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466 results on '"Biggerstaff, Matthew"'

1. 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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2. Multiple models for outbreak decision support in the face of uncertainty.

Author

Shea, Katriona, Borchering, Rebecca K, Probert, William JM, Howerton, Emily, Bogich, Tiffany L, Li, Shou-Li, van Panhuis, Willem G, Viboud, Cecile, Aguás, Ricardo, Belov, Artur A, Bhargava, Sanjana H, Cavany, Sean M, Chang, Joshua C, Chen, Cynthia, Chen, Jinghui, Chen, Shi, Chen, YangQuan, Childs, Lauren M, Chow, Carson C, Crooker, Isabel, Del Valle, Sara Y, España, Guido, Fairchild, Geoffrey, Gerkin, Richard C, Germann, Timothy C, Gu, Quanquan, Guan, Xiangyang, Guo, Lihong, Hart, Gregory R, Hladish, Thomas J, Hupert, Nathaniel, Janies, Daniel, Kerr, Cliff C, Klein, Daniel J, Klein, Eili Y, Lin, Gary, Manore, Carrie, Meyers, Lauren Ancel, Mittler, John E, Mu, Kunpeng, Núñez, Rafael C, Oidtman, Rachel J, Pasco, Remy, Pastore Y Piontti, Ana, Paul, Rajib, Pearson, Carl AB, Perdomo, Dianela R, Perkins, T Alex, Pierce, Kelly, Pillai, Alexander N, Rael, Rosalyn Cherie, Rosenfeld, Katherine, Ross, Chrysm Watson, Spencer, Julie A, Stoltzfus, Arlin B, Toh, Kok Ben, Vattikuti, Shashaank, Vespignani, Alessandro, Wang, Lingxiao, White, Lisa J, Xu, Pan, Yang, Yupeng, Yogurtcu, Osman N, Zhang, Weitong, Zhao, Yanting, Zou, Difan, Ferrari, Matthew J, Pannell, David, Tildesley, Michael J, Seifarth, Jack, Johnson, Elyse, Biggerstaff, Matthew, Johansson, Michael A, Slayton, Rachel B, Levander, John D, Stazer, Jeff, Kerr, Jessica, and Runge, Michael C

Subjects
Humans, Uncertainty, Public Health, Disease Outbreaks, Pandemics, COVID-19, cognitive biases, decision theory, multi-model aggregation, Prevention, Brain Disorders, Good Health and Well Being
Abstract

Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020.

Published
2023

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. Redefining pandemic preparedness: Multidisciplinary insights from the CERP modelling workshop in infectious diseases, workshop report

Author

Nunes, Marta C., Thommes, Edward, Fröhlich, Holger, Flahault, Antoine, Arino, Julien, Baguelin, Marc, Biggerstaff, Matthew, Bizel-Bizellot, Gaston, Borchering, Rebecca, Cacciapaglia, Giacomo, Cauchemez, Simon, Barbier--Chebbah, Alex, Claussen, Carsten, Choirat, Christine, Cojocaru, Monica, Commaille-Chapus, Catherine, Hon, Chitin, Kong, Jude, Lambert, Nicolas, Lauer, Katharina B., Lehr, Thorsten, Mahe, Cédric, Marechal, Vincent, Mebarki, Adel, Moghadas, Seyed, Niehus, Rene, Opatowski, Lulla, Parino, Francesco, Pruvost, Gery, Schuppert, Andreas, Thiébaut, Rodolphe, Thomas-Bachli, Andrea, Viboud, Cecile, Wu, Jianhong, Crépey, Pascal, and Coudeville, Laurent

Published
2024
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6. Wastewater Surveillance for Influenza A Virus and H5 Subtype Concurrent with the Highly Pathogenic Avian Influenza A(H5N1) Virus Outbreak in Cattle and Poultry and Associated Human Cases-United States, May 12-July 13, 2024

