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1. Wastewater-based Epidemiology for COVID-19 Surveillance and Beyond: A Survey

Author

Chen, Chen, Wang, Yunfan, Kaur, Gursharn, Adiga, Aniruddha, Espinoza, Baltazar, Venkatramanan, Srinivasan, Warren, Andrew, Lewis, Bryan, Crow, Justin, Singh, Rekha, Lorentz, Alexandra, Toney, Denise, and Marathe, Madhav

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

The pandemic of COVID-19 has imposed tremendous pressure on public health systems and social economic ecosystems over the past years. To alleviate its social impact, it is important to proactively track the prevalence of COVID-19 within communities. The traditional way to estimate the disease prevalence is to estimate from reported clinical test data or surveys. However, the coverage of clinical tests is often limited and the tests can be labor-intensive, requires reliable and timely results, and consistent diagnostic and reporting criteria. Recent studies revealed that patients who are diagnosed with COVID-19 often undergo fecal shedding of SARS-CoV-2 virus into wastewater, which makes wastewater-based epidemiology for COVID-19 surveillance a promising approach to complement traditional clinical testing. In this paper, we survey the existing literature regarding wastewater-based epidemiology for COVID-19 surveillance and summarize the current advances in the area. Specifically, we have covered the key aspects of wastewater sampling, sample testing, and presented a comprehensive and organized summary of wastewater data analytical methods. Finally, we provide the open challenges on current wastewater-based COVID-19 surveillance studies, aiming to encourage new ideas to advance the development of effective wastewater-based surveillance systems for general infectious diseases.

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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4. Novel multi-cluster workflow system to support real-time HPC-enabled epidemic science: Investigating the impact of vaccine acceptance on COVID-19 spread

Author

Bhattacharya, Parantapa, Machi, Dustin, Chen, Jiangzhuo, Hoops, Stefan, Lewis, Bryan, Mortveit, Henning, Venkatramanan, Srinivasan, Wilson, Mandy L., Marathe, Achla, Porebski, Przemyslaw, Klahn, Brian, Outten, Joseph, Vullikanti, Anil, Xie, Dawen, Adiga, Abhijin, Brown, Shawn, Barrett, Christopher, and Marathe, Madhav

Published
2024
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7. Examining Deep Learning Models with Multiple Data Sources for COVID-19 Forecasting

Author

Wang, Lijing, Adiga, Aniruddha, Venkatramanan, Srinivasan, Chen, Jiangzhuo, Lewis, Bryan, and Marathe, Madhav

Subjects
Computer Science - Machine Learning, Statistics - Applications
Abstract

The COVID-19 pandemic represents the most significant public health disaster since the 1918 influenza pandemic. During pandemics such as COVID-19, timely and reliable spatio-temporal forecasting of epidemic dynamics is crucial. Deep learning-based time series models for forecasting have recently gained popularity and have been successfully used for epidemic forecasting. Here we focus on the design and analysis of deep learning-based models for COVID-19 forecasting. We implement multiple recurrent neural network-based deep learning models and combine them using the stacking ensemble technique. In order to incorporate the effects of multiple factors in COVID-19 spread, we consider multiple sources such as COVID-19 confirmed and death case count data and testing data for better predictions. To overcome the sparsity of training data and to address the dynamic correlation of the disease, we propose clustering-based training for high-resolution forecasting. The methods help us to identify the similar trends of certain groups of regions due to various spatio-temporal effects. We examine the proposed method for forecasting weekly COVID-19 new confirmed cases at county-, state-, and country-level. A comprehensive comparison between different time series models in COVID-19 context is conducted and analyzed. The results show that simple deep learning models can achieve comparable or better performance when compared with more complicated models. We are currently integrating our methods as a part of our weekly forecasts that we provide state and federal authorities.

Published
2020

8. Data-driven modeling for different stages of pandemic response

Author

Adiga, Aniruddha, Chen, Jiangzhuo, Marathe, Madhav, Mortveit, Henning, Venkatramanan, Srinivasan, and Vullikanti, Anil

Subjects
Quantitative Biology - Populations and Evolution, Physics - Physics and Society
Abstract

Some of the key questions of interest during the COVID-19 pandemic (and all outbreaks) include: where did the disease start, how is it spreading, who is at risk, and how to control the spread. There are a large number of complex factors driving the spread of pandemics, and, as a result, multiple modeling techniques play an increasingly important role in shaping public policy and decision making. As different countries and regions go through phases of the pandemic, the questions and data availability also changes. Especially of interest is aligning model development and data collection to support response efforts at each stage of the pandemic. The COVID-19 pandemic has been unprecedented in terms of real-time collection and dissemination of a number of diverse datasets, ranging from disease outcomes, to mobility, behaviors, and socio-economic factors. The data sets have been critical from the perspective of disease modeling and analytics to support policymakers in real-time. In this overview article, we survey the data landscape around COVID-19, with a focus on how such datasets have aided modeling and response through different stages so far in the pandemic. We also discuss some of the current challenges and the needs that will arise as we plan our way out of the pandemic.

Published
2020

9. Models for COVID-19 Pandemic: A Comparative Analysis

Author

Adiga, Aniruddha, Dubhashi, Devdatt, Lewis, Bryan, Marathe, Madhav, Venkatramanan, Srinivasan, and Vullikanti, Anil

Subjects
Quantitative Biology - Populations and Evolution, Computer Science - Artificial Intelligence
Abstract

COVID-19 pandemic represents an unprecedented global health crisis in the last 100 years. Its economic, social and health impact continues to grow and is likely to end up as one of the worst global disasters since the 1918 pandemic and the World Wars. Mathematical models have played an important role in the ongoing crisis; they have been used to inform public policies and have been instrumental in many of the social distancing measures that were instituted worldwide. In this article we review some of the important mathematical models used to support the ongoing planning and response efforts. These models differ in their use, their mathematical form and their scope.

