Search

Your search keyword '"Cramer, Estee Y."' showing total 31 results
Start Over Author "Cramer, Estee Y."
31 results on '"Cramer, Estee Y."'

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
Full Text
View/download PDF

2. 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

3. The United States COVID-19 Forecast Hub dataset

Author

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, and Reich, Nicholas G

Subjects
Earth Sciences, Atmospheric Sciences, COVID-19, Centers for Disease Control and Prevention, U.S., Forecasting, Humans, Pandemics, United States, US COVID-19 Forecast Hub Consortium
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

4. 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, VP, Guertin, Stephanie L, Hulme-Lowe, Christopher, Turner, Stephen D, Shi, Yunfeng, Ban, Xuegang, Walraven, Robert, Hong, Qi-Jun, Kong, Stanley, and van de Walle, Axel

Subjects
Bioengineering, Good Health and Well Being, COVID-19, Data Accuracy, Forecasting, Humans, Pandemics, Probability, Public Health, United States, forecasting, ensemble forecast, model evaluation
Abstract

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

Published
2022

5. Serological surveys to estimate cumulative incidence of SARS-CoV-2 infection in adults (Sero-MAss study), Massachusetts, July–August 2020: a mail-based cross-sectional study

Author

Snyder, Teah, Ravenhurst, Johanna, Cramer, Estee Y, Reich, Nicholas G, Balzer, Laura, Alfandari, Dominique, and Lover, Andrew A

Subjects
Medical Microbiology, Biomedical and Clinical Sciences, Vaccine Related, Clinical Research, Prevention, Biodefense, Emerging Infectious Diseases, Infectious Diseases, Infection, Good Health and Well Being, Adult, Antibodies, Viral, COVID-19, COVID-19 Testing, Cross-Sectional Studies, Humans, Incidence, Male, Postal Service, SARS-CoV-2, Seroepidemiologic Studies, epidemiology, public health, infectious disease, serology, Clinical Sciences, Public Health and Health Services, Other Medical and Health Sciences, Biomedical and clinical sciences, Health sciences, Psychology
Abstract

ObjectivesTo estimate the seroprevalence of anti-SARS-CoV-2 IgG and IgM among Massachusetts residents and to better understand asymptomatic SARS-CoV-2 transmission during the summer of 2020.DesignMail-based cross-sectional survey.SettingMassachusetts, USA.ParticipantsPrimary sampling group: sample of undergraduate students at the University of Massachusetts, Amherst (n=548) and a member of their household (n=231).Secondary sampling group: sample of graduate students, faculty, librarians and staff (n=214) and one member of their household (n=78). All participants were residents of Massachusetts without prior COVID-19 diagnosis.Primary and secondary outcome measuresPrevalence of SARS-CoV-2 seropositivity. Association of seroprevalence with variables including age, gender, race, geographic region, occupation and symptoms.ResultsApproximately 27 000 persons were invited via email to assess eligibility. 1001 households were mailed dried blood spot sample kits, 762 returned blood samples for analysis. In the primary sample group, 36 individuals (4.6%) had IgG antibodies detected for an estimated weighted prevalence in this population of 5.3% (95% CI: 3.5 to 8.0). In the secondary sampling group, 10 participants (3.4%) had IgG antibodies detected for an estimated adjusted prevalence of 4.0% (95% CI: 2.2 to 7.4). No samples were IgM positive. No association was found in either group between seropositivity and self-reported work duties or customer-facing hours. In the primary sampling group, self-reported febrile illness since February 2020, male sex and minority race (Black or American Indian/Alaskan Native) were associated with seropositivity. No factors except geographic regions within the state were associated with evidence of prior SARS-CoV-2 infection in the secondary sampling group.ConclusionsThis study fills a critical gap in estimating the levels of subclinical and asymptomatic infection. Estimates can be used to calibrate models estimating levels of population immunity over time, and these data are critical for informing public health interventions and policy.

Published
2021

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

10. Measuring effects of ivermectin-treated cattle on potential malaria vectors in Vietnam: A cluster-randomized trial.

Author

Cramer, Estee Y., Nguyen, Xuan Quang, Hertz, Jeffrey C., Nguyen, Do Van, Quang, Huynh Hong, Mendenhall, Ian H., and Lover, Andrew A.