Author

Louis, Souci, Mark-Carew, Miguella, Biggerstaff, Matthew, Yoder, Jonathan, Boehm, Alexandria B., Wolfe, Marlene K., Flood, Matthew, Peters, Susan, Stobierski, Mary Grace, Coyle, Joseph, Leslie, Matthew T., Sinner, Mallory, Nims, Dawn, Salinas, Victoria, Lustri, Layla, Bojes, Heidi, Shetty, Varun, Burnor, Elisabeth, Rabe, Angela, Ellison-Giles, Guinevere, Yu, Alexander T., Bell, Austin, Meyer, Stephanie, Lynfield, Ruth, Sutton, Melissa, Scholz, Ryan, Falender, Rebecca, Matzinger, Shannon, Wheeler, Allison, Ahmed, Farah S., Anderson, John, Harris, Kate, Walkins, Austin, Bohra, Surabhi, O'Dell, Victoria, Guidry, Virginia T., Christensen, Ariel, Moore, Zack, Wilson, Erica, Clayton, Joshua L., Parsons, Hannah, Kniss, Krista, Budd, Alicia, Mercante, Jeffrey W., Reese, Heather E., Welton, Michael, Bias, Megan, Webb, Jenna, Cornforth, Daniel, Santibanez, Scott, Soelaeman, Rieza H., Kaur, Manpreet, Kirby, Amy E., Barnes, John R., Fehrenbach, Nicole, Olsen, Sonja J., and Honein, Margaret A.

Subjects
Avian influenza -- Analysis, Milk -- Analysis, Respiratory tract diseases -- Analysis, Wastewater -- Analysis, Avian influenza viruses -- Analysis, Poultry industry -- Analysis, Cattle -- Analysis, Sewage -- Purification
Abstract

Introduction Wastewater surveillance is used to monitor human shedding of pathogens, including SARS-CoV-2, at a community level and is independent of symptoms, testing access, and care-seeking behavior (1). Some sites [...]

Published
2024

7. Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty

Author

Howerton, Emily, Contamin, Lucie, Mullany, Luke C., Qin, Michelle, Reich, Nicholas G., Bents, Samantha, Borchering, Rebecca K., Jung, Sung-mok, Loo, Sara L., Smith, Claire P., Levander, John, Kerr, Jessica, Espino, J., van Panhuis, Willem G., Hochheiser, Harry, Galanti, Marta, Yamana, Teresa, Pei, Sen, Shaman, Jeffrey, Rainwater-Lovett, Kaitlin, Kinsey, Matt, Tallaksen, Kate, Wilson, Shelby, Shin, Lauren, Lemaitre, Joseph C., Kaminsky, Joshua, Hulse, Juan Dent, Lee, Elizabeth C., McKee, Clifton D., Hill, Alison, Karlen, Dean, Chinazzi, Matteo, Davis, Jessica T., Mu, Kunpeng, Xiong, Xinyue, Pastore y Piontti, Ana, Vespignani, Alessandro, Rosenstrom, Erik T., Ivy, Julie S., Mayorga, Maria E., Swann, Julie L., España, Guido, Cavany, Sean, Moore, Sean, Perkins, Alex, Hladish, Thomas, Pillai, Alexander, Ben Toh, Kok, Longini, Jr., Ira, Chen, Shi, Paul, Rajib, Janies, Daniel, Thill, Jean-Claude, Bouchnita, Anass, Bi, Kaiming, Lachmann, Michael, Fox, Spencer J., Meyers, Lauren Ancel, Srivastava, Ajitesh, Porebski, Przemyslaw, Venkatramanan, Srini, Adiga, Aniruddha, Lewis, Bryan, Klahn, Brian, Outten, Joseph, Hurt, Benjamin, Chen, Jiangzhuo, Mortveit, Henning, Wilson, Amanda, Marathe, Madhav, Hoops, Stefan, Bhattacharya, Parantapa, Machi, Dustin, Cadwell, Betsy L., Healy, Jessica M., Slayton, Rachel B., Johansson, Michael A., Biggerstaff, Matthew, Truelove, Shaun, Runge, Michael C., Shea, Katriona, Viboud, Cécile, and Lessler, Justin

Published
2023
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11. 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

12. Outbreak of Highly Pathogenic Avian Influenza A(H5N1) Viruses in U.S. Dairy Cattle and Detection of Two Human Cases--United States, 2024

Author

Garg, Shikha, Reed, Carrie, Davis, C. Todd, Uyeki, Timothy M., Behravesh, Casey Barton, Kniss, Krista, Budd, Alicia, Biggerstaff, Matthew, Adjemian, Jennifer, Barnes, John R., Kirby, Marie K., Basler, Colin, Szablewski, Christine M., Richmond-Crum, Malia, Burns, Erin, Limbago, Brandi, Daskalakis, Demetre C., Armstrong, Kimberly, Boucher, David, Shimabukuro, Tom T., Jhung, Michael A., Olsen, Sonja J., and Dugan, Vivien

Subjects
United States. Department of Agriculture, United States. Food and Drug Administration, United States. National Institute of Allergy and Infectious Diseases, Avian influenza -- Health aspects, Milk -- Health aspects, Avian influenza viruses -- Health aspects, Dairy cattle -- Health aspects, Dairy farming -- Health aspects, Conjunctivitis -- Health aspects, Public health -- Health aspects, Virus diseases -- Health aspects, Health
Abstract

On May 24, 2024, this report was posted as an MMWR Early Release on the MMWR website (https://www.cdc.gov/mmwr). Investigation and Findings Identification of Two Human Cases of Influenza A(H5) Virus [...]

