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1. Finding Novel Links in COVID-19 Knowledge Graph Using Graph Embedding Techniques

Author

Patel, Ankit, Pai, Saeel Shrivallabh, Rajamohan, Haresh Rengaraj, Bongarala, Manohar, Samyak, Rajanala, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Nichols, Jeffrey, editor, Maccabe, Arthur ‘Barney’, editor, Nutaro, James, editor, Pophale, Swaroop, editor, Devineni, Pravallika, editor, Ahearn, Theresa, editor, and Verastegui, Becky, editor

Published
2022
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3. Statistical summaries of unlabelled evolutionary trees.

Author

Samyak, Rajanala and Palacios, Julia A

Subjects
*MARKOV chain Monte Carlo, *OPTIMIZATION algorithms, *COVID-19 pandemic
Abstract

Rooted and ranked phylogenetic trees are mathematical objects that are useful in modelling hierarchical data and evolutionary relationships with applications to many fields such as evolutionary biology and genetic epidemiology. Bayesian phylogenetic inference usually explores the posterior distribution of trees via Markov chain Monte Carlo methods. However, assessing uncertainty and summarizing distributions remains challenging for these types of structures. While labelled phylogenetic trees have been extensively studied, relatively less literature exists for unlabelled trees that are increasingly useful, for example when one seeks to summarize samples of trees obtained with different methods, or from different samples and environments, and wishes to assess the stability and generalizability of these summaries. In our paper, we exploit recently proposed distance metrics of unlabelled ranked binary trees and unlabelled ranked genealogies, or trees equipped with branch lengths, to define the Fréchet mean, variance and interquartile sets as summaries of these tree distributions. We provide an efficient combinatorial optimization algorithm for computing the Fréchet mean of a sample or of distributions on unlabelled ranked tree shapes and unlabelled ranked genealogies. We show the applicability of our summary statistics for studying popular tree distributions and for comparing the SARS-CoV-2 evolutionary trees across different locations during the COVID-19 epidemic in 2020. Our current implementations are publicly available at https://github.com/RSamyak/fmatrix. [ABSTRACT FROM AUTHOR]

Published
2024
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5. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Author

