The origins of dengue and chikungunya viruses in Ecuador following increased migration from Venezuela and Colombia

This work was funded by the Armed Forces Health Surveillance Branch (AFHSB) and its Global Emerging Infections Surveillance (GEIS) Section, FY2018 ProMIS ID P0108_18_WR.Background: In recent years, Ecuador and other South American countries have experienced an increase in arboviral diseases. A rise in dengue infections was followed by introductions of chikungunya and Zika, two viruses never before seen in many of these areas. Furthermore, the latest socioeconomic and political instability in Venezuela and the mass migration of its population into the neighboring countries has given rise to concerns of infectious disease spillover and escalation of arboviral spread in the region. Results: We performed phylogeographic analyses of dengue (DENV) and chikungunya (CHIKV) virus genomes sampled from a surveillance site in Ecuador in 2014-2015, along with genomes from the surrounding countries. Our results revealed at least two introductions of DENV, in 2011 and late 2013, that initially originated from Venezuela and/or Colombia. The introductions were subsequent to increases in the influx of Venezuelan and Colombian citizens into Ecuador, which in 2013 were 343% and 214% higher than in 2009, respectively. However, we show that Venezuela has historically been an important source of DENV dispersal in this region, even before the massive exodus of its population, suggesting already established paths of viral distribution. Like DENV, CHIKV was introduced into Ecuador at multiple time points in 2013-2014, but unlike DENV, these introductions were associated with the Caribbean. Our findings indicated no direct CHIKV connection between Ecuador, Colombia, and Venezuela as of 2015, suggesting that CHIKV was, at this point, not following the paths of DENV spread. Conclusion: Our results reveal that Ecuador is vulnerable to arbovirus import from many geographic locations, emphasizing the need of continued surveillance and more diversified prevention strategies. Importantly, increase in human movement along established paths of viral dissemination, combined with regional outbreaks and epidemics, may facilitate viral spread and lead to novel virus introductions. Thus, strengthening infectious disease surveillance and control along migration routes and improving access to healthcare for the vulnerable populations is of utmost importance.Publisher PDFPeer reviewe

Effect of Low-Passage Number on Dengue Consensus Genomes and Intra-host Variant Frequencies

Intra-host single nucleotide variants (iSNVs) have been increasingly used in genomic epidemiology to increase phylogenetic resolution and reconstruct fine-scale outbreak dynamics. These analyses are preferably done on sequence data from direct clinical samples, but in many cases due to low viral loads, there might not be enough genetic material for deep sequencing and iSNV determination. Isolation of the virus from clinical samples with low-passage number increases viral load, but few studies have investigated how dengue virus (DENV) culture isolation from a clinical sample impacts the consensus sequence and the intra-host virus population frequencies. In this study, we investigate consensus and iSNV frequency differences between DENV sequenced directly from clinical samples and their corresponding low-passage isolates. Twenty five DENV1 and DENV2 posi- tive sera and their corresponding viral isolates (T. splendens inoculation and C6/36 passage) were obtained from a prospective cohort study in the Philippines. These were sequenced on MiSeq with minimum nucleotide depth of coverage of 500×, and iSNVs were detected using LoFreq. For both DENV1 and DENV2, we found a maximum of one consensus nucleotide difference between clinical sample and isolate. Interestingly, we found that iSNVs with frequencies ≥5% were often preserved between the samples, and that the number of iSNV positions, and sample diversity, at this frequency cutoff did not differ significantly between the sample pairs (clinical sample and isolate) in either DENV1 or DENV2 data. Our results show that low-passage DENV isolate consensus genomes are largely representative of their direct sample parental viruses, and that low-passage isolates often mirror high frequency within-host variants from direct samples

