Determinants of dengue virus dispersal in the Americas

Dengue viruses (DENVs) are classified into four serotypes, each of which contains multiple genotypes. DENV genotypes introduced into the Americas over the past five decades have exhibited different rates and patterns of spatial dispersal. In order to understand factors underlying these patterns, we utilized a statistical framework that allows for the integration of ecological, socioeconomic, and air transport mobility data as predictors of viral diffusion while inferring the phylogeographic history. Predictors describing spatial diffusion based on several covariates were compared using a generalized linear model approach, where the support for each scenario and its contribution is estimated simultaneously from the data set. Although different predictors were identified for different serotypes, our analysis suggests that overall diffusion of DENV-1, -2, and -3 in the Americas was associated with airline traffic. The other significant predictors included human population size, the geographical distance between countries and between urban centers and the density of people living in urban environments

Global disparities in SARS-CoV-2 genomic surveillance

Genomic sequencing is essential to track the evolution and spread of SARS-CoV-2, optimize molecular tests, treatments, vaccines, and guide public health responses. To investigate the global SARS-CoV-2 genomic surveillance, we used sequences shared via GISAID to estimate the impact of sequencing intensity and turnaround times on variant detection in 189 countries. In the first two years of the pandemic, 78% of high-income countries sequenced >0.5% of their COVID-19 cases, while 42% of low- and middle-income countries reached that mark. Around 25% of the genomes from high income countries were submitted within 21 days, a pattern observed in 5% of the genomes from low- and middle-income countries. We found that sequencing around 0.5% of the cases, with a turnaround time <21 days, could provide a benchmark for SARS-CoV-2 genomic surveillance. Socioeconomic inequalities undermine the global pandemic preparedness, and efforts must be made to support low- and middle-income countries improve their local sequencing capacity

Global disparities in SARS-CoV-2 genomic surveillance

Genomic sequencing is essential to track the evolution and spread of SARS-CoV-2, optimize molecular tests, treatments, vaccines, and guide public health responses. To investigate the global SARS-CoV-2 genomic surveillance, we used sequences shared via GISAID to estimate the impact of sequencing intensity and turnaround times on variant detection in 189 countries. In the first two years of the pandemic, 78% of high-income countries sequenced >0.5% of their COVID-19 cases, while 42% of low- and middle-income countries reached that mark. Around 25% of the genomes from high income countries were submitted within 21 days, a pattern observed in 5% of the genomes from low- and middle-income countries. We found that sequencing around 0.5% of the cases, with a turnaround time <21 days, could provide a benchmark for SARS-CoV-2 genomic surveillance. Socioeconomic inequalities undermine the global pandemic preparedness, and efforts must be made to support low- and middle-income countries improve their local sequencing capacity

Genomics-informed outbreak investigations of SARS-CoV-2 using civet

The scale of data produced during the SARS-CoV-2 pandemic has been unprecedented, with more than 13 million sequences shared publicly at the time of writing. This wealth of sequence data provides important context for interpreting local outbreaks. However, placing sequences of interest into national and international context is difficult given the size of the global dataset. Often outbreak investigations and genomic surveillance efforts require running similar analyses again and again on the latest dataset and producing reports. We developed civet (cluster investigation and virus epidemiology tool) to aid these routine analyses and facilitate virus outbreak investigation and surveillance. Civet can place sequences of interest in the local context of background diversity, resolving the query into different ’catchments’ and presenting the phylogenetic results alongside metadata in an interactive, distributable report. Civet can be used on a fine scale for clinical outbreak investigation, for local surveillance and cluster discovery, and to routinely summarise the virus diversity circulating on a national level. Civet reports have helped researchers and public health bodies feedback genomic information in the appropriate context within a timeframe that is useful for public health

Tracking the international spread of SARS-CoV-2 lineages B.1.1.7 and B.1.351/501Y-V2

Publisher Copyright: © 2021 O'Toole Á et al.Late in 2020, two genetically-distinct clusters of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with mutations of biological concern were reported, one in the United Kingdom and one in South Africa. Using a combination of data from routine surveillance, genomic sequencing and international travel we track the international dispersal of lineages B.1.1.7 and B.1.351 (variant 501Y-V2). We account for potential biases in genomic surveillance efforts by including passenger volumes from location of where the lineage was first reported, London and South Africa respectively. Using the software tool grinch (global report investigating novel coronavirus haplotypes), we track the international spread of lineages of concern with automated daily reports, Further, we have built a custom tracking website (cov-lineages.org/global_report.html) which hosts this daily report and will continue to include novel SARS-CoV-2 lineages of concern as they are detected.Peer reviewe

