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1. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Author

Cramer, Estee Y, Ray, Evan L, Lopez, Velma K, Bracher, Johannes, Brennen, Andrea, Rivadeneira, Alvaro J Castro, Gerding, Aaron, Gneiting, Tilmann, House, Katie H, Huang, Yuxin, Jayawardena, Dasuni, Kanji, Abdul H, Khandelwal, Ayush, Le, Khoa, Mühlemann, Anja, Niemi, Jarad, Shah, Apurv, Stark, Ariane, Wang, Yijin, Wattanachit, Nutcha, Zorn, Martha W, Gu, Youyang, Jain, Sansiddh, Bannur, Nayana, Deva, Ayush, Kulkarni, Mihir, Merugu, Srujana, Raval, Alpan, Shingi, Siddhant, Tiwari, Avtansh, White, Jerome, Abernethy, Neil F, Woody, Spencer, Dahan, Maytal, Fox, Spencer, Gaither, Kelly, Lachmann, Michael, Meyers, Lauren Ancel, Scott, James G, Tec, Mauricio, Srivastava, Ajitesh, George, Glover E, Cegan, Jeffrey C, Dettwiller, Ian D, England, William P, Farthing, Matthew W, Hunter, Robert H, Lafferty, Brandon, Linkov, Igor, Mayo, Michael L, Parno, Matthew D, Rowland, Michael A, Trump, Benjamin D, Zhang-James, Yanli, Chen, Samuel, Faraone, Stephen V, Hess, Jonathan, Morley, Christopher P, Salekin, Asif, Wang, Dongliang, Corsetti, Sabrina M, Baer, Thomas M, Eisenberg, Marisa C, Falb, Karl, Huang, Yitao, Martin, Emily T, McCauley, Ella, Myers, Robert L, Schwarz, Tom, Sheldon, Daniel, Gibson, Graham Casey, Yu, Rose, Gao, Liyao, Ma, Yian, Wu, Dongxia, Yan, Xifeng, Jin, Xiaoyong, Wang, Yu-Xiang, Chen, YangQuan, Guo, Lihong, Zhao, Yanting, Gu, Quanquan, Chen, Jinghui, Wang, Lingxiao, Xu, Pan, Zhang, Weitong, Zou, Difan, Biegel, Hannah, Lega, Joceline, McConnell, Steve, Nagraj, VP, Guertin, Stephanie L, Hulme-Lowe, Christopher, Turner, Stephen D, Shi, Yunfeng, Ban, Xuegang, Walraven, Robert, Hong, Qi-Jun, Kong, Stanley, and van de Walle, Axel

Subjects
Bioengineering, Good Health and Well Being, COVID-19, Data Accuracy, Forecasting, Humans, Pandemics, Probability, Public Health, United States, forecasting, ensemble forecast, model evaluation
Abstract

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

Published
2022

2. XSEDE: Accelerating Scientific Discovery

Author

Towns, John, Cockerill, Timothy, Dahan, Maytal, Foster, Ian, Gaither, Kelly, Grimshaw, Andrew, Hazlewood, Victor, Lathrop, Scott, Lifka, Dave, Peterson, Gregory D, Roskies, Ralph, Scott, J Ray, and Wilkins-Diehr, Nancy

Subjects
Information and Computing Sciences, Library and Information Studies, Genome, Assembly, Alternative haplotype, Haploid, Purge, Deduplication, Scaffolding, Animals, Anopheles, Chromosome Mapping, Genomics, Diploidy, Genes, Insect, Software, Mathematical Sciences, Biological Sciences, Bioinformatics, Biological sciences, Information and computing sciences, Mathematical sciences
Abstract