Estee Y. Cramer, Evan L. Ray, Velma K. Lopez, Johannes Bracher, Andrea Brennen, Alvaro J. Castro Rivadeneira, Aaron Gerding, Tilmann Gneiting, Katie H. House, Yuxin Huang, Dasuni Jayawardena, Abdul H. Kanji, Ayush Khandelwal, Khoa Le, Anja Mühlemann, Jarad Niemi, Apurv Shah, Ariane Stark, Yijin Wang, Nutcha Wattanachit, Martha W. Zorn, Youyang Gu, Sansiddh Jain, Nayana Bannur, Ayush Deva, Mihir Kulkarni, Srujana Merugu, Alpan Raval, Siddhant Shingi, Avtansh Tiwari, Jerome White, Neil F. Abernethy, Spencer Woody, Maytal Dahan, Spencer Fox, Kelly Gaither, Michael Lachmann, Lauren Ancel Meyers, James G. Scott, Mauricio Tec, Ajitesh Srivastava, Glover E. George, Jeffrey C. Cegan, Ian D. Dettwiller, William P. England, Matthew W. Farthing, Robert H. Hunter, Brandon Lafferty, Igor Linkov, Michael L. Mayo, Matthew D. Parno, Michael A. Rowland, Benjamin D. Trump, Yanli Zhang-James, Samuel Chen, Stephen V. Faraone, Jonathan Hess, Christopher P. Morley, Asif Salekin, Dongliang Wang, Sabrina M. Corsetti, Thomas M. Baer, Marisa C. Eisenberg, Karl Falb, Yitao Huang, Emily T. Martin, Ella McCauley, Robert L. Myers, Tom Schwarz, Daniel Sheldon, Graham Casey Gibson, Rose Yu, Liyao Gao, Yian Ma, Dongxia Wu, Xifeng Yan, Xiaoyong Jin, Yu-Xiang Wang, YangQuan Chen, Lihong Guo, Yanting Zhao, Quanquan Gu, Jinghui Chen, Lingxiao Wang, Pan Xu, Weitong Zhang, Difan Zou, Hannah Biegel, Joceline Lega, Steve McConnell, V. P. Nagraj, Stephanie L. Guertin, Christopher Hulme-Lowe, Stephen D. Turner, Yunfeng Shi, Xuegang Ban, Robert Walraven, Qi-Jun Hong, Stanley Kong, Axel van de Walle, James A. Turtle, Michal Ben-Nun, Steven Riley, Pete Riley, Ugur Koyluoglu, David DesRoches, Pedro Forli, Bruce Hamory, Christina Kyriakides, Helen Leis, John Milliken, Michael Moloney, James Morgan, Ninad Nirgudkar, Gokce Ozcan, Noah Piwonka, Matt Ravi, Chris Schrader, Elizabeth Shakhnovich, Daniel Siegel, Ryan Spatz, Chris Stiefeling, Barrie Wilkinson, Alexander Wong, Sean Cavany, Guido España, Sean Moore, Rachel Oidtman, Alex Perkins, David Kraus, Andrea Kraus, Zhifeng Gao, Jiang Bian, Wei Cao, Juan Lavista Ferres, Chaozhuo Li, Tie-Yan Liu, Xing Xie, Shun Zhang, Shun Zheng, Alessandro Vespignani, Matteo Chinazzi, Jessica T. Davis, Kunpeng Mu, Ana Pastore y Piontti, Xinyue Xiong, Andrew Zheng, Jackie Baek, Vivek Farias, Andreea Georgescu, Retsef Levi, Deeksha Sinha, Joshua Wilde, Georgia Perakis, Mohammed Amine Bennouna, David Nze-Ndong, Divya Singhvi, Ioannis Spantidakis, Leann Thayaparan, Asterios Tsiourvas, Arnab Sarker, Ali Jadbabaie, Devavrat Shah, Nicolas Della Penna, Leo A. Celi, Saketh Sundar, Russ Wolfinger, Dave Osthus, Lauren Castro, Geoffrey Fairchild, Isaac Michaud, Dean Karlen, Matt Kinsey, Luke C. Mullany, Kaitlin Rainwater-Lovett, Lauren Shin, Katharine Tallaksen, Shelby Wilson, Elizabeth C. Lee, Juan Dent, Kyra H. Grantz, Alison L. Hill, Joshua Kaminsky, Kathryn Kaminsky, Lindsay