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1. From Noise to Signal: Unveiling Treatment Effects from Digital Health Data through Pharmacology-Informed Neural-SDE

Author

Pakravan, Samira, Evangelou, Nikolaos, Usdin, Maxime, Brooks, Logan, and Lu, James

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Mathematics - Dynamical Systems, I.2, G.3
Abstract

Digital health technologies (DHT), such as wearable devices, provide personalized, continuous, and real-time monitoring of patient. These technologies are contributing to the development of novel therapies and personalized medicine. Gaining insight from these technologies requires appropriate modeling techniques to capture clinically-relevant changes in disease state. The data generated from these devices is characterized by being stochastic in nature, may have missing elements, and exhibits considerable inter-individual variability - thereby making it difficult to analyze using traditional longitudinal modeling techniques. We present a novel pharmacology-informed neural stochastic differential equation (SDE) model capable of addressing these challenges. Using synthetic data, we demonstrate that our approach is effective in identifying treatment effects and learning causal relationships from stochastic data, thereby enabling counterfactual simulation., Comment: 6 figures

Published
2024

2. Evaluation of FluSight influenza forecasting in the 2021–22 and 2022–23 seasons with a new target laboratory-confirmed influenza hospitalizations

Author

Mathis, Sarabeth M., Webber, Alexander E., León, Tomás M., Murray, Erin L., Sun, Monica, White, Lauren A., Brooks, Logan C., Green, Alden, Hu, Addison J., Rosenfeld, Roni, Shemetov, Dmitry, Tibshirani, Ryan J., McDonald, Daniel J., Kandula, Sasikiran, Pei, Sen, Yaari, Rami, Yamana, Teresa K., Shaman, Jeffrey, Agarwal, Pulak, Balusu, Srikar, Gururajan, Gautham, Kamarthi, Harshavardhan, Prakash, B. Aditya, Raman, Rishi, Zhao, Zhiyuan, Rodríguez, Alexander, Meiyappan, Akilan, Omar, Shalina, Baccam, Prasith, Gurung, Heidi L., Suchoski, Brad T., Stage, Steve A., Ajelli, Marco, Kummer, Allisandra G., Litvinova, Maria, Ventura, Paulo C., Wadsworth, Spencer, Niemi, Jarad, Carcelen, Erica, Hill, Alison L., Loo, Sara L., McKee, Clifton D., Sato, Koji, Smith, Claire, Truelove, Shaun, Jung, Sung-mok, Lemaitre, Joseph C., Lessler, Justin, McAndrew, Thomas, Ye, Wenxuan, Bosse, Nikos, Hlavacek, William S., Lin, Yen Ting, Mallela, Abhishek, Gibson, Graham C., Chen, Ye, Lamm, Shelby M., Lee, Jaechoul, Posner, Richard G., Perofsky, Amanda C., Viboud, Cécile, Clemente, Leonardo, Lu, Fred, Meyer, Austin G., Santillana, Mauricio, Chinazzi, Matteo, Davis, Jessica T., Mu, Kunpeng, Pastore y Piontti, Ana, Vespignani, Alessandro, Xiong, Xinyue, Ben-Nun, Michal, Riley, Pete, Turtle, James, Hulme-Lowe, Chis, Jessa, Shakeel, Nagraj, V. P., Turner, Stephen D., Williams, Desiree, Basu, Avranil, Drake, John M., Fox, Spencer J., Suez, Ehsan, Cojocaru, Monica G., Thommes, Edward W., Cramer, Estee Y., Gerding, Aaron, Stark, Ariane, Ray, Evan L., Reich, Nicholas G., Shandross, Li, Wattanachit, Nutcha, Wang, Yijin, Zorn, Martha W., Aawar, Majd Al, Srivastava, Ajitesh, Meyers, Lauren A., Adiga, Aniruddha, Hurt, Benjamin, Kaur, Gursharn, Lewis, Bryan L., Marathe, Madhav, Venkatramanan, Srinivasan, Butler, Patrick, Farabow, Andrew, Ramakrishnan, Naren, Muralidhar, Nikhil, Reed, Carrie, Biggerstaff, Matthew, and Borchering, Rebecca K.

Published
2024
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3. Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States

Author

Ray, Evan L., Brooks, Logan C., Bien, Jacob, Biggerstaff, Matthew, Bosse, Nikos I., Bracher, Johannes, Cramer, Estee Y., Funk, Sebastian, Gerding, Aaron, Johansson, Michael A., Rumack, Aaron, Wang, Yijin, Zorn, Martha, Tibshirani, Ryan J., and Reich, Nicholas G.

Subjects
Statistics - Methodology
Abstract

The U.S. COVID-19 Forecast Hub aggregates forecasts of the short-term burden of COVID-19 in the United States from many contributing teams. We study methods for building an ensemble that combines forecasts from these teams. These experiments have informed the ensemble methods used by the Hub. To be most useful to policy makers, ensemble forecasts must have stable performance in the presence of two key characteristics of the component forecasts: (1) occasional misalignment with the reported data, and (2) instability in the relative performance of component forecasters over time. Our results indicate that in the presence of these challenges, an untrained and robust approach to ensembling using an equally weighted median of all component forecasts is a good choice to support public health decision makers. In settings where some contributing forecasters have a stable record of good performance, trained ensembles that give those forecasters higher weight can also be helpful.

Published
2022

4. An open repository of real-time COVID-19 indicators

Author

Reinhart, Alex, Brooks, Logan, Jahja, Maria, Rumack, Aaron, Tang, Jingjing, Agrawal, Sumit, Saeed, Wael Al, Arnold, Taylor, Basu, Amartya, Bien, Jacob, Cabrera, Ángel A, Chin, Andrew, Chua, Eu Jing, Clark, Brian, Colquhoun, Sarah, DeFries, Nat, Farrow, David C, Forlizzi, Jodi, Grabman, Jed, Gratzl, Samuel, Green, Alden, Haff, George, Han, Robin, Harwood, Kate, Hu, Addison J, Hyde, Raphael, Hyun, Sangwon, Joshi, Ananya, Kim, Jimi, Kuznetsov, Andrew, La Motte-Kerr, Wichada, Lee, Yeon Jin, Lee, Kenneth, Lipton, Zachary C, Liu, Michael X, Mackey, Lester, Mazaitis, Kathryn, McDonald, Daniel J, McGuinness, Phillip, Narasimhan, Balasubramanian, O’Brien, Michael P, Oliveira, Natalia L, Patil, Pratik, Perer, Adam, Politsch, Collin A, Rajanala, Samyak, Rucker, Dawn, Scott, Chris, Shah, Nigam H, Shankar, Vishnu, Sharpnack, James, Shemetov, Dmitry, Simon, Noah, Smith, Benjamin Y, Srivastava, Vishakha, Tan, Shuyi, Tibshirani, Robert, Tuzhilina, Elena, Van Nortwick, Ana Karina, Ventura, Valérie, Wasserman, Larry, Weaver, Benjamin, Weiss, Jeremy C, Whitman, Spencer, Williams, Kristin, Rosenfeld, Roni, and Tibshirani, Ryan J

Subjects
Bioengineering, Ambulatory Care, COVID-19, Databases, Factual, Epidemiologic Methods, Health Status Indicators, Humans, Internet, Physical Distancing, Surveys and Questionnaires, Travel, United States, open data, digital surveillance, internet surveys, medical insurance claims
Abstract

The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.

Published
2021

7. Correction for Johansson et al., An open challenge to advance probabilistic forecasting for dengue epidemics

Author

Johansson, Michael A, Apfeldorf, Karyn M, Dobson, Scott, Devita, Jason, Buczak, Anna L, Baugher, Benjamin, Moniz, Linda J, Bagley, Thomas, Babin, Steven M, Guven, Erhan, Yamana, Teresa K, Shaman, Jeffrey, Moschou, Terry, Lothian, Nick, Lane, Aaron, Osborne, Grant, Jiang, Gao, Brooks, Logan C, Farrow, David C, Hyun, Sangwon, Tibshirani, Ryan J, Rosenfeld, Roni, Lessler, Justin, Reich, Nicholas G, Cummings, Derek AT, Lauer, Stephen A, Moore, Sean M, Clapham, Hannah E, Lowe, Rachel, Bailey, Trevor C, Garcia-Diez, Markel, Carvalho, Marilia Sa, Rodo, Xavier, Sardar, Tridip, Paul, Richard, Ray, Evan L, Sakrejda, Krzysztof, Brown, Alexandria C, Meng, Xi, Osoba, Osonde, Vardavas, Raffaele, Manheim, David, Moore, Melinda, Rao, Dhananjai M, Porco, Travis C, Ackley, Sarah, Liu, Fengchen, Worden, Lee, Convertino, Matteo, Liu, Yang, Reddy, Abraham, Ortiz, Eloy, Rivero, Jorge, Brito, Humberto, Juarrero, Alicia, Johnson, Leah R, Gramacy, Robert B, Cohen, Jeremy M, Mordecai, Erin A, Murdock, Courtney C, Rohr, Jason R, Ryan, Sadie J, Stewart-Ibarra, Anna M, Weikel, Daniel P, Jutla, Antarpreet, Khan, Rakibul, Poultney, Marissa, Colwell, Rita R, Rivera-Garcia, Brenda, Barker, Christopher M, Bell, Jesse E, Biggerstaff, Matthew, Swerdlow, David, Mier-Y-Teran-Romero, Luis, Forshey, Brett M, Trtanj, Juli, Asher, Jason, Clay, Matt, Margolis, Harold S, Hebbeler, Andrew M, George, Dylan, and Chretien, Jean-Paul