BackgroundDespite marked recent improvements in long-read sequencing technology, the assembly of diploid genomes remains a difficult task. A major obstacle is distinguishing between alternative contigs that represent highly heterozygous regions. If primary and secondary contigs are not properly identified, the primary assembly will overrepresent both the size and complexity of the genome, which complicates downstream analysis such as scaffolding.ResultsHere we illustrate a new method, which we call HapSolo, that identifies secondary contigs and defines a primary assembly based on multiple pairwise contig alignment metrics. HapSolo evaluates candidate primary assemblies using BUSCO scores and then distinguishes among candidate assemblies using a cost function. The cost function can be defined by the user but by default considers the number of missing, duplicated and single BUSCO genes within the assembly. HapSolo performs hill climbing to minimize cost over thousands of candidate assemblies. We illustrate the performance of HapSolo on genome data from three species: the Chardonnay grape (Vitis vinifera), with a genome of 490 Mb, a mosquito (Anopheles funestus; 200 Mb) and the Thorny Skate (Amblyraja radiata; 2650 Mb).ConclusionsHapSolo rapidly identified candidate assemblies that yield improvements in assembly metrics, including decreased genome size and improved N50 scores. Contig N50 scores improved by 35%, 9% and 9% for Chardonnay, mosquito and the thorny skate, respectively, relative to unreduced primary assemblies. The benefits of HapSolo were amplified by down-stream analyses, which we illustrated by scaffolding with Hi-C data. We found, for example, that prior to the application of HapSolo, only 52% of the Chardonnay genome was captured in the largest 19 scaffolds, corresponding to the number of chromosomes. After the application of HapSolo, this value increased to ~ 84%. The improvements for the mosquito's largest three scaffolds, representing the number of chromosomes, were from 61 to 86%, and the improvement was even more pronounced for thorny skate. We compared the scaffolding results to assemblies that were based on PurgeDups for identifying secondary contigs, with generally superior results for HapSolo.

Published
2021

3. Cost-benefit Analysis of Visualization in Virtual Environments

Author

Chen, Min, Gaither, Kelly, John, Nigel W., and McCann, Brian

Subjects
Computer Science - Human-Computer Interaction, Computer Science - Graphics
Abstract

Visualization and virtual environments (VEs) have been two interconnected parallel strands in visual computing for decades. Some VEs have been purposely developed for visualization applications, while many visualization applications are exemplary showcases in general-purpose VEs. Because of the development and operation costs of VEs, the majority of visualization applications in practice are yet to benefit from the capacity of VEs. In this paper, we examine this perplexity from an information-theoretic perspective. Our objectives are to conduct cost-benefit analysis on typical VE systems (including augmented and mixed reality, theatre-based systems, and large powerwalls), to explain why some visualization applications benefit more from VEs than others, and to sketch out pathways for the future development of visualization applications in VEs. We support our theoretical propositions and analysis using theories and discoveries in the literature of cognitive sciences and the practical evidence reported in the literatures of visualization and VEs., Comment: Submitted to SciVis 2017 on 31 March 2017 and was not accepted. Authors' feedback about the SciVis 2017 reviews can be found in the cover letter accompanying the EuroVis submission. Revised with a major extension and submitted to EuroVis 2018 on 13 December 2017, but was not accepted

Published
2018
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4. Visualization of Feature Separation in Advected Scalar Fields

Author

Karch, Grzegorz K., Sadlo, Filip, Boblest, Sebastian, Ertl, Moritz, Weigand, Bernhard, Gaither, Kelly, and Ertl, Thomas

Subjects
Computer Science - Graphics
Abstract

Scalar features in time-dependent fluid flow are traditionally visualized using 3D representation, and their topology changes over time are often conveyed with abstract graphs. Using such techniques, however, the structural details of feature separation and the temporal evolution of features undergoing topological changes are difficult to analyze. In this paper, we propose a novel approach for the spatio-temporal visualization of feature separation that segments feature volumes into regions with respect to their contribution to distinct features after separation. To this end, we employ particle-based feature tracking to find volumetric correspondences between features at two different instants of time. We visualize this segmentation by constructing mesh boundaries around each volume segment of a feature at the initial time that correspond to the separated features at the later time. To convey temporal evolution of the partitioning within the investigated time interval, we complement our approach with spatio-temporal separation surfaces. For the application of our approach to multiphase flow, we additionally present a feature-based corrector method to ensure phase-consistent particle trajectories. The utility of our technique is demonstrated by application to two-phase (liquid-gas) and multi-component (liquid-liquid) flows where the scalar field represents the fraction of one of the phases.