T. Keegan, Stephen A. Lauer, Joseph C. Lemaitre, Justin Lessler, Hannah R. Meredith, Javier Perez-Saez, Sam Shah, Claire P. Smith, Shaun A. Truelove, Josh Wills, Maximilian Marshall, Lauren Gardner, Kristen Nixon, John C. Burant, Lily Wang, Lei Gao, Zhiling Gu, Myungjin Kim, Xinyi Li, Guannan Wang, Yueying Wang, Shan Yu, Robert C. Reiner, Ryan Barber, Emmanuela Gakidou, Simon I. Hay, Steve Lim, Chris Murray, David Pigott, Heidi L. Gurung, Prasith Baccam, Steven A. Stage, Bradley T. Suchoski, B. Aditya Prakash, Bijaya Adhikari, Jiaming Cui, Alexander Rodríguez, Anika Tabassum, Jiajia Xie, Pinar Keskinocak, John Asplund, Arden Baxter, Buse Eylul Oruc, Nicoleta Serban, Sercan O. Arik, Mike Dusenberry, Arkady Epshteyn, Elli Kanal, Long T. Le, Chun-Liang Li, Tomas Pfister, Dario Sava, Rajarishi Sinha, Thomas Tsai, Nate Yoder, Jinsung Yoon, Leyou Zhang, Sam Abbott, Nikos I. Bosse, Sebastian Funk, Joel Hellewell, Sophie R. Meakin, Katharine Sherratt, Mingyuan Zhou, Rahi Kalantari, Teresa K. Yamana, Sen Pei, Jeffrey Shaman, Michael L. Li, Dimitris Bertsimas, Omar Skali Lami, Saksham Soni, Hamza Tazi Bouardi, Turgay Ayer, Madeline Adee, Jagpreet Chhatwal, Ozden O. Dalgic, Mary A. Ladd, Benjamin P. Linas, Peter Mueller, Jade Xiao, Yuanjia Wang, Qinxia Wang, Shanghong Xie, Donglin Zeng, Alden Green, Jacob Bien, Logan Brooks, Addison J. Hu, Maria Jahja, Daniel McDonald, Balasubramanian Narasimhan, Collin Politsch, Samyak Rajanala, Aaron Rumack, Noah Simon, Ryan J. Tibshirani, Rob Tibshirani, Valerie Ventura, Larry Wasserman, Eamon B. O’Dea, John M. Drake, Robert Pagano, Quoc T. Tran, Lam Si Tung Ho, Huong Huynh, Jo W. Walker, Rachel B. Slayton, Michael A. Johansson, Matthew Biggerstaff, Nicholas G. Reich, Cramer, Estee Y [0000-0003-1373-3177], Ray, Evan L [0000-0003-4035-0243], Lopez, Velma K [0000-0003-2926-4010], Bracher, Johannes [0000-0002-3777-1410], Gneiting, Tilmann [0000-0001-9397-3271], Niemi, Jarad [0000-0002-5079-158X], White, Jerome [0000-0003-4148-8834], Woody, Spencer [0000-0002-2882-3450], Fox, Spencer [0000-0003-1969-3778], Gaither, Kelly [0000-0002-4272-175X], Meyers, Lauren Ancel [0000-0002-5828-8874], Tec, Mauricio [0000-0002-1853-5842], George, Glover E [0000-0003-4779-8702], Cegan, Jeffrey C [0000-0002-3065-3403], Hunter, Robert H [0000-0002-2382-7938], Lafferty, Brandon [0000-0002-2618-3787], Mayo, Michael L [0000-0001-9014-1859], Rowland, Michael A [0000-0002-6759-8225], Chen, Samuel [0000-0002-1070-9801], Salekin, Asif [0000-0002-0807-8967], Corsetti, Sabrina M [0000-0003-2216-2492], Falb, Karl [0000-0002-3465-3988], Huang, Yitao [0000-0001-7846-2174], Sheldon, Daniel [0000-0002-4257-2432], Guo, Lihong [0000-0003-4804-4005], Gu, Quanquan [0000-0001-9830-793X], Xu, Pan [0000-0002-2559-8622], Lega, Joceline [0000-0003-2064-229X], McConnell, Steve [0000-0002-0294-3737], Turner, Stephen D [0000-0001-9140-9028], Shi, Yunfeng [0000-0003-1700-6049], Walraven, Robert [0000-0002-5755-4325], van de Walle, Axel [0000-0002-3415-1494], Turtle, James A [0000-0003-0735-7769], Ben-Nun, Michal [0000-0002-9164-0008], Riley, Steven [0000-0001-7904-4804], Koyluoglu, Ugur [0000-0002-6286-351X], Cavany, Sean [0000-0002-2559-797X], España, Guido [0000-0002-9915-8056], Moore, Sean [0000-0001-9062-6100], Oidtman, Rachel [0000-0003-1773-9533], Perkins, Alex [0000-0002-7518-4014], Kraus, David [0000-0003-4376-3932], Cao, Wei [0000-0001-5640-0917], Lavista Ferres, Juan [0000-0002-9654-3178], Vespignani, Alessandro [0000-0003-3419-4205], Sinha, Deeksha [0000-0002-9788-728X], Perakis, Georgia [0000-0002-0888-9030], Bennouna, Mohammed Amine [0000-0002-9123-8588], Spantidakis, Ioannis [0000-0002-5149-6320], Tsiourvas, Asterios [0000-0002-2979-6300], Sarker, Arnab [0000-0003-1680-9421], Jadbabaie, Ali [0000-0003-1122-3069], Shah, Devavrat [0000-0003-0737-3259], Celi, Leo A [0000-0001-6712-6626], Osthus, Dave [0000-0002-4681-091X], Fairchild, Geoffrey [0000-0001-5500-8120], Mullany, Luke C [0000-0003-4668-9803], Rainwater-Lovett, Kaitlin [0000-0002-8707-7339], Lee, Elizabeth C [0000-0002-4156-9637], Dent, Juan [0000-0003-3154-0731], Hill, Alison L [0000-0002-6583-3623], Keegan, Lindsay T [0000-0002-8526-3007], Lemaitre, Joseph C [0000-0002-2677-6574], Truelove, Shaun A [0000-0003-0538-0607], Wills, Josh [0000-0001-7285-9349], Gao, Lei [0000-0002-4707-0933], Gu, Zhiling [0000-0002-8052-7608], Yu, Shan [0000-0002-0271-5726], Hay, Simon I [0000-0002-0611-7272], Murray, Chris [0000-0002-4930-9450], Stage, Steven A [0000-0001-5361-6464], Prakash, B Aditya [0000-0002-3252-455X], Rodríguez, Alexander [0000-0002-4313-9913], Xie, Jiajia [0000-0001-6530-2489], Keskinocak, Pinar [0000-0003-2686-546X], Baxter, Arden [0000-0002-6345-2229], Oruc, Buse Eylul [0000-0003-2431-3864], Sinha, Rajarishi [0000-0001-9157-674X], Yoder, Nate [0000-0003-4153-4722], Zhang, Leyou [0000-0002-2454-0082], Funk, Sebastian [0000-0002-2842-3406], Meakin, Sophie R [0000-0002-6385-2652], Sherratt, Katharine [0000-0003-2049-3423], Yamana, Teresa K [0000-0001-8349-3151], Pei, Sen [0000-0002-7072-2995], Shaman, Jeffrey [0000-0002-7216-7809], Li, Michael L [0000-0002-2456-4834], Bertsimas, Dimitris [0000-0002-1985-1003], Skali Lami, Omar [0000-0002-8208-3035], Soni, Saksham [0000-0002-8898-5726], Tazi Bouardi, Hamza [0000-0002-7871-325X], Wang, Yuanjia [0000-0002-1510-3315], McDonald, Daniel [0000-0002-0443-4282], Politsch, Collin [0000-0003-3727-9167], Rajanala, Samyak [0000-0002-5791-3789], Rumack, Aaron [0000-0002-9181-1794], Tibshirani, Ryan J [0000-0002-2158-8304], Drake, John M [0000-0003-4646-1235], Ho, Lam Si Tung [0000-0002-0453-8444], Slayton, Rachel B [0000-0003-4699-8040], Johansson, Michael A [0000-0002-5090-7722], Biggerstaff, Matthew [0000-0001-5108-8311], Reich, Nicholas G [0000-0003-3503-9899], and Apollo - University of Cambridge Repository

