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21.
Cliff C. Kerr Robyn M. Stuart Dina Mistry Romesh G. Abeysuriya Katherine Rosenfeld Gregory R. Hart Rafael C. Núez Jamie A. Cohen Prashanth Selvaraj Brittany Hagedorn Lauren George Micha Jastrzbski Amanda S. Izzo Greer Fowler Anna Palmer Dominic Delport Nick Scott Sherrie L. Kelly Caroline S. Bennette Bradley G. Wagner Stewart T. Chang Assaf P. Oron Edward A. Wenger Jasmina Panovska-Griffiths Michael Famulare Daniel J. Klein 《PLoS computational biology》2021,17(7)
The COVID-19 pandemic has created an urgent need for models that can project epidemic trends, explore intervention scenarios, and estimate resource needs. Here we describe the methodology of Covasim (COVID-19 Agent-based Simulator), an open-source model developed to help address these questions. Covasim includes country-specific demographic information on age structure and population size; realistic transmission networks in different social layers, including households, schools, workplaces, long-term care facilities, and communities; age-specific disease outcomes; and intrahost viral dynamics, including viral-load-based transmissibility. Covasim also supports an extensive set of interventions, including non-pharmaceutical interventions, such as physical distancing and protective equipment; pharmaceutical interventions, including vaccination; and testing interventions, such as symptomatic and asymptomatic testing, isolation, contact tracing, and quarantine. These interventions can incorporate the effects of delays, loss-to-follow-up, micro-targeting, and other factors. Implemented in pure Python, Covasim has been designed with equal emphasis on performance, ease of use, and flexibility: realistic and highly customized scenarios can be run on a standard laptop in under a minute. In collaboration with local health agencies and policymakers, Covasim has already been applied to examine epidemic dynamics and inform policy decisions in more than a dozen countries in Africa, Asia-Pacific, Europe, and North America. 相似文献
22.
Erika de Wit Wayne Delport Chimusa E. Rugamika Ayton Meintjes Marlo Möller Paul D. van Helden Cathal Seoighe Eileen G. Hoal 《Human genetics》2010,128(2):145-153
Admixed populations present unique opportunities to discover the genetic factors underlying many multifactorial diseases.
The geographical position and complex history of South Africa has led to the establishment of the unique admixed population
known as the South African Coloured. Not much is known about the genetic make-up of this population, and the historical record
is patchy. We genotyped 959 individuals from the Western Cape area, self-identified as belonging to this population, using
the Affymetrix 500k genotyping platform. This resulted in nearly 75,000 autosomal SNPs that could be compared with populations
represented in the International HapMap Project and the Human Genome Diversity Project. Analysis by means of both the admixture
and linkage models in STRUCTURE revealed that the major ancestral components of this population are predominantly Khoesan
(32–43%), Bantu-speaking Africans (20–36%), European (21–28%) and a smaller Asian contribution (9–11%), depending on the model
used. This is consistent with historical data. While of great historical and genealogical interest, this information is also
essential for future admixture mapping of disease genes in this population. 相似文献
23.
Hydrobiologia - Silica (Si) plays a significant role in alleviating the effects of biotic and abiotic stressors in many plants, especially in an agricultural context. With increased use of Si-based... 相似文献