Subjects
*AEDES aegypti, *CATTLE, *NEGATIVE binomial distribution, *MALARIA, *GENERALIZED estimating equations, *BOVINE viral diarrhea, *FIELD research
Abstract

Background: Malaria elimination using current tools has stalled in many areas. Ivermectin (IVM) is a broad-antiparasitic drug and mosquitocide and has been proposed as a tool for accelerating progress towards malaria elimination. Under laboratory conditions, IVM has been shown to reduce the survival of adult Anopheles populations that have fed on IVM-treated mammals. Treating cattle with IVM has been proposed as an important contribution to malaria vector management, however, the impacts of IVM in this One Health use case have been untested in field trials in Southeast Asia. Methods: Through a randomized village-based trial, this study quantified the effect of IVM-treated cattle on anopheline populations in treated vs. untreated villages in Central Vietnam. Local zebu cattle in six rural villages were included in this study. In three villages, cattle were treated with IVM at established veterinary dosages, and in three additional villages cattle were left as untreated controls. For the main study outcome, the mosquito populations in all villages were sampled using cattle-baited traps for six nights before, and six nights after a 2-day treatment IVM-administration (intervention) period. Anopheline species were characterized using taxonomic keys. The impact of the intervention was analyzed using a difference-in-differences (DID) approach with generalized estimating equations (with negative binomial distribution and robust errors). This intervention was powered to detect a 50% reduction in total nightly Anopheles spp. vector catches from cattle-baited traps. Given the unusual diversity in anopheline populations, exploratory analyses examined taxon-level differences in the ecological population diversity. Results: Across the treated villages, 1,112 of 1,527 censused cows (73% overall; range 67% to 83%) were treated with IVM. In both control and treated villages, there was a 30% to 40% decrease in total anophelines captured in the post-intervention period as compared to the pre-intervention period. In the control villages, there were 1,873 captured pre-intervention and 1,079 captured during the post-intervention period. In the treated villages, there were 1,594 captured pre-intervention, and 1,101 captured during the post-intervention period. The difference in differences model analysis comparing total captures between arms was not statistically significant (p = 0.61). Secondary outcomes of vector population diversity found that in three villages (one control and two treatment) Brillouin's index increased, and in three villages (two control and one treatment) Brillouin's index decreased. When examining biodiversity by trapping-night, there were no clear trends in treated or untreated vector populations. Additionally, there were no clear trends when examining the components of biodiversity: richness and evenness. Conclusions: The ability of this study to quantify the impacts of IVM treatment was limited due to unexpectedly large spatiotemporal variability in trapping rates; an area-wide decrease in trapping counts across all six villages post-intervention; and potential spillover effects. However, this study provides important data to directly inform future studies in the GMS and beyond for IVM-based vector control. Author summary: Malaria incidence in Vietnam has substantially declined in the last decade, however, current prevention strategies may be insufficient to achieve national elimination goals. A proposed tool for use as a mosquitocide is zooprophylaxis-aided ivermectin-based elimination (i.e., treating cattle with ivermectin to kill feeding mosquitoes). Ivermectin (IVM) is an inexpensive helminthicide and is safe for use in mammals. In laboratory studies, mosquitoes feeding on IVM-treated cattle have increased mortality compared to controls. Presented here is a randomized village-based trial to determine whether IVM treatment can reduce the number of captured mosquitoes. Six villages in Central Vietnam were randomly assigned to either receive IVM treatment or remain a control. Mosquitoes in each village were captured for six trap nights, followed by treatment in three villages, followed by trapping for an additional six nights. A difference-in-differences model did not show any statistically significant differences in capture rates between the treated and control villages. Factors including the amount of circulating IVM in the cattle population, crossover of mosquito populations, and differential feeding habits may have affected captures. A total of 18 species were identified using dichotomous keys. In post hoc analyses, no clear trends were observed in the anopheline populations after IVM treatment, as measured by ecological diversity metrics. Future studies should include additional villages, greater control of cattle movements, and consider potential vector movements. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

11. 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
Full Text
View/download PDF

15. Forgotten People, Forgotten Diseases: The Neglected Tropical Diseases and Their Impact on Global Health and Development

Author

Cramer, Estee Y. and Lover, Andrew A.

Subjects
Forgotten People, Forgotten Diseases: The Neglected Tropical Diseases and Their Impact on Global Health and Development (Nonfiction work) -- Hotez, Peter J., Books -- Book reviews, Health
Abstract

Forgotten People, Forgotten Diseases: The Neglected Tropical Diseases and Their Impact on Global Health and Development Peter J. Hotez John Wiley & Sons, Hoboken, NJ, USA, 2021;ISBN: 9781683673897 (eBook); ISBN: [...]