Subjects
Economics, Biological Sciences, Applied Economics, Good Health and Well Being
Abstract

Correction for “An open challenge to advance probabilistic forecasting for dengue epidemics,” by Michael A. Johansson, Karyn M. Apfeldorf, Scott Dobson, Jason Devita, Anna L. Buczak, Benjamin Baugher, Linda J. Moniz, Thomas Bagley, Steven M. Babin, Erhan Guven, Teresa K. Yamana, Jeffrey Shaman, Terry Moschou, Nick Lothian, Aaron Lane, Grant Osborne, Gao Jiang, Logan C. Brooks, David C. Farrow, Sangwon Hyun, Ryan J. Tibshirani, Roni Rosenfeld, Justin Lessler, Nicholas G. Reich, Derek A. T. Cummings, Stephen A. Lauer, Sean M. Moore, Hannah E. Clapham, Rachel Lowe, Trevor C. Bailey, Markel García-Díez, Marilia Sá Carvalho, Xavier Rodó, Tridip Sardar, Richard Paul, Evan L. Ray, Krzysztof Sakrejda, Alexandria C. Brown, Xi Meng, Osonde Osoba, Raffaele Vardavas, David Manheim, Melinda Moore, Dhananjai M. Rao, Travis C. Porco, Sarah Ackley, Fengchen Liu, Lee Worden, Matteo Convertino, Yang Liu, Abraham Reddy, Eloy Ortiz, Jorge Rivero, Humberto Brito, Alicia Juarrero, Leah R. Johnson, Robert B. Gramacy, Jeremy M. Cohen, Erin A. Mordecai, Courtney C. Murdock, Jason R. Rohr, Sadie J. Ryan, Anna M. Stewart-Ibarra, Daniel P. Weikel, Antarpreet Jutla, Rakibul Khan, Marissa Poultney, Rita R. Colwell, Brenda Rivera-García, Christopher M. Barker, Jesse E. Bell, Matthew Biggerstaff, David Swerdlow, Luis Mier-y-Teran-Romero, Brett M. Forshey, Juli Trtanj, Jason Asher, Matt Clay, Harold S. Margolis, Andrew M. Hebbeler, Dylan George, and Jean-Paul Chretien, which was first published November 11, 2019; 10.1073/pnas.1909865116 (Proc. Natl. Acad. Sci. U.S.A. 116, 24268–24274). The authors note that the following statement should be added to the Acknowledgments: “E.A.M. was supported by NIH Grant R35GM133439.

Published
2020

8. An open challenge to advance probabilistic forecasting for dengue epidemics

Author

Johansson, Michael A, Apfeldorf, Karyn M, Dobson, Scott, Devita, Jason, Buczak, Anna L, Baugher, Benjamin, Moniz, Linda J, Bagley, Thomas, Babin, Steven M, Guven, Erhan, Yamana, Teresa K, Shaman, Jeffrey, Moschou, Terry, Lothian, Nick, Lane, Aaron, Osborne, Grant, Jiang, Gao, Brooks, Logan C, Farrow, David C, Hyun, Sangwon, Tibshirani, Ryan J, Rosenfeld, Roni, Lessler, Justin, Reich, Nicholas G, Cummings, Derek AT, Lauer, Stephen A, Moore, Sean M, Clapham, Hannah E, Lowe, Rachel, Bailey, Trevor C, García-Díez, Markel, Carvalho, Marilia Sá, Rodó, Xavier, Sardar, Tridip, Paul, Richard, Ray, Evan L, Sakrejda, Krzysztof, Brown, Alexandria C, Meng, Xi, Osoba, Osonde, Vardavas, Raffaele, Manheim, David, Moore, Melinda, Rao, Dhananjai M, Porco, Travis C, Ackley, Sarah, Liu, Fengchen, Worden, Lee, Convertino, Matteo, Liu, Yang, Reddy, Abraham, Ortiz, Eloy, Rivero, Jorge, Brito, Humberto, Juarrero, Alicia, Johnson, Leah R, Gramacy, Robert B, Cohen, Jeremy M, Mordecai, Erin A, Murdock, Courtney C, Rohr, Jason R, Ryan, Sadie J, Stewart-Ibarra, Anna M, Weikel, Daniel P, Jutla, Antarpreet, Khan, Rakibul, Poultney, Marissa, Colwell, Rita R, Rivera-García, Brenda, Barker, Christopher M, Bell, Jesse E, Biggerstaff, Matthew, Swerdlow, David, Mier-y-Teran-Romero, Luis, Forshey, Brett M, Trtanj, Juli, Asher, Jason, Clay, Matt, Margolis, Harold S, Hebbeler, Andrew M, George, Dylan, and Chretien, Jean-Paul

Subjects
Emerging Infectious Diseases, Rare Diseases, Vaccine Related, Infectious Diseases, Vector-Borne Diseases, Prevention, Infection, Good Health and Well Being, Dengue, Disease Outbreaks, Epidemics, Epidemiologic Methods, Humans, Incidence, Models, Statistical, Peru, Puerto Rico, forecast, dengue, epidemic
Abstract

A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project-integration with public health needs, a common forecasting framework, shared and standardized data, and open participation-can help advance infectious disease forecasting beyond dengue.

Published
2019

9. Collaborative efforts to forecast seasonal influenza in the United States, 2015–2016

Author

McGowan, Craig J, Biggerstaff, Matthew, Johansson, Michael, Apfeldorf, Karyn M, Ben-Nun, Michal, Brooks, Logan, Convertino, Matteo, Erraguntla, Madhav, Farrow, David C, Freeze, John, Ghosh, Saurav, Hyun, Sangwon, Kandula, Sasikiran, Lega, Joceline, Liu, Yang, Michaud, Nicholas, Morita, Haruka, Niemi, Jarad, Ramakrishnan, Naren, Ray, Evan L, Reich, Nicholas G, Riley, Pete, Shaman, Jeffrey, Tibshirani, Ryan, Vespignani, Alessandro, Zhang, Qian, and Reed, Carrie

Subjects
Emerging Infectious Diseases, Biodefense, Infectious Diseases, Influenza, Vaccine Related, Prevention, Pneumonia & Influenza, Centers for Disease Control and Prevention, U.S., Disease Outbreaks, Humans, Influenza, Human, Models, Statistical, Morbidity, Seasons, United States, Influenza Forecasting Working Group
Abstract

Since 2013, the Centers for Disease Control and Prevention (CDC) has hosted an annual influenza season forecasting challenge. The 2015-2016 challenge consisted of weekly probabilistic forecasts of multiple targets, including fourteen models submitted by eleven teams. Forecast skill was evaluated using a modified logarithmic score. We averaged submitted forecasts into a mean ensemble model and compared them against predictions based on historical trends. Forecast skill was highest for seasonal peak intensity and short-term forecasts, while forecast skill for timing of season onset and peak week was generally low. Higher forecast skill was associated with team participation in previous influenza forecasting challenges and utilization of ensemble forecasting techniques. The mean ensemble consistently performed well and outperformed historical trend predictions. CDC and contributing teams will continue to advance influenza forecasting and work to improve the accuracy and reliability of forecasts to facilitate increased incorporation into public health response efforts.

Published
2019

10. Results from the second year of a collaborative effort to forecast influenza seasons in the United States

Author

Biggerstaff, Matthew, Johansson, Michael, Alper, David, Brooks, Logan C, Chakraborty, Prithwish, Farrow, David C, Hyun, Sangwon, Kandula, Sasikiran, McGowan, Craig, Ramakrishnan, Naren, Rosenfeld, Roni, Shaman, Jeffrey, Tibshirani, Rob, Tibshirani, Ryan J, Vespignani, Alessandro, Yang, Wan, Zhang, Qian, and Reed, Carrie

Subjects
Epidemiology, Health Sciences, Emerging Infectious Diseases, Biodefense, Vaccine Related, Prevention, Influenza, Pneumonia & Influenza, Cooperative Behavior, Data Collection, Epidemics, Forecasting, Humans, Influenza, Human, Public Health, Seasons, United States, Prediction, Modeling, Clinical Sciences, Public Health and Health Services
Abstract

Accurate forecasts could enable more informed public health decisions. Since 2013, CDC has worked with external researchers to improve influenza forecasts by coordinating seasonal challenges for the United States and the 10 Health and Human Service Regions. Forecasted targets for the 2014-15 challenge were the onset week, peak week, and peak intensity of the season and the weekly percent of outpatient visits due to influenza-like illness (ILI) 1-4 weeks in advance. We used a logarithmic scoring rule to score the weekly forecasts, averaged the scores over an evaluation period, and then exponentiated the resulting logarithmic score. Poor forecasts had a score near 0, and perfect forecasts a score of 1. Five teams submitted forecasts from seven different models. At the national level, the team scores for onset week ranged from

