Last data update: Jun 17, 2024. (Total: 47034 publications since 2009)
Records 1-30 (of 49 Records) |
Query Trace: Reich J [original query] |
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Challenges of COVID-19 case forecasting in the US, 2020-2021
Lopez VK , Cramer EY , Pagano R , Drake JM , O'Dea EB , Adee M , Ayer T , Chhatwal J , Dalgic OO , Ladd MA , Linas BP , Mueller PP , Xiao J , Bracher J , Castro Rivadeneira AJ , Gerding A , Gneiting T , Huang Y , Jayawardena D , Kanji AH , Le K , Mühlemann A , Niemi J , Ray EL , Stark A , Wang Y , Wattanachit N , Zorn MW , Pei S , Shaman J , Yamana TK , Tarasewicz SR , Wilson DJ , Baccam S , Gurung H , Stage S , Suchoski B , Gao L , Gu Z , Kim M , Li X , Wang G , Wang L , Wang Y , Yu S , Gardner L , Jindal S , Marshall M , Nixon K , Dent J , Hill AL , Kaminsky J , Lee EC , Lemaitre JC , Lessler J , Smith CP , Truelove S , Kinsey M , Mullany LC , Rainwater-Lovett K , Shin L , Tallaksen K , Wilson S , Karlen D , Castro L , Fairchild G , Michaud I , Osthus D , Bian J , Cao W , Gao Z , Lavista Ferres J , Li C , Liu TY , Xie X , Zhang S , Zheng S , Chinazzi M , Davis JT , Mu K , Pastore YPiontti A , Vespignani A , Xiong X , Walraven R , Chen J , Gu Q , Wang L , Xu P , Zhang W , Zou D , Gibson GC , Sheldon D , Srivastava A , Adiga A , Hurt B , Kaur G , Lewis B , Marathe M , Peddireddy AS , Porebski P , Venkatramanan S , Wang L , Prasad PV , Walker JW , Webber AE , Slayton RB , Biggerstaff M , Reich NG , Johansson MA . PLoS Comput Biol 2024 20 (5) e1011200 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. |
Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty
Howerton E , Contamin L , Mullany LC , Qin M , Reich NG , Bents S , Borchering RK , Jung SM , Loo SL , Smith CP , Levander J , Kerr J , Espino J , van Panhuis WG , Hochheiser H , Galanti M , Yamana T , Pei S , Shaman J , Rainwater-Lovett K , Kinsey M , Tallaksen K , Wilson S , Shin L , Lemaitre JC , Kaminsky J , Hulse JD , Lee EC , McKee CD , Hill A , Karlen D , Chinazzi M , Davis JT , Mu K , Xiong X , Pastore YPiontti A , Vespignani A , Rosenstrom ET , Ivy JS , Mayorga ME , Swann JL , España G , Cavany S , Moore S , Perkins A , Hladish T , Pillai A , Ben Toh K , Longini I Jr , Chen S , Paul R , Janies D , Thill JC , Bouchnita A , Bi K , Lachmann M , Fox SJ , Meyers LA , Srivastava A , Porebski P , Venkatramanan S , Adiga A , Lewis B , Klahn B , Outten J , Hurt B , Chen J , Mortveit H , Wilson A , Marathe M , Hoops S , Bhattacharya P , Machi D , Cadwell BL , Healy JM , Slayton RB , Johansson MA , Biggerstaff M , Truelove S , Runge MC , Shea K , Viboud C , Lessler J . Nat Commun 2023 14 (1) 7260 ![]() Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make months ahead projections of SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we find SMH performance varied widely as a function of both scenario validity and model calibration. We show scenarios remained close to reality for 22 weeks on average before the arrival of unanticipated SARS-CoV-2 variants invalidated key assumptions. An ensemble of participating models that preserved variation between models (using the linear opinion pool method) was consistently more reliable than any single model in periods of valid scenario assumptions, while projection interval coverage was near target levels. SMH projections were used to guide pandemic response, illustrating the value of collaborative hubs for longer-term scenario projections. |
Post-marketing safety surveillance of a hexavalent vaccine in the Vaccine Adverse Event Reporting System
Moro PL , Zhang B , Marquez P , Reich J . J Pediatr 2023 262 113643 We assessed the safety of hexavalent vaccine (DTaP-IPV-Hib-HepB) in the Vaccine Adverse Event Reporting System. Five hundred-one reports of adverse events (AEs) were identified; 21 (4.2%) were serious. Most frequently reported AEs were fever (10.2%) and injection site erythema (5.4%). AEs reported were consistent with findings from pre-licensure studies. |
The United States COVID-19 Forecast Hub dataset (preprint)
Cramer EY , Huang Y , Wang Y , Ray EL , Cornell M , Bracher J , Brennen A , Rivadeneira AJC , Gerding A , House K , Jayawardena D , Kanji AH , Khandelwal A , Le K , Mody V , Mody V , Niemi J , Stark A , Shah A , Wattanchit N , Zorn MW , Reich NG , US COVID-19 Forecast Hub Consortium , Lopez VK , Walker JW , Slayton RB , Johansson MA , Biggerstaff M . medRxiv 2021 2021.11.04.21265886 ![]() 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 hospitalizations, incident cases, incident deaths, and cumulative deaths due to COVID-19 at national, state, and county 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.Competing Interest StatementAV, MC, and APP report grants from Metabiota Inc outside the submitted work. Funding StatementFor teams that reported receiving funding for their work, we report the sources and disclosures below: AIpert-pwllnod: Natural Sciences and Engineering Research Council of Canada; Caltech-CS156: Gary Clinard Innovation Fund; CEID-Walk: University of Georgia; CMU-TimeSeries: CDC Center of Excellence, gifts from Google and Facebook; COVIDhub: This work has been supported by the US Centers for Disease Control and Prevention (1U01IP001122) and the National Institutes of General Medical Sciences (R35GM119582). The content is solely the responsibility of the authors and does not necessarily represent the official views of CDC, NIGMS or the National Institutes of Health; Johannes Bracher was supported by the Helmholtz Foundation via the SIMCARD Information & Data Science Pilot Project; Tilmann Gneiting gratefully acknowledges support by the Klaus Tschira Foundation; CU-select: NSF DMS-2027369 and a gift from the Morris-Singer Foundation; DDS-NBDS: NSF III-1812699; epiforecasts-ensemble1: Wellcome Trust (210758/Z/18/Z) FDANIHASU: supported by the Intramural Research Program of the NIH/NIDDK; GT_CHHS-COVID19: William W. George Endowment, Virginia C. and Joseph C. Mello Endowment, NSF DGE-1650044, NSF MRI 1828187, research cyberinfrastructure resources and services provided by the Partnership for an Advanced Computing Environment (PACE) at Georgia Tech, and the following benefactors at Georgia Tech: Andrea Laliberte, Joseph C. Mello, Richard Rick E. & Charlene Zalesky, and Claudia & Paul Raines, CDC MInD-Healthcare U01CK000531-Supplement; IHME: This work was supported by the Bill & Melinda Gates Foundation, as well as funding from the state of Washington and the National Science Foundation (award no. FAIN: 2031096); Imperial-ensemble1: SB acknowledges funding from the Wellcome Trust (219415); Institute of Business Forecasting: IBF; IowaStateLW-STEM: NSF DMS-1916204, Iowa State University Plant Sciences Institute Scholars Program, NSF DMS-1934884, Laurence H. Baker Center for Bioinformatics and Biological Statistics; IUPUI CIS: NSF; JHU_CSSE-DECOM: JHU CSSE: National Science