Published
2017

15. ITS_LIVE: A Cloud-Native Approach to Monitoring Glaciers From Space.

Author

Lopez, Luis A., Gardner, Alex S., Greene, Chad A., Kennedy, Joseph H., Liukis, Maria, Fahnestock, Mark A., Scambos, Ted, Fahnestock, Jacob R., Borgo, Rita, and Gaither, Kelly

Subjects
OPEN scholarship, DATA libraries, EARTH sciences, CLOUD computing, REMOTE sensing, GLACIERS
Abstract

NASA's ITS_LIVE project delivers information on global glacier dynamics and provides historical context for climate change with a record of how every glacier in the world has evolved over decades of satellite observation. To handle petabytes of data in the satellite archives and a constant influx of new observations, the project has adopted a cloud-native approach that is scalable, performant, user friendly, and embraces transparent and collaborative code development. As every discipline of Earth science is being transformed by a new era of remote sensing and cloud computing, ITS_LIVE offers a progressive approach to maximizing scientific advancements through open science. [ABSTRACT FROM AUTHOR]

Published
2023
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18. Building Inclusive Communities

Author

Schmidt, Johanna, primary, Gaither, Kelly, additional, Glencross, Mashhuda, additional, Borkin, Michelle, additional, and Isenberg, Petra, additional

Published
2019
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23. Cross-Scale, Multi-Scale, and Multi-Source Data Visualization and Analysis Issues and Opportunities

Author

Ebert, David, Gaither, Kelly, Jang, Yun, Lasher-Trapp, Sonia, Farin, Gerald, Series editor, Hege, Hans-Christian, Series editor, Hoffman, David, Series editor, Johnson, Christopher R., Series editor, Polthier, Konrad, Series editor, Rumpf, Martin, Series editor, Hansen, Charles D., editor, Chen, Min, editor, Kaufman, Arie E., editor, and Hagen, Hans, editor

Published
2014
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24. Large-Scale Integration-Based Vector Field Visualization

Author

Garth, Christoph, Gaither, Kelly, Farin, Gerald, Series editor, Hege, Hans-Christian, Series editor, Hoffman, David, Series editor, Johnson, Christopher R., Series editor, Polthier, Konrad, Series editor, Rumpf, Martin, Series editor, Hansen, Charles D., editor, Chen, Min, editor, Kaufman, Arie E., editor, and Hagen, Hans, editor

Published
2014
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28. Data Visualization for Digital Twins.

Author

Comba, Joao L. D., Santos, Nicolau O., Rivera, Jonathan C., Romeu, Regis K., Abel, Mara, Borgo, Rita, and Gaither, Kelly

Subjects
DIGITAL twins, DATA visualization, TASK analysis
Abstract

Visualization techniques are useful in the analysis and insight generation for applications in computing in science and engineering. In this article, we describe the importance of visualization to a digital twin (DT), a virtual representation of a physical object, process or system that can be applied for different tasks, such as data-driven simulation, analysis or monitoring. We illustrate tasks in DTs and give examples of how visualization techniques can be applied for DTs in different application areas. [ABSTRACT FROM AUTHOR]

Published
2023
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29. Real-time pandemic surveillance using hospital admissions and mobility data

