1,721,038 research outputs found
Data integration and simulation of population immunity at the beginning of a pandemic
Full text linkAccurate knowledge of population exposure at the outset of a pandemic has critical ramifications for preparedness plans for future epidemic waves. In this thesis, I developed a mechanistically informed statistical model to integrate multiple epidemiological datasets in different settings and in different population and to estimate key epidemiological parameters as well as population exposure using Bayesian inference.
First, I present a dynamic model to link together three key metrics for evaluating the progress of COVID-19 epidemic in England: seroprevalence, PR-PCR test positivity and death. While estimating the IgG antibody seroreversion rate and region-specific infection fatality ratios, I find that epidemic progression resulted in an increasing gap between measured serology prevalence levels and cumulative population exposure to the virus. Ultimately, this may mean that twice as many, or more, people have been exposed to the virus relative to the number of people who are seropositive by the end of 2020.
Moreover, I demonstrate that the model could reconstruct the first, unobserved, epidemic wave of COVID-19 in England from March 2020 to June 2020 as long as two or three serological measurements are given as inputs, with the second wave during the winter of 2020 validated by the estimates from the ONS Coronavirus Infection Survey. Comparing with the inferred exposure, I find that the UK official COVID-9 online dashboard reported COVID-19 cases only accounted for less than ten percent by the end of October 2020. I then generalise the model to account for the undocumented COVID-19-related mortality and sparse measurements of seroprevalence. I apply this in the context of Afghanistan COVID- 19 epidemic and find the population exposure in nine regions of Afghanistan were all higher than the seroprevalence survey suggested by July 2020.
Finally, I assess the impact of shielding among pregnant patients by comparing their exposure with the estimated exposure of the general population. To approach this, I develop a dynamic model to link RT-PCR and antibody testing results from patients who gave birth and then apply Bayesian inference to estimate transmission parameters and exposure among pregnant patients. I find that after considering the duration of each pregnancy pre-COVID onset and after, the impact of shielding on reducing the level of exposure among pregnant patients during early 2020 who gave birth in this New York City hospital were approximately 50%
Viral fitness, genomic recombination, and immune evasion: Mechanisms of HIV evolution across scales
Full text linkThe within-host evolution of HIV is characterised by rapid selection of immune-evasion mutations, pervasive recombination, and vast fitness landscapes, making it one of the fastest evolving organisms. The enormous evolutionary capacity of the virus has challenged therapeutic development, with a cure remaining elusive and drug resistance continues to be a concern. In contrast, between-host evolution shows evidence of neutral evolution, with multiple subtypes coexisting globally. Selection pressures and fitness trade-offs can vary or conflict across within and between-host scales, and the impact of within-host selection on the virus circulating at a population level is not fully understood. In this thesis, I investigate the evolutionary forces driving viral dynamics, focusing on how selection and recombination shape viral populations within and between hosts. Using whole-genome deep sequencing data from hundreds of longitudinally sampled transmission pairs in sub-Saharan Africa, I explore several aspects of viral evolution. First, I examine virulence evolution as a classic example of the conflict between withinhost and between-host selection. Through modelling viral load as determined by the number of weakly deleterious mutations across many segregating sites, I demonstrate how between-host selection for transmission fitness can overcome short-term evolutionary pressures within-host as a result of the balance between mutation and selection pressure. I then investigate recombination, a major source of viral genetic diversity, discussing challenges in accurately inferring recombination rates in dynamic, rapidly evolving viral populations. I identify consistent recombination hot and cold spots across the genome that persist across subtypes and sequencing platforms, corresponding with previously identified inter-subtype patterns. I then quantify the evolutionary rate at both the within and between-host scales across the genome, proposing that transient, high-frequency mutations (“toggling” mutations) explain discrepancies in rate estimation across the two scales. Finally, I show that CTL escape mutations dominate early viral evolution and undergo selection and reversion during transmission, linking these dynamics to within-host toggling at the amino acid level. This work reveals general principles of viral evolution that persist across hosts, subtypes, and scales. The findings have important implications for understanding the long-term impact of immune pressures, the balance of selection forces, and methodological approaches to studying rapidly evolving viruses
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Invisible foes Contagion: How Commerce Has Spread Disease, Mark Harrison (Yale University Press, New Haven, CT; 2013), ISBN: 978-0-300-12357-9
No full textUnknow
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