Published
2018

11. Nonmechanistic forecasts of seasonal influenza with iterative one-week-ahead distributions

Author

Brooks, Logan C, Farrow, David C, Hyun, Sangwon, Tibshirani, Ryan J, and Rosenfeld, Roni

Subjects
Information and Computing Sciences, Statistics, Mathematical Sciences, Influenza, Emerging Infectious Diseases, Prevention, Infectious Diseases, Good Health and Well Being, Centers for Disease Control and Prevention, U.S., Communicable Diseases, Epidemics, Forecasting, Humans, Influenza, Human, Models, Biological, Models, Statistical, Public Health, Retrospective Studies, Seasons, United States, Biological Sciences, Bioinformatics
Abstract

Accurate and reliable forecasts of seasonal epidemics of infectious disease can assist in the design of countermeasures and increase public awareness and preparedness. This article describes two main contributions we made recently toward this goal: a novel approach to probabilistic modeling of surveillance time series based on "delta densities", and an optimization scheme for combining output from multiple forecasting methods into an adaptively weighted ensemble. Delta densities describe the probability distribution of the change between one observation and the next, conditioned on available data; chaining together nonparametric estimates of these distributions yields a model for an entire trajectory. Corresponding distributional forecasts cover more observed events than alternatives that treat the whole season as a unit, and improve upon multiple evaluation metrics when extracting key targets of interest to public health officials. Adaptively weighted ensembles integrate the results of multiple forecasting methods, such as delta density, using weights that can change from situation to situation. We treat selection of optimal weightings across forecasting methods as a separate estimation task, and describe an estimation procedure based on optimizing cross-validation performance. We consider some details of the data generation process, including data revisions and holiday effects, both in the construction of these forecasting methods and when performing retrospective evaluation. The delta density method and an adaptively weighted ensemble of other forecasting methods each improve significantly on the next best ensemble component when applied separately, and achieve even better cross-validated performance when used in conjunction. We submitted real-time forecasts based on these contributions as part of CDC's 2015/2016 FluSight Collaborative Comparison. Among the fourteen submissions that season, this system was ranked by CDC as the most accurate.

Published
2018

12. A human judgment approach to epidemiological forecasting

Author

Farrow, David C, Brooks, Logan C, Hyun, Sangwon, Tibshirani, Ryan J, Burke, Donald S, and Rosenfeld, Roni

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Infectious Diseases, Bioengineering, Influenza, Networking and Information Technology R&D (NITRD), Clinical Research, Emerging Infectious Diseases, Infection, Generic health relevance, Good Health and Well Being, Communicable Diseases, Disease Outbreaks, Epidemiologic Methods, Forecasting, Humans, Models, Statistical, Prevalence, Reproducibility of Results, Risk Assessment, Sensitivity and Specificity, United States, Mathematical Sciences, Biological Sciences, Information and Computing Sciences, Bioinformatics
Abstract

Infectious diseases impose considerable burden on society, despite significant advances in technology and medicine over the past century. Advanced warning can be helpful in mitigating and preparing for an impending or ongoing epidemic. Historically, such a capability has lagged for many reasons, including in particular the uncertainty in the current state of the system and in the understanding of the processes that drive epidemic trajectories. Presently we have access to data, models, and computational resources that enable the development of epidemiological forecasting systems. Indeed, several recent challenges hosted by the U.S. government have fostered an open and collaborative environment for the development of these technologies. The primary focus of these challenges has been to develop statistical and computational methods for epidemiological forecasting, but here we consider a serious alternative based on collective human judgment. We created the web-based "Epicast" forecasting system which collects and aggregates epidemic predictions made in real-time by human participants, and with these forecasts we ask two questions: how accurate is human judgment, and how do these forecasts compare to their more computational, data-driven alternatives? To address the former, we assess by a variety of metrics how accurately humans are able to predict influenza and chikungunya trajectories. As for the latter, we show that real-time, combined human predictions of the 2014-2015 and 2015-2016 U.S. flu seasons are often more accurate than the same predictions made by several statistical systems, especially for short-term targets. We conclude that there is valuable predictive power in collective human judgment, and we discuss the benefits and drawbacks of this approach.

Published
2017

13. Computational Modeling of Drug Response Identifies Mutant-Specific Constraints for Dosing panRAF and MEK Inhibitors in Melanoma.

Author

Goetz, Andrew, Shanahan, Frances, Brooks, Logan, Lin, Eva, Mroue, Rana, Dela Cruz, Darlene, Hunsaker, Thomas, Czech, Bartosz, Dixit, Purushottam, Segal, Udi, Martin, Scott, Foster, Scott A., and Gerosa, Luca

Subjects
PROTEIN kinase inhibitors, COMPUTER simulation, IN vitro studies, MELANOMA, RESEARCH funding, ANTINEOPLASTIC agents, XENOGRAFTS, CELLULAR signal transduction, DESCRIPTIVE statistics, DOSE-effect relationship in pharmacology, MICE, CELL lines, BIOINFORMATICS, ANIMAL experimentation, WESTERN immunoblotting, PROTEIN-tyrosine kinases, GENETIC mutation, INDIVIDUALIZED medicine
Abstract

Simple Summary: Combining drugs is crucial for enhancing anti-cancer responses. However, the potential of pre-clinical data in identifying suitable combinations and dosage is often underutilized. In this study, we leverage pre-clinical in vitro cell line drug response data and computational modeling of signal transduction and of pharmacokinetics to elucidate distinct dose requirements for the combination of pan-RAF and MEK inhibitors in melanoma. Our findings reveal a more synergistic but narrower dosing landscape in NRAS vs. BRAF mutant melanoma, which we link to a mechanism of adaptive resistance through negative feedback. Further, our analysis suggests the importance of drug dosing strategies to optimize synergy based on mutational context yet highlights the real-world challenges of maintaining a narrow dose range. This approach establishes a framework for translational investigation of drug responses in the refinement of combination therapy, balancing the potential for synergy and practical feasibility in cancer treatment planning. Purpose: This study explores the potential of pre-clinical in vitro cell line response data and computational modeling in identifying the optimal dosage requirements of pan-RAF (Belvarafenib) and MEK (Cobimetinib) inhibitors in melanoma treatment. Our research is motivated by the critical role of drug combinations in enhancing anti-cancer responses and the need to close the knowledge gap around selecting effective dosing strategies to maximize their potential. Results: In a drug combination screen of 43 melanoma cell lines, we identified specific dosage landscapes of panRAF and MEK inhibitors for NRAS vs. BRAF mutant melanomas. Both experienced benefits, but with a notably more synergistic and narrow dosage range for NRAS mutant melanoma (mean Bliss score of 0.27 in NRAS vs. 0.1 in BRAF mutants). Computational modeling and follow-up molecular experiments attributed the difference to a mechanism of adaptive resistance by negative feedback. We validated the in vivo translatability of in vitro dose–response maps by predicting tumor growth in xenografts with high accuracy in capturing cytostatic and cytotoxic responses. We analyzed the pharmacokinetic and tumor growth data from Phase 1 clinical trials of Belvarafenib with Cobimetinib to show that the synergy requirement imposes stricter precision dose constraints in NRAS mutant melanoma patients. Conclusion: Leveraging pre-clinical data and computational modeling, our approach proposes dosage strategies that can optimize synergy in drug combinations, while also bringing forth the real-world challenges of staying within a precise dose range. Overall, this work presents a framework to aid dose selection in drug combinations. [ABSTRACT FROM AUTHOR]

Published
2024
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14. Title evaluation of FluSight influenza forecasting in the 2021–22 and 2022–23 seasons with a new target laboratory-confirmed influenza hospitalizations.