Subjects
model evaluation, Multidisciplinary, COVID-19, prediction, United States, Data Accuracy, 510 Mathematics, 360 Social problems & social services, weather, Humans, Public Health, ddc:510, ensemble forecast, Pandemics, Mathematics, Forecasting, Probability
Abstract

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

Published
2022
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6. An Open Repository of Real-Time COVID-19 Indicators

Author

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

Subjects
medicine.medical_specialty, Medical Sciences, Databases, Factual, Coronavirus disease 2019 (COVID-19), Computer science, Physical Distancing, open data, Bioengineering, medical insurance claims, Databases, Software, Public health surveillance, Phone, Surveys and Questionnaires, Claims data, Ambulatory Care, medicine, Health Status Indicators, Humans, Factual, computer.programming_language, Internet, Travel, Multidisciplinary, business.industry, Public health, COVID-19, Biological Sciences, Python (programming language), Data science, digital surveillance, United States, internet surveys, The Internet, Epidemiologic Methods, business, computer
Abstract

Significance To study the COVID-19 pandemic, its effects on society, and measures for reducing its spread, researchers need detailed data on the course of the pandemic. Standard public health data streams suffer inconsistent reporting and frequent, unexpected revisions. They also miss other aspects of a population’s behavior that are worthy of consideration. We present an open database of COVID signals in the United States, measured at the county level and updated daily. This includes traditionally reported COVID cases and deaths, and many others: measures of mobility, social distancing, internet search trends, self-reported symptoms, and patterns of COVID-related activity in deidentified medical insurance claims. The database provides all signals in a common, easy-to-use format, empowering both public health research and operational decision-making., 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
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7. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US