Genomics-informed outbreak investigations of SARS-CoV-2 using civet

The scale of data produced during the SARS-CoV-2 pandemic has been unprecedented, with more than 13 million sequences shared publicly at the time of writing. This wealth of sequence data provides important context for interpreting local outbreaks. However, placing sequences of interest into national and international context is difficult given the size of the global dataset. Often outbreak investigations and genomic surveillance efforts require running similar analyses again and again on the latest dataset and producing reports. We developed civet (cluster investigation and virus epidemiology tool) to aid these routine analyses and facilitate virus outbreak investigation and surveillance. Civet can place sequences of interest in the local context of background diversity, resolving the query into different ’catchments’ and presenting the phylogenetic results alongside metadata in an interactive, distributable report. Civet can be used on a fine scale for clinical outbreak investigation, for local surveillance and cluster discovery, and to routinely summarise the virus diversity circulating on a national level. Civet reports have helped researchers and public health bodies feedback genomic information in the appropriate context within a timeframe that is useful for public health

Determinants of dengue virus dispersal in the Americas.

Dengue viruses (DENVs) are classified into four serotypes, each of which contains multiple genotypes. DENV genotypes introduced into the Americas over the past five decades have exhibited different rates and patterns of spatial dispersal. In order to understand factors underlying these patterns, we utilized a statistical framework that allows for the integration of ecological, socioeconomic, and air transport mobility data as predictors of viral diffusion while inferring the phylogeographic history. Predictors describing spatial diffusion based on several covariates were compared using a generalized linear model approach, where the support for each scenario and its contribution is estimated simultaneously from the data set. Although different predictors were identified for different serotypes, our analysis suggests that overall diffusion of DENV-1, -2, and -3 in the Americas was associated with airline traffic. The other significant predictors included human population size, the geographical distance between countries and between urban centers and the density of people living in urban environments

Molecular Characterisation of Chikungunya Virus Infections in Trinidad and Comparison of Clinical and Laboratory Features with Dengue and Other Acute Febrile Cases

<div><p>Local transmission of Chikungunya virus (CHIKV) was first documented in Trinidad and Tobago (T&T) in July 2014 preceding a large epidemic. At initial presentation, it is difficult to distinguish chikungunya fever (CHIKF) from other acute undifferentiated febrile illnesses (AUFIs), including life-threatening dengue disease. We characterised and compared dengue virus (DENV) and CHIKV infections in 158 patients presenting with suspected dengue fever (DF) and CHIKF at a major hospital in T&T, and performed phylogenetic analyses on CHIKV genomic sequences recovered from 8 individuals. The characteristics of patients with and without PCR-confirmed CHIKV were compared using Pearson’s χ^<i>2</i> and student’s t-tests, and adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were determined using logistic regression. We then compared signs and symptoms of people with RT-qPCR-confirmed CHIKV and DENV infections using the Mann-Whitney U, Pearson’s χ^<i>2</i> and Fisher’s exact tests. Among the 158 persons there were 8 (6%) RT-qPCR-confirmed DENV and 30 (22%) RT-qPCR-confirmed CHIKV infections. Phylogenetic analyses showed that the CHIKV strains belonged to the Asian genotype and were most closely related to a British Virgin Islands strain isolated at the beginning of the 2013/14 outbreak in the Americas. Compared to persons who were RT-qPCR-negative for CHIKV, RT-qPCR-positive individuals were significantly more likely to have joint pain (aOR: 4.52 [95% CI: 1.28–16.00]), less likely to be interviewed at a later stage of illness (days post onset of fever—aOR: 0.56 [0.40–0.78]) and had a lower white blood cell count (aOR: 0.83 [0.71–0.96]). Among the 38 patients with RT-qPCR-confirmed CHIKV or DENV, there were no significant differences in symptomatic presentation. However when individuals with serological evidence of recent DENV or CHIKV infection were included in the analyses, there were key differences in clinical presentation between CHIKF and other AUFIs including DF, which can be used to triage patients for appropriate care in the clinical setting.</p></div

Positive predictive values (PPVs), negative predictive values (NPVs), sensitivity, specificity and likelihood ratios of clinical features used to distinguish patients of the APCF of the EWMSC, T&T (Dec 2013 –Nov 2014) with DF ^ⱡ and CHIKF ^ⱡ from patients with other AUFIs.

<p>Positive predictive values (PPVs), negative predictive values (NPVs), sensitivity, specificity and likelihood ratios of clinical features used to distinguish patients of the APCF of the EWMSC, T&T (Dec 2013 –Nov 2014) with DF <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004199#t004fn001" target="_blank">^ⱡ</a> and CHIKF <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004199#t004fn001" target="_blank">^ⱡ</a> from patients with other AUFIs.</p