Published
2020

11. Correction: Recommended reporting items for epidemic forecasting and prediction research: The EPIFORGE 2020 guidelines

Author

Pollett, Simon, Johansson, Michael A., Reich, Nicholas G., Brett-Major, David, Del Valle, Sara Y., Venkatramanan, Srinivasan, Lowe, Rachel, Porco, Travis, Berry, Irina Maljkovic, Deshpande, Alina, Kraemer, Moritz U. G., Blazes, David L., Pan-ngum, Wirichada, Vespigiani, Alessandro, Mate, Suzanne E., Silal, Sheetal P., Kandula, Sasikiran, Sippy, Rachel, Quandelacy, Talia M., Morgan, Jeffrey J., Ball, Jacob, Morton, Lindsay C., Althouse, Benjamin M, Pavlin, Julie, van Panhuis, Wilbert, Riley, Steven, Biggerstaff, Matthew, Viboud, Cecile, Brady, Oliver, and Rivers, Caitlin

Subjects
Biological sciences
Abstract

Author(s): Simon Pollett, Michael A. Johansson, Nicholas G. Reich, David Brett-Major, Sara Y. Del Valle, Srinivasan Venkatramanan, Rachel Lowe, Travis Porco, Irina Maljkovic Berry, Alina Deshpande, Moritz U. G. Kraemer, [...]

Published
2023
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13. A Framework for Evaluating Epidemic Forecasts

Author

Tabataba, Farzaneh Sadat, Chakraborty, Prithwish, Ramakrishnan, Naren, Venkatramanan, Srinivasan, Chen, Jiangzhuo, Lewis, Bryan, and Marathe, Madhav

Subjects
Computer Science - Social and Information Networks, Physics - Physics and Society, Quantitative Biology - Populations and Evolution
Abstract

Background: Over the past few decades, numerous forecasting methods have been proposed in the field of epidemic forecasting. Such methods can be classified into different categories such as deterministic vs. probabilistic, comparative methods vs. generative methods, and so on. In some of the more popular comparative methods, researchers compare observed epidemiological data from early stages of an outbreak with the output of proposed models to forecast the future trend and prevalence of the pandemic. A significant problem in this area is the lack of standard well-defined evaluation measures to select the best algorithm among different ones, as well as for selecting the best possible configuration for a particular algorithm. Results: In this paper, we present an evaluation framework which allows for combining different features, error measures, and ranking schema to evaluate forecasts. We describe the various epidemic features (Epi-features) included to characterize the output of forecasting methods and provide suitable error measures that could be used to evaluate the accuracy of the methods with respect to these Epi-features. We focus on long-term predictions rather than short-term forecasting and demonstrate the utility of the framework by evaluating six forecasting methods for predicting influenza in the United States. Our results demonstrate that different error measures lead to different rankings even for a single Epi-feature. Further, our experimental analyses show that no single method dominates the rest in predicting all Epi-features, when evaluated across error measures. As an alternative, we provide various consensus ranking schema that summarizes individual rankings, thus accounting for different error measures. We believe that a comprehensive evaluation framework, as presented in this paper, will add value to the computational epidemiology community., Comment: Submitted to BMC infectious disease Journal, 2016. Accepted in 2017

Published
2017

15. A Population Model for the Academic Ecosystem

Author

Wu, Yan, Venkatramanan, Srinivasan, and Chiu, Dah Ming

Subjects
Computer Science - Social and Information Networks, Computer Science - Digital Libraries, Physics - Physics and Society
Abstract

In recent times, the academic ecosystem has seen a tremendous growth in number of authors and publications. While most temporal studies in this area focus on evolution of co-author and citation network structure, this systemic inflation has received very little attention. In this paper, we address this issue by proposing a population model for academia, derived from publication records in the Computer Science domain. We use a generalized branching process as an overarching framework, which enables us to describe the evolution and composition of the research community in a systematic manner. Further, the observed patterns allow us to shed light on researchers' lifecycle encompassing arrival, academic life expectancy, activity, productivity and offspring distribution in the ecosystem. We believe such a study will help develop better bibliometric indices which account for the inflation, and also provide insights into sustainable and efficient resource management for academia.

Published
2015

16. Modeling and Analysis of Scholar Mobility on Scientific Landscape

Author

Ying, Qiu Fang, Venkatramanan, Srinivasan, and Chiu, Dah Ming

Subjects
Computer Science - Social and Information Networks, Computer Science - Digital Libraries, Physics - Physics and Society
Abstract

Scientific literature till date can be thought of as a partially revealed landscape, where scholars continue to unveil hidden knowledge by exploring novel research topics. How do scholars explore the scientific landscape , i.e., choose research topics to work on? We propose an agent-based model of topic mobility behavior where scholars migrate across research topics on the space of science following different strategies, seeking different utilities. We use this model to study whether strategies widely used in current scientific community can provide a balance between individual scientific success and the efficiency and diversity of the whole academic society. Through extensive simulations, we provide insights into the roles of different strategies, such as choosing topics according to research potential or the popularity. Our model provides a conceptual framework and a computational approach to analyze scholars' behavior and its impact on scientific production. We also discuss how such an agent-based modeling approach can be integrated with big real-world scholarly data., Comment: To appear in BigScholar, WWW 2015

Published
2015

18. 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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19. A Multi-Team Multi-Model Collaborative Covid-19 Forecasting Hub for India