Author

Mathis, Sarabeth M., Webber, Alexander E., León, Tomás M., Murray, Erin L., Sun, Monica, White, Lauren A., Brooks, Logan C., Green, Alden, Hu, Addison J., Rosenfeld, Roni, Shemetov, Dmitry, Tibshirani, Ryan J., McDonald, Daniel J., Kandula, Sasikiran, Pei, Sen, Yaari, Rami, Yamana, Teresa K., Shaman, Jeffrey, Agarwal, Pulak, and Balusu, Srikar

Subjects
INFLUENZA, FORECASTING, HOSPITAL admission & discharge, SEASONS, HOSPITAL care, SEASONAL influenza
Abstract

Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. For the 2021–22 and 2022–23 influenza seasons, 26 forecasting teams provided national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one-to-four weeks ahead. Forecast skill is evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperform the baseline model across forecast weeks and locations in 2021–22 and 12 out of 18 models in 2022–23. Averaging across all forecast targets, the FluSight ensemble is the 2nd most accurate model measured by WIS in 2021–22 and the 5th most accurate in the 2022–23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degrade over longer forecast horizons. In this work we demonstrate that while the FluSight ensemble was a robust predictor, even ensembles face challenges during periods of rapid change. This manuscript evaluates forecasts of laboratory-confirmed influenza hospital admissions, a new target for influenza forecasting in the United States. Across two influenza seasons, the FluSight ensemble is robust compared to submitted models. [ABSTRACT FROM AUTHOR]

Published
2024
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15. Flexible Modeling of Epidemics with an Empirical Bayes Framework

Author

Brooks, Logan C., Farrow, David C., Hyun, Sangwon, Tibshirani, Ryan J., and Rosenfeld, Roni

Subjects
Quantitative Biology - Populations and Evolution, Statistics - Applications
Abstract

Seasonal influenza epidemics cause consistent, considerable, widespread loss annually in terms of economic burden, morbidity, and mortality. With access to accurate and reliable forecasts of a current or upcoming influenza epidemic's behavior, policy makers can design and implement more effective countermeasures. We developed a framework for in-season forecasts of epidemics using a semiparametric Empirical Bayes framework, and applied it to predict the weekly percentage of outpatient doctors visits for influenza-like illness, as well as the season onset, duration, peak time, and peak height, with and without additional data from Google Flu Trends, as part of the CDC's 2013--2014 "Predict the Influenza Season Challenge". Previous work on epidemic modeling has focused on developing mechanistic models of disease behavior and applying time series tools to explain historical data. However, these models may not accurately capture the range of possible behaviors that we may see in the future. Our approach instead produces possibilities for the epidemic curve of the season of interest using modified versions of data from previous seasons, allowing for reasonable variations in the timing, pace, and intensity of the seasonal epidemics, as well as noise in observations. Since the framework does not make strict domain-specific assumptions, it can easily be applied to other diseases as well. Another important advantage of this method is that it produces a complete posterior distribution for any desired forecasting target, rather than mere point predictions. We report prospective influenza-like-illness forecasts that were made for the 2013--2014 U.S. influenza season, and compare the framework's cross-validated prediction error on historical data to that of a variety of simpler baseline predictors., Comment: 52 pages

Published
2014
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17. Flexible Modeling of Epidemics with an Empirical Bayes Framework

Author

Brooks, Logan C, Farrow, David C, Hyun, Sangwon, Tibshirani, Ryan J, and Rosenfeld, Roni

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Influenza, Infectious Diseases, Prevention, Biodefense, Emerging Infectious Diseases, Pneumonia & Influenza, Vaccine Related, 2.5 Research design and methodologies (aetiology), Aetiology, Infection, Good Health and Well Being, Bayes Theorem, Centers for Disease Control and Prevention, U.S., Computational Biology, Epidemics, Humans, Influenza, Human, Models, Biological, Models, Statistical, Reproducibility of Results, United States, Mathematical Sciences, Biological Sciences, Information and Computing Sciences, Bioinformatics
Abstract

Seasonal influenza epidemics cause consistent, considerable, widespread loss annually in terms of economic burden, morbidity, and mortality. With access to accurate and reliable forecasts of a current or upcoming influenza epidemic's behavior, policy makers can design and implement more effective countermeasures. This past year, the Centers for Disease Control and Prevention hosted the "Predict the Influenza Season Challenge", with the task of predicting key epidemiological measures for the 2013-2014 U.S. influenza season with the help of digital surveillance data. We developed a framework for in-season forecasts of epidemics using a semiparametric Empirical Bayes framework, and applied it to predict the weekly percentage of outpatient doctors visits for influenza-like illness, and the season onset, duration, peak time, and peak height, with and without using Google Flu Trends data. Previous work on epidemic modeling has focused on developing mechanistic models of disease behavior and applying time series tools to explain historical data. However, tailoring these models to certain types of surveillance data can be challenging, and overly complex models with many parameters can compromise forecasting ability. Our approach instead produces possibilities for the epidemic curve of the season of interest using modified versions of data from previous seasons, allowing for reasonable variations in the timing, pace, and intensity of the seasonal epidemics, as well as noise in observations. Since the framework does not make strict domain-specific assumptions, it can easily be applied to some other diseases with seasonal epidemics. This method produces a complete posterior distribution over epidemic curves, rather than, for example, solely point predictions of forecasting targets. We report prospective influenza-like-illness forecasts made for the 2013-2014 U.S. influenza season, and compare the framework's cross-validated prediction error on historical data to that of a variety of simpler baseline predictors.

Published
2015

18. Deep‐NCA: A deep learning methodology for performing noncompartmental analysis of pharmacokinetic data.

Author

Liu, Gengbo, Brooks, Logan, Canty, John, Lu, Dan, Jin, Jin Y., and Lu, James

Subjects
*DATA analysis, *DEEP learning, *DRUG development, *PHARMACOKINETICS, *DATA modeling
Abstract

Noncompartmental analysis (NCA) is a model‐independent approach for assessing pharmacokinetics (PKs). Although the existing NCA algorithms are very well‐established and widely utilized, they suffer from low accuracies in the setting of sparse PK samples. In response, we developed Deep‐NCA, a deep learning (DL) model to improve the prediction of key noncompartmental PK parameters. Our methodology utilizes synthetic PK data for model training and uses an innovative patient‐specific normalization method for data preprocessing. Deep‐NCA demonstrated adequate performance across six previously unseen simulated drugs under multiple dosing, showcasing effective generalization. Compared to traditional NCA, Deep‐NCA exhibited superior performance for sparse PK data. This study advances the application of DL to PK studies and introduces an effective method for handling sparse PK data. With further validation and refinement, Deep‐NCA could significantly enhance the efficiency of drug development by providing more accurate NCA estimates while requiring fewer PK samples. [ABSTRACT FROM AUTHOR]

Published
2024
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20. Evaluation of FluSight influenza forecasting in the 2021-22 and 2022-23 seasons with a new target laboratory-confirmed influenza hospitalizations

Author

Mathis, Sarabeth M., primary, Webber, Alexander E., additional, León, Tomás M., additional, Murray, Erin L., additional, Sun, Monica, additional, White, Lauren A., additional, Brooks, Logan C., additional, Green, Alden, additional, Hu, Addison J., additional, McDonald, Daniel J., additional, Rosenfeld, Roni, additional, Shemetov, Dmitry, additional, Tibshirani, Ryan J., additional, Kandula, Sasikiran, additional, Pei, Sen, additional, Shaman, Jeffrey, additional, Yaari, Rami, additional, Yamana, Teresa K., additional, Agarwal, Pulak, additional, Balusu, Srikar, additional, Gururajan, Gautham, additional, Kamarthi, Harshavardhan, additional, Prakash, B. Aditya, additional, Raman, Rishi, additional, Rodríguez, Alexander, additional, Zhao, Zhiyuan, additional, Meiyappan, Akilan, additional, Omar, Shalina, additional, Baccam, Prasith, additional, Gurung, Heidi L., additional, Stage, Steve A., additional, Suchoski, Brad T., additional, Ajelli, Marco, additional, Kummer, Allisandra G., additional, Litvinova, Maria, additional, Ventura, Paulo C., additional, Wadsworth, Spencer, additional, Niemi, Jarad, additional, Carcelen, Erica, additional, Hill, Alison L, additional, Jung, Sung-mok, additional, Lemaitre, Joseph C., additional, Lessler, Justin, additional, Loo, Sara L, additional, McKee, Clifton D., additional, Sato, Koji, additional, Smith, Claire, additional, Truelove, Shaun, additional, McAndrew, Thomas, additional, Ye, Wenxuan, additional, Bosse, Nikos, additional, Hlavacek, William S., additional, Lin, Yen Ting, additional, Mallela, Abhishek, additional, Chen, Ye, additional, Lamm, Shelby M., additional, Lee, Jaechoul, additional, Posner, Richard G., additional, Perofsky, Amanda C., additional, Viboud, Cécile, additional, Clemente, Leonardo, additional, Lu, Fred, additional, Meyer, Austin G, additional, Santillana, Mauricio, additional, Chinazzi, Matteo, additional, Davis, Jessica T., additional, Mu, Kunpeng, additional, Pastore y Piontti, Ana, additional, Vespignani, Alessandro, additional, Xiong, Xinyue, additional, Ben-Nun, Michal, additional, Riley, Pete, additional, Turtle, James, additional, Hulme-Lowe, Chis, additional, Jessa, Shakeel, additional, Nagraj, V.P., additional, Turner, Stephen D., additional, Williams, Desiree, additional, Basu, Avranil, additional, Drake, John M., additional, Fox, Spencer J., additional, Gibson, Graham C., additional, Suez, Ehsan, additional, Thommes, Edward W., additional, Cojocaru, Monica G., additional, Cramer, Estee Y., additional, Gerding, Aaron, additional, Stark, Ariane, additional, Ray, Evan L., additional, Reich, Nicholas G., additional, Shandross, Li, additional, Wattanachit, Nutcha, additional, Wang, Yijin, additional, Zorn, Martha W., additional, Aawar, Majd Al, additional, Srivastava, Ajitesh, additional, Meyers, Lauren A., additional, Adiga, Aniruddha, additional, Hurt, Benjamin, additional, Kaur, Gursharn, additional, Lewis, Bryan L., additional, Marathe, Madhav, additional, Venkatramanan, Srinivasan, additional, Butler, Patrick, additional, Farabow, Andrew, additional, Muralidhar, Nikhil, additional, Ramakrishnan, Naren, additional, Reed, Carrie, additional, Biggerstaff, Matthew, additional, and Borchering, Rebecca K., additional

Published
2023
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21. gPKPDviz: A flexible R shiny tool for pharmacokinetic/pharmacodynamic simulations using mrgsolve.