Analysis of cell-associated DENV RNA by oligo(dT) primed 5’ capture scRNAseq

Dengue is one of the most widespread vector-borne viral diseases in the world. However, the size, heterogeneity, and temporal dynamics of the cell-associated viral reservoir during acute dengue virus (DENV) infection remains unclear. In this study, we analyzed cells infected in vitro with DENV and PBMC from an individual experiencing a natural DENV infection utilizing 5’ capture single cell RNA sequencing (scRNAseq). Both positive- and negative-sense DENV RNA was detected in reactions containing either an oligo(dT) primer alone, or in reactions supplemented with a DENV-specific primer. The addition of a DENV-specific primer did not increase the total amount of DENV RNA captured or the fraction of cells identified as containing DENV RNA. However, inclusion of a DENV-specific cDNA primer did increase the viral genome coverage immediately 5’ to the primer binding site. Furthermore, while the majority of intracellular DENV sequence captured in this analysis mapped to the 5’ end of the viral genome, distinct patterns of enhanced coverage within the DENV polyprotein coding region were observed. The 5’ capture scRNAseq analysis of PBMC not only recapitulated previously published reports by detecting virally infected memory and naïve B cells, but also identified cell-associated genomic variants not observed in contemporaneous serum samples. These results demonstrate that oligo(dT) primed 5’ capture scRNAseq can detect DENV RNA and quantify virus-infected cells in physiologically relevant conditions, and provides insight into viral sequence variability within infected cells

Using outbreak science to strengthen the use of models during epidemics.

Infectious disease modeling has played a prominent role in recent outbreaks, yet integrating these analyses into public health decision-making has been challenging. We recommend establishing ‘outbreak science’ as an inter-disciplinary field to improve applied epidemic modeling

Identification and Evaluation of Epidemic Prediction and Forecasting Reporting Guidelines: A Systematic Review and a Call for Action

INTRODUCTION: High quality epidemic forecasting and prediction are critical to support response to local, regional and global infectious disease threats. Other fields of biomedical research use consensus reporting guidelines to ensure standardization and quality of research practice among researchers, and to provide a framework for end-users to interpret the validity of study results. The purpose of this study was to determine whether guidelines exist specifically for epidemic forecast and prediction publications. METHODS: We undertook a formal systematic review to identify and evaluate any published infectious disease epidemic forecasting and prediction reporting guidelines. This review leveraged a team of 18 investigators from US Government and academic sectors. RESULTS: A literature database search through May 26, 2019, identified 1467 publications (MEDLINE n = 584, EMBASE n = 883), and a grey-literature review identified a further 407 publications, yielding a total 1777 unique publications. A paired-reviewer system screened in 25 potentially eligible publications, of which two were ultimately deemed eligible. A qualitative review of these two published reporting guidelines indicated that neither were specific for epidemic forecasting and prediction, although they described reporting items which may be relevant to epidemic forecasting and prediction studies. CONCLUSIONS: This systematic review confirms that no specific guidelines have been published to standardize the reporting of epidemic forecasting and prediction studies. These findings underscore the need to develop such reporting guidelines in order to improve the transparency, quality and implementation of epidemic forecasting and prediction research in operational public health

Reconstructing unseen transmission events to infer dengue dynamics from viral sequences.

For most pathogens, transmission is driven by interactions between the behaviours of infectious individuals, the behaviours of the wider population, the local environment, and immunity. Phylogeographic approaches are currently unable to disentangle the relative effects of these competing factors. We develop a spatiotemporally structured phylogenetic framework that addresses these limitations by considering individual transmission events, reconstructed across spatial scales. We apply it to geocoded dengue virus sequences from Thailand (N = 726 over 18 years). We find infected individuals spend 96% of their time in their home community compared to 76% for the susceptible population (mainly children) and 42% for adults. Dynamic pockets of local immunity make transmission more likely in places with high heterotypic immunity and less likely where high homotypic immunity exists. Age-dependent mixing of individuals and vector distributions are not important in determining spread. This approach provides previously unknown insights into one of the most complex disease systems known and will be applicable to other pathogens

Identification and evaluation of epidemic prediction and forecasting reporting guidelines : a systematic review and a call for action