Author

Adiga, Aniruddha, primary, Hurt, Benjamin, additional, Kaur, Gursharn, additional, Lewis, Bryan, additional, Marathe, Madhav, additional, Porebski, Przemyslaw, additional, Venkatramanan, Srinivasan, additional, Dukkipati, Ambedkar, additional, Gracious, Tony, additional, Gupta, Shubham, additional, Rathod, Nihesh, additional, Sundaresan, Rajesh, additional, Yasodharan, Sarath, additional, Bhimala, Kantha Rao, additional, Mudkavi, Vidyadhar, additional, Patra, Gopal Krishna, additional, and Athreya, Siva, additional

Published
2023
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20. Influence Spread in Social Networks: A Study via a Fluid Limit of the Linear Threshold Model

Author

Venkatramanan, Srinivasan and Kumar, Anurag

Subjects
Computer Science - Social and Information Networks, Physics - Physics and Society
Abstract

Threshold based models have been widely used in characterizing collective behavior on social networks. An individual's threshold indicates the minimum level of influence that must be exerted, by other members of the population engaged in some activity, before the individual will join the activity. In this work, we begin with a homogeneous version of the Linear Threshold model proposed by Kempe et al. in the context of viral marketing, and generalize this model to arbitrary threshold distributions. We show that the evolution can be modeled as a discrete time Markov chain, and, by using a certain scaling, we obtain a fluid limit that provides an ordinary differential equation model (o.d.e.). We find that the threshold distribution appears in the o.d.e. via its hazard rate function. We demonstrate the accuracy of the o.d.e. approximation and derive explicit expressions for the trajectory of influence under the uniform threshold distribution. Also, for an exponentially distributed threshold, we show that the fluid dynamics are equivalent to the well-known SIR model in epidemiology. We also numerically study how other hazard functions (obtained from the Weibull and loglogistic distributions) provide qualitative different characteristics of the influence evolution, compared to traditional epidemic models, even in a homogeneous setting. We finally show how the model can be extended to a setting with multiple communities and conclude with possible future directions.

Published
2014

21. Forecasting influenza activity using machine-learned mobility map

Author

Venkatramanan, Srinivasan, Sadilek, Adam, Fadikar, Arindam, Barrett, Christopher L., Biggerstaff, Matthew, Chen, Jiangzhuo, Dotiwalla, Xerxes, Eastham, Paul, Gipson, Bryant, Higdon, Dave, Kucuktunc, Onur, Lieber, Allison, Lewis, Bryan L., Reynolds, Zane, Vullikanti, Anil K., Wang, Lijing, and Marathe, Madhav

Published
2021
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24. Spread of Influence and Content in Mobile Opportunistic Networks

Author

Venkatramanan, Srinivasan and Kumar, Anurag

Subjects
Computer Science - Social and Information Networks, Computer Science - Multiagent Systems, Computer Science - Systems and Control
Abstract

We consider a setting in which a single item of content (such as a song or a video clip) is disseminated in a population of mobile nodes by opportunistic copying when pairs of nodes come in radio contact. We propose and study models that capture the joint evolution of the population of nodes interested in the content (referred to as destinations), and the population of nodes that possess the content. The evolution of interest in the content is captured using an influence spread model and the content spread occurs via epidemic copying. Nodes not yet interested in the content are called relays; the influence spread process converts relays into destinations. We consider the decentralized setting, where interest in the content and the spread of the content evolve by pairwise interactions between the mobiles. We derive fluid limits for the joint evolution models and obtain optimal policies for copying to relay nodes in order to deliver content to a desired fraction of destinations. We prove that a time-threshold policy is optimal while copying to relays. We then provide insights into the effects of various system parameters on the co-evolution model through simulations., Comment: 16 pages, 19 Figures. arXiv admin note: substantial text overlap with arXiv:1107.5851

Published
2012

25. Co-evolution of Content Popularity and Delivery in Mobile P2P Networks

Author

Venkatramanan, Srinivasan and Kumar, Anurag

Subjects
Computer Science - Social and Information Networks, Computer Science - Multiagent Systems
Abstract

Mobile P2P technology provides a scalable approach to content delivery to a large number of users on their mobile devices. In this work, we study the dissemination of a \emph{single} content (e.g., an item of news, a song or a video clip) among a population of mobile nodes. Each node in the population is either a \emph{destination} (interested in the content) or a potential \emph{relay} (not yet interested in the content). There is an interest evolution process by which nodes not yet interested in the content (i.e., relays) can become interested (i.e., become destinations) on learning about the popularity of the content (i.e., the number of already interested nodes). In our work, the interest in the content evolves under the \emph{linear threshold model}. The content is copied between nodes when they make random contact. For this we employ a controlled epidemic spread model. We model the joint evolution of the copying process and the interest evolution process, and derive the joint fluid limit ordinary differential equations. We then study the selection of the parameters under the content provider's control, for the optimization of various objective functions that aim at maximizing content popularity and efficient content delivery., Comment: 21 pages, 16 figures

Published
2011

26. New Insights from an Analysis of Social Influence Networks under the Linear Threshold Model

Author

Venkatramanan, Srinivasan and Kumar, Anurag

Subjects
Computer Science - Other Computer Science, Physics - Physics and Society, F.2.2
Abstract