Author

Lu, Tong, Poon, Victor, Brooks, Logan, Velasquez, Erick, Anderson, Eric, Baron, Kyle, Jin, Jin Y., and Kågedal, Matts

Subjects
PHARMACOKINETICS, DEBUGGING, CLINICAL pharmacology, CLINICAL trials
Abstract

GPKPDviz is a Shiny application (app) dedicated to real‐time simulation, visualization, and assessment of the pharmacokinetic/pharmacodynamic (PK/PD) models. Within the app, gPKPDviz is capable of generating virtual populations and complex dosing and sampling scenarios, which, together with the streamlined workflow, is designed to efficiently assess the impact of covariates and dosing regimens on PK/PD end points. The actual population data from clinical trials can be loaded into the app for simulation if desired. The app‐generated dosing regimens include single or multiple dosing, and more complex regimens, such as loading doses or intermittent dosing. When necessary, the dosing regimens can be defined externally and loaded to the app for simulation. Using mrgsolve as the simulation engine, gPKPDviz is typically used for population simulation, however, with a slight modification of the mrgsolve model, gPKPDviz is capable of performing individual simulations with individual post hoc parameters, individual dosing logs, and individual sampling timepoints through an external dataset. A built‐in text editor has a debugging feature for the mrgsolve model, providing the same error messages as model compilation in R. GPKPDviz has had stringent validation by comparing simulation results between the app and using mrgsolve in R. GPKPDviz is a member of the suite of Modeling and Simulation Shiny apps developed at Genentech to facilitate the typical modeling work in Clinical Pharmacology. For broader access to the Pharmacometric community, gPKPDviz has been published as an open‐source application in GitHub under the terms of GNU General Public License. [ABSTRACT FROM AUTHOR]

Published
2024
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22. Randomized Phase IIb Study of Brimonidine Drug Delivery System Generation 2 for Geographic Atrophy in Age-Related Macular Degeneration

Author

Freeman, William R., primary, Bandello, Francesco, additional, Souied, Eric, additional, Guymer, Robyn H., additional, Garg, Sunir J., additional, Chen, Fred K., additional, Rich, Ryan, additional, Holz, Frank G., additional, Patel, Sunil S., additional, Kim, Kimmie, additional, López, Francisco J., additional, Chen, Fred, additional, Guymer, Robyn, additional, Korobelnik, Jean-Francois, additional, Holz, Frank, additional, Ziemssen, Focke, additional, Campos, Emilio, additional, Grignolo/Eandi, Chiara, additional, Midena, Edoardo, additional, Peiretti, Enrico, additional, Staurenghi, Giovanni, additional, Viola, Francesco, additional, Bailey, Clare, additional, Esposti, Simona Degli, additional, Jackson, Timothy, additional, Menon, Geeta, additional, Pagliarini, Sergio, additional, Quhill, Fahd, additional, Antoszyk, Andrew, additional, Brooks, Logan, additional, Callanan, David, additional, Csaky, Karl, additional, Edwards, Albert, additional, Eichenbaum, David, additional, Freeman, William, additional, Garg, Sunir, additional, Ghuman, Avtar Thomas, additional, Gonzalez, Victor, additional, Gupta, Sunil, additional, Hamilton, Richard, additional, Khurana, Rahul, additional, Kunimoto, Derek, additional, Kuppermann, Baruch, additional, Lauer, Andreas, additional, Lee, Seong Young, additional, Maturi, Raj, additional, Patel, Sunil, additional, Reddy, Rahul, additional, Rivellese, Mark, additional, Rose, Steven, additional, Segal, Zachary, additional, and Wong, Robert, additional

Published
2023
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23. Concentration QTc analysis of giredestrant: Overcoming QT /heart rate confounding in the presence of drug‐induced heart rate changes

Author

Brooks, Logan, primary, Dolton, Michael, additional, Langenhorst, Jurgen, additional, Yoshida, Kenta, additional, Lien, Yi Ting (Kayla), additional, Malhi, Vikram, additional, Li, Chunze, additional, Perez‐Moreno, Pablo, additional, Bond, John, additional, Chen, Ya‐Chi, additional, and Yu, Jiajie, additional

Published
2023
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24. Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States

Author

Ray, Evan L., primary, Brooks, Logan C., additional, Bien, Jacob, additional, Biggerstaff, Matthew, additional, Bosse, Nikos I., additional, Bracher, Johannes, additional, Cramer, Estee Y., additional, Funk, Sebastian, additional, Gerding, Aaron, additional, Johansson, Michael A., additional, Rumack, Aaron, additional, Wang, Yijin, additional, Zorn, Martha, additional, Tibshirani, Ryan J., additional, and Reich, Nicholas G., additional

Published
2022
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25. Management der neovaskulären altersbedingten Makuladegeneration (nAMD) mit dem Port-Delivery-System mit Ranibizumab (PDS): 2-Jahres-Ergebnisse der Phase-3-Studie ARCHWAY

Author

Priglinger, Siegfried, Wykoff, Charles, Brooks, Logan, Mittra, Robert, Rizzo, Shemra, Callaway, Natalia, DeGraaf, Stephanie, Fung, Anne, Gune, Shamika, and Barteselli, Giulio

Subjects
Medicine and health
Abstract

Zielsetzung: Das in der USA (FDA) zur Behandlung der nAMD bereits zugelassene Port-Delivery-System mit Ranibizumab (PDS) ist ein innovatives nachfüllbares intraokulares Implantat zur kontinuierlichen Abgabe einer Ranibizumab-Formulierung in den Glaskörper. Hier berichten wir über die [zum vollständigen Text gelangen Sie über die oben angegebene URL]

Published
2022
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26. A Phase 1 study of GDC-0134, a dual leucine zipper kinase inhibitor, in ALS

Author

Neurologen, Projectafdeling ALS, Brain, Regenerative Medicine and Stem Cells, Katz, Jonathan S, Rothstein, Jeffrey D, Cudkowicz, Merit E, Genge, Angela, Oskarsson, Björn, Hains, Avis B, Chen, Chen, Galanter, Joshua, Burgess, Braydon L, Cho, William, Kerchner, Geoffrey A, Yeh, Felix L, Ghosh, Arundhati Sengupta, Cheeti, Sravanthi, Brooks, Logan, Honigberg, Lee, Couch, Jessica A, Rothenberg, Michael E, Brunstein, Flavia, Sharma, Khema R, van den Berg, Leonard, Berry, James D, Glass, Jonathan D, Neurologen, Projectafdeling ALS, Brain, Regenerative Medicine and Stem Cells, Katz, Jonathan S, Rothstein, Jeffrey D, Cudkowicz, Merit E, Genge, Angela, Oskarsson, Björn, Hains, Avis B, Chen, Chen, Galanter, Joshua, Burgess, Braydon L, Cho, William, Kerchner, Geoffrey A, Yeh, Felix L, Ghosh, Arundhati Sengupta, Cheeti, Sravanthi, Brooks, Logan, Honigberg, Lee, Couch, Jessica A, Rothenberg, Michael E, Brunstein, Flavia, Sharma, Khema R, van den Berg, Leonard, Berry, James D, and Glass, Jonathan D

Published
2022

27. Concentration QTc analysis of giredestrant: Overcoming QT/heart rate confounding in the presence of drug‐induced heart rate changes.

Author

Brooks, Logan, Dolton, Michael, Langenhorst, Jurgen, Yoshida, Kenta, Lien, Yi Ting, Malhi, Vikram, Li, Chunze, Perez‐Moreno, Pablo, Bond, John, Chen, Ya‐Chi, and Yu, Jiajie

Subjects
*HEART beat, *DRUG side effects, *ANTINEOPLASTIC agents, *ESTROGEN receptors, *DRUG development, *CONFIDENCE intervals
Abstract

Concentration‐QTc (C‐QTc) analysis has become a common approach for evaluating proarrhythmic risk and delayed cardiac repolarization of oncology drug candidates. Significant heart rate (HR) change has been associated with certain classes of oncology drugs and can result in over‐ or underestimation of the true QT prolongation risk. Because oncology early clinical trials typically lack a placebo control arm or time‐matched, treatment‐free baseline electrocardiogram collection, significant HR change brings additional challenges to C‐QTc analysis in the oncology setting. In this work, a spline‐based correction method (QTcSPL) was explored to mitigate the impact of HR changes in giredestrant C‐QTc analysis. Giredestrant is a selective estrogen receptor degrader being developed for the treatment of patients with estrogen receptor‐positive (ER+) breast cancer. A dose‐related HR decrease has been observed in patients under giredestrant treatment, with significant reductions (>10 bpm) observed at supratherapeutic doses. The QTcSPL method demonstrated superior functionality to reduce the correlation between QTc and HR as compared with the Fridericia correction (QTcF). The effect of giredestrant exposure on QTc was evaluated at the clinical dose of 30 mg and supratherapeutic dose of 100 mg based on a prespecified linear mixed effect model. The upper 90% confidence interval of ΔQTcSPL and ΔQTcF were below the 10 ms at both clinical and supratherapeutic exposures, suggesting giredestrant has a low risk of QT prolongation at clinically relevant concentrations. This work demonstrated the use case of QTcSPL to address HR confounding challenges in the context of oncology drug development for the first time. [ABSTRACT FROM AUTHOR]