Author

Fox, Spencer J., primary, Lachmann, Michael, additional, Tec, Mauricio, additional, Pasco, Remy, additional, Woody, Spencer, additional, Du, Zhanwei, additional, Wang, Xutong, additional, Ingle, Tanvi A., additional, Javan, Emily, additional, Dahan, Maytal, additional, Gaither, Kelly, additional, Escott, Mark E., additional, Adler, Stephen I., additional, Johnston, S. Claiborne, additional, Scott, James G., additional, and Meyers, Lauren Ancel, additional

Published
2022
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30. The United States COVID-19 Forecast Hub dataset

Author

US COVID-19 Forecast Hub Consortium, Cramer, Estee Y., Huang, Yuxin, Wang, Yijin, Ray, Evan L., Cornell, Matthew, Bracher, Johannes, Brennen, Andrea, Rivadeneira, Alvaro J. Castro, Gerding, Aaron, House, Katie, Jayawardena, Dasuni, Kanji, Abdul Hannan, Khandelwal, Ayush, Le, Khoa, Mody, Vidhi, Mody, Vrushti, Niemi, Jarad, Stark, Ariane, Shah, Apurv, Wattanchit, Nutcha, Zorn, Martha W., Reich, Nicholas G., Gneiting, Tilmann, Mühlemann, Anja, Gu, Youyang, Chen, Yixian, Chintanippu, Krishna, Jivane, Viresh, Khurana, Ankita, Kumar, Ajay, Lakhani, Anshul, Mehrotra, Prakhar, Pasumarty, Sujitha, Shrivastav, Monika, You, Jialu, Bannur, Nayana, Deva, Ayush, Jain, Sansiddh, Kulkarni, Mihir, Merugu, Srujana, Raval, Alpan, Shingi, Siddhant, Tiwari, Avtansh, White, Jerome, Adiga, Aniruddha, Hurt, Benjamin, Lewis, Bryan, Marathe, Madhav, Peddireddy, Akhil Sai, Porebski, Przemyslaw, Venkatramanan, Srinivasan, Wang, Lijing, Dahan, Maytal, Fox, Spencer, Gaither, Kelly, Lachmann, Michael, Meyers, Lauren Ancel, Scott, James G., Tec, Mauricio, Woody, Spencer, Srivastava, Ajitesh, Xu, Tianjian, Cegan, Jeffrey C., Dettwiller, Ian D., England, William P., Farthing, Matthew W., George, Glover E., Hunter, Robert H., Lafferty, Brandon, Linkov, Igor, Mayo, Michael L., Parno, Matthew D., Rowland, Michael A., Trump, Benjamin D., Chen, Samuel, Faraone, Stephen V., Hess, Jonathan, Morley, Christopher P., Salekin, Asif, Wang, Dongliang, Zhang-James, Yanli, Baer, Thomas M., Corsetti, Sabrina M., Eisenberg, Marisa C., Falb, Karl, Huang, Yitao, Martin, Emily T., McCauley, Ella, Myers, Robert L., Schwarz, Tom, Gibson, Graham Casey, Sheldon, Daniel, Gao, Liyao, Ma, Yian, Wu, Dongxia, Yu, Rose, Jin, Xiaoyong, Wang, Yu-Xiang, Yan, Xifeng, Chen, YangQuan, Guo, Lihong, Zhao, Yanting, Chen, Jinghui, Gu, Quanquan, Wang, Lingxiao, Xu, Pan, Zhang, Weitong, Zou, Difan, Chattopadhyay, Ishanu, Huang, Yi, Lu, Guoqing, Pfeiffer, Ruth, Sumner, Timothy, Wang, Dongdong, Wang, Liqiang, Zhang, Shunpu, Zou, Zihang, Biegel, Hannah, Lega, Joceline, Hussain, Fazle, Khan, Zeina, Van