Search

Your search keyword '"Adee, Madeline"' showing total 22 results
Start Over Author "Adee, Madeline"
22 results on '"Adee, Madeline"'

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

Author

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

Published
2022

9. Challenges of COVID-19 Case Forecasting in the US, 2020–2021.

Author

Lopez, Velma K., Cramer, Estee Y., Pagano, Robert, Drake, John M., O'Dea, Eamon B., Adee, Madeline, Ayer, Turgay, Chhatwal, Jagpreet, Dalgic, Ozden O., Ladd, Mary A., Linas, Benjamin P., Mueller, Peter P., Xiao, Jade, Bracher, Johannes, Castro Rivadeneira, Alvaro J., Gerding, Aaron, Gneiting, Tilmann, Huang, Yuxin, Jayawardena, Dasuni, and Kanji, Abdul H.

Subjects
COVID-19 pandemic, BASIC reproduction number, GENERALIZED estimating equations, INFECTIOUS disease transmission, DECISION making, FORECASTING
Abstract

During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1–4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making. Author summary: As SARS-CoV-2 began to spread throughout the world in early 2020, modelers played a critical role in predicting how the epidemic could take shape. Short-term forecasts of epidemic outcomes (for example, infections, cases, hospitalizations, or deaths) provided useful information to support pandemic planning, resource allocation, and intervention. Yet, infectious disease forecasting is still a nascent science, and the reliability of different types of forecasts is unclear. We retrospectively evaluated COVID-19 case forecasts, which were often unreliable. For example, forecasts did not anticipate the speed of increase in cases in early winter 2020. This analysis provides insights on specific problems that could be addressed in future research to improve forecasts and their use. Identifying the strengths and weaknesses of forecasts is critical to improving forecasting for current and future public health responses. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

13. 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

14. Assessing the impact of simplified HCV care on linkage to care amongst high-risk patients at primary healthcare clinics in Malaysia: a prospective observational study

Author

Markby, Jessica, primary, Shilton, Sonjelle, additional, Sem, Xiaohui, additional, Chan, Huan Keat, additional, Md Said, Rosaida, additional, Siva, Sasikala, additional, Zainuddin, Zalwani, additional, Abu Bakar, Norasiah, additional, Omar, Haniza, additional, Ruiz, Ryan Jose III, additional, Gaeddert, Mary, additional, Tyshkovskiy, Alexander, additional, Adee, Madeline, additional, Chhatwal, Jagpreet, additional, Kumar, Suresh, additional, Piedagnel, Jean-Michel, additional, Mohd Zain, Rozainanee, additional, Menétrey, Caroline, additional, Yuswan, Fazidah, additional, Hairizan Nasir, Nazrila, additional, Andrieux-Meyer, Isabelle, additional, Ismail, Fatanah, additional, Zakaria, Rozita, additional, Hasim, Ruziaton, additional, Murad, Shahnaz, additional, Easterbrook, Philippa, additional, and Hassan, Muhammad Radzi Abu, additional

Published
2021
Full Text
View/download PDF

15. Feasibility, effectiveness and cost of a decentralized HCV care model among the general population in Delhi, India

Author

Markby, Jessica, primary, Gupta, Ekta, additional, Soni, Divya, additional, Sarin, Sanjay, additional, Murya, Mugil, additional, Katapur, Preetishirin, additional, Tewatia, Navneet, additional, Ramachandran, Babu Entoor, additional, Ruiz, Ryan Jose, additional, Gaeddert, Mary, additional, Tyshkovskiy, Alexander, additional, Adee, Madeline, additional, Chhatwal, Jagpreet, additional, Miglani, Sundeep, additional, Easterbrook, Philippa, additional, Sarin, Shiv K., additional, and Shilton, Sonjelle, additional

Published
2021
Full Text
View/download PDF

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

Author

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

Published
2021
Full Text
View/download PDF

20. Feasibility, effectiveness and cost of a decentralized HCV care model among the general population in Delhi, India.

Author

Markby, Jessica, Gupta, Ekta, Soni, Divya, Sarin, Sanjay, Murya, Mugil, Katapur, Preetishirin, Tewatia, Navneet, Ramachandran, Babu Entoor, Ruiz, Ryan Jose, Gaeddert, Mary, Tyshkovskiy, Alexander, Adee, Madeline, Chhatwal, Jagpreet, Miglani, Sundeep, Easterbrook, Philippa, Sarin, Shiv K., and Shilton, Sonjelle

Subjects
COST effectiveness, HEPATITIS C virus, VIRAL load, CAMP nursing
Abstract

Background and aims: India has a significant burden of hepatitis C virus (HCV) infection and has committed to achieving national elimination by 2030. This will require a substantial scale‐up in testing and treatment. The "HEAD‐Start Project Delhi" aimed to enhance HCV diagnosis and treatment pathways among the general population. Methods: A prospective study was conducted at 5 district hospitals (Arm 1: one‐stop shop), 15 polyclinics (Arm 2: referral for viral load (VL) testing and treatment) and 62 screening camps (Arm 3: referral for treatment). HCV prevalence, retention in the HCV care cascade, and turn‐around time were measured. Results: Between January and September 2019, 37 425 participants were screened for HCV. The median (IQR) age of participants was 35 (26‐48) years, with 50.4% male and 49.6% female. A significantly higher proportion of participants in Arm 1 (93.7%) and Arm 3 (90.3%) received a VL test compared with Arm 2 (52.5%, P <.001). Of those confirmed positive, treatment was initiated at significantly higher rates for participants in both Arms 1 (85.6%) and 2 (73.7%) compared to Arm 3 (41.8%, P <.001). Arm 1 was found to be a cost‐saving strategy compared to Arm 2, Arm 3, and no action. Conclusions: Delivery of all services at a single site (district hospitals) resulted in a higher yield of HCV seropositive cases and retention compared with sites where participants were referred elsewhere for VL testing and/or treatment. The highest level of retention in the care cascade was also associated with the shortest turn‐around times. [ABSTRACT FROM AUTHOR]

Published
2022
Full Text
View/download PDF

22. Funding Hepatitis C Treatment in Correctional Facilities by Using a Nominal Pricing Mechanism.

Author

Spaulding, Anne C., Chhatwal, Jagpreet, Adee, Madeline G., Lawrence, Robert T., Beckwith, Curt G., and von Oehsen, William

Subjects
HEPATITIS C prevention, HEPATITIS C treatment, MEDICAL care cost laws, CORRECTIONAL institutions, COST control, RESEARCH funding, VIRAL hepatitis, GOVERNMENT regulation, VACCINATION, ECONOMICS
Abstract

The cost of treating all incarcerated people who have hepatitis C with direct-acting antiviral agents (DAAs) greatly stresses correctional facility budgets. Complex federal laws bar pharmaceutical companies from simply discounting expensive medications to prices that facilities can afford. This article discusses means by which correctional facilities may qualify under federal law as "safety-net providers" to allow sale of DAAs at a price <10% of the average manufacturer price (AMP). No new laws would need to be enacted to implement this strategy. Using fiscal year 2018 pricing data from the Georgia Department of Corrections, we derived an estimate for the AMP and then used this estimate to calculate a nominal price. The United States would save ~$3 billion if manufacturers sold DAAs at a nominal price to correctional facilities. Use of this strategy would help solve the conundrum of how state and county governments can pay for hepatitis C treatment and would ultimately save money for society. [ABSTRACT FROM AUTHOR]

Published
2019
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results