Published
2022
Full Text
View/download PDF

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

Author

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

Published
2022
Full Text
View/download PDF

17. Additional file 1 of Optimizing the implementation of a participant-collected, mail-based SARS-CoV-2 serological survey in university-affiliated populations: lessons learned and practical guidance

Author

Cramer, Estee Y., Snyder, Teah, Ravenhurst, Johanna, and Lover, Andrew A.

Abstract

Additional file 1. Eligibility, Consent, and Demographic survey administered to UMass affiliates and their household members. REDCap surveys were administered to participants through campus email accounts. Supplemental Fig. 1. Instructional guides for collecting and repackaging samples. PDF versions of files were included with every box giving users instructions on how to collect blood spots and properly mail back the packages.

Published
2022
Full Text
View/download PDF

18. 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

19. The United States COVID-19 Forecast Hub dataset

Author

Cramer, Estee Y, primary, Huang, Yuxin, additional, Wang, Yijin, additional, Ray, Evan L, additional, Cornell, Matthew, additional, Bracher, Johannes, additional, Brennen, Andrea, additional, Castero Rivadeneira, Alvaro J, additional, Gerding, Aaron, additional, House, Katie, additional, Jayawardena, Dasuni, additional, Kanji, Abdul H, additional, Khandelwal, Ayush, additional, Le, Khoa, additional, Niemi, Jarad, additional, Stark, Ariane, additional, Shah, Apurv, additional, Wattanchit, Nutcha, additional, Zorn, Martha W, additional, and Reich, Nicholas G, additional

Published
2021
Full Text
View/download PDF

23. 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

24. Ensemble Forecasts of Coronavirus Disease 2019 (COVID-19) in the U.S.

Author

Ray, Evan L, primary, Wattanachit, Nutcha, additional, Niemi, Jarad, additional, Kanji, Abdul Hannan, additional, House, Katie, additional, Cramer, Estee Y, additional, Bracher, Johannes, additional, Zheng, Andrew, additional, Yamana, Teresa K, additional, Xiong, Xinyue, additional, Woody, Spencer, additional, Wang, Yuanjia, additional, Wang, Lily, additional, Walraven, Robert L, additional, Tomar, Vishal, additional, Sherratt, Katharine, additional, Sheldon, Daniel, additional, Reiner, Robert C, additional, Prakash, B. Aditya, additional, Osthus, Dave, additional, Li, Michael Lingzhi, additional, Lee, Elizabeth C, additional, Koyluoglu, Ugur, additional, Keskinocak, Pinar, additional, Gu, Youyang, additional, Gu, Quanquan, additional, George, Glover E., additional, España, Guido, additional, Corsetti, Sabrina, additional, Chhatwal, Jagpreet, additional, Cavany, Sean, additional, Biegel, Hannah, additional, Ben-Nun, Michal, additional, Walker, Jo, additional, Slayton, Rachel, additional, Lopez, Velma, additional, Biggerstaff, Matthew, additional, Johansson, Michael A, additional, and Reich, Nicholas G, additional

Published
2020
Full Text
View/download PDF

26. Risk Factors for Malaria Infection in Central Madagascar: Insights from a Cross-Sectional Population Survey

Author

Howes, Rosalind E., primary, Zikursh, Melinda, additional, Cramer, Estee Y., additional, Ramiranirina, Brune, additional, Ratsimbasoa, Arsène, additional, Kang, Su Y., additional, Zimmerman, Peter A., additional, Ramboarina, Stéphanie, additional, Tisch, Daniel J., additional, Rakotomanga, Tovonahary Angelo, additional, and Franchard, Thierry, additional

