to estimates from several US states over 2014-17, a pre-pandemic period\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. More generally, our EV-D68 BDSKY derived R\u003csub\u003et\u003c/sub\u003e estimates are consistent with other respiratory pathogens particularly other enteroviruses\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan additionalcitationids=\"CR47 CR48\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. As with Park et al., (2021) \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, we found delays in the increase of R\u003csub\u003et\u003c/sub\u003e were associated with outbreaks occurring farther north within North America.\u003c/p\u003e \u003cp\u003eThe WGS-based substitution rates reported here, 0.0148 substitution per site per year (95% HPD 0.0112, 0.0185) for ON-2022 and 0.0113 substitution per site per year (95% HPD 0.0078, 0.0154) for MD-2022, are substantially higher than reported previously, 0.003 substitution per site per year\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. The 38 WGS used in the analysis Eshaghi et al., (2017) \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e come from 14 different countries, span 1960\u0026ndash;2014 and therefore come from different EV-D68 clades, whereas, the WGS being used in our analyses are from one regional outbreak of a single sub-clade, B3. Time varying evolutionary metrics have been observed before, with faster rates observed when samples are drawn from shorter time periods\u003csup\u003e\u003cspan additionalcitationids=\"CR51\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Ghafari et al., (2022)\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e demonstrated that during the SARS-CoV-2 and pH1N1 influenza pandemics this time varying evolutionary rate could be attributed to a short-term buildup of mildly deleterious mutations, that were eradicated over a longer term through purifying selection. This process of incomplete purifying selection may be the reason for the discrepancy between the EV-D68 substitution rates reported here and earlier\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe estimated 2022 EV-D68 epidemic origin and TMRCA statistics from BDSS models for both regions coincide with the periods when measures to reduce social contact, known as Non-Pharmaceutical Interventions (NPIs), were relaxed. Specifically, in Ontario, Canada, the period was from January 31, 2022 to March 14, 2022, coinciding with the decline of the Omicron COVID-19 wave\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Meanwhile, the Maryland data corresponded with the phase of winding down several NPIs aimed at curtailing the spread of COVID-19, from early February 2021 to August 13, 2021\u003csup\u003e55\u0026ndash;57\u003c/sup\u003e. NPIs aimed at controlling COVID-19 transmission have also significantly reduced influenza cases, virtually eliminated respiratory syncytial virus (RSV) hospitalization and diminish detectable circulation of several enteroviruses\u003csup\u003e\u003cspan additionalcitationids=\"CR59 CR60\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. Therefore, it is possible that the coinciding of our epidemic origin and TMRCA estimates with the lifting of NPIs demonstrates the suppressing effect of NPIs on EV-D68 transmission. However, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e depicts 2022 EV-D68 Maryland and Ontario sequences interspersed with each other and sequences from Sweden. This pattern suggests that the 2022 EV-D68 outbreaks in Ontario and Maryland may be the result of several independent introductions into their respective populations, and not a single introduction. This would mean that our R\u003csub\u003et\u003c/sub\u003e estimates are more likely to be for EV-D68 outbreaks in regions greater than Ontario or Maryland the further back in time the estimate is. Likewise, this may mean that our TMRCA and origin estimates are for EV-D68 outbreaks occurring over a much wider region than Ontario or Maryland.\u003c/p\u003e \u003cp\u003eThis study has limitations that should be addressed in further research efforts. For instance, the above caveats over R\u003csub\u003et\u003c/sub\u003e, TMRCA and origin estimates have, in part, come about through sampling in acute healthcare settings during an ongoing transmission within the wider community (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). It is important that sampling efforts are broader and capture more localities nationally, as well as broadly in North America, if not globally. As seen in the wider phylogenetic analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) there are long branches across the phylogeny which may indicate prolonged periods of within-host evolution, missed infections or un-sampled diversity. Thus, active surveillance is critical in identifying major source and sink populations for the EV-D68 virus, directing intervention efforts effectively. In addition to sampling biases, it is important that clinical observation studies of positive cases are conducted to validate the in-silico estimates of infection period for EV-D68 viruses to robustly model epidemiological dynamics further.\u003c/p\u003e \u003cp\u003eFuture study of EV-D68 in a phylodynamic framework will not only be bolstered by wider sampling efforts but will also be aided by the inclusion of secondary metadata to study the importance of different host traits on viral evolution and diffusion. If metadata pertaining to severity of infection, age, and travel history of a patient is available phylodynamic methods can be used to determine the importance of traits in the diffusion process and potentially identify host characteristics that can inform control measures\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e,\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. In summary, this study underscores the importance of pathogen genome surveillance combined with phylodynamics in complementing conventional epidemiological approaches within public health investigations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: M.G and V.R.D., methodology: M.G., A.E., S.N., A.P., A.G., C.C., V.R.D., formal analysis: M.G., A.E., P.B., and A.G, funding acquisition: J.W. and V.R.D, investigation: M.G., and A.E., supervision: V.R.D, validation: L.D., A.G., L.D.P., V.R.D, visualization: M.G., writing - original draft preparation: M.G., writing \u0026ndash; review \u0026amp; editing: A.E., L.D., S.N., A.G., T.B., S.C., A.P., S.I., P.B., L.D.P., C.L.M., C.C., S.M., M.H, J.B., H.H.M., J.B.G., S.N.P., J.W., and V.R.D.