Bussel, Frank, McConnell, Steve, Guertin, Stephanie L., Hulme-Lowe, Christopher, Nagraj, V. P., Turner, Stephen D., Bejar, Benjamín, Choirat, Christine, Flahault, Antoine, Krymova, Ekaterina, Lee, Gavin, Manetti, Elisa, Namigai, Kristen, Obozinski, Guillaume, Sun, Tao, Thanou, Dorina, Ban, Xuegang, Shi, Yunfeng, Walraven, Robert, Hong, Qi-Jun, Van De Walle, Axel, Ben-Nun, Michal, Riley, Steven, Riley, Pete, Turtle, James, Cao, Duy, Galasso, Joseph, Cho, Jae H., Jo, Areum, DesRoches, David, Forli, Pedro, Hamory, Bruce, Koyluoglu, Ugur, Kyriakides, Christina, Leis, Helen, Milliken, John, Moloney, Michael, Morgan, James, Nirgudkar, Ninad, Ozcan, Gokce, Piwonka, Noah, Ravi, Matt, Schrader, Chris, Shakhnovich, Elizabeth, Siegel, Daniel, Spatz, Ryan, Stiefeling, Chris, Wilkinson, Barrie, Wong, Alexander, Cavany, Sean, España, Guido, Moore, Sean, Oidtman, Rachel, Perkins, Alex, Ivy, Julie S., Mayorga, Maria E., Mele, Jessica, Rosenstrom, Erik T., Swann, Julie L., Kraus, Andrea, Kraus, David, Bian, Jiang, Cao, Wei, Gao, Zhifeng, Ferres, Juan Lavista, Li, Chaozhuo, Liu, Tie-Yan, Xie, Xing, Zhang, Shun, Zheng, Shun, Chinazzi, Matteo, Vespignani, Alessandro, Xiong, Xinyue, Davis, Jessica T., Mu, Kunpeng, Piontti, Ana Pastore Y, Baek, Jackie, Farias, Vivek, Georgescu, Andreea, Levi, Retsef, Sinha, Deeksha, Wilde, Joshua, Zheng, Andrew, Lami, Omar Skali, Bennouna, Amine, Ndong, David Nze, Perakis, Georgia, Singhvi, Divya, Spantidakis, Ioannis, Thayaparan, Leann, Tsiourvas, Asterios, Weisberg, Shane, Jadbabaie, Ali, Sarker, Arnab, Shah, Devavrat, Celi, Leo A., Penna, Nicolas D., Sundar, Saketh, Berlin, Abraham, Gandhi, Parth D., McAndrew, Thomas, Piriya, Matthew, Chen, Ye, Hlavacek, William, Lin, Yen Ting, Mallela, Abhishek, Miller, Ely, Neumann, Jacob, Posner, Richard, Wolfinger, Russ, Castro, Lauren, Fairchild, Geoffrey, Michaud, Isaac, Osthus, Dave, Wolffram, Daniel, Karlen, Dean, Panaggio, Mark J., Kinsey, Matt, Mullany, Luke C., Rainwater-Lovett, Kaitlin, Shin, Lauren, Tallaksen, Katharine, Wilson, Shelby, Brenner, Michael, Coram, Marc, Edwards, Jessie K., Joshi, Keya, Klein, Ellen, Hulse, Juan Dent, Grantz, Kyra H., Hill, Alison L., Kaminsky, Kathryn, Kaminsky, Joshua, Keegan, Lindsay T., Lauer, Stephen A., Lee, Elizabeth C., Lemaitre, Joseph C., Lessler, Justin, Meredith, Hannah R., Perez-Saez, Javier, Shah, Sam, Smith, Claire P., Truelove, Shaun A., Wills, Josh, Gardner, Lauren, Marshall, Maximilian, Nixon, Kristen, Burant, John