Foundation (NSF) RAPID Real-time Forecasting of COVID-19 risk in the USA. 2021-2022. Award ID: 2108526. National Science Foundation (NSF) RAPID Development of an interactive web-based dashboard to track COVID-19 in real-time. 2020. Award ID: 2028604; JHU_IDD-CovidSP: State of California, US Dept of Health and Human Services, US Dept of Homeland Security, Johns Hopkins Health System, Office of the Dean at Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University Modeling and Policy Hub, Centers for Disease Control and Prevention (5U01CK000538-03), University of Utah Immunology, Inflammation, & Infectious Disease Initiative (26798 Seed Grant); JHU_UNC_GAS-StatMechP ol: NIH NIGMS: R01GM140564; JHUAPL-Bucky: US Dept of Health and Human Services; KITmetricslab-select_ensemble: Daniel Wolffram gratefully acknowledges support by the Klaus Tschira Foundation; LANL-GrowthRate: LANL LDRD 20200700ER; MIT-Cassandra: MIT Quest for Intelligence; MOBS-GLEAM_COVID: COVID Supplement CDC-HHS-6U01IP001137-01; CA NU38OT000297 from the Council of State and Territorial Epidemiologists (CSTE); NotreDame-FRED: NSF RAPID DEB 2027718; NotreDame-mobility: NSF RAPID DEB 2027718; PSI-DRAFT: NSF RAPID Grant # 2031536; QJHong-Encounter: NSF DMR-2001411 and DMR-1835939; SDSC_ISG-TrendModel: The development of the dashboard was partly funded by the Fondation Privee des Hopitaux Universitaires de Geneve; UA-EpiCovDA: NSF RAPID Grant # 2028401; UChicagoCHATTOPADHYAY-UnIT: Defense Advanced Research Projects Agency (DARPA) #HR00111890043/P00004 (I. Chattopadhyay, University of Chicago); UCSB-ACTS: NSF RAPID IIS 2029626; UCSD_NEU-DeepGLEAM: Google Faculty Award, W31P4Q-21-C-0014; UMass-MechBayes: NIGMS #R35GM119582, NSF #1749854, NIGMS #R35GM119582; UMich-RidgeTfReg: This project is funded by the University of Michigan Physics Department and the University of Michigan Office of Research; UVA-Ensemble: National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and Virginia Dept of Health Grant VDH-21-501-0141; Wadnwani_AI-BayesOpt: This study is made possible by the generous support of the American People through the United States Agency for International Development (USAID). The work described in this article was implemented under the TRACETB Project, managed by WIAI under the terms of Cooperative Agreement Number 72038620CA00006. The contents of this manuscript are the sole responsibility of the authors and do not necessarily reflect the views of USAID or the United States Government; WalmartLabsML-LogForecasting: Team acknowledges Walmart to support this study Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesI confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAll data produced are available online at https://github.com/reichlab/covid19-forecast-hub https://github.com/reichlab/covid19-forecast-hub |
Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US (preprint)
Cramer EY , Ray EL , Lopez VK , Bracher J , Brennen A , Castro Rivadeneira AJ , Gerding A , Gneiting T , House KH , Huang Y , Jayawardena D , Kanji AH , Khandelwal A , Le K , Mühlemann A , Niemi J , Shah A , Stark A , Wang Y , Wattanachit N , Zorn MW , Gu Y , Jain S , Bannur N , Deva A , Kulkarni M , Merugu S , Raval A , Shingi S , Tiwari A , White J , Abernethy NF , Woody S , Dahan M , Fox S , Gaither K , Lachmann M , Meyers LA , Scott JG , Tec M , Srivastava A , George GE , Cegan JC , Dettwiller ID , England WP , Farthing MW , Hunter RH , Lafferty B , Linkov I , Mayo ML , Parno MD , Rowland MA , Trump BD , Zhang-James Y , Chen S , Faraone SV , Hess J , Morley CP , Salekin A , Wang D , Corsetti SM , Baer TM , Eisenberg MC , Falb K , Huang Y , Martin ET , McCauley E , Myers RL , Schwarz T , Sheldon D , Gibson GC , Yu R , Gao L , Ma Y , Wu D , Yan X , Jin X , Wang YX , Chen Y , Guo L , Zhao Y , Gu Q , Chen J , Wang L , Xu P , Zhang W , Zou D , Biegel H , Lega J , McConnell S , Nagraj VP , Guertin SL , Hulme-Lowe C , Turner SD , Shi Y , Ban X , Walraven R , Hong QJ , Kong S , van de Walle A , Turtle JA , Ben-Nun M , Riley S , Riley P , Koyluoglu U , DesRoches D , Forli P , Hamory B , Kyriakides C , Leis H , Milliken J , Moloney M , Morgan J , Nirgudkar N , Ozcan G , Piwonka N , Ravi M , Schrader C , Shakhnovich E , Siegel D , Spatz R , Stiefeling C , Wilkinson B , Wong A , Cavany S , España G , Moore S , Oidtman R , Perkins A , Kraus D , Kraus A , Gao Z , Bian J , Cao W , Lavista Ferres J , Li C , Liu TY , Xie X , Zhang S , Zheng S , Vespignani A , Chinazzi M , Davis JT , Mu K , Pastore YPiontti A , Xiong X , Zheng A , Baek J , Farias V , Georgescu A , Levi R , Sinha D , Wilde J , Perakis G , Bennouna MA , Nze-Ndong D , Singhvi D , Spantidakis I , Thayaparan L , Tsiourvas A , Sarker A , Jadbabaie A , Shah D , Della Penna N , Celi LA , Sundar S , Wolfinger R , Osthus D , Castro L , Fairchild G , Michaud I , Karlen D , Kinsey M , Mullany LC , Rainwater-Lovett K , Shin L , Tallaksen K , Wilson S , Lee EC , Dent J , Grantz KH , Hill AL , Kaminsky J , Kaminsky K , Keegan LT , Lauer SA , Lemaitre JC , Lessler J , Meredith HR , Perez-Saez J , Shah S , Smith CP , Truelove SA , Wills J , Marshall M , Gardner L , Nixon K , Burant JC , Wang L , Gao L , Gu Z , Kim M , Li X , Wang G , Wang Y , Yu S , Reiner RC , Barber R , Gakidou E , Hay SI , Lim S , Murray C , Pigott D , Gurung HL , Baccam P , Stage SA , Suchoski BT , Prakash BA , Adhikari B , Cui J , Rodríguez A , Tabassum A , Xie J , Keskinocak P , Asplund J , Baxter A , Oruc BE , Serban N , Arik SO , Dusenberry M , Epshteyn A , Kanal E , Le LT , Li CL , Pfister T , Sava D , Sinha R , Tsai T , Yoder N , Yoon J , Zhang L , Abbott S , Bosse NI , Funk S , Hellewell J , Meakin SR , Sherratt K , Zhou M , Kalantari R , Yamana TK , Pei S , Shaman J , Li ML , Bertsimas D , Skali Lami O , Soni S , Tazi Bouardi H , Ayer T , Adee M , Chhatwal J , Dalgic OO , Ladd MA , Linas BP , Mueller P , Xiao J , Wang Y , Wang Q , Xie S , Zeng D , Green A , Bien J , Brooks L , Hu AJ , Jahja M , McDonald D , Narasimhan B , Politsch C , Rajanala S , Rumack A , Simon N , Tibshirani RJ , Tibshirani R , Ventura V , Wasserman L , O'Dea EB , Drake JM , Pagano R , Tran QT , Ho LST , Huynh H , Walker JW , Slayton RB , Johansson MA , Biggerstaff M , Reich NG . medRxiv 2021 2021.02.03.21250974 ![]() 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. In 2020, the COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized hundreds of thousands of specific predictions from more than 50 different academic, industry, and independent research groups. This manuscript systematically evaluates 23 models that regularly submitted forecasts of reported weekly incident COVID-19 mortality counts in the US at the state and national level. One of these models was a multi-model ensemble that combined all available forecasts each week. The performance of individual models showed high variability across time, geospatial units, and forecast