We study the spread of influence in a social network based on the Linear Threshold model. We derive an analytical expression for evaluating the expected size of the eventual influenced set for a given initial set, using the probability of activation for each node in the social network. We then provide an equivalent interpretation for the influence spread, in terms of acyclic path probabilities in the Markov chain obtained by reversing the edges in the social network influence graph. We use some properties of such acyclic path probabilities to provide an alternate proof for the submodularity of the influence function. We illustrate the usefulness of the analytical expression in estimating the most influential set, in special cases such as the UILT(Uniform Influence Linear Threshold), USLT(Uniform Susceptance Linear Threshold) and node-degree based influence models. We show that the PageRank heuristic is either provably optimal or performs very well in the above models, and explore its limitations in more general cases. Finally, based on the insights obtained from the analytical expressions, we provide an efficient algorithm which approximates the greedy algorithm for the influence maximization problem., Comment: 13 pages, 6 figures

Published
2010

28. Potential impact of annual vaccination with reformulated COVID-19 vaccines: lessons from the U.S. COVID-19 Scenario Modeling Hub

Author

Jung, Sung-mok, primary, Loo, Sara L, additional, Howerton, Emily, additional, Contamin, Lucie, additional, Smith, Claire P, additional, Carcelén, Erica C, additional, Yan, Katie, additional, Bents, Samantha J, additional, Espino, Jessi, additional, Levander, John, additional, Reich, Nicholas G, additional, Lemaitre, Joseph C, additional, Sato, Koji, additional, McKee, Clifton D, additional, Hill, Alison L, additional, Chinazzi, Matteo, additional, Davis, Jessica T, additional, Mu, Kunpeng, additional, Vespignani, Alessandro, additional, Rosenstrom, Erik T, additional, Rodriguez-Cartes, Sebastian A, additional, Ivy, Julie S, additional, Mayorga, Maria E, additional, Swann, Julie L, additional, España, Guido, additional, Cavany, Sean, additional, Moore, Sean M, additional, Perkins, Alex, additional, Chen, Shi, additional, Paul, Rajib, additional, Janies, Daniel, additional, Thill, Jean-Claude, additional, Srivastava, Ajitesh, additional, Aawar, Majd Al, additional, Bi, Kaiming, additional, Bandekar, Shraddha Ramdas, additional, Bouchnita, Anass, additional, Fox, Spencer J, additional, Meyers, Lauren Ancel, additional, Porebski, Przemyslaw, additional, Venkatramanan, Srinivasan, additional, Adiga, Aniruddha, additional, Lewis, Bryan, additional, Klahn, Brian, additional, Hurt, Benjamin, additional, Chen, Jiangzhuo, additional, Wilson, Amanda, additional, Hoops, Stefan, additional, Bhattacharya, Parantapa, additional, Machi, Dustin, additional, Outten, Joseph, additional, Mortveit, Henning, additional, Vullikanti, Anil, additional, Marathe, Madhav, additional, Hochheiser, Harry, additional, Runge, Michael C, additional, Shea, Katriona, additional, Truelove, Shaun, additional, Viboud, Cécile, additional, and Lessler, Justin, additional

Published
2023
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29. Scenario Design for Infectious Disease Projections: Integrating Concepts from Decision Analysis and Experimental Design

Author

Runge, Michael C, primary, Shea, Katriona, additional, Howerton, Emily, additional, Yan, Katie, additional, Hochheiser, Harry, additional, Rosenstrom, Erik, additional, Probert, William J M, additional, Borchering, Rebecca, additional, Marathe, Madhav V, additional, Lewis, Bryan, additional, Venkatramanan, Srinivasan, additional, Truelove, Shaun A, additional, Lessler, Justin, additional, and Viboud, Cecile, additional

Published
2023
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32. Recommended reporting items for epidemic forecasting and prediction research: The EPIFORGE 2020 guidelines

Author

Pollett, Simon, Johansson, Michael A., Reich, Nicholas G., Brett-Major, David, Del Valle, Sara Y., Venkatramanan, Srinivasan, Lowe, Rachel, Porco, Travis, Berry, Irina Maljkovic, Deshpande, Alina, Kraemer, Moritz U. G., Blazes, David L., Pan-ngum, Wirichada, Vespigiani, Alessandro, Mate, Suzanne E., Silal, Sheetal P., Kandula, Sasikiran, Sippy, Rachel, Quandelacy, Talia M., Morgan, Jeffrey J., Ball, Jacob, Morton, Lindsay C., Althouse, Benjamin M., Pavlin, Julie, van Panhuis, Wilbert, Riley, Steven, Biggerstaff, Matthew, Viboud, Cecile, Brady, Oliver, and Rivers, Caitlin

Subjects
Epidemics -- Forecasts and trends -- United States, Medical research, Medicine, Experimental, Epidemiologic methods -- Research, Technical reports -- Methods -- Health aspects -- Laws, regulations and rules, Government regulation, Market trend/market analysis, Biological sciences
Abstract

Background The importance of infectious disease epidemic forecasting and prediction research is underscored by decades of communicable disease outbreaks, including COVID-19. Unlike other fields of medical research, such as clinical trials and systematic reviews, no reporting guidelines exist for reporting epidemic forecasting and prediction research despite their utility. We therefore developed the EPIFORGE checklist, a guideline for standardized reporting of epidemic forecasting research. Methods and findings We developed this checklist using a best-practice process for development of reporting guidelines, involving a Delphi process and broad consultation with an international panel of infectious disease modelers and model end users. The objectives of these guidelines are to improve the consistency, reproducibility, comparability, and quality of epidemic forecasting reporting. The guidelines are not designed to advise scientists on how to perform epidemic forecasting and prediction research, but rather to serve as a standard for reporting critical methodological details of such studies. Conclusions These guidelines have been submitted to the EQUATOR network, in addition to hosting by other dedicated webpages to facilitate feedback and journal endorsement., Author(s): Simon Pollett 1, Michael A. Johansson 2, Nicholas G. Reich 3, David Brett-Major 4, Sara Y. Del Valle 5, Srinivasan Venkatramanan 6, Rachel Lowe 7,8, Travis Porco 9, Irina [...]