Published
2023
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28. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

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, V. P., 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, Lavista Ferres, Juan, 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, Pastore y Piontti, Ana, 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, Della Penna, Nicolas, 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, additional, Pigott, David, additional, Gurung, Heidi L., additional, Baccam, Prasith, additional, Stage, Steven A., additional, Suchoski, Bradley T., additional, Prakash, B. Aditya, additional, Adhikari, Bijaya, additional, Cui, Jiaming, additional, Rodríguez, Alexander, additional, Tabassum, Anika, additional, Xie, Jiajia, additional, Keskinocak, Pinar, additional, Asplund, John, additional, Baxter, Arden, additional, Oruc, Buse Eylul, additional, Serban, Nicoleta, additional, Arik, Sercan O., additional, Dusenberry, Mike, additional, Epshteyn, Arkady, additional, Kanal, Elli, additional, Le, Long T., additional, Li, Chun-Liang, additional, Pfister, Tomas, additional, Sava, Dario, additional, Sinha, Rajarishi, additional, Tsai, Thomas, additional, Yoder, Nate, additional, Yoon, Jinsung, additional, Zhang, Leyou, additional, Abbott, Sam, additional, Bosse, Nikos I., additional, Funk, Sebastian, additional, Hellewell, Joel, additional, Meakin, Sophie R., additional, Sherratt, Katharine, additional, Zhou, Mingyuan, additional, Kalantari, Rahi, additional, Yamana, Teresa K., additional, Pei, Sen, additional, Shaman, Jeffrey, additional, Li, Michael L., additional, Bertsimas, Dimitris, additional, Skali Lami, Omar, additional, Soni, Saksham, additional, Tazi Bouardi, Hamza, additional, Ayer, Turgay, additional, Adee, Madeline, additional, Chhatwal, Jagpreet, additional, Dalgic, Ozden O., additional, Ladd, Mary A., additional, Linas, Benjamin P., additional, Mueller, Peter, additional, Xiao, Jade, additional, Wang, Yuanjia, additional, Wang, Qinxia, additional, Xie, Shanghong, additional, Zeng, Donglin, additional, Green, Alden, additional, Bien, Jacob, additional, Brooks, Logan, additional, Hu, Addison J., additional, Jahja, Maria, additional, McDonald, Daniel, additional, Narasimhan, Balasubramanian, additional, Politsch, Collin, additional, Rajanala, Samyak, additional, Rumack, Aaron, additional, Simon, Noah, additional, Tibshirani, Ryan J., additional, Tibshirani, Rob, additional, Ventura, Valerie, additional, Wasserman, Larry, additional, O’Dea, Eamon B., additional, Drake, John M., additional, Pagano, Robert, additional, Tran, Quoc T., additional, Ho, Lam Si Tung, additional, Huynh, Huong, additional, Walker, Jo W., additional, Slayton, Rachel B., additional, Johansson, Michael A., additional, Biggerstaff, Matthew, additional, and Reich, Nicholas G., additional

Published
2022
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29. 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

30. A Phase 1 study of GDC ‐0134, a dual leucine zipper kinase inhibitor, in ALS

Author

Katz, Jonathan S., primary, Rothstein, Jeffrey D., additional, Cudkowicz, Merit E., additional, Genge, Angela, additional, Oskarsson, Björn, additional, Hains, Avis B., additional, Chen, Chen, additional, Galanter, Joshua, additional, Burgess, Braydon L., additional, Cho, William, additional, Kerchner, Geoffrey A., additional, Yeh, Felix L., additional, Ghosh, Arundhati Sengupta, additional, Cheeti, Sravanthi, additional, Brooks, Logan, additional, Honigberg, Lee, additional, Couch, Jessica A., additional, Rothenberg, Michael E., additional, Brunstein, Flavia, additional, Sharma, Khema R., additional, Berg, Leonard, additional, Berry, James D., additional, and Glass, Jonathan D., additional

Published
2022
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32. Safety, Tolerability, and Pharmacokinetics of High‐Volume Subcutaneous Crenezumab, With and Without Recombinant Human Hyaluronidase in Healthy Volunteers

Author

Dolton, Michael J., primary, Chesterman, Aaron, additional, Moein, Anita, additional, Sink, Kaycee M., additional, Waitz, Ariel, additional, Blondeau, Kathleen, additional, Kerchner, Geoffrey A., additional, Hu, Nan, additional, Brooks, Logan, additional, Wetzel‐Smith, Monica K., additional, Roden, Amanda, additional, Deshmukh, Ajay, additional, Peng, Kun, additional, Carrasco‐Triguero, Montserrat, additional, Smith, Jill, additional, Ostrowitzki, Susanne, additional, and Quartino, Angelica, additional

Published
2021
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36. An Open Repository of Real-Time COVID-19 Indicators

Author

Reinhart, Alex, primary, Brooks, Logan, additional, Jahja, Maria, additional, Rumack, Aaron, additional, Tang, Jingjing, additional, Agrawal, Sumit, additional, Saeed, Wael Al, additional, Arnold, Taylor, additional, Basu, Amartya, additional, Bien, Jacob, additional, Cabrera, Ángel A., additional, Chin, Andrew, additional, Chua, Eu Jing, additional, Clark, Brian, additional, Colquhoun, Sarah, additional, DeFries, Nat, additional, Farrow, David C., additional, Forlizzi, Jodi, additional, Grabman, Jed, additional, Gratzl, Samuel, additional, Green, Alden, additional, Haff, George, additional, Han, Robin, additional, Harwood, Kate, additional, Hu, Addison J., additional, Hyde, Raphael, additional, Hyun, Sangwon, additional, Joshi, Ananya, additional, Kim, Jimi, additional, Kuznetsov, Andrew, additional, Motte-Kerr, Wichada La, additional, Lee, Yeon Jin, additional, Lee, Kenneth, additional, Lipton, Zachary C., additional, Liu, Michael X., additional, Mackey, Lester, additional, Mazaitis, Kathryn, additional, McDonald, Daniel J., additional, McGuinness, Phillip, additional, Narasimhan, Balasubramanian, additional, O’Brien, Michael P., additional, Oliveira, Natalia L., additional, Patil, Pratik, additional, Perer, Adam, additional, Politsch, Collin A., additional, Rajanala, Samyak, additional, Rucker, Dawn, additional, Scott, Chris, additional, Shah, Nigam H., additional, Shankar, Vishnu, additional, Sharpnack, James, additional, Shemetov, Dmitry, additional, Simon, Noah, additional, Smith, Benjamin Y., additional, Srivastava, Vishakha, additional, Tan, Shuyi, additional, Tibshirani, Robert, additional, Tuzhilina, Elena, additional, Van Nortwick, Ana Karina, additional, Ventura, Valérie, additional, Wasserman, Larry, additional, Weaver, Benjamin, additional, Weiss, Jeremy C., additional, Whitman, Spencer, additional, Williams, Kristin, additional, Rosenfeld, Roni, additional, and Tibshirani, Ryan J., additional

Published
2021
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37. 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. Aditya, additional, Adhikari, Bijaya, additional, Cui, Jiaming, additional, Rodríguez, Alexander, additional, Tabassum, Anika, additional, Xie, Jiajia, additional, Keskinocak, Pinar, additional, Asplund, John, additional, Baxter, Arden, additional, Oruc, Buse Eylul, additional, Serban, Nicoleta, additional, Arik, Sercan O, additional, Dusenberry, Mike, additional, Epshteyn, Arkady, additional, Kanal, Elli, additional, Le, Long T, additional, Li, Chun-Liang, additional, Pfister, Tomas, additional, Sava, Dario, additional, Sinha, Rajarishi, additional, Tsai, Thomas, additional, Yoder, Nate, additional, Yoon, Jinsung, additional, Zhang, Leyou, additional, Abbott, Sam, additional, Bosse, Nikos I, additional, Funk, Sebastian, additional, Hellewell, Joel, additional, Meakin, Sophie R, additional, Sherratt, Katharine, additional, Zhou, Mingyuan, additional, Kalantari, Rahi, additional, Yamana, Teresa K, additional, Pei, Sen, additional, Shaman, Jeffrey, additional, Li, Michael L, additional, Bertsimas, Dimitris, additional, Lami, Omar Skali, additional, Soni, Saksham, additional, Bouardi, Hamza Tazi, additional, Ayer, Turgay, additional, Adee, Madeline, additional, Chhatwal, Jagpreet, additional, Dalgic, Ozden O, additional, Ladd, Mary A, additional, Linas, Benjamin P, additional, Mueller, Peter, additional, Xiao, Jade, additional, Wang, Yuanjia, additional, Wang, Qinxia, additional, Xie, Shanghong, additional, Zeng, Donglin, additional, Green, Alden, additional, Bien, Jacob, additional, Brooks, Logan, additional, Hu, Addison J, additional, Jahja, Maria, additional, McDonald, Daniel, additional, Narasimhan, Balasubramanian, additional, Politsch, Collin, additional, Rajanala, Samyak, additional, Rumack, Aaron, additional, Simon, Noah, additional, Tibshirani, Ryan J, additional, Tibshirani, Rob, additional, Ventura, Valerie, additional, Wasserman, Larry, additional, O’Dea, Eamon B, additional, Drake, John M, additional, Pagano, Robert, additional, Tran, Quoc T, additional, Tung Ho, Lam Si, additional, Huynh, Huong, additional, Walker, Jo W, additional, Slayton, Rachel B, additional, Johansson, Michael A, additional, Biggerstaff, Matthew, additional, and Reich, Nicholas G, additional