horizons. Half of the models evaluated showed better accuracy than a naïve baseline model. In combining the forecasts from all teams, the ensemble showed the best overall probabilistic accuracy of any model. Forecast accuracy degraded as models made predictions farther into the future, with probabilistic accuracy at a 20-week horizon more than 5 times worse than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.Competing Interest StatementAV, MC, and APP report grants from Metabiota Inc outside the submitted work.Funding StatementFor teams that reported receiving funding for their work, we report the sources and disclosures below. CMU-TimeSeries: CDC Center of Excellence, gifts from Google and Facebook. CU-select: NSF DMS-2027369 and a gift from the Morris-Singer Foundation. COVIDhub: This work has been supported by the US Centers for Disease Control and Prevention (1U01IP001122) and the National Institutes of General Medical Sciences (R35GM119582). The content is solely the responsibility of the authors and does not necessarily represent the official views of CDC, NIGMS or the National Institutes of Health. Johannes Bracher was supported by the Helmholtz Foundation via the SIMCARD Information& Data Science Pilot Project. Tilmann Gneiting gratefully acknowledges support by the Klaus Tschira Foundation. DDS-NBDS: NSF III-1812699. EPIFORECASTS-ENSEMBLE1: Wellcome Trust (210758/Z/18/Z) GT_CHHS-COVID19: William W. George Endowment, Virginia C. and Joseph C. Mello Endowments, NSF DGE-1650044, NSF MRI 1828187, research cyberinfrastructure resources and services provided by the Partnership for an Advanced Computing Environment (PACE) at Georgia Tech, and the following benefactors at Georgia Tech: Andrea Laliberte, Joseph C. Mello, Richard Rick E. & Charlene Zalesky, and Claudia & Paul Raines GT-DeepCOVID: CDC MInD-Healthcare U01CK000531-Supplement. NSF (Expeditions CCF-1918770, CAREER IIS-2028586, RAPID IIS-2027862, Medium IIS-1955883, NRT DGE-1545362), CDC MInD program, ORNL and funds/computing resources from Georgia Tech and GTRI. IHME: This work was supported by the Bill & Melinda Gates Foundation, as well as funding from the state of Washington and the National Science Foundation (award no. FAIN: 2031096). IowaStateLW-STEM: Iowa State University Plant Sciences Institute Scholars Program, NSF DMS-1916204, NSF CCF-1934884, Laurence H. Baker Center for Bioinformatics and Biological Statistics. JHU_IDD-CovidSP: State of California, US Dept of Health and Human Services, US Dept of Homeland Security, US Office of Foreign Disaster Assistance, Johns Hopkins Health System, Office of the Dean at Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University Modeling and Policy Hub, Centers fo Disease Control and Prevention (5U01CK000538-03), University of Utah Immunology, Inflammation, & Infectious Disease Initiative (26798 Seed Grant). LANL-GrowthRate: LANL LDRD 20200700ER. MOBS-GLEAM_COVID: COVID Supplement CDC-HHS-6U01IP001137-01. NotreDame-mobility and NotreDame-FRED: NSF RAPID DEB 2027718 UA-EpiCovDA: NSF RAPID Grant # 2028401. UCSB-ACTS: NSF RAPID IIS 2029626. UCSD-NEU: Google Faculty Award, DARPA W31P4Q-21-C-0014, COVID Supplement CDC-HHS-6U01IP001137-01. UMass-MechBayes: NIGMS R35GM119582, NSF 1749854. UMich-RidgeTfReg: The University of Michigan Physics Department and the University of Michigan Office of Research.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:UMass-Amherst IRBAll necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAll data and code referred to in the manuscript are publicly available. https://github.com/reichlab/covid19-forecast-hub/ https://github.com/reichlab/covidEnsembles https://zoltardata.com/project/44 |
A Collaborative Multi-Model Ensemble for Real-Time Influenza Season Forecasting in the U.S (preprint)
Reich NG , McGowan CJ , Yamana TK , Tushar A , Ray EL , Osthus D , Kandula S , Brooks LC , Crawford-Crudell W , Gibson GC , Moore E , Silva R , Biggerstaff M , Johansson MA , Rosenfeld R , Shaman J . bioRxiv 2019 566604 ![]() Seasonal influenza results in substantial annual morbidity and mortality in the United States and worldwide. Accurate forecasts of key features of influenza epidemics, such as the timing and severity of the peak incidence in a given season, can inform public health response to outbreaks. As part of ongoing efforts to incorporate data and advanced analytical methods into public health decision-making, the United States Centers for Disease Control and Prevention (CDC) has organized seasonal influenza forecasting challenges since the 2013/2014 season. In the 2017/2018 season, 22 teams participated. A subset of four teams created a research consortium called the FluSight Network in early 2017. During the 2017/2018 season they worked together to produce a collaborative multi-model ensemble that combined 21 separate component models into a single model using a machine learning technique called stacking. This approach creates a weighted average of predictive densities where the weight for each component is based on that component’s forecast accuracy in past seasons. In the 2017/2018 influenza season, one of the largest seasonal outbreaks in the last 15 years, this multi-model ensemble performed better on average than all individual component models and placed second overall in the CDC challenge. It also outperformed the baseline multi-model ensemble created by the CDC that took a simple average of all models submitted to the forecasting challenge. This project shows that collaborative efforts between research teams to develop ensemble forecasting approaches can bring measurable improvements in forecast accuracy and important reductions in the variability of performance from year to year. Efforts such as this, that emphasize real-time testing and evaluation of forecasting models and facilitate the close collaboration between public health officials and modeling researchers, are essential to improving our understanding of how best to use forecasts to improve public health response to seasonal and emerging epidemic threats. |
Forecasting seasonal influenza in the U.S.: A collaborative multi-year, multi-model assessment of forecast performance (preprint)