C., Budzinski, Jozef, Chiang, Wen-Hao, Mohler, George, Gao, Junyi, Glass, Lucas, Qian, Cheng, Romberg, Justin, Sharma, Rakshith, Spaeder, Jeffrey, Sun, Jimeng, Xiao, Cao, Gao, Lei, Gu, Zhiling, Kim, Myungjin, Li, Xinyi, Wang, Yueying, Wang, Guannan, Wang, Lily, Yu, Shan, Jain, Chaman, Bhatia, Sangeeta, Nouvellet, Pierre, Barber, Ryan, Gaikedu, Emmanuela, Hay, Simon, Lim, Steve, Murray, Chris, Pigott, David, Reiner, Robert C., Baccam, Prasith, Gurung, Heidi L., Stage, Steven A., Suchoski, Bradley T., Fong, Chung-Yan, Yeung, Dit-Yan, Adhikari, Bijaya, Cui, Jiaming, Prakash, B. Aditya, Rodríguez, Alexander, Tabassum, Anika, Xie, Jiajia, Asplund, John, Baxter, Arden, Keskinocak, Pinar, Oruc, Buse Eylul, Serban, Nicoleta, Arik, Sercan O., Dusenberry, Mike, Epshteyn, Arkady, Kanal, Elli, Le, Long T., Li, Chun-Liang, Pfister, Tomas, Sinha, Rajarishi, Tsai, Thomas, Yoder, Nate, Yoon, Jinsung, Zhang, Leyou, Wilson, Daniel, Belov, Artur A., Chow, Carson C., Gerkin, Richard C., Yogurtcu, Osman N., Ibrahim, Mark, Lacroix, Timothee, Le, Matthew, Liao, Jason, Nickel, Maximilian, Sagun, Levent, Abbott, Sam, Bosse, Nikos I., Funk, Sebastian, Hellewell, Joel, Meakin, Sophie R., Sherratt, Katharine, Kalantari, Rahi, Zhou, Mingyuan, Karimzadeh, Morteza, Lucas, Benjamin, Ngo, Thoai, Zoraghein, Hamidreza, Vahedi, Behzad, Wang, Zhongying, Pei, Sen, Shaman, Jeffrey, Yamana, Teresa K., Bertsimas, Dimitris, Li, Michael L., Soni, Saksham, Bouardi, Hamza Tazi, Adee, Madeline, Ayer, Turgay, Chhatwal, Jagpreet, Dalgic, Ozden O., Ladd, Mary A., Linas, Benjamin P., Mueller, Peter, Xiao, Jade, Bosch, Jurgen, Wilson, Austin, Zimmerman, Peter, Wang, Qinxia, Wang, Yuanjia, Xie, Shanghong, Zeng, Donglin, Bien, Jacob, Brooks, Logan, Green, Alden, Hu, Addison J., Jahja, Maria, McDonald, Daniel, Narasimhan, Balasubramanian, Politsch, Collin, Rajanala, Samyak, Rumack, Aaron, Simon, Noah, Tibshirani, Ryan J., Tibshirani, Rob, Ventura, Valerie, Wasserman, Larry, Drake, John M., O’Dea, Eamon B., Abu-Mostafa, Yaser, Bathwal, Rahil, Chang, Nicholas A., Chitta, Pavan, Erickson, Anne, Goel, Sumit, Gowda, Jethin, Jin, Qixuan, Jo, HyeongChan, Kim, Juhyun, Kulkarni, Pranav, Lushtak, Samuel M., Mann, Ethan, Popken, Max, Soohoo, Connor, Tirumala, Kushal, Tseng, Albert, Varadarajan, Vignesh, Vytheeswaran, Jagath, Wang, Christopher, Yeluri, Akshay, Yurk, Dominic, Zhang, Michael, Zlokapa, Alexander, Pagano, Robert, Jain, Chandini, Tomar, Vishal, Ho, Lam, Huynh, Huong, Tran, Quoc, Lopez, Velma K., Walker, Jo W., Slayton, Rachel B., Johansson, Michael A., and Biggerstaff, Matthew