Published
2021
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38. Correction for Johansson et al., An open challenge to advance probabilistic forecasting for dengue epidemics

Author

Johansson, Michael A, Apfeldorf, Karyn M, Dobson, Scott, Devita, Jason, Buczak, Anna L, Baugher, Benjamin, Moniz, Linda J, Bagley, Thomas, Babin, Steven M, Guven, Erhan, Yamana, Teresa K, Shaman, Jeffrey, Moschou, Terry, Lothian, Nick, Lane, Aaron, Osborne, Grant, Jiang, Gao, Brooks, Logan C, Farrow, David C, Hyun, Sangwon, Tibshirani, Ryan J, Rosenfeld, Roni, Lessler, Justin, Reich, Nicholas G, Cummings, Derek AT, Lauer, Stephen A, Moore, Sean M, Clapham, Hannah E, Lowe, Rachel, Bailey, Trevor C, Garcia-Diez, Markel, Carvalhou, Marilia Sa, Rodo, Xavier, Sardar, Tridip, Paul, Richard, Ray, Evan L, Sakrejda, Krzysztof, Brown, Alexandria C, Meng, Xi, Osobaa, Osonde, Vardavas, Raffaele, Manheim, David, Moore, Melinda, Rao, Dhananjai M, Porco, Travis C, Ackley, Sarah, Liu, Fengchen, Worden, Lee, Convertino, Matteo, Liu, Yang, Reddy, Abraham, Ortiz, Eloy, Rivero, Jorge, Britog, Humberto, Juarrero, Alicia, Johnson, Leah R, Gramacy, Robert B, Cohen, Jeremy M, Mordecai, Erin A, Murdock, Courtney C, Rohr, Jason R, Ryan, Sadie J, Stewart-Ibarra, Annam, Weikel, Daniel P, Jutla, Antarpreet, Khan, Rakibul, Poultney, Marissa, Colwell, Rita R, Rivera-Garcia, Brenda, Barker, Christopher M, Bell, Jesse E, Biggerstaff, Matthew, Swerdlow, David, Mier-y-Teran-Romero, Luis, Forshey, Brett M, Trtanj, Juli, Asher, Jason, Clay, Matt, Margolis, Harold S, Hebbeler, Andrew M, George, Dylan, and Chretien, Jean-Paul

Abstract

Correction for “An open challenge to advance probabilistic forecasting for dengue epidemics,” by Michael A. Johansson, Karyn M. Apfeldorf, Scott Dobson, Jason Devita, Anna L. Buczak, Benjamin Baugher, Linda J. Moniz, Thomas Bagley, Steven M. Babin, Erhan Guven, Teresa K. Yamana, Jeffrey Shaman, Terry Moschou, Nick Lothian, Aaron Lane, Grant Osborne, Gao Jiang, Logan C. Brooks, David C. Farrow, Sangwon Hyun, Ryan J. Tibshirani, Roni Rosenfeld, Justin Lessler, Nicholas G. Reich, Derek A. T. Cummings, Stephen A. Lauer, Sean M. Moore, Hannah E. Clapham, Rachel Lowe, Trevor C. Bailey, Markel García-Díez, Marilia Sá Carvalho, Xavier Rodó, Tridip Sardar, Richard Paul, Evan L. Ray, Krzysztof Sakrejda, Alexandria C. Brown, Xi Meng, Osonde Osoba, Raffaele Vardavas, David Manheim, Melinda Moore, Dhananjai M. Rao, Travis C. Porco, Sarah Ackley, Fengchen Liu, Lee Worden, Matteo Convertino, Yang Liu, Abraham Reddy, Eloy Ortiz, Jorge Rivero, Humberto Brito, Alicia Juarrero, Leah R. Johnson, Robert B. Gramacy, Jeremy M. Cohen, Erin A. Mordecai, Courtney C. Murdock, Jason R. Rohr, Sadie J. Ryan, Anna M. Stewart-Ibarra, Daniel P. Weikel, Antarpreet Jutla, Rakibul Khan, Marissa Poultney, Rita R. Colwell, Brenda Rivera-García, Christopher M. Barker, Jesse E. Bell, Matthew Biggerstaff, David Swerdlow, Luis Mier-y-Teran-Romero, Brett M. Forshey, Juli Trtanj, Jason Asher, Matt Clay, Harold S. Margolis, Andrew M. Hebbeler, Dylan George, and Jean-Paul Chretien, which was first published November 11, 2019; 10.1073/pnas.1909865116. The authors note that the affiliation for Xavier Rodó should instead appear as Catalan Institution for Research and Advanced Studies (ICREA) and Climate and Health Program, Barcelona Institute for Global Health (ISGlobal). The corrected author and affiliation lines appear below. The online version has been corrected.

Published
2019

39. An open challenge to advance probabilistic forecasting for dengue epidemics

Author

Johansson, Michael A., Apfeldorf, Karyn M., Dobson, Scott, Devita, Jason, Buczak, Anna L., Baugher, Benjamin, Moniz, Linda J., Bagley, Thomas, Babin, Steven M., Guven, Erhan, Yamana, Teresa K., Shaman, Jeffrey, Moschou, Terry, Lothian, Nick, Lane, Aaron, Osborne, Grant, Jiang, Gao, Brooks, Logan C., Farrow, David C., Hyun, Sangwon, Tibshirani, Ryan J., Rosenfeld, Roni, Lessler, Justin, Reich, Nicholas G., Cummings, Derek AT T., Lauer, Stephen A., Moore, Sean M., Clapham, Hannah E., Lowe, Rachel, Bailey, Trevor C., Garcia-Diez, Markel, Carvalho, Marilia Sa, Rodo, Xavier, Sardar, Tridip, Paul, Richard, Ray, Evan L., Sakrejda, Krzysztof, Brown, Alexandria C., Meng, Xi, Osoba, Osonde, Vardavas, Raffaele, Manheim, David, Moore, Melinda, Rao, Dhananjai M., Porco, Travis C., Ackley, Sarah, Liu, Fengchen, Worden, Lee, Convertino, Matteo, Liu, Yang, Reddy, Abraham, Ortiz, Eloy, Rivero, Jorge, Brito, Humberto, Juarrero, Alicia, Johnson, Leah R., Gramacy, Robert B., Cohen, Jeremy M., Mordecai, Erin A., Murdock, Courtney C., Rohr, Jason R., Ryan, Sadie J., Stewart-Ibarra, Anna M., Weikel, Daniel P., Jutla, Antarpreet, Khan, Rakibul, Poultney, Marissa, Colwell, Rita R., Rivera-Garcia, Brenda, Barker, Christopher M., Bell, Jesse E., Biggerstaff, Matthew, Swerdlow, David, Mier-y-Teran-Romero, Luis, Forshey, Brett M., Trtanj, Juli, Asher, Jason, Clay, Matt, Margolis, Harold S., Hebbeler, Andrew M., George, Dylan, and Chretien, Jean-Paul

Subjects
Models, Statistical, Incidence, education, forecast, Puerto Rico, social sciences, dengue, epidemic, Disease Outbreaks, Peru, population characteristics, Humans, BURDEN, Epidemiologic Methods, Epidemics, health care economics and organizations, DEPENDENT ENHANCEMENT
Abstract

A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project-integration with public health needs, a common forecasting framework, shared and standardized data, and open participation-can help advance infectious disease forecasting beyond dengue. Published version

Published
2019

40. Collaborative efforts to forecast seasonal influenza in the United States, 2015–2016

Author

McGowan, Craig J., Biggerstaff, Matthew, Johansson, Michael, Apfeldorf, Karyn M., Ben-Nun, Michal, Brooks, Logan, Convertino, Matteo, Erraguntla, Madhav, Farrow, David C., Freeze, John, Ghosh, Saurav, Hyun, Sangwon, Kandula, Sasikiran, Lega, Joceline, Liu, Yang, Michaud, Nicholas, Morita, Haruka, Niemi, Jarad, Ramakrishnan, Naren, Ray, Evan L., Reich, Nicholas G., Riley, Pete, Shaman, Jeffrey, Tibshirani, Ryan, Vespignani, Alessandro, Zhang, Qian, Reed, Carrie, Rosenfeld, Roni, Ulloa, Nehemias, Will, Katie, Turtle, James, Bacon, David, Riley, Steven, Yang, Wan, and The Influenza Forecasting Working Group

Subjects
education, population characteristics, social sciences, health care economics and organizations
Abstract

Since 2013, the Centers for Disease Control and Prevention (CDC) has hosted an annual influenza season forecasting challenge. The 2015–2016 challenge consisted of weekly probabilistic forecasts of multiple targets, including fourteen models submitted by eleven teams. Forecast skill was evaluated using a modified logarithmic score. We averaged submitted forecasts into a mean ensemble model and compared them against predictions based on historical trends. Forecast skill was highest for seasonal peak intensity and short-term forecasts, while forecast skill for timing of season onset and peak week was generally low. Higher forecast skill was associated with team participation in previous influenza forecasting challenges and utilization of ensemble forecasting techniques. The mean ensemble consistently performed well and outperformed historical trend predictions. CDC and contributing teams will continue to advance influenza forecasting and work to improve the accuracy and reliability of forecasts to facilitate increased incorporation into public health response efforts. © 2019, The Author(s).