Published
2021
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34. Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Author

Sherratt, Katharine, primary, Gruson, Hugo, additional, Grah, Rok, additional, Johnson, Helen, additional, Niehus, Rene, additional, Prasse, Bastian, additional, Sandmann, Frank, additional, Deuschel, Jannik, additional, Wolffram, Daniel, additional, Abbott, Sam, additional, Ullrich, Alexander, additional, Gibson, Graham, additional, Ray, Evan L, additional, Reich, Nicholas G, additional, Sheldon, Daniel, additional, Wang, Yijin, additional, Wattanachit, Nutcha, additional, Wang, Lijing, additional, Trnka, Jan, additional, Obozinski, Guillaume, additional, Sun, Tao, additional, Thanou, Dorina, additional, Pottier, Loic, additional, Krymova, Ekaterina, additional, Meinke, Jan H, additional, Barbarossa, Maria Vittoria, additional, Leithauser, Neele, additional, Mohring, Jan, additional, Schneider, Johanna, additional, Wlazlo, Jaroslaw, additional, Fuhrmann, Jan, additional, Lange, Berit, additional, Rodiah, Isti, additional, Baccam, Prasith, additional, Gurung, Heidi, additional, Stage, Steven, additional, Suchoski, Bradley, additional, Budzinski, Jozef, additional, Walraven, Robert, additional, Villanueva, Inmaculada, additional, Tucek, Vit, additional, Smid, Martin, additional, Zajicek, Milan, additional, Perez Alvarez, Cesar, additional, Reina, Borja, additional, Bosse, Nikos I, additional, Meakin, Sophie R, additional, Castro, Lauren, additional, Fairchild, Geoffrey, additional, Michaud, Isaac, additional, Osthus, Dave, additional, Alaimo Di Loro, Pierfrancesco, additional, Maruotti, Antonello, additional, Eclerova, Veronika, additional, Kraus, Andrea, additional, Kraus, David, additional, Pribylova, Lenka, additional, Dimitris, Bertsimas, additional, Li, Michael Lingzhi, additional, Saksham, Soni, additional, Dehning, Jonas, additional, Mohr, Sebastian, additional, Priesemann, Viola, additional, Redlarski, Grzegorz, additional, Bejar, Benjamin, additional, Ardenghi, Giovanni, additional, Parolini, Nicola, additional, Ziarelli, Giovanni, additional, Bock, Wolfgang, additional, Heyder, Stefan, additional, Hotz, Thomas, additional, Singh, David E, additional, Guzman-Merino, Miguel, additional, Aznarte, Jose L, additional, Morina, David, additional, Alonso, Sergio, additional, Alvarez, Enric, additional, Lopez, Daniel, additional, Prats, Clara, additional, Burgard, Jan Pablo, additional, Rodloff, Arne, additional, Zimmermann, Tom, additional, Kuhlmann, Alexander, additional, Zibert, Janez, additional, Pennoni, Fulvia, additional, Divino, Fabio, additional, Catala, Marti, additional, Lovison, Gianfranco, additional, Giudici, Paolo, additional, Tarantino, Barbara, additional, Bartolucci, Francesco, additional, Jona Lasinio, Giovanna, additional, Mingione, Marco, additional, Farcomeni, Alessio, additional, Srivastava, Ajitesh, additional, Montero-Manso, Pablo, additional, Adiga, Aniruddha, additional, Hurt, Benjamin, additional, Lewis, Bryan, additional, Marathe, Madhav, additional, Porebski, Przemyslaw, additional, Venkatramanan, Srinivasan, additional, Bartczuk, Rafal P, additional, Dreger, Filip, additional, Gambin, Anna, additional, Gogolewski, Krzysztof, additional, Gruziel-Slomka, Magdalena, additional, Krupa, Bartosz, additional, Moszyński, Antoni, additional, Niedzielewski, Karol, additional, Nowosielski, Jedrzej, additional, Radwan, Maciej, additional, Rakowski, Franciszek, additional, Semeniuk, Marcin, additional, Szczurek, Ewa, additional, Zielinski, Jakub, additional, Kisielewski, Jan, additional, Pabjan, Barbara, additional, Holger, Kirsten, additional, Kheifetz, Yuri, additional, Scholz, Markus, additional, Przemyslaw, Biecek, additional, Bodych, Marcin, additional, Filinski, Maciej, additional, Idzikowski, Radoslaw, additional, Krueger, Tyll, additional, Ozanski, Tomasz, additional, Bracher, Johannes, additional, and Funk, Sebastian, additional

Published
2023
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35. Coupled models of genomic surveillance and evolving pandemics with applications for timely public health interventions.

Author

Espinoza, Baltazar, Adiga, Aniruddha, Venkatramanan, Srinivasan, Warren, Andrew Scott, Jiangzhuo Chen, Lewis, Bryan Leroy, Vullikanti, Anil, Swarup, Samarth, Moon, Sifat, Barrett, Christopher Louis, Athreya, Siva, Sundaresan, Rajesh, Chandru, Vijay, Laxminarayan, Ramanan, Schaffer, Benjamin, Poor, H. Vincent, Levin, Simon A., and Marathe, Madhav V.