Published
2018
Full Text
View/download PDF

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

Author

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

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

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

Published
2022

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

Author

Mathis SM, Webber AE, León TM, Murray EL, Sun M, White LA, Brooks LC, Green A, Hu AJ, Rosenfeld R, Shemetov D, Tibshirani RJ, McDonald DJ, Kandula S, Pei S, Yaari R, Yamana TK, Shaman J, Agarwal P, Balusu S, Gururajan G, Kamarthi H, Prakash BA, Raman R, Zhao Z, Rodríguez A, Meiyappan A, Omar S, Baccam P, Gurung HL, Suchoski BT, Stage SA, Ajelli M, Kummer AG, Litvinova M, Ventura PC, Wadsworth S, Niemi J, Carcelen E, Hill AL, Loo SL, McKee CD, Sato K, Smith C, Truelove S, Jung SM, Lemaitre JC, Lessler J, McAndrew T, Ye W, Bosse N, Hlavacek WS, Lin YT, Mallela A, Gibson GC, Chen Y, Lamm SM, Lee J, Posner RG, Perofsky AC, Viboud C, Clemente L, Lu F, Meyer AG, Santillana M, Chinazzi M, Davis JT, Mu K, Pastore Y Piontti A, Vespignani A, Xiong X, Ben-Nun M, Riley P, Turtle J, Hulme-Lowe C, Jessa S, Nagraj VP, Turner SD, Williams D, Basu A, Drake JM, Fox SJ, Suez E, Cojocaru MG, Thommes EW, Cramer EY, Gerding A, Stark A, Ray EL, Reich NG, Shandross L, Wattanachit N, Wang Y, Zorn MW, Aawar MA, Srivastava A, Meyers LA, Adiga A, Hurt B, Kaur G, Lewis BL, Marathe M, Venkatramanan S, Butler P, Farabow A, Ramakrishnan N, Muralidhar N, Reed C, Biggerstaff M, and Borchering RK

Subjects
Humans, Models, Statistical, Influenza, Human epidemiology, Hospitalization statistics & numerical data, Forecasting methods, Seasons
Abstract

Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. For the 2021-22 and 2022-23 influenza seasons, 26 forecasting teams provided national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one-to-four weeks ahead. Forecast skill is evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperform the baseline model across forecast weeks and locations in 2021-22 and 12 out of 18 models in 2022-23. Averaging across all forecast targets, the FluSight ensemble is the 2 nd most accurate model measured by WIS in 2021-22 and the 5 th most accurate in the 2022-23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degrade over longer forecast horizons. In this work we demonstrate that while the FluSight ensemble was a robust predictor, even ensembles face challenges during periods of rapid change., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Published
2024
Full Text
View/download PDF

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

Author

Mathis SM, Webber AE, León TM, Murray EL, Sun M, White LA, Brooks LC, Green A, Hu AJ, McDonald DJ, Rosenfeld R, Shemetov D, Tibshirani RJ, Kandula S, Pei S, Shaman J, Yaari R, Yamana TK, Agarwal P, Balusu S, Gururajan G, Kamarthi H, Prakash BA, Raman R, Rodríguez A, Zhao Z, Meiyappan A, Omar S, Baccam P, Gurung HL, Stage SA, Suchoski BT, Ajelli M, Kummer AG, Litvinova M, Ventura PC, Wadsworth S, Niemi J, Carcelen E, Hill AL, Jung SM, Lemaitre JC, Lessler J, Loo SL, McKee CD, Sato K, Smith C, Truelove S, McAndrew T, Ye W, Bosse N, Hlavacek WS, Lin YT, Mallela A, Chen Y, Lamm SM, Lee J, Posner RG, Perofsky AC, Viboud C, Clemente L, Lu F, Meyer AG, Santillana M, Chinazzi M, Davis JT, Mu K, Piontti APY, Vespignani A, Xiong X, Ben-Nun M, Riley P, Turtle J, Hulme-Lowe C, Jessa S, Nagraj VP, Turner SD, Williams D, Basu A, Drake JM, Fox SJ, Gibson GC, Suez E, Thommes EW, Cojocaru MG, Cramer EY, Gerding A, Stark A, Ray EL, Reich NG, Shandross L, Wattanachit N, Wang Y, Zorn MW, Al Aawar M, Srivastava A, Meyers LA, Adiga A, Hurt B, Kaur G, Lewis BL, Marathe M, Venkatramanan S, Butler P, Farabow A, Muralidhar N, Ramakrishnan N, Reed C, Biggerstaff M, and Borchering RK

Abstract

Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. Forecasting teams were asked to provide national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one through four weeks ahead for the 2021-22 and 2022-23 influenza seasons. Across both seasons, 26 teams submitted forecasts, with the submitting teams varying between seasons. Forecast skill was evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperformed the baseline model across forecast weeks and locations in 2021-22 and 12 out of 18 models in 2022-23. Averaging across all forecast targets, the FluSight ensemble was the 2 nd most accurate model measured by WIS in 2021-22 and the 5 th most accurate in the 2022-23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degraded over longer forecast horizons and during periods of rapid change. Current influenza forecasting efforts help inform situational awareness, but research is needed to address limitations, including decreased performance during periods of changing epidemic dynamics., Competing Interests: Competing interests: E.W.T. is an employee of Sanofi, which manufactures influenza vaccines. J.S. and Columbia University disclose partial ownership of SK Analytics. J.S. discloses consulting for BNI.