NGR reports funding by NIGMS grant R35GM119582. BMA is supported by Bill and Melinda Gates Foundation through the Global Good Fund. SP and IMB were funded by the Armed Forces Health Surveillance Branch (GEIS: P0116_19_WR_03.11).Introduction: High quality epidemic forecasting and prediction are critical to support response to local, regional and global infectious disease threats. Other fields of biomedical research use consensus reporting guidelines to ensure standardization and quality of research practice among researchers, and to provide a framework for end-users to interpret the validity of study results. The purpose of this study was to determine whether guidelines exist specifically for epidemic forecast and prediction publications. Methods: We undertook a formal systematic review to identify and evaluate any published infectious disease epidemic forecasting and prediction reporting guidelines. This review leveraged a team of 18 investigators from US Government and academic sectors. Results: A literature database search through May 26, 2019, identified 1467 publications (MEDLINE n = 584, EMBASE n = 883), and a grey-literature review identified a further 407 publications, yielding a total 1777 unique publications. A paired-reviewer system screened in 25 potentially eligible publications, of which two were ultimately deemed eligible. A qualitative review of these two published reporting guidelines indicated that neither were specific for epidemic forecasting and prediction, although they described reporting items which may be relevant to epidemic forecasting and prediction studies. Conclusions: This systematic review confirms that no specific guidelines have been published to standardize the reporting of epidemic forecasting and prediction studies. These findings underscore the need to develop such reporting guidelines in order to improve the transparency, quality and implementation of epidemic forecasting and prediction research in operational public health.Publisher PDFPeer reviewe

Recommended reporting items for epidemic forecasting and prediction research : the EPIFORGE 2020 guidelines

Funding: MIDAS Coordination Center and the National Institutes of General Medical Sciences (NIGMS 1U24GM132013) for supporting travel to the face-to-face consensus meeting by members of the Working Group. NGR was supported by the National Institutes of General Medical Sciences (R35GM119582). Travel for SV was supported by the National Institutes of General Medical Sciences (1U24GM132013-01). BMA was supported by Bill & Melinda Gates through the Global Good Fund. RL was funded by a Royal Society Dorothy Hodgkin Fellowship.Background  The importance of infectious disease epidemic forecasting and prediction research is underscored by decades of communicable disease outbreaks, including COVID-19. Unlike other fields of medical research, such as clinical trials and systematic reviews, no reporting guidelines exist for reporting epidemic forecasting and prediction research despite their utility. We therefore developed the EPIFORGE checklist, a guideline for standardized reporting of epidemic forecasting research. Methods and findings  We developed this checklist using a best-practice process for development of reporting guidelines, involving a Delphi process and broad consultation with an international panel of infectious disease modelers and model end users. The objectives of these guidelines are to improve the consistency, reproducibility, comparability, and quality of epidemic forecasting reporting. The guidelines are not designed to advise scientists on how to perform epidemic forecasting and prediction research, but rather to serve as a standard for reporting critical methodological details of such studies. Conclusions  These guidelines have been submitted to the EQUATOR network, in addition to hosting by other dedicated webpages to facilitate feedback and journal endorsement.Publisher PDFNon peer reviewe

Long-term cellular immunity of vaccines for Zaire Ebola Virus Diseases

Recent Ebola outbreaks underscore the importance of continuous prevention and disease control efforts. Authorized vaccines include Merck’s Ervebo (rVSV-ZEBOV) and Johnson & Johnson’s two-dose combination (Ad26.ZEBOV/MVA-BN-Filo). Here, in a five-year follow-up of the PREVAC randomized trial (NCT02876328), we report the results of the immunology ancillary study of the trial. The primary endpoint is to evaluate long-term memory T-cell responses induced by three vaccine regimens: Ad26–MVA, rVSV, and rVSV–booster. Polyfunctional EBOV-specific CD4+ T-cell responses increase after Ad26 priming and are further boosted by MVA, whereas minimal responses are observed in the rVSV groups, declining after one year. In-vitro expansion for eight days show sustained EBOV-specific T-cell responses for up to 60 months post-prime vaccination with both Ad26-MVA and rVSV, with no decline. Cytokine production analysis identify shared biomarkers between the Ad26-MVA and rVSV groups. In secondary endpoint, we observed an elevation of pro-inflammatory cytokines at Day 7 in the rVSV group. Finally, we establish a correlation between EBOV-specific T-cell responses and anti-EBOV IgG responses. Our findings can guide booster vaccination recommendations and help identify populations likely to benefit from revaccination