Subjects
PANDEMICS, DISEASE outbreaks, PUBLIC health, EPIDEMICS
Abstract

Disease surveillance systems provide early warnings of disease outbreaks before they become public health emergencies. However, pandemics containment would be challenging due to the complex immunity landscape created by multiple variants. Genomic surveillance is critical for detecting novel variants with diverse characteristics and importation/emergence times. Yet, a systematic study incorporating genomic monitoring, situation assessment, and intervention strategies is lacking in the literature. We formulate an integrated computational modeling framework to study a realistic course of action based on sequencing, analysis, and response. We study the effects of the second variant's importation time, its infectiousness advantage and, its cross-infection on the novel variant's detection time, and the resulting intervention scenarios to contain epidemics driven by two-variants dynamics. Our results illustrate the limitation in the intervention's effectiveness due to the variants' competing dynamics and provide the following insights: i) There is a set of importation times that yields the worst detection time for the second variant, which depends on the first variant's basic reproductive number; ii) When the second variant is imported relatively early with respect to the first variant, the cross-infection level does not impact the detection time of the second variant. We found that depending on the target metric, the best outcomes are attained under different interventions' regimes. Our results emphasize the importance of sustained enforcement of Non-Pharmaceutical Interventions on preventing epidemic resurgence due to importation/emergence of novel variants. We also discuss how our methods can be used to study when a novel variant emerges within a population. [ABSTRACT FROM AUTHOR]

Published
2023
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36. Author response: Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Author

Sherratt, Katharine, primary, Gruson, Hugo, additional, Grah, Rok, additional, Johnson, Helen, additional, Niehus, Rene, additional, Prasse, Bastian, additional, Sandmann, Frank, additional, Deuschel, Jannik, additional, Wolffram, Daniel, additional, Abbott, Sam, additional, Ullrich, Alexander, additional, Gibson, Graham, additional, Ray, Evan L, additional, Reich, Nicholas G, additional, Sheldon, Daniel, additional, Wang, Yijin, additional, Wattanachit, Nutcha, additional, Wang, Lijing, additional, Trnka, Jan, additional, Obozinski, Guillaume, additional, Sun, Tao, additional, Thanou, Dorina, additional, Pottier, Loic, additional, Krymova, Ekaterina, additional, Meinke, Jan H, additional, Barbarossa, Maria Vittoria, additional, Leithauser, Neele, additional, Mohring, Jan, additional, Schneider, Johanna, additional, Wlazlo, Jaroslaw, additional, Fuhrmann, Jan, additional, Lange, Berit, additional, Rodiah, Isti, additional, Baccam, Prasith, additional, Gurung, Heidi, additional, Stage, Steven, additional, Suchoski, Bradley, additional, Budzinski, Jozef, additional, Walraven, Robert, additional, Villanueva, Inmaculada, additional, Tucek, Vit, additional, Smid, Martin, additional, Zajicek, Milan, additional, Perez Alvarez, Cesar, additional, Reina, Borja, additional, Bosse, Nikos I, additional, Meakin, Sophie R, additional, Castro, Lauren, additional, Fairchild, Geoffrey, additional, Michaud, Isaac, additional, Osthus, Dave, additional, Alaimo Di Loro, Pierfrancesco, additional, Maruotti, Antonello, additional, Eclerova, Veronika, additional, Kraus, Andrea, additional, Kraus, David, additional, Pribylova, Lenka, additional, Dimitris, Bertsimas, additional, Li, Michael Lingzhi, additional, Saksham, Soni, additional, Dehning, Jonas, additional, Mohr, Sebastian, additional, Priesemann, Viola, additional, Redlarski, Grzegorz, additional, Bejar, Benjamin, additional, Ardenghi, Giovanni, additional, Parolini, Nicola, additional, Ziarelli, Giovanni, additional, Bock, Wolfgang, additional, Heyder, Stefan, additional, Hotz, Thomas, additional, Singh, David E, additional, Guzman-Merino, Miguel, additional, Aznarte, Jose L, additional, Morina, David, additional, Alonso, Sergio, additional, Alvarez, Enric, additional, Lopez, Daniel, additional, Prats, Clara, additional, Burgard, Jan Pablo, additional, Rodloff, Arne, additional, Zimmermann, Tom, additional, Kuhlmann, Alexander, additional, Zibert, Janez, additional, Pennoni, Fulvia, additional, Divino, Fabio, additional, Catala, Marti, additional, Lovison, Gianfranco, additional, Giudici, Paolo, additional, Tarantino, Barbara, additional, Bartolucci, Francesco, additional, Jona Lasinio, Giovanna, additional, Mingione, Marco, additional, Farcomeni, Alessio, additional, Srivastava, Ajitesh, additional, Montero-Manso, Pablo, additional, Adiga, Aniruddha, additional, Hurt, Benjamin, additional, Lewis, Bryan, additional, Marathe, Madhav, additional, Porebski, Przemyslaw, additional, Venkatramanan, Srinivasan, additional, Bartczuk, Rafal P, additional, Dreger, Filip, additional, Gambin, Anna, additional, Gogolewski, Krzysztof, additional, Gruziel-Slomka, Magdalena, additional, Krupa, Bartosz, additional, Moszyński, Antoni, additional, Niedzielewski, Karol, additional, Nowosielski, Jedrzej, additional, Radwan, Maciej, additional, Rakowski, Franciszek, additional, Semeniuk, Marcin, additional, Szczurek, Ewa, additional, Zielinski, Jakub, additional, Kisielewski, Jan, additional, Pabjan, Barbara, additional, Holger, Kirsten, additional, Kheifetz, Yuri, additional, Scholz, Markus, additional, Przemyslaw, Biecek, additional, Bodych, Marcin, additional, Filinski, Maciej, additional, Idzikowski, Radoslaw, additional, Krueger, Tyll, additional, Ozanski, Tomasz, additional, Bracher, Johannes, additional, and Funk, Sebastian, additional

Published
2023
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39. Data-driven scalable pipeline using national agent-based models for real-time pandemic response and decision support