Published
2023
Full Text
View/download PDF

30. Assessing the utility of COVID-19 case reports as a leading indicator for hospitalization forecasting in the United States.

Author

Reich NG, Wang Y, Burns M, Ergas R, Cramer EY, and Ray EL

Abstract

Identifying data streams that can consistently improve the accuracy of epidemiological forecasting models is challenging. Using models designed to predict daily state-level hospital admissions due to COVID-19 in California and Massachusetts, we investigated whether incorporating COVID-19 case data systematically improved forecast accuracy. Additionally, we considered whether using case data aggregated by date of test or by date of report from a surveillance system made a difference to the forecast accuracy. Evaluating forecast accuracy in a test period, after first having selected the best-performing methods in a validation period, we found that overall the difference in accuracy between approaches was small, especially at forecast horizons of less than two weeks. However, forecasts from models using cases aggregated by test date showed lower accuracy at longer horizons and at key moments in the pandemic, such as the peak of the Omicron wave in January 2022. Overall, these results highlight the challenge of finding a modeling approach that can generate accurate forecasts of outbreak trends both during periods of relative stability and during periods that show rapid growth or decay of transmission rates. While COVID-19 case counts seem to be a natural choice to help predict COVID-19 hospitalizations, in practice any benefits we observed were small and inconsistent.

Published
2023
Full Text
View/download PDF

31. Serological surveys to estimate cumulative incidence of SARS-CoV-2 infection in adults (Sero-MAss study), Massachusetts, July-August 2020: a mail-based cross-sectional study.

Author

Snyder T, Ravenhurst J, Cramer EY, Reich NG, Balzer LB, Alfandari D, and Lover AA

Abstract

Background: The SARS-CoV-2 pandemic is an unprecedented global health crisis. The state of Massachusetts was especially impacted during the initial stages; however, the extent of asymptomatic transmission remains poorly understood due to limited asymptomatic testing in the "first wave." To address this gap, a geographically representative and contact-free seroprevalence survey was conducted in July-August 2020, to estimate prior undetected SARS-CoV-2 infections., Methods: Students, faculty, librarians and staff members at the University of Massachusetts, Amherst without a previous COVID-19 diagnosis were invited to participate in this study along with one member of their household in June 2020. Two separate sampling frames were generated from administrative lists: all undergraduates and their household members (primary sampling group) were randomly selected with probability proportional to population size. All staff, faculty, graduate students and librarians (secondary sampling group) were selected as a simple random sample. After informed consent and a socio-behavioral survey, participants were mailed test kits and asked to return self-collected dried blood spot (DBS) samples. Samples were analyzed via ELISA for anti-SARS-CoV-2 IgG antibodies, and then IgM antibodies if IgG-positive. Seroprevalence estimates were adjusted for survey non-response. Binomial models were used to assess factors associated with seropositivity in both sample groups separately., Results: Approximately 27,000 persons were invited via email to assess eligibility. Of the 1,001 individuals invited to participate in the study, 762 (76%) returned blood samples for analysis. In the primary sampling group 548 returned samples, of which 230 enrolled a household member. Within the secondary sampling group of 214 individuals, 79 enrolled a household member. In the primary sample group, 36 (4.6%) had IgG antibodies detected for an estimated weighed prevalence for this population of 5.3% (95% CI: 3.5 to 8.0). In the secondary sampling group, 10 (3.4%) of 292 individuals had IgG antibodies detected for an estimated adjusted prevalence of 4.0% (95% CI: 2.2 to 7.4). No samples were IgM positive. No association was found in either sample group between seropositivity and self-reported work duties or customer-facing hours. In the primary sampling group, self-reported febrile illness since Feb 2020, male sex, and minority race (Black or American Indian/Alaskan Native) were associated with seropositivity. No factors except geographic regions within the state were associated with evidence of prior SARS-CoV-2 infection in the secondary sampling group., Interpretation: This study provides insight into the seroprevalence of university-related populations and their household members across the state of Massachusetts during the summer of 2020 of the pandemic and helps to fill a critical gap in estimating the levels of sub-clinical and asymptomatic infection. Estimates like these can be used to calibrate models that estimate levels of population immunity over time to inform public health interventions and policy., Competing Interests: COI statements None of the authors report any conflicts of interest, financial or non-financial, regarding this study.

Published
2021
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results