Reich NG , Brooks LC , Fox SJ , Kandula S , McGowan CJ , Moore E , Osthus D , Ray EL , Tushar A , Yamana TK , Biggerstaff M , Johansson MA , Rosenfeld R , Shaman J . bioRxiv 2018 397190 Influenza infects an estimated 9 to 35 million individuals each year in the United States and is a contributing cause for between 12,000 and 56,000 deaths annually. Seasonal outbreaks of influenza are common in temperate regions of the world, with highest incidence typically occurring in colder and drier months of the year. Real-time forecasts of influenza transmission can inform public health response to outbreaks. We present the results of a multi-institution collaborative effort to standardize the collection and evaluation of forecasting models for influenza in the US for the 2010/2011 through 2016/2017 influenza seasons. For these seven seasons, we assembled weekly real-time forecasts of 7 targets of public health interest from 22 different models. We compared forecast accuracy of each model relative to a historical baseline seasonal average. Across all regions of the US, over half of the models showed consistently better performance than the historical baseline when forecasting incidence of influenza-like illness 1, 2 and 3 weeks ahead of available data and when forecasting the timing and magnitude of the seasonal peak. In some regions, delays in data reporting were strongly and negatively associated with forecast accuracy. More timely reporting and an improved overall accessibility to novel and traditional data sources are needed to improve forecasting accuracy and its integration with real-time public health decision-making. |
Quantifying the Risk and Cost of Active Monitoring for Infectious Diseases (preprint)
Reich NG , Lessler J , Varma JK , Vora NM . bioRxiv 2017 156497 During outbreaks of deadly emerging pathogens (e.g., Ebola, MERS-CoV) and bioterror threats (e.g., smallpox), actively monitoring potentially infected individuals aims to limit disease transmission and morbidity. Guidance issued by CDC on active monitoring was a cornerstone of its response to the West Africa Ebola outbreak. There are limited data on how to balance the costs and performance of this important public health activity. We present a framework that estimates the risks and costs of specific durations of active monitoring for pathogens of significant public health concern. We analyze data from New York City’s Ebola active monitoring program over a 16-month period in 2014-2016. For monitored individuals, we identified unique durations of active monitoring that minimize expected costs for those at “low (but not zero) risk” and “some or high risk”: 21 and 31 days, respectively. Extending our analysis to smallpox and MERS-CoV, we found that the optimal length of active monitoring relative to the median incubation period was reduced compared to Ebola due to less variable incubation periods. Active monitoring can save lives but is expensive. Resources can be most effectively allocated by using exposure-risk categories to modify the duration or intensity of active monitoring. |
Projected resurgence of COVID-19 in the United States in July-December 2021 resulting from the increased transmissibility of the Delta variant and faltering vaccination (preprint)
Truelove S , Smith CP , Qin M , Mullany LC , Borchering RK , Lessler J , Shea K , Howerton E , Contamin L , Levander J , Salerno J , Hochheiser H , Kinsey M , Tallaksen K , Wilson S , Shin L , Rainwater-Lovett K , Lemaitre JC , Dent J , Kaminsky J , Lee EC , Perez-Saez J , Hill A , Karlen D , Chinazzi M , Davis JT , Mu K , Xiong X , Piontti APY , Vespignani A , Srivastava A , Porebski P , Venkatramanan S , Adiga A , Lewis B , Klahn B , Outten J , Schlitt J , Corbett P , Telionis PA , Wang L , Peddireddy AS , Hurt B , Chen J , Vullikanti A , Marathe M , Hoops S , Bhattacharya P , Machi D , Chen S , Paul R , Janies D , Thill JC , Galanti M , Yamana T , Pei S , Shaman J , Reich NG , Healy JM , Slayton RB , Biggerstaff M , Johansson MA , Runge MC , Viboud C . medRxiv 2021 WHAT IS ALREADY KNOWN ABOUT THIS TOPIC? The highly transmissible SARS-CoV-2 Delta variant has begun to cause increases in cases, hospitalizations, and deaths in parts of the United States. With slowed vaccination uptake, this novel variant is expected to increase the risk of pandemic resurgence in the US in July-December 2021. WHAT IS ADDED BY THIS REPORT? Data from nine mechanistic models project substantial resurgences of COVID-19 across the US resulting from the more transmissible Delta variant. These resurgences, which have now been observed in most states, were projected to occur across most of the US, coinciding with school and business reopening. Reaching higher vaccine coverage in July-December 2021 reduces the size and duration of the projected resurgence substantially. The expected impact of the outbreak is largely concentrated in a subset of states with lower vaccination coverage. WHAT ARE THE IMPLICATIONS FOR PUBLIC HEALTH PRACTICE? Renewed efforts to increase vaccination uptake are critical to limiting transmission and disease, particularly in states with lower current vaccination coverage. Reaching higher vaccination goals in the coming months can potentially avert 1.5 million cases and 21,000 deaths and improve the ability to safely resume social contacts, and educational and business activities. Continued or renewed non-pharmaceutical interventions, including masking, can also help limit transmission, particularly as schools and businesses reopen. |
CASCADIA: a prospective community-based study protocol for assessing SARS-CoV-2 vaccine effectiveness in children and adults using a remote nasal swab collection and web-based survey design
Babu TM , Feldstein LR , Saydah S , Acker Z , Boisvert CL , Briggs-Hagen M , Carone M , Casto A , Cox SN , Ehmen B , Englund JA , Fortmann SP , Frivold CJ , Groom H , Han PD , Kuntz JL , Lockwood T , Midgley CM , Mularski RA , Ogilvie T , Reich SL , Schmidt MA , Smith N , Starita L , Stone J , Vandermeer M , Weil AA , Wolf CR , Chu HY , Naleway AL . BMJ Open 2023 13 (7) e071446 ![]() INTRODUCTION: Although SARS-CoV-2 vaccines were first approved under Emergency Use Authorization by the Food and Drug Administration in late 2020 for adults, authorisation for young children 6 months to <5 years of age did not occur until 2022. These authorisations were based on clinical trials, understanding real-world vaccine effectiveness (VE) in the setting of emerging variants is critical. The primary goal of this study is to evaluate SARS-CoV-2 VE against infection among children aged >6 months and adults aged <50 years. METHODS: CASCADIA is a 4-year community-based prospective study of SARS-CoV-2 VE among 3500 adults and paediatric populations aged 6 months to 49 years in Oregon and Washington, USA. At enrolment and regular intervals, participants complete a sociodemographic questionnaire. Individuals provide a blood sample at enrolment and annually thereafter, with optional blood draws every 6 months and after infection and vaccination. Participants complete weekly self-collection of anterior nasal swabs and symptom questionnaires. Swabs are tested for SARS-CoV-2 and other respiratory pathogens by reverse transcription-PCR, with results of selected pathogens returned to participants; nasal swabs with SARS-CoV-2 detected will undergo whole genome sequencing. Participants who test positive for SARS-CoV-2 undergo serial swab collection every 3 days for 21 days. Serum samples are tested for SARS-CoV-2 antibody by binding and neutralisation assays. ANALYSIS: The primary outcome is SARS-CoV-2 infection. Cox regression models will be used to estimate the incidence rate ratio associated with SARS-CoV-2 vaccination among the paediatric and adult population, controlling for demographic factors and other potential confounders. ETHICS AND DISSEMINATION: All study materials including the protocol, consent forms, data collection instruments, participant communication and recruitment materials, were approved by the Kaiser Permanente Interregional Institutional Review Board, the IRB of record for the study. Results will be disseminated through peer-reviewed publications, presentations, participant newsletters and appropriate general news media. |