Subjects
ddc:510, Mathematics
Abstract

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.

Published
2022

35. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US

Author

Cramer, Estee Y, primary, Ray, Evan L, additional, Lopez, Velma K, additional, Bracher, Johannes, additional, Brennen, Andrea, additional, Castro Rivadeneira, Alvaro J, additional, Gerding, Aaron, additional, Gneiting, Tilmann, additional, House, Katie H, additional, Huang, Yuxin, additional, Jayawardena, Dasuni, additional, Kanji, Abdul H, additional, Khandelwal, Ayush, additional, Le, Khoa, additional, Mühlemann, Anja, additional, Niemi, Jarad, additional, Shah, Apurv, additional, Stark, Ariane, additional, Wang, Yijin, additional, Wattanachit, Nutcha, additional, Zorn, Martha W, additional, Gu, Youyang, additional, Jain, Sansiddh, additional, Bannur, Nayana, additional, Deva, Ayush, additional, Kulkarni, Mihir, additional, Merugu, Srujana, additional, Raval, Alpan, additional, Shingi, Siddhant, additional, Tiwari, Avtansh, additional, White, Jerome, additional, Abernethy, Neil F, additional, Woody, Spencer, additional, Dahan, Maytal, additional, Fox, Spencer, additional, Gaither, Kelly, additional, Lachmann, Michael, additional, Meyers, Lauren Ancel, additional, Scott, James G, additional, Tec, Mauricio, additional, Srivastava, Ajitesh, additional, George, Glover E, additional, Cegan, Jeffrey C, additional, Dettwiller, Ian D, additional, England, William P, additional, Farthing, Matthew W, additional, Hunter, Robert H, additional, Lafferty, Brandon, additional, Linkov, Igor, additional, Mayo, Michael L, additional, Parno, Matthew D, additional, Rowland, Michael A, additional, Trump, Benjamin D, additional, Zhang-James, Yanli, additional, Chen, Samuel, additional, Faraone, Stephen V, additional, Hess, Jonathan, additional, Morley, Christopher P, additional, Salekin, Asif, additional, Wang, Dongliang, additional, Corsetti, Sabrina M, additional, Baer, Thomas M, additional, Eisenberg, Marisa C, additional, Falb, Karl, additional, Huang, Yitao, additional, Martin, Emily T, additional, McCauley, Ella, additional, Myers, Robert L, additional, Schwarz, Tom, additional, Sheldon, Daniel, additional, Gibson, Graham Casey, additional, Yu, Rose, additional, Gao, Liyao, additional, Ma, Yian, additional, Wu, Dongxia, additional, Yan, Xifeng, additional, Jin, Xiaoyong, additional, Wang, Yu-Xiang, additional, Chen, YangQuan, additional, Guo, Lihong, additional, Zhao, Yanting, additional, Gu, Quanquan, additional, Chen, Jinghui, additional, Wang, Lingxiao, additional, Xu, Pan, additional, Zhang, Weitong, additional, Zou, Difan, additional, Biegel, Hannah, additional, Lega, Joceline, additional, McConnell, Steve, additional, Nagraj, VP, additional, Guertin, Stephanie L, additional, Hulme-Lowe, Christopher, additional, Turner, Stephen D, additional, Shi, Yunfeng, additional, Ban, Xuegang, additional, Walraven, Robert, additional, Hong, Qi-Jun, additional, Kong, Stanley, additional, van de Walle, Axel, additional, Turtle, James A, additional, Ben-Nun, Michal, additional, Riley, Steven, additional, Riley, Pete, additional, Koyluoglu, Ugur, additional, DesRoches, David, additional, Forli, Pedro, additional, Hamory, Bruce, additional, Kyriakides, Christina, additional, Leis, Helen, additional, Milliken, John, additional, Moloney, Michael, additional, Morgan, James, additional, Nirgudkar, Ninad, additional, Ozcan, Gokce, additional, Piwonka, Noah, additional, Ravi, Matt, additional, Schrader, Chris, additional, Shakhnovich, Elizabeth, additional, Siegel, Daniel, additional, Spatz, Ryan, additional, Stiefeling, Chris, additional, Wilkinson, Barrie, additional, Wong, Alexander, additional, Cavany, Sean, additional, España, Guido, additional, Moore, Sean, additional, Oidtman, Rachel, additional, Perkins, Alex, additional, Kraus, David, additional, Kraus, Andrea, additional, Gao, Zhifeng, additional, Bian, Jiang, additional, Cao, Wei, additional, Ferres, Juan