Published
2024

13. 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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14. 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

15. 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

16. 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

18. High Influenza Incidence and Disease Severity Among Children and Adolescents Aged <18 Years--United States, 2022-23 Season

Author

White, Elizabeth B., O'Halloran, Alissa, Sundaresan, Devi, Gilmer, Matthew, Threlkel, Ryan, Colon, Arielle, Tastad, Katie, Chai, Shua J., Alden, Nisha B., Yousey-Hindes, Kimberly, Openo, Kyle P., Ryan, Patricia A., Kim, Sue, Lynfield, Ruth, Spina, Nancy, Tesini, Brenda L., Martinez, Marc, Schmidt, Zachary, Sutton, Melissa, Talbot, H. Keipp, Hill, Mary, Biggerstaff, Matthew, Budd, Alicia, Garg, Shikha, Reed, Carrie, Iuliano, A. Danielle, and Bozio, Catherine H.

Subjects
United States. Department of Health and Human Services, Children -- Diseases, Vaccination, Antiviral agents, Influenza vaccines, Influenza, Health, Council of State and Territorial Epidemiologists
Abstract

Introduction During the 2022-23 season, influenza activity in the United States began in early October, earlier than in most previous seasons, and returned to pre-COVID-19 levels (1). In addition, high [...]

Published
2023

19. Identification and evaluation of epidemic prediction and forecasting reporting guidelines: A systematic review and a call for action

Author

Pollett, Simon, Johansson, Michael, Biggerstaff, Matthew, Morton, Lindsay C., Bazaco, Sara L., Brett Major, David M., Stewart-Ibarra, Anna M., Pavlin, Julie A., Mate, Suzanne, Sippy, Rachel, Hartman, Laurie J., Reich, Nicholas G., Maljkovic Berry, Irina, Chretien, Jean-Paul, Althouse, Benjamin M., Myer, Diane, Viboud, Cecile, and Rivers, Caitlin

Published
2020
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20. Influenza virus shedding and symptoms: Dynamics and implications from a multi-season household transmission study

Author

Morris, Sinead E, primary, Nguyen, Huong Q, additional, Grijalva, Carlos G, additional, Hanson, Kayla E, additional, Zhu, Yuwei, additional, Biddle, Jessica E, additional, Meece, Jennifer K, additional, Halasa, Natasha B, additional, Chappell, James D, additional, Mellis, Alexandra M, additional, Reed, Carrie, additional, Biggerstaff, Matthew, additional, Belongia, Edward A, additional, Talbot, H Keipp, additional, and Rolfes, Melissa A, additional

Published
2024
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24. Update : Influenza Activity in the United States During the 2018–19 Season and Composition of the 2019–20 Influenza Vaccine

Author

Xu, Xiyan, Blanton, Lenee, Elal, Anwar Isa Abd, Alabi, Noreen, Barnes, John, Biggerstaff, Matthew, Brammer, Lynnette, Budd, Alicia P., Burns, Erin, Cummings, Charisse N., Garg, Shikha, Kondor, Rebecca, Gubareva, Larisa, Kniss, Krista, Nyanseor, Sankan, O’Halloran, Alissa, Rolfes, Melissa, Sessions, Wendy, Dugan, Vivien G., Fry, Alicia M., Wentworth, David E., Stevens, James, and Jernigan, Daniel

Published
2019

28. Nowcasting (Short-Term Forecasting) of Influenza Epidemics in Local Settings, Sweden, 2008-2019

Author

Spreco, Armin, Eriksson, Olle, Dahlstrom, Orjan, Cowling, Benjamin John, Biggerstaff, Matthew, Ljunggren, Gunnar, Joud, Anna, Istefan, Emanuel, and Timpka, Toomas

Subjects
Epidemics -- Statistics -- Forecasts and trends, Influenza -- Statistics -- Forecasts and trends, Market trend/market analysis, Health
Abstract

Reliable forecasts of the timing and spatial spread of influenza during seasons and pandemics can meaningfully advance the timing of public health communication campaigns and implementation of resource allocation in [...]

Published
2020
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29. 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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31. Modeling the Impacts of Antiviral Prophylaxis Strategies in Mitigating Seasonal Influenza Outbreaks in Nursing Homes.