Author

Bhattacharya, Parantapa, primary, Chen, Jiangzhuo, additional, Hoops, Stefan, additional, Machi, Dustin, additional, Lewis, Bryan, additional, Venkatramanan, Srinivasan, additional, Wilson, Mandy L., additional, Klahn, Brian, additional, Adiga, Aniruddha, additional, Hurt, Benjamin, additional, Outten, Joseph, additional, Adiga, Abhijin, additional, Warren, Andrew, additional, Baek, Young Yun, additional, Porebski, Przemyslaw, additional, Marathe, Achla, additional, Xie, Dawen, additional, Swarup, Samarth, additional, Vullikanti, Anil, additional, Mortveit, Henning, additional, Eubank, Stephen, additional, Barrett, Christopher L., additional, and Marathe, Madhav, additional

Published
2022
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42. Multimodeling approach to evaluating the efficacy of layering pharmaceutical and nonpharmaceutical interventions for influenza pandemics.

Author

Prasad, Pragati V., Steele, Molly K., Reed, Carrie, Meyers, Lauren Ancel, Zhanwei Du, Pasco, Remy, Alfaro-Murillo, Jorge A., Lewis, Bryan, Venkatramanan, Srinivasan, Schlitt, James, Jiangzhuo Chen, Orr, Mark, Wilson, Mandy L., Eubank, Stephen, Lijing Wang, Chinazzi, Matteo, y Piontti, Ana Pastore, Davis, Jessica T., Halloran, M. Elizabeth, and Longini, Ira

Subjects
SCHOOL closings, PANDEMICS, INFLUENZA, VIRAL transmission, VACCINE development, MULTIPLE comparisons (Statistics)
Abstract

When an influenza pandemic emerges, temporary school closures and antiviral treat- ment may slow virus spread, reduce the overall disease burden, and provide time for vaccine development, distribution, and administration while keeping a larger portion of the general population infection free. The impact of such measures will depend on the transmissibility and severity of the virus and the timing and extent of their imple- mentation. To provide robust assessments of layered pandemic intervention strategies, the Centers for Disease Control and Prevention (CDC) funded a network of academic groups to build a framework for the development and comparison of multiple pandemic influenza models. Research teams from Columbia University, Imperial College London/ Princeton University, Northeastern University, the University of Texas at Austin/Yale University, and the University of Virginia independently modeled three prescribed sets of pandemic influenza scenarios developed collaboratively by the CDC and network members. Results provided by the groups were aggregated into a mean-based ensemble. The ensemble and most component models agreed on the ranking of the most and least effective intervention strategies by impact but not on the magnitude of those impacts. In the scenarios evaluated, vaccination alone, due to the time needed for development, approval, and deployment, would not be expected to substantially reduce the numbers of illnesses, hospitalizations, and deaths that would occur. Only strategies that included early implementation of school closure were found to substantially mitigate early spread and allow time for vaccines to be developed and administered, especially under a highly transmissible pandemic scenario. [ABSTRACT FROM AUTHOR]

Published
2023
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43. Effective Social Network-Based Allocation of COVID-19 Vaccines

Author

Chen, Jiangzhuo, primary, Hoops, Stefan, additional, Marathe, Achla, additional, Mortveit, Henning, additional, Lewis, Bryan, additional, Venkatramanan, Srinivasan, additional, Haddadan, Arash, additional, Bhattacharya, Parantapa, additional, Adiga, Abhijin, additional, Vullikanti, Anil, additional, Srinivasan, Aravind, additional, Wilson, Mandy L., additional, Ehrlich, Gal, additional, Fenster, Maier, additional, Eubank, Stephen, additional, Barrett, Christopher, additional, and Marathe, Madhav, additional

Published
2022
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45. Projected resurgence of COVID-19 in the United States in July—December 2021 resulting from the increased transmissibility of the Delta variant and faltering vaccination

Author

Truelove, Shaun, primary, Smith, Claire P, primary, Qin, Michelle, additional, Mullany, Luke C, additional, Borchering, Rebecca K, additional, Lessler, Justin, additional, Shea, Katriona, additional, Howerton, Emily, additional, Contamin, Lucie, additional, Levander, John, additional, Kerr, Jessica, additional, Hochheiser, Harry, additional, Kinsey, Matt, additional, Tallaksen, Kate, additional, Wilson, Shelby, additional, Shin, Lauren, additional, Rainwater-Lovett, Kaitlin, additional, Lemairtre, Joseph C, additional, Dent, Juan, additional, Kaminsky, Joshua, additional, Lee, Elizabeth C, additional, Perez-Saez, Javier, additional, Hill, Alison, additional, Karlen, Dean, additional, Chinazzi, Matteo, additional, Davis, Jessica T, additional, Mu, Kunpeng, additional, Xiong, Xinyue, additional, Pastore y Piontti, Ana, additional, Vespignani, Alessandro, additional, Srivastava, Ajitesh, additional, Porebski, Przemyslaw, additional, Venkatramanan, Srinivasan, additional, Adiga, Aniruddha, additional, Lewis, Bryan, additional, Klahn, Brian, additional, Outten, Joseph, additional, Orr, Mark, additional, Harrison, Galen, additional, Hurt, Benjamin, additional, Chen, Jiangzhuo, additional, Vullikanti, Anil, additional, Marathe, Madhav, additional, Hoops, Stefan, additional, Bhattacharya, Parantapa, additional, Machi, Dustin, additional, Chen, Shi, additional, Paul, Rajib, additional, Janies, Daniel, additional, Thill, Jean-Claude, additional, Galanti, Marta, additional, Yamana, Teresa K, additional, Pei, Sen, additional, Shaman, Jeffrey L, additional, Healy, Jessica M, additional, Slayton, Rachel B, additional, Biggerstaff, Matthew, additional, Johansson, Michael A, additional, Runge, Michael C, additional, and Viboud, Cecile, additional

