Dengue is the fastest spreading arboviral disease, posing great challenges on global public health. A reproduceable and comparable global genotyping framework for contextualizing spatiotemporal epidemiological data of dengue virus (DENV) is essential for research studies and collaborative surveillance. Targeting DENV-1 spreading prominently in recent decades, by reconciling all qualified complete E gene sequences of 5003 DENV-1 strains with epidemiological information from 78 epidemic countries/areas ranging from 1944 to 2018, we established and characterized a unified global high-resolution genotyping framework using phylogenetics, population genetics, phylogeography, and phylodynamics. The defined framework was discriminated with three hierarchical layers of genotype, subgenotype and clade with respective mean pairwise distances 2–6%, 0.8–2%, and ≤ 0.8%. The global epidemic patterns of DENV-1 showed strong geographic constraints representing stratified spatial-genetic epidemic pairs of Continent-Genotype, Region-Subgenotype and Nation-Clade, thereby identifying 12 epidemic regions which prospectively facilitates the region-based coordination. The increasing cross-transmission trends were also demonstrated. The traditional endemic countries such as Thailand, Vietnam and Indonesia displayed as persisting dominant source centers, while the emerging epidemic countries such as China, Australia, and the USA, where dengue outbreaks were frequently triggered by importation, showed a growing trend of DENV-1 diffusion. The probably hidden epidemics were found especially in Africa and India. Then, our framework can be utilized in an accurate stratified coordinated surveillance based on the defined viral population compositions. Thereby it is prospectively valuable for further hampering the ongoing transition process of epidemic to endemic, addressing the issue of inadequate monitoring, and warning us to be concerned about the cross-national, cross-regional, and cross-continental diffusions of dengue, which can potentially trigger large epidemics. The framework and its utilization in quantitatively assessing DENV-1 epidemics has laid a foundation and re-unveiled the urgency for establishing a stratified coordinated surveillance platform for blocking global spreading of dengue. This framework is also expected to bridge classical DENV-1 genotyping with genomic epidemiology and risk modeling. We will promote it to the public and update it periodically.
New AI-assisted method could help counter COVID-19 mutations
USC researchers have developed a new method to counter emergent mutations of the coronavirus and hasten vaccine development to stop the pathogen responsible for killing thousands of people and ruining the economy.
Using artificial intelligence (AI), the research team at the USC Viterbi School of Engineering developed a method to speed the analysis of vaccines and zero in on the best potential preventive medical therapy.
The method is easily adaptable to analyze potential mutations of the virus, ensuring the best possible vaccines are quickly identified -- solutions that give humans a big advantage over the evolving contagion. Their machine-learning model can accomplish vaccine design cycles that once took months or years in a matter of seconds and minutes, the study says.
Scientists develop AI tool that can help world stay ahead of Covid mutation
SECTIONS
Scientists develop AI tool that can help world stay ahead of Covid mutationBy Seema Hakhu Kachru, PTI
Last Updated: Feb 06, 2021, 01:57 PM IST
Share
AFP
The research team using artificial intelligence developed a method to speed the analysis of vaccines and zero in on the best potential preventive medical therapy, the varsity said in a statement.
Related
HOUSTON: Researchers have developed a new method to counter emergent mutations of the deadly coronavirus and hasten vaccine development to stop the pathogen responsible for killing thousands of people and ruining the economy, according to a study.
New AI Tool Can Thwart Coronavirus Mutations: Study
FOLLOW US ON:
Kachru Houston: Researchers have developed a new method to counter emergent mutations of the deadly coronavirus and hasten vaccine development to stop the pathogen responsible for killing thousands of people and ruining the economy, according to a study. The research team at the University of Southern California Viterbi School of Engineering, using artificial intelligence developed a method to speed the analysis of vaccines and zero in on the best potential preventive medical therapy, the varsity said in a statement. The method is easily adaptable to analyse potential mutations of the virus, ensuring the best possible vaccines are quickly identified — solutions that give humans a big advantage over the evolving contagion. Their machine-learning model can accomplish vaccine design cycles that once took months or years in a matter of seconds and minutes, the study says.
E-Mail
USC researchers have developed a new method to counter emergent mutations of the coronavirus and hasten vaccine development to stop the pathogen responsible for killing thousands of people and ruining the economy.
Using artificial intelligence (AI), the research team at the USC Viterbi School of Engineering developed a method to speed the analysis of vaccines and zero in on the best potential preventive medical therapy.
The method is easily adaptable to analyze potential mutations of the virus, ensuring the best possible vaccines are quickly identified -- solutions that give humans a big advantage over the evolving contagion. Their machine-learning model can accomplish vaccine design cycles that once took months or years in a matter of seconds and minutes, the study says.