Published
2019

41. Accuracy of real-time multi-model ensemble forecasts for seasonal influenza in the U.S.

Author

Reich, Nicholas G., primary, McGowan, Craig J., additional, Yamana, Teresa K., additional, Tushar, Abhinav, additional, Ray, Evan L., additional, Osthus, Dave, additional, Kandula, Sasikiran, additional, Brooks, Logan C., additional, Crawford-Crudell, Willow, additional, Gibson, Graham Casey, additional, Moore, Evan, additional, Silva, Rebecca, additional, Biggerstaff, Matthew, additional, Johansson, Michael A., additional, Rosenfeld, Roni, additional, and Shaman, Jeffrey, additional

Published
2019
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42. A Collaborative Multi-Model Ensemble for Real-Time Influenza Season Forecasting in the U.S

Author

Reich, Nicholas G, primary, McGowan, Craig J, additional, Yamana, Teresa K, additional, Tushar, Abhinav, additional, Ray, Evan L, additional, Osthus, Dave, additional, Kandula, Sasikiran, additional, Brooks, Logan C, additional, Crawford-Crudell, Willow, additional, Gibson, Graham Casey, additional, Moore, Evan, additional, Silva, Rebecca, additional, Biggerstaff, Matthew, additional, Johansson, Michael A, additional, Rosenfeld, Roni, additional, and Shaman, Jeffrey, additional

Published
2019
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43. Results from the second year of a collaborative effort to forecast influenza seasons in the United States

Author

Biggerstaff, Matthew, primary, Johansson, Michael, additional, Alper, David, additional, Brooks, Logan C., additional, Chakraborty, Prithwish, additional, Farrow, David C., additional, Hyun, Sangwon, additional, Kandula, Sasikiran, additional, McGowan, Craig, additional, Ramakrishnan, Naren, additional, Rosenfeld, Roni, additional, Shaman, Jeffrey, additional, Tibshirani, Rob, additional, Tibshirani, Ryan J., additional, Vespignani, Alessandro, additional, Yang, Wan, additional, Zhang, Qian, additional, and Reed, Carrie, additional

Published
2018
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44. Forecasting seasonal influenza in the U.S.: A collaborative multi-year, multi-model assessment of forecast performance

Author

Reich, Nicholas G, primary, Brooks, Logan, additional, Fox, Spencer, additional, Kandula, Sasikiran, additional, McGowan, Craig, additional, Moore, Evan, additional, Osthus, Dave, additional, Ray, Evan, additional, Tushar, Abhinav, additional, Yamana, Teresa, additional, Biggerstaff, Matthew, additional, Johansson, Michael A, additional, Rosenfeld, Roni, additional, and Shaman, Jeffrey, additional

Published
2018
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47. Results from the centers for disease control and prevention’s predict the 2013–2014 Influenza Season Challenge

Author

Biggerstaff, Matthew, Alper, David, Dredze, Mark, Fox, Spencer, Fung, Isaac Chun Hai, Hickmann, Kyle S., Lewis, Bryan, Rosenfeld, Roni, Shaman, Jeffrey, Tsou, Ming Hsiang, Velardi, Paola, Vespignani, Alessandro, Finelli, Lyn, Chandra, Priyadarshini, Kaup, Hemchandra, Krishnan, Ramesh, Madhavan, Satish, Markar, Ashirwad, Pashley, Bryanne, Paul, Michael, Meyers, Lauren Ancel, Eggo, Rosalind, Henderson, Jette, Ramakrishnan, Anurekha, Scott, James, Singh, Bismark, Srinivasan, Ravi, Bakach, Iurii, Hao, Yi, Schaible, Braydon J., Sexton, Jessica K., Del Valle, Sara Y., Deshpande, Alina, Fairchild, Geoffrey, Generous, Nicholas, Priedhorsky, Reid, Hickman, Kyle S., Hyman, James M., Brooks, Logan, Farrow, David, Hyun, Sangwon, Tibshirani, Ryan J., Yang, Wan, Allen, Christopher, Aslam, Anoshã, Nagel, Anna, Stilo, Giovanni, Basagni, Stefano, Zhang, Qian, Perra, Nicola, Chakraborty, Prithwish, Butler, Patrick, Khadivi, Pejman, Ramakrishnan, Naren, Chen, Jiangzhuo, Barrett, Chris, Bisset, Keith, Eubank, Stephen, Anil Kumar, V. S., Laskowski, Kathy, Lum, Kristian, Marathe, Madhav, Aman, Susan, Brownstein, John S., Goldstein, Ed, Lipsitch, Marc, Mekaru, Sumiko R., Nsoesie, Elaine O., Gesualdo, Francesco, Tozzi, Alberto E., Broniatowski, David, Karspeck, Alicia, Tse, Zion Tsz Ho, Ying, Yuchen, Gambhir, Manoj, and Scarpino, Sam

Subjects
0301 basic medicine, Veterinary medicine, Influenza season, Models, Biological, 03 medical and health sciences, 0302 clinical medicine, Public health surveillance, Models, Centers for Disease Control and Prevention (U.S.), Influenza, Human, Influenza prevention, Expert evaluation, Humans, Medicine, Public Health Surveillance, Research article, National level, 030212 general & internal medicine, Duration (project management), health care economics and organizations, Models, Statistical, business.industry, Modeling, social sciences, Statistical, Biological, Disease control, United States, Influenza, 3. Good health, 030104 developmental biology, Infectious Diseases, Forecasting, Prediction, Seasons, Human, Centers for Disease Control and Prevention, U.S, business, Research Article, Demography
Abstract

Background Early insights into the timing of the start, peak, and intensity of the influenza season could be useful in planning influenza prevention and control activities. To encourage development and innovation in influenza forecasting, the Centers for Disease Control and Prevention (CDC) organized a challenge to predict the 2013–14 Unites States influenza season. Methods Challenge contestants were asked to forecast the start, peak, and intensity of the 2013–2014 influenza season at the national level and at any or all Health and Human Services (HHS) region level(s). The challenge ran from December 1, 2013–March 27, 2014; contestants were required to submit 9 biweekly forecasts at the national level to be eligible. The selection of the winner was based on expert evaluation of the methodology used to make the prediction and the accuracy of the prediction as judged against the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). Results Nine teams submitted 13 forecasts for all required milestones. The first forecast was due on December 2, 2013; 3/13 forecasts received correctly predicted the start of the influenza season within one week, 1/13 predicted the peak within 1 week, 3/13 predicted the peak ILINet percentage within 1 %, and 4/13 predicted the season duration within 1 week. For the prediction due on December 19, 2013, the number of forecasts that correctly forecasted the peak week increased to 2/13, the peak percentage to 6/13, and the duration of the season to 6/13. As the season progressed, the forecasts became more stable and were closer to the season milestones. Conclusion Forecasting has become technically feasible, but further efforts are needed to improve forecast accuracy so that policy makers can reliably use these predictions. CDC and challenge contestants plan to build upon the methods developed during this contest to improve the accuracy of influenza forecasts. Electronic supplementary material The online version of this article (doi:10.1186/s12879-016-1669-x) contains supplementary material, which is available to authorized users.

Published
2016
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49. Endophthalmitis in Eyes Treated with the Port Delivery System with Ranibizumab

Author

Eichenbaum, David A., Freeman, William R., Chang, Margaret A., Brooks, Logan, Chaudhry, Nauman, Dadgostar, Hajir, McCannel, Colin A., Michels, Mark, Mittra, Robert A., Wolfe, Jeremy D., Beindl, Victoria C., Jaycock, Philip, Bobbala, Ashwini, Gune, Shamika, Spicer, Galin, and Callaway, Natalia

Abstract

The Port Delivery System with ranibizumab (PDS) is approved in the United States for neovascular age-related macular degeneration. The United States Prescribing Information has a Boxed Warning for endophthalmitis and reports the incidence rate in patients developing endophthalmitis after receiving the PDS compared with monthly intravitreal ranibizumab. Endophthalmitis cases noted in the Boxed Warning, treatment outcomes, potential contributing factors, and potential mitigations are summarized.

Published
2024
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