Lavista, additional, Li, Chaozhuo, additional, Liu, Tie-Yan, additional, Xie, Xing, additional, Zhang, Shun, additional, Zheng, Shun, additional, Vespignani, Alessandro, additional, Chinazzi, Matteo, additional, Davis, Jessica T, additional, Mu, Kunpeng, additional, y Piontti, Ana Pastore, additional, Xiong, Xinyue, additional, Zheng, Andrew, additional, Baek, Jackie, additional, Farias, Vivek, additional, Georgescu, Andreea, additional, Levi, Retsef, additional, Sinha, Deeksha, additional, Wilde, Joshua, additional, Perakis, Georgia, additional, Bennouna, Mohammed Amine, additional, Nze-Ndong, David, additional, Singhvi, Divya, additional, Spantidakis, Ioannis, additional, Thayaparan, Leann, additional, Tsiourvas, Asterios, additional, Sarker, Arnab, additional, Jadbabaie, Ali, additional, Shah, Devavrat, additional, Penna, Nicolas Della, additional, Celi, Leo A, additional, Sundar, Saketh, additional, Wolfinger, Russ, additional, Osthus, Dave, additional, Castro, Lauren, additional, Fairchild, Geoffrey, additional, Michaud, Isaac, additional, Karlen, Dean, additional, Kinsey, Matt, additional, Mullany, Luke C., additional, Rainwater-Lovett, Kaitlin, additional, Shin, Lauren, additional, Tallaksen, Katharine, additional, Wilson, Shelby, additional, Lee, Elizabeth C, additional, Dent, Juan, additional, Grantz, Kyra H, additional, Hill, Alison L, additional, Kaminsky, Joshua, additional, Kaminsky, Kathryn, additional, Keegan, Lindsay T, additional, Lauer, Stephen A, additional, Lemaitre, Joseph C, additional, Lessler, Justin, additional, Meredith, Hannah R, additional, Perez-Saez, Javier, additional, Shah, Sam, additional, Smith, Claire P, additional, Truelove, Shaun A, additional, Wills, Josh, additional, Marshall, Maximilian, additional, Gardner, Lauren, additional, Nixon, Kristen, additional, Burant, John C., additional, Wang, Lily, additional, Gao, Lei, additional, Gu, Zhiling, additional, Kim, Myungjin, additional, Li, Xinyi, additional, Wang, Guannan, additional, Wang, Yueying, additional, Yu, Shan, additional, Reiner, Robert C, additional, Barber, Ryan, additional, Gakidou, Emmanuela, additional, Hay, Simon I., additional, Lim, Steve, additional, Murray, Chris J.L., additional, Pigott, David, additional, Gurung, Heidi L, additional, Baccam, Prasith, additional, Stage, Steven A, additional, Suchoski, Bradley T, additional, Prakash, B. 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Published
2021
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41. Computational medicine, present and the future: obstetrics and gynecology perspective

Author

Bukowski, Radek, primary, Schulz, Karl, additional, Gaither, Kelly, additional, Stephens, Keri K., additional, Semeraro, Dave, additional, Drake, Justin, additional, Smith, Gordon, additional, Cordola, Craig, additional, Zariphopoulou, Thaleia, additional, Hughes, Thomas J.R., additional, Zarins, Christopher, additional, Kusnezov, Dimitri, additional, Howard, Donna, additional, and Oden, Tinsley, additional

Published
2021
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