Published
2022
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46. Author response: Projected resurgence of COVID-19 in the United States in July—December 2021 resulting from the increased transmissibility of the Delta variant and faltering vaccination

Author

Truelove, Shaun, primary, Smith, Claire P, primary, Qin, Michelle, additional, Mullany, Luke C, additional, Borchering, Rebecca K, additional, Lessler, Justin, additional, Shea, Katriona, additional, Howerton, Emily, additional, Contamin, Lucie, additional, Levander, John, additional, Kerr, Jessica, additional, Hochheiser, Harry, additional, Kinsey, Matt, additional, Tallaksen, Kate, additional, Wilson, Shelby, additional, Shin, Lauren, additional, Rainwater-Lovett, Kaitlin, additional, Lemairtre, Joseph C, additional, Dent, Juan, additional, Kaminsky, Joshua, additional, Lee, Elizabeth C, additional, Perez-Saez, Javier, additional, Hill, Alison, additional, Karlen, Dean, additional, Chinazzi, Matteo, additional, Davis, Jessica T, additional, Mu, Kunpeng, additional, Xiong, Xinyue, additional, Pastore y Piontti, Ana, additional, Vespignani, Alessandro, additional, Srivastava, Ajitesh, additional, Porebski, Przemyslaw, additional, Venkatramanan, Srinivasan, additional, Adiga, Aniruddha, additional, Lewis, Bryan, additional, Klahn, Brian, additional, Outten, Joseph, additional, Orr, Mark, additional, Harrison, Galen, additional, Hurt, Benjamin, additional, Chen, Jiangzhuo, additional, Vullikanti, Anil, additional, Marathe, Madhav, additional, Hoops, Stefan, additional, Bhattacharya, Parantapa, additional, Machi, Dustin, additional, Chen, Shi, additional, Paul, Rajib, additional, Janies, Daniel, additional, Thill, Jean-Claude, additional, Galanti, Marta, additional, Yamana, Teresa K, additional, Pei, Sen, additional, Shaman, Jeffrey L, additional, Healy, Jessica M, additional, Slayton, Rachel B, additional, Biggerstaff, Matthew, additional, Johansson, Michael A, additional, Runge, Michael C, additional, and Viboud, Cecile, additional

Published
2022
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47. 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

48. AI-Driven Agent-Based Models to Study the Role of Vaccine Acceptance in Controlling COVID-19 Spread in the US

Author

Bhattacharya, Parantapa, primary, Machi, Dustin, additional, Chen, Jiangzhuo, additional, Hoops, Stefan, additional, Lewis, Bryan, additional, Mortveit, Henning, additional, Venkatramanan, Srinivasan, additional, Wilson, Mandy L., additional, Marathe, Achla, additional, Porebski, Przemyslaw, additional, Klahn, Brian, additional, Outten, Joseph, additional, Vullikanti, Anil, additional, Xie, Dawen, additional, Adiga, Abhijin, additional, Brown, Shawn, additional, Barrett, Christopher, additional, and Marathe, Madhav, additional

Published
2021
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49. High Performance Agent-Based Modeling to Study Realistic Contact Tracing Protocols

Author

Hoops, Stefan, primary, Chen, Jiangzhuo, additional, Adiga, Abhijin, additional, Lewis, Bryan, additional, Mortveit, Henning, additional, Baek, Hannah, additional, Wilson, Mandy, additional, Xie, Dawen, additional, Swarup, Samarth, additional, Venkatramanan, Srinivasan, additional, Crow, Justin, additional, Diskin, Elena, additional, Levine, Seth, additional, Tazelaar, Helen, additional, Rossheim, Brooke, additional, Ghaemmaghami, Chris, additional, Early, Rebecca, additional, Barrett, Chris, additional, Marathe, Madhav V., additional, and Price, Carter, additional

Published
2021
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50. Data-driven scalable pipeline using national agent-based models for real-time pandemic response and decision support.

Author

Bhattacharya, Parantapa, Chen, Jiangzhuo, Hoops, Stefan, Machi, Dustin, Lewis, Bryan, Venkatramanan, Srinivasan, Wilson, Mandy L., Klahn, Brian, Adiga, Aniruddha, Hurt, Benjamin, Outten, Joseph, Adiga, Abhijin, Warren, Andrew, Baek, Young Yun, Porebski, Przemyslaw, Marathe, Achla, Xie, Dawen, Swarup, Samarth, Vullikanti, Anil, and Mortveit, Henning

Subjects
HIGH performance computing, PANDEMICS, DIGITAL twins, SOCIAL contact, AUTOMATED planning & scheduling
Abstract

This paper describes an integrated, data-driven operational pipeline based on national agent-based models to support federal and state-level pandemic planning and response. The pipeline consists of (i) an automatic semantic-aware scheduling method that coordinates jobs across two separate high performance computing systems; (ii) a data pipeline to collect, integrate and organize national and county-level disaggregated data for initialization and post-simulation analysis; (iii) a digital twin of national social contact networks made up of 288 Million individuals and 12.6 Billion time-varying interactions covering the US states and DC; (iv) an extension of a parallel agent-based simulation model to study epidemic dynamics and associated interventions. This pipeline can run 400 replicates of national runs in less than 33 h, and reduces the need for human intervention, resulting in faster turnaround times and higher reliability and accuracy of the results. Scientifically, the work has led to significant advances in real-time epidemic sciences. [ABSTRACT FROM AUTHOR]

Published
2023
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