CASCADIA: A prospective community-based study protocol for assessing SARS-CoV-2 vaccine effectiveness in children and adults utilizing a remote nasal swab collection and web-based survey design (preprint)
Babu TM , Feldstein LR , Saydah S , Acker Z , Boisvert CL , Briggs-Hagen M , Carone M , Casto A , Cox SN , Ehmen B , Englund JA , Fortmann SP , Frivold CJ , Groom H , Han P , Kuntz JL , Lockwood T , Midgley CM , Mularski RA , Ogilvie T , Reich S , Schmidt MA , Smith N , Starita L , Stone J , Vandermeer M , Weil AA , Wolf CR , Chu HY , Naleway AL . medRxiv 2023 07 Introduction: Although SARS-CoV-2 vaccines were first approved under Emergency Use Authorization by the FDA in late 2020 for adults, approval for young children 6 months to < 5 years of age did not occur until 2022. Understanding real world vaccine effectiveness in the setting of emerging variants is critical. The primary goal of this study is to evaluate SARS-CoV-2 vaccine effectiveness (VE) against infection among children aged >6 months and adults aged <50 years. Method(s): CASCADIA is a four-year community-based prospective study of SARS-CoV-2 VE among adult and pediatric populations aged 6 months to 49 years in Oregon and Washington. At enrollment and regular intervals, participants complete a sociodemographic questionnaire. Individuals provide a blood sample at enrollment and annually thereafter, with additional, optional blood draws after infection and vaccination. Participants complete weekly self-collection of anterior nasal swabs and symptom questionnaires. Swabs are tested for SARS-CoV-2 and other respiratory pathogens by RT-PCR, with results of selected pathogens returned to participants; nasal swabs with SARS-CoV-2 detected will undergo whole genome sequencing. Participants who report symptoms outside of their weekly swab collection and symptom survey are asked to collect an additional swab. Participants who test positive for SARS-CoV-2 undergo serial swab collection every three days for three weeks. Serum samples are tested for SARS-CoV-2 antibody by binding and neutralization assays. Analysis: Cox regression models will be used to estimate the hazard ratio associated with SARS-CoV-2 vaccination among the pediatric and adult population, controlling for demographic factors and potential confounders, including clustering within households. Ethics and dissemination: All study materials including the protocol, consent forms, participant communication and recruitment materials, and data collection instruments were approved by the Kaiser Permanente Northwest (KPNW) Institutional Review Board, the IRB of record for the study. Copyright The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available for use under a CC0 license. |
Impact of SARS-CoV-2 vaccination of children ages 5-11 years on COVID-19 disease burden and resilience to new variants in the United States, November 2021-March 2022: a multi-model study (preprint)
Borchering RK , Mullany LC , Howerton E , Chinazzi M , Smith CP , Qin M , Reich NG , Contamin L , Levander J , Kerr J , Espino J , Hochheiser H , Lovett K , Kinsey M , Tallaksen K , Wilson S , Shin L , Lemaitre JC , Hulse JD , Kaminsky J , Lee EC , Davis JT , Mu K , Xiong X , Pastore y Piontti A , Vespignani A , Srivastava A , Porebski P , Venkatramanan S , Adiga A , Lewis B , Klahn B , Outten J , Hurt B , Chen J , Mortveit H , Wilson A , Marathe M , Hoops S , Bhattacharya P , Machi D , Chen S , Paul R , Janies D , Thill JC , Galanti M , Yamana T , Pei S , Shaman J , Espana G , Cavany S , Moore S , Perkins A , Healy JM , Slayton RB , Johansson MA , Biggerstaff M , Shea K , Truelove SA , Runge MC , Viboud C , Lessler J . medRxiv 2022 10 Background SARS-CoV-2 vaccination of persons aged 12 years and older has reduced disease burden in the United States. The COVID-19 Scenario Modeling Hub convened multiple modeling teams in September 2021 to project the impact of expanding vaccine administration to children 5-11 years old on anticipated COVID-19 burden and resilience against variant strains. Methods Nine modeling teams contributed state- and national-level projections for weekly counts of cases, hospitalizations, and deaths in the United States for the period September 12, 2021 to March 12, 2022. Four scenarios covered all combinations of: 1) presence vs. absence of vaccination of children ages 5-11 years starting on November 1, 2021; and 2) continued dominance of the Delta variant vs. emergence of a hypothetical more transmissible variant on November 15, 2021. Individual team projections were combined using linear pooling. The effect of childhood vaccination on overall and age-specific outcomes was estimated by meta-analysis approaches. Findings Absent a new variant, COVID-19 cases, hospitalizations, and deaths among all ages were projected to decrease nationally through mid-March 2022. Under a set of specific assumptions, models projected that vaccination of children 5-11 years old was associated with reductions in all-age cumulative cases (7.2%, mean incidence ratio [IR] 0.928, 95% confidence interval [CI] 0.880-0.977), hospitalizations (8.7%, mean IR 0.913, 95% CI 0.834-0.992), and deaths (9.2%, mean IR 0.908, 95% CI 0.797-1.020) compared with scenarios where children were not vaccinated. This projected effect of vaccinating children 5-11 years old increased in the presence of a more transmissible variant, assuming no change in vaccine effectiveness by variant. Larger relative reductions in cumulative cases, hospitalizations, and deaths were observed for children than for the entire U.S. population. Substantial state-level variation was projected in epidemic trajectories, vaccine benefits, and variant impacts. Conclusions Results from this multi-model aggregation study suggest that, under a specific set of scenario assumptions, expanding vaccination to children 5-11 years old would provide measurable direct benefits to this age group and indirect benefits to the all-age U.S. population, including resilience to more transmissible variants. Copyright The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license. |
The United States COVID-19 Forecast Hub dataset.
