5 results
Search Results
2. Women are underrepresented in computational biology: An analysis of the scholarly literature in biology, computer science and computational biology.
- Author
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Bonham, Kevin S. and Stefan, Melanie I.
- Subjects
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STEM education , *COMPUTATIONAL biology , *BIBLIOMETRICS , *SCIENCE publishing , *SCIENCE & state - Abstract
While women are generally underrepresented in STEM fields, there are noticeable differences between fields. For instance, the gender ratio in biology is more balanced than in computer science. We were interested in how this difference is reflected in the interdisciplinary field of computational/quantitative biology. To this end, we examined the proportion of female authors in publications from the PubMed and arXiv databases. There are fewer female authors on research papers in computational biology, as compared to biology in general. This is true across authorship position, year, and journal impact factor. A comparison with arXiv shows that quantitative biology papers have a higher ratio of female authors than computer science papers, placing computational biology in between its two parent fields in terms of gender representation. Both in biology and in computational biology, a female last author increases the probability of other authors on the paper being female, pointing to a potential role of female PIs in influencing the gender balance. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
3. Even a good influenza forecasting model can benefit from internet-based nowcasts, but those benefits are limited.
- Author
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Osthus, Dave, Daughton, Ashlynn R., and Priedhorsky, Reid
- Subjects
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INFLUENZA , *RESPIRATORY infections , *PUBLIC health , *MATHEMATICAL models of forecasting - Abstract
The ability to produce timely and accurate flu forecasts in the United States can significantly impact public health. Augmenting forecasts with internet data has shown promise for improving forecast accuracy and timeliness in controlled settings, but results in practice are less convincing, as models augmented with internet data have not consistently outperformed models without internet data. In this paper, we perform a controlled experiment, taking into account data backfill, to improve clarity on the benefits and limitations of augmenting an already good flu forecasting model with internet-based nowcasts. Our results show that a good flu forecasting model can benefit from the augmentation of internet-based nowcasts in practice for all considered public health-relevant forecasting targets. The degree of forecast improvement due to nowcasting, however, is uneven across forecasting targets, with short-term forecasting targets seeing the largest improvements and seasonal targets such as the peak timing and intensity seeing relatively marginal improvements. The uneven forecasting improvements across targets hold even when “perfect” nowcasts are used. These findings suggest that further improvements to flu forecasting, particularly seasonal targets, will need to derive from other, non-nowcasting approaches. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
4. Ten simple rules for measuring the impact of workshops.
- Author
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Sufi, Shoaib, Nenadic, Aleksandra, Silva, Raniere, Balzano, Melissa, Coelho, Sara, Ford, Heather, Jones, Catherine, Higgins, Vanessa, Duckles, Beth, Simera, Iveta, de Beyer, Jennifer A., Struthers, Caroline, Nurmikko-Fuller, Terhi, Bellis, Louisa, Miah, Wadud, Wilde, Adriana, Emsley, Iain, and Philippe, Olivier
- Subjects
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FORUMS , *RESEARCH , *DECISION making , *STRATEGIC planning , *PARTICIPATION - Abstract
Workshops are used to explore a specific topic, to transfer knowledge, to solve identified problems, or to create something new. In funded research projects and other research endeavours, workshops are the mechanism used to gather the wider project, community, or interested people together around a particular topic. However, natural questions arise: how do we measure the impact of these workshops? Do we know whether they are meeting the goals and objectives we set for them? What indicators should we use? In response to these questions, this paper will outline rules that will improve the measurement of the impact of workshops. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
5. Targeted pandemic containment through identifying local contact network bottlenecks
- Author
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Chris T. Bauch, Di Wang, Shenghao Yang, Priyabrata Senapati, and Kimon Fountoulakis
- Subjects
FOS: Computer and information sciences ,Viral Diseases ,Facebook ,Computer science ,Epidemiology ,Distributed computing ,Social Sciences ,01 natural sciences ,Systems Science ,Oregon ,Medical Conditions ,Sociology ,Agent-Based Modeling ,Medicine and Health Sciences ,Centrality ,Biology (General) ,Computer Networks ,0303 health sciences ,education.field_of_study ,Ecology ,Simulation and Modeling ,Quebec ,Social Communication ,Computer Science - Social and Information Networks ,Infectious Diseases ,Computational Theory and Mathematics ,Social Networks ,Modeling and Simulation ,Convex optimization ,Physical Sciences ,Network Analysis ,Algorithms ,Research Article ,Physics - Physics and Society ,Computer and Information Sciences ,QH301-705.5 ,Population ,FOS: Physical sciences ,Physics and Society (physics.soc-ph) ,Research and Analysis Methods ,Models, Biological ,Bottleneck ,03 medical and health sciences ,Cellular and Molecular Neuroscience ,0103 physical sciences ,Genetics ,Humans ,Computer Simulation ,Quantitative Biology - Populations and Evolution ,010306 general physics ,education ,Molecular Biology ,Pandemics ,Ecology, Evolution, Behavior and Systematics ,030304 developmental biology ,Social and Information Networks (cs.SI) ,Simulation modeling ,Populations and Evolution (q-bio.PE) ,COVID-19 ,Covid 19 ,Flow network ,Communications ,Transmission (telecommunications) ,Flow (mathematics) ,FOS: Biological sciences ,Social Media ,Mathematics - Abstract
Decision-making about pandemic mitigation often relies upon simulation modelling. Models of disease transmission through networks of contacts--between individuals or between population centres--are increasingly used for these purposes. Real-world contact networks are rich in structural features that influence infection transmission, such as tightly-knit local communities that are weakly connected to one another. In this paper, we propose a new flow-based edge-betweenness centrality method for detecting bottleneck edges that connect nodes in contact networks. In particular, we utilize convex optimization formulations based on the idea of diffusion with p-norm network flow. Using simulation models of COVID-19 transmission through real network data at both individual and county levels, we demonstrate that targeting bottleneck edges identified by the proposed method reduces the number of infected cases by up to 10% more than state-of-the-art edge-betweenness methods. Furthermore, the proposed method is orders of magnitude faster than existing methods., Comment: 38 pages, 21 figures
- Published
- 2021
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