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the authors, originating and submitting laboratories of the sequences from NCBI Database on which this research is based. MG and JW were supported by the NSERC-PHAC Emerging Infectious Disease Modeling Initiative Mathematics for Public Health. MG received The Emerging and Pandemic Infections Consortium (EPIC) 2023 researcher mobility award to participate in the \u0026lsquo;Taming the Beast\u0026rdquo; workshop at Squamish, British Columbia, Canada.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJBG is a paid consultant scientific editor for GIDEON Informatics, Inc., which is unrelated to the current work. Other authors have no conflicts of interest to disclose.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003ch1\u003eThis project has received ethics review clearance from Public Health\u0026nbsp;Ontario\u0026rsquo;s Ethics Review Board.\u003c/h1\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSchieble, J. H., Fox, V. 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Journal of Clinical Virology 169, 105617 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLemey, P., Rambaut, A., Drummond, A. J. \u0026amp; Suchard, M. A. Bayesian phylogeography finds its roots. PLoS Computational Biology 5, e1000520 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLemey, P. \u003cem\u003eet al.\u003c/em\u003e Accommodating individual travel history and unsampled diversity in Bayesian phylogeographic inference of SARS-CoV-2. Nature Communications 11, 1\u0026ndash;14 (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-viruses","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Viruses](https://www.nature.com/npjviruses)","snPcode":"44298","submissionUrl":"https://submission.springernature.com/new-submission/44298/3","title":"npj Viruses","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Enterovirus-D68, Whole Genome Sequence Data, Case Counts, Phylodynamics, Outbreak Potential, Transmission Dynamics, Reproduction Number, Infection Duration, Infection Period","lastPublishedDoi":"10.21203/rs.3.rs-4362075/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4362075/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEnterovirus D68 (EV-D68) has emerged as a significant cause of acute respiratory illness in children globally, notably following its extensive outbreak in North America in 2014. A recent outbreak of EV-D68 was observed in Ontario, Canada, from August to October 2022. Our phylogenetic analysis revealed a notable genetic similarity between the Ontario outbreak and a concurrent outbreak in Maryland, USA. Utilizing Bayesian phylodynamic modeling on whole genome sequences (WGS) from both outbreaks, we determined the median peak time-varying reproduction number (R\u003csub\u003et\u003c/sub\u003e) to be 2.70 (95% HPD 1.76, 4.08) in Ontario and 2.10 (95% HPD 1.41, 3.17) in Maryland. The R\u003csub\u003et\u003c/sub\u003e trends in Ontario closely matched those derived via EpiEstim using reported case numbers. Our study also provides new insights into the median infection duration of EV-D68, estimated at 7.94 days (95% HPD 4.55, 12.8) in Ontario and 10.8 days (95% HPD 5.85, 18.6) in Maryland, addressing the gap in the existing literature surrounding EV-D68\u0026rsquo;s infection period. We observed that the estimated Time since the Most Recent Common Ancestor (TMRCA) and the epidemic's origin coincided with the easing of COVID-19 related social contact restrictions in both areas. This suggests that the relaxation of non-pharmaceutical interventions, initially implemented to control COVID-19, may have inadvertently facilitated the spread of EV-D68. These findings underscore the effectiveness of phylodynamic methods in public health, demonstrating their broad application from local to global scales and underscoring the critical role of pathogen genomic data in enhancing public health surveillance and outbreak characterization.\u003c/p\u003e","manuscriptTitle":"Inferring Enterovirus D68 Transmission Dynamics from the Genomic Data of Two 2022 North American outbreaks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-15 15:42:31","doi":"10.21203/rs.3.rs-4362075/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-07T08:31:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-25T07:20:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-12T22:29:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"48195388180393793696111532462910915326","date":"2024-05-09T18:27:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"450906315959805989355424830349290596","date":"2024-05-09T14:25:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"251328583963877649600560611248879416756","date":"2024-05-09T00:28:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-07T10:57:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-07T07:25:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-07T06:58:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Viruses","date":"2024-05-03T05:31:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-viruses","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Viruses](https://www.nature.com/npjviruses)","snPcode":"44298","submissionUrl":"https://submission.springernature.com/new-submission/44298/3","title":"npj Viruses","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3c1eb833-6962-4a49-ab08-a5326d2aa2e9","owner":[],"postedDate":"May 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":31822381,"name":"Biological sciences/Microbiology/Virology/Viral epidemiology"},{"id":31822382,"name":"Biological sciences/Microbiology/Virology/Viral evolution"},{"id":31822383,"name":"Health sciences/Medical research/Epidemiology"}],"tags":[],"updatedAt":"2024-07-18T10:26:05+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-15 15:42:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4362075","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4362075","identity":"rs-4362075","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}