Cramer EY , Huang Y , Wang Y , Ray EL , Cornell M , Bracher J , Brennen A , Rivadeneira AJC , Gerding A , House K , Jayawardena D , Kanji AH , Khandelwal A , Le K , Mody V , Mody V , Niemi J , Stark A , Shah A , Wattanchit N , Zorn MW , Reich NG , US COVID-19 Forecast Hub Consortium , Lopez VK , Walker JW , Slayton RB , Johansson MA , Biggerstaff M . Sci Data 2022 9 (1) 462 ![]() ![]() 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. |
Reply to Bracher: Scoring probabilistic forecasts to maximize public health interpretability
Reich NG , Osthus D , Ray EL , Yamana TK , Biggerstaff M , Johansson MA , Rosenfeld R , Shaman J . Proc Natl Acad Sci U S A 2019 116 (42) 20811-20812 Evaluating probabilistic forecasts in the context of a real-time public health surveillance system is a complicated business. We agree with Bracher’s (1) observations that the scores established by the US Centers for Disease Control and Prevention (CDC) and used to evaluate our forecasts of seasonal influenza in the United States are not “proper” by definition (2). We thank him for raising this important issue. | | A key advantage of proper scoring is that it incentivizes forecasters to provide their best probabilistic estimates of the fundamental unit of prediction. In the case of the FluSight competition targets, the units are intervals or bins containing dates or values representing influenza-like illness (ILI) activity. A forecast assigns probabilities to each bin. |
Informative censoring-a cause of bias in estimating COVID-19 mortality using hospital data
Lin HM , Liu STH , Levin MA , Williamson J , Bouvier NM , Aberg JA , Reich D , Egorova N . Life (Basel) 2023 13 (1) Background: Several retrospective observational analyzed treatment outcomes for COVID-19; (2) Methods: Inverse probability of censoring weighting (IPCW) was applied to correct for bias due to informative censoring in database of hospitalized patients who did and did not receive convalescent plasma; (3) Results: When compared with an IPCW analysis, overall mortality was overestimated using an unadjusted Kaplan-Meier curve, and hazard ratios for the older age group compared to the youngest were underestimated using the Cox proportional hazard models and 30-day mortality; (4) Conclusions: An IPCW analysis provided stabilizing weights by hospital admission. |
Impact of SARS-CoV-2 vaccination of children ages 5-11 years on COVID-19 disease burden and resilience to new variants in the United States, November 2021-March 2022: A multi-model study.
Borchering RK , Mullany LC , Howerton E , Chinazzi M , Smith CP , Qin M , Reich NG , Contamin L , Levander J , Kerr J , Espino J , Hochheiser H , Lovett K , Kinsey M , Tallaksen K , Wilson S , Shin L , Lemaitre JC , Hulse JD , Kaminsky J , Lee EC , Hill AL , Davis JT , Mu K , Xiong X , Pastore YPiontti A , Vespignani A , Srivastava A , Porebski P , Venkatramanan S , Adiga A , Lewis B , Klahn B , Outten J , Hurt B , Chen J , Mortveit H , Wilson A , Marathe M , Hoops S , Bhattacharya P , Machi D , Chen S , Paul R , Janies D , Thill JC , Galanti M , Yamana T , Pei S , Shaman J , España G , Cavany S , Moore S , Perkins A , Healy JM , Slayton RB , Johansson MA , Biggerstaff M , Shea K , Truelove SA , Runge MC , Viboud C , Lessler J . Lancet Reg Health Am 2023 17 100398 ![]() BACKGROUND: The COVID-19 Scenario Modeling Hub convened nine modeling teams to project the impact of expanding SARS-CoV-2 vaccination to children aged 5-11 years on COVID-19 burden and resilience against variant strains. METHODS: Teams contributed state- and national-level weekly projections of cases, hospitalizations, and deaths in the United States from September 12, 2021 to March 12, 2022. Four scenarios covered all combinations of 1) vaccination (or not) of children aged 5-11 years (starting November 1, 2021), and 2) emergence (or not) of a variant more transmissible than the Delta variant (emerging November 15, 2021). Individual team projections were linearly pooled. The effect of childhood vaccination on overall and age-specific outcomes was estimated using meta-analyses. FINDINGS: Assuming that a new variant would not emerge, all-age COVID-19 outcomes were projected to decrease nationally through mid-March 2022. In this setting, vaccination of children 5-11 years old was associated with reductions in projections for all-age cumulative cases (7.2%, mean incidence ratio [IR] 0.928, 95% confidence interval [CI] 0.880-0.977), hospitalizations (8.7%, mean IR 0.913, 95% CI 0.834-0.992), and deaths (9.2%, mean IR 0.908, 95% CI 0.797-1.020) compared with scenarios without childhood vaccination. Vaccine benefits increased for scenarios including a hypothesized more transmissible variant, assuming similar vaccine effectiveness. Projected relative reductions in cumulative outcomes were larger for children than for the entire population. State-level variation was observed. INTERPRETATION: Given the scenario assumptions (defined before the emergence of Omicron), expanding vaccination to children 5-11 years old would provide measurable direct benefits, as well as indirect benefits to the all-age U.S. population, including resilience to more transmissible variants. FUNDING: Various (see acknowledgments). |
Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States.
Ray EL , Brooks LC , Bien J , Biggerstaff M , Bosse NI , Bracher J , Cramer EY , Funk S , Gerding A , Johansson MA , Rumack A , Wang Y , Zorn M , Tibshirani RJ , Reich NG . Int J Forecast 2022 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. |
Collaborative Hubs: Making the Most of Predictive Epidemic Modeling.
Reich NG , Lessler J , Funk S , Viboud C , Vespignani A , Tibshirani RJ , Shea K , Schienle M , Runge MC , Rosenfeld R , Ray EL , Niehus R , Johnson HC , Johansson MA , Hochheiser H , Gardner L , Bracher J , Borchering RK , Biggerstaff M . Am J Public Health 2022 112 (6) e1-e4 ![]() ![]() The COVID-19 pandemic has made it clear that epidemic models play an important role in how governments and the public respond to infectious disease crises. Early in the pandemic, models were used to estimate the true number of infections. Later, they estimated key parameters, generated short-term forecasts of outbreak trends, and quantified possible effects of interventions on the unfolding epidemic.1,2 In contrast to the coordinating role played by major national or international agencies in weather-related emergencies, pandemic modeling efforts were initially scattered across many research institutions. Differences in modeling approaches led to contrasting results, contributing to confusion in public perception of the pandemic. Efforts to coordinate modeling efforts in so-called hubs have provided governments, healthcare agencies, and the public with assessments and forecasts that reflect the consensus in the modeling community.36 This has been achieved by openly synthesizing uncertainties across different modeling approaches and facilitating comparisons between them. |
Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.