Author

Estee Y Cramer, Evan L Ray, Velma K Lopez, Johannes Bracher, Andrea Brennen, Alvaro J Castro Rivadeneira, Aaron Gerding, Tilmann Gneiting, Katie H House, Yuxin Huang, Dasuni Jayawardena, Abdul H Kanji, Ayush Khandelwal, Khoa Le, Anja Mühlemann, Jarad Niemi, Apurv Shah, Ariane Stark, Yijin Wang, Nutcha Wattanachit, Martha W Zorn, Youyang Gu, Sansiddh Jain, Nayana Bannur, Ayush Deva, Mihir Kulkarni, Srujana Merugu, Alpan Raval, Siddhant Shingi, Avtansh Tiwari, Jerome White, Neil F Abernethy, Spencer Woody, Maytal Dahan, Spencer Fox, Kelly Gaither, Michael Lachmann, Lauren Ancel Meyers, James G Scott, Mauricio Tec, Ajitesh Srivastava, Glover E George, Jeffrey C Cegan, Ian D Dettwiller, William P England, Matthew W Farthing, Robert H Hunter, Brandon Lafferty, Igor Linkov, Michael L Mayo, Matthew D Parno, Michael A Rowland, Benjamin D Trump, Yanli Zhang-James, Samuel Chen, Stephen V Faraone, Jonathan Hess, Christopher P Morley, Asif Salekin, Dongliang Wang, Sabrina M Corsetti, Thomas M Baer, Marisa C Eisenberg, Karl Falb, Yitao Huang, Emily T Martin, Ella McCauley, Robert L Myers, Tom Schwarz, Daniel Sheldon, Graham Casey Gibson, Rose Yu, Liyao Gao, Yian Ma, Dongxia Wu, Xifeng Yan, Xiaoyong Jin, Yu-Xiang Wang, YangQuan Chen, Lihong Guo, Yanting Zhao, Quanquan Gu, Jinghui Chen, Lingxiao Wang, Pan Xu, Weitong Zhang, Difan Zou, Hannah Biegel, Joceline Lega, Steve McConnell, VP Nagraj, Stephanie L Guertin, Christopher Hulme-Lowe, Stephen D Turner, Yunfeng Shi, Xuegang Ban, Robert Walraven, Qi-Jun Hong, Stanley Kong, Axel van de Walle, James A Turtle, Michal Ben-Nun, Steven Riley, Pete Riley, Ugur Koyluoglu, David DesRoches, Pedro Forli, Bruce Hamory, Christina Kyriakides, Helen Leis, John Milliken, Michael Moloney, James Morgan, Ninad Nirgudkar, Gokce Ozcan, Noah Piwonka, Matt Ravi, Chris Schrader, Elizabeth Shakhnovich, Daniel Siegel, Ryan Spatz, Chris Stiefeling, Barrie Wilkinson, Alexander Wong, Sean Cavany, Guido España, Sean Moore, Rachel Oidtman, Alex Perkins, David Kraus, Andrea Kraus, Zhifeng Gao, Jiang Bian, Wei Cao, Juan Lavista Ferres, Chaozhuo Li, Tie-Yan Liu, Xing Xie, Shun Zhang, Shun Zheng, Alessandro Vespignani, Matteo Chinazzi, Jessica T Davis, Kunpeng Mu, Ana Pastore y Piontti, Xinyue Xiong, Andrew Zheng, Jackie Baek, Vivek Farias, Andreea Georgescu, Retsef Levi, Deeksha Sinha, Joshua Wilde, Georgia Perakis, Mohammed Amine Bennouna, David Nze-Ndong, Divya Singhvi, Ioannis Spantidakis, Leann Thayaparan, Asterios Tsiourvas, Arnab Sarker, Ali Jadbabaie, Devavrat Shah, Nicolas Della Penna, Leo A Celi, Saketh Sundar, Russ Wolfinger, Dave Osthus, Lauren Castro, Geoffrey Fairchild, Isaac Michaud, Dean Karlen, Matt Kinsey, Luke C. Mullany, Kaitlin Rainwater-Lovett, Lauren Shin, Katharine Tallaksen, Shelby Wilson, Elizabeth C Lee, Juan Dent, Kyra H Grantz, Alison L Hill, Joshua Kaminsky, Kathryn Kaminsky, Lindsay T Keegan, Stephen A Lauer, Joseph C Lemaitre, Justin Lessler, Hannah R Meredith, Javier Perez-Saez, Sam Shah, Claire P Smith, Shaun A Truelove, Josh Wills, Maximilian Marshall, Lauren Gardner, Kristen Nixon, John C. Burant, Lily Wang, Lei Gao, Zhiling Gu, Myungjin Kim, Xinyi Li, Guannan Wang, Yueying Wang, Shan Yu, Robert C Reiner, Ryan Barber, Emmanuela Gakidou, Simon I. Hay, Steve Lim, Chris J.L. Murray, David Pigott, Heidi L Gurung, Prasith Baccam, Steven A Stage, Bradley T Suchoski, B. Aditya Prakash, Bijaya Adhikari, Jiaming Cui, Alexander Rodríguez, Anika Tabassum, Jiajia Xie, Pinar Keskinocak, John Asplund, Arden Baxter, Buse Eylul Oruc, Nicoleta Serban, Sercan O Arik, Mike Dusenberry, Arkady Epshteyn, Elli Kanal, Long T Le, Chun-Liang Li, Tomas Pfister, Dario Sava, Rajarishi Sinha, Thomas Tsai, Nate Yoder, Jinsung Yoon, Leyou Zhang, Sam Abbott, Nikos I Bosse, Sebastian Funk, Joel Hellewell, Sophie R Meakin, Katharine Sherratt, Mingyuan Zhou, Rahi Kalantari, Teresa K Yamana, Sen Pei, Jeffrey Shaman, Michael L Li, Dimitris Bertsimas, Omar Skali Lami, Saksham Soni, Hamza Tazi Bouardi, Turgay Ayer, Madeline Adee, Jagpreet Chhatwal, Ozden O Dalgic, Mary A Ladd, Benjamin P Linas, Peter Mueller, Jade Xiao, Yuanjia Wang, Qinxia Wang, Shanghong Xie, Donglin Zeng, Alden Green, Jacob Bien, Logan Brooks, Addison J Hu, Maria Jahja, Daniel McDonald, Balasubramanian Narasimhan, Collin Politsch, Samyak Rajanala, Aaron Rumack, Noah Simon, Ryan J Tibshirani, Rob Tibshirani, Valerie Ventura, Larry Wasserman, Eamon B O’Dea, John M Drake, Robert Pagano, Quoc T Tran, Lam Si Tung Ho, Huong Huynh, Jo W Walker, Rachel B Slayton, Michael A Johansson, Matthew Biggerstaff, and Nicholas G Reich

Subjects
Geospatial analysis, Coronavirus disease 2019 (COVID-19), Computer science, business.industry, Probabilistic logic, Staffing, computer.software_genre, Scientific modelling, Health care, Econometrics, National level, business, computer, Independent research
Abstract

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-week horizon 3-5 times larger than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks. Significance Statement This paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the US. Results show high variation in accuracy between and within stand-alone models, and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public health action.

Published
2021
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