Author

Morris, Sinead E, Zipfel, Casey M, Peer, Komal, Madewell, Zachary J, Brenner, Stephan, Garg, Shikha, Paul, Prabasaj, Slayton, Rachel B, and Biggerstaff, Matthew

Subjects
PREVENTION of infectious disease transmission, PREVENTION of epidemics, CHEMOPREVENTION, STATISTICAL models, PATIENT compliance, HOSPITAL care, MEDICAL care, TREATMENT effectiveness, INFLUENZA, DESCRIPTIVE statistics, ANTIVIRAL agents, NURSING care facilities, PRE-exposure prophylaxis, CONCEPTUAL structures, DRUGS, DATA analysis software, SEASONAL influenza
Abstract

Background Antiviral chemoprophylaxis is recommended for use during influenza outbreaks in nursing homes to prevent transmission and severe disease among non-ill residents. Centers for Disease Control and Prevention (CDC) guidance recommends prophylaxis be initiated for all non-ill residents once an influenza outbreak is detected and be continued for at least 14 days and until 7 days after the last laboratory-confirmed influenza case is identified. However, not all facilities strictly adhere to this guidance and the impact of such partial adherence is not fully understood. Methods We developed a stochastic compartmental framework to model influenza transmission within an average-sized US nursing home. We compared the number of symptomatic illnesses and hospitalizations under varying prophylaxis implementation strategies, in addition to different levels of prophylaxis uptake and adherence by residents and healthcare personnel (HCP). Results Prophylaxis implemented according to current guidance reduced total symptomatic illnesses and hospitalizations among residents by a median of 12% and 36%, respectively, compared with no prophylaxis. We did not find evidence that alternative implementations of prophylaxis were more effective: compared to full adoption of current guidance, partial adoption resulted in increased symptomatic illnesses and/or hospitalizations, and longer or earlier adoption offered no additional improvements. In addition, increasing uptake and adherence among nursing home residents was effective in reducing resident illnesses and hospitalizations, but increasing HCP uptake had minimal indirect impacts for residents. Conclusions The greatest benefits of influenza prophylaxis during nursing home outbreaks will likely be achieved through increasing uptake and adherence among residents and following current CDC guidance. [ABSTRACT FROM AUTHOR]

Published
2024
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34. 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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36. Responding to the Return of Influenza in the United States by Applying Centers for Disease Control and Prevention Surveillance, Analysis, and Modeling to Inform Understanding of Seasonal Influenza (Preprint)

Author

Borchering, Rebecca K, primary, Biggerstaff, Matthew, additional, Brammer, Lynnette, additional, Budd, Alicia, additional, Garg, Shikha, additional, Fry, Alicia M, additional, Iuliano, A Danielle, additional, and Reed, Carrie, additional

Published
2023
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37. Update : Influenza Activity in the United States During the 2017–18 Season and Composition of the 2018–19 Influenza Vaccine

Author

Garten, Rebecca, Blanton, Lenee, Elal, Anwar Isa Abd, Alabi, Noreen, Barnes, John, Biggerstaff, Matthew, Brammer, Lynnette, Budd, Alicia P., Burns, Erin, Cummings, Charisse N., Davis, Todd, Garg, Shikha, Gubareva, Larisa, Jang, Yunho, Kniss, Krista, Kramer, Natalie, Lindstrom, Stephen, Mustaquim, Desiree, O’Halloran, Alissa, Sessions, Wendy, Taylor, Calli, Xu, Xiyan, Dugan, Vivien G., Fry, Alicia M., Wentworth, David E., Katz, Jacqueline, and Jernigan, Daniel

Published
2018

41. 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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43. Outbreak of Influenza A(H3N2) Variant Virus Infections Among Persons Attending Agricultural Fairs Housing Infected Swine — Michigan and Ohio, July–August 2016

Author

Schicker, Rebekah S., Rossow, John, Eckel, Seth, Fisher, Nicolas, Bidol, Sally, Tatham, Lilith, Matthews-Greer, Janice, Sohner, Kevin, Bowman, Andrew S., Avrill, James, Forshey, Tony, Blanton, Lenee, Davis, C. Todd, Schiltz, John, Skorupski, Susan, Berman, LaShondra, Jang, Yunho, Bresee, Joseph S., Lindstrom, Stephen, Trock, Susan C., Wentworth, David, Fry, Alicia M., Fijter, Sietske de, Signs, Kimberly, DiOrio, Mary, Olsen, Sonja J., and Biggerstaff, Matthew

Published
2016

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