Cramer EY , Ray EL , Lopez VK , Bracher J , Brennen A , Castro Rivadeneira AJ , Gerding A , Gneiting T , House KH , Huang Y , Jayawardena D , Kanji AH , Khandelwal A , Le K , Mühlemann A , Niemi J , Shah A , Stark A , Wang Y , Wattanachit N , Zorn MW , Gu Y , Jain S , Bannur N , Deva A , Kulkarni M , Merugu S , Raval A , Shingi S , Tiwari A , White J , Abernethy NF , Woody S , Dahan M , Fox S , Gaither K , Lachmann M , Meyers LA , Scott JG , Tec M , Srivastava A , George GE , Cegan JC , Dettwiller ID , England WP , Farthing MW , Hunter RH , Lafferty B , Linkov I , Mayo ML , Parno MD , Rowland MA , Trump BD , Zhang-James Y , Chen S , Faraone SV , Hess J , Morley CP , Salekin A , Wang D , Corsetti SM , Baer TM , Eisenberg MC , Falb K , Huang Y , Martin ET , McCauley E , Myers RL , Schwarz T , Sheldon D , Gibson GC , Yu R , Gao L , Ma Y , Wu D , Yan X , Jin X , Wang YX , Chen Y , Guo L , Zhao Y , Gu Q , Chen J , Wang L , Xu P , Zhang W , Zou D , Biegel H , Lega J , McConnell S , Nagraj VP , Guertin SL , Hulme-Lowe C , Turner SD , Shi Y , Ban X , Walraven R , Hong QJ , Kong S , van de Walle A , Turtle JA , Ben-Nun M , Riley S , Riley P , Koyluoglu U , DesRoches D , Forli P , Hamory B , Kyriakides C , Leis H , Milliken J , Moloney M , Morgan J , Nirgudkar N , Ozcan G , Piwonka N , Ravi M , Schrader C , Shakhnovich E , Siegel D , Spatz R , Stiefeling C , Wilkinson B , Wong A , Cavany S , España G , Moore S , Oidtman R , Perkins A , Kraus D , Kraus A , Gao Z , Bian J , Cao W , Lavista Ferres J , Li C , Liu TY , Xie X , Zhang S , Zheng S , Vespignani A , Chinazzi M , Davis JT , Mu K , Pastore YPiontti A , Xiong X , Zheng A , Baek J , Farias V , Georgescu A , Levi R , Sinha D , Wilde J , Perakis G , Bennouna MA , Nze-Ndong D , Singhvi D , Spantidakis I , Thayaparan L , Tsiourvas A , Sarker A , Jadbabaie A , Shah D , Della Penna N , Celi LA , Sundar S , Wolfinger R , Osthus D , Castro L , Fairchild G , Michaud I , Karlen D , Kinsey M , Mullany LC , Rainwater-Lovett K , Shin L , Tallaksen K , Wilson S , Lee EC , Dent J , Grantz KH , Hill AL , Kaminsky J , Kaminsky K , Keegan LT , Lauer SA , Lemaitre JC , Lessler J , Meredith HR , Perez-Saez J , Shah S , Smith CP , Truelove SA , Wills J , Marshall M , Gardner L , Nixon K , Burant JC , Wang L , Gao L , Gu Z , Kim M , Li X , Wang G , Wang Y , Yu S , Reiner RC , Barber R , Gakidou E , Hay SI , Lim S , Murray C , Pigott D , Gurung HL , Baccam P , Stage SA , Suchoski BT , Prakash BA , Adhikari B , Cui J , Rodríguez A , Tabassum A , Xie J , Keskinocak P , Asplund J , Baxter A , Oruc BE , Serban N , Arik SO , Dusenberry M , Epshteyn A , Kanal E , Le LT , Li CL , Pfister T , Sava D , Sinha R , Tsai T , Yoder N , Yoon J , Zhang L , Abbott S , Bosse NI , Funk S , Hellewell J , Meakin SR , Sherratt K , Zhou M , Kalantari R , Yamana TK , Pei S , Shaman J , Li ML , Bertsimas D , Skali Lami O , Soni S , Tazi Bouardi H , Ayer T , Adee M , Chhatwal J , Dalgic OO , Ladd MA , Linas BP , Mueller P , Xiao J , Wang Y , Wang Q , Xie S , Zeng D , Green A , Bien J , Brooks L , Hu AJ , Jahja M , McDonald D , Narasimhan B , Politsch C , Rajanala S , Rumack A , Simon N , Tibshirani RJ , Tibshirani R , Ventura V , Wasserman L , O'Dea EB , Drake JM , Pagano R , Tran QT , Ho LST , Huynh H , Walker JW , Slayton RB , Johansson MA , Biggerstaff M , Reich NG . Proc Natl Acad Sci U S A 2022 119 (15) e2113561119 ![]() SignificanceThis paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the United States. Results show high variation in accuracy between and within stand-alone models and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public-health action. |
Cost-effectiveness of long-acting injectable HIV preexposure prophylaxis in the United States : A cost-effectiveness analysis
Neilan AM , Landovitz RJ , Le MH , Grinsztejn B , Freedberg KA , McCauley M , Wattananimitgul N , Cohen MS , Ciaranello AL , Clement ME , Reddy KP , Hyle EP , Paltiel AD , Walensky RP . Ann Intern Med 2022 175 (4) 479-489 BACKGROUND: The HIV Prevention Trials Network (HPTN) 083 trial demonstrated the superiority of long-acting injectable cabotegravir (CAB-LA) compared with oral emtricitabine-tenofovir disoproxil fumarate (F/TDF) for HIV preexposure prophylaxis (PrEP). OBJECTIVE: To identify the maximum price premium (that is, greatest possible price differential) that society should be willing to accept for the additional benefits of CAB-LA over tenofovir-based PrEP among men who have sex with men and transgender women (MSM/TGW) in the United States. DESIGN: Simulation, cost-effectiveness analysis. DATA SOURCES: Trial and published data, including estimated HIV incidence (5.32, 1.33, and 0.26 per 100 person-years for off PrEP, generic F/TDF and branded emtricitabine-tenofovir alafenamide (F/TAF), and CAB-LA, respectively); 28% 6-year PrEP retention. Annual base-case drug costs: $360 and $16800 for generic F/TDF and branded F/TAF. Fewer side effects with branded F/TAF versus generic F/TDF were assumed. TARGET POPULATION: 476700 MSM/TGW at very high risk for HIV (VHR). TIME HORIZON: 10 years. PERSPECTIVE: Health care system. INTERVENTION: CAB-LA versus generic F/TDF or branded F/TAF for HIV PrEP. OUTCOME MEASURES: Primary transmissions, quality-adjusted life-years (QALYs), costs (2020 U.S. dollars), incremental cost-effectiveness ratios (ICERs; U.S. dollars per QALY), maximum price premium for CAB-LA versus tenofovir-based PrEP. RESULTS OF BASE-CASE ANALYSIS: Compared with generic F/TDF (or branded F/TAF), CAB-LA increased life expectancy by 28000 QALYs (26000 QALYs) among those at VHR. Branded F/TAF cost more per QALY gained than generic F/TDF compared with no PrEP. At 10 years, CAB-LA could achieve an ICER of at most $100000 per QALY compared with generic F/TDF at a maximum price premium of $3700 per year over generic F/TDF (CAB-LA price <$4100 per year). RESULTS OF SENSITIVITY ANALYSIS: In a PrEP-eligible population at high risk for HIV, rather than at VHR (n = 1906800; off PrEP incidence: 1.54 per 100 person-years), CAB-LA could achieve an ICER of at most $100000 per QALY versus generic F/TDF at a maximum price premium of $1100 per year over generic F/TDF (CAB-LA price <$1500 per year). LIMITATION: Uncertain clinical and economic benefits of averting future transmissions. CONCLUSION: Effective oral PrEP limits the additional price society should be willing to pay for CAB-LA. PRIMARY FUNDING SOURCE: FHI 360; Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Allergy and Infectious Diseases; National Heart, Lung, and Blood Institute; National Institute on Drug Abuse; the Reich HIV Scholar Award; and the Steve and Deborah Gorlin MGH Research Scholars Award. |
Recommended reporting items for epidemic forecasting and prediction research: The EPIFORGE 2020 guidelines.
Pollett S , Johansson MA , Reich NG , Brett-Major D , Del Valle SY , Venkatramanan S , Lowe R , Porco T , Berry IM , Deshpande A , Kraemer MUG , Blazes DL , Pan-Ngum W , Vespigiani A , Mate SE , Silal SP , Kandula S , Sippy R , Quandelacy TM , Morgan JJ , Ball J , Morton LC , Althouse BM , Pavlin J , van Panhuis W , Riley S , Biggerstaff M , Viboud C , Brady O , Rivers C . PLoS Med 2021 18 (10) e1003793 ![]() ![]() BACKGROUND: The importance of infectious disease epidemic forecasting and prediction research is underscored by decades of communicable disease outbreaks, including COVID-19. Unlike other fields of medical research, such as clinical trials and systematic reviews, no reporting guidelines exist for reporting epidemic forecasting and prediction research despite their utility. We therefore developed the EPIFORGE checklist, a guideline for standardized reporting of epidemic forecasting research. METHODS AND FINDINGS: We developed this checklist using a best-practice process for development of reporting guidelines, involving a Delphi process and broad consultation with an international panel of infectious disease modelers and model end users. The objectives of these guidelines are to improve the consistency, reproducibility, comparability, and quality of epidemic forecasting reporting. The guidelines are not designed to advise scientists on how to perform epidemic forecasting and prediction research, but rather to serve as a standard for reporting critical methodological details of such studies. CONCLUSIONS: These guidelines have been submitted to the EQUATOR network, in addition to hosting by other dedicated webpages to facilitate feedback and journal endorsement. |
Estimating Under-recognized COVID-19 Deaths, United States, March 2020-May 2021 using an Excess Mortality Modelling Approach.
Iuliano AD , Chang HH , Patel NN , Threlkel R , Kniss K , Reich J , Steele M , Hall AJ , Fry AM , Reed C . Lancet Reg Health Am 2021 1 100019 ![]() BACKGROUND: In the United States, Coronavirus Disease 2019 (COVID-19) deaths are captured through the National Notifiable Disease Surveillance System and death certificates reported to the National Vital Statistics System (NVSS). However, not all COVID-19 deaths are recognized and reported because of limitations in testing, exacerbation of chronic health conditions that are listed as the cause of death, or delays in reporting. Estimating deaths may provide a more comprehensive understanding of total COVID-19-attributable deaths. METHODS: We estimated COVID-19 unrecognized attributable deaths, from March 2020-April 2021, using all-cause deaths reported to NVSS by week and six age groups (0-17, 18-49, 50-64, 65-74, 75-84, and ≥85 years) for 50 states, New York City, and the District of Columbia using a linear time series regression model. Reported COVID-19 deaths were subtracted from all-cause deaths before applying the model. Weekly expected deaths, assuming no SARS-CoV-2 circulation and predicted all-cause deaths using SARS-CoV-2 weekly percent positive as a covariate were modelled by age group and including state as a random intercept. COVID-19-attributable unrecognized deaths were calculated for each state and age group by subtracting the expected all-cause deaths from the predicted deaths. FINDINGS: We estimated that 766,611 deaths attributable to COVID-19 occurred in the United States from March 8, 2020-May 29, 2021. Of these, 184,477 (24%) deaths were not documented on death certificates. Eighty-two percent of unrecognized deaths were among persons aged ≥65 years; the proportion of unrecognized deaths were 0•24-0•31 times lower among those 0-17 years relative to all other age groups. More COVID-19-attributable deaths were not captured during the early months of the pandemic (March-May 2020) and during increases in SARS-CoV-2 activity (July 2020, November 2020-February 2021). DISCUSSION: Estimating COVID-19-attributable unrecognized deaths provides a better understanding of the COVID-19 mortality burden and may better quantify the severity of the COVID-19 pandemic. FUNDING: None. |
Impact of mandatory vaccination of healthcare personnel on rates of influenza and other viral respiratory pathogens
Simberkoff MS , Rattigan SM , Gaydos CA , Gibert CL , Gorse GJ , Nyquist AC , Price CS , Reich N , Rodriguez-Barradas MC , Bessesen M , Brown A , Cummings DAT , Radonovich LJ , Perl TM . Infect Control Hosp Epidemiol 2021 43 (9) 1-5 OBJECTIVE: The implementation of mandatory influenza vaccination policies among healthcare personnel (HCP) is controversial. Thus, we examined the affect of mandatory influenza vaccination policies among HCP working in outpatient settings. SETTING: Four Veterans' Affairs (VA) health systems and three non-VA medical centers. METHODS: We analyzed rates of influenza and other viral causes of respiratory infections among HCP working in outpatient sites at 4 VA health systems without mandatory influenza vaccination policies and 3 non-VA health systems with mandatory influenza vaccination policies. RESULTS: Influenza vaccination was associated with a decreased risk of influenza (odds ratio, 0.17; 95% confidence interval [CI], 0.13-0.22) but an increased risk of other respiratory viral infections (incidence rate ratio, 1.26; 95% CI, 1.02-1.57). CONCLUSIONS: Our fitted regression models suggest that if influenza vaccination rates in clinics where vaccination was not mandated had equalled those where vaccine was mandated, HCP influenza infections would have been reduced by 52.1% (95% CI, 51.3%-53.0%). These observations, their possible causes, and additional strategies to reduce influenza and other viral respiratory illnesses among HCP working in ambulatory clinics warrant further investigation. |
Outpatient Healthcare Personnel Knowledge and Attitudes Towards Infection Prevention Measures for Protection from Respiratory Infections
Bessesen MT , Rattigan S , Frederick J , Cummings DAT , Gaydos CA , Gibert CL , Gorse GJ , Nyquist AC , Price CS , Reich NG , Simberkoff MS , Brown AC , Radonovich LJ Jr , Perl TM , Rodriguez-Barradas MC . Am J Infect Control 2021 49 (11) 1369-1375 BACKGROUND: Healthcare personnel (HCP) knowledge and attitudes toward infection control measures are important determinants of practices that can protect them from transmission of infectious diseases. METHODS: Healthcare personnel were recruited from Emergency Departments and outpatient clinics at seven sites. They completed knowledge surveys at the beginning and attitude surveys at the beginning and end of each season of participation. Attitudes toward infection prevention and control measures, especially medical masks and N95 respirators, were compared. The proportion of participants who correctly identified all components of an infection control bundle for seven clinical scenarios was calculated. RESULTS: The proportion of participants in the medical mask group who reported at least one reason to avoid using medical masks fell from 88.5% on the pre-season survey to 39.6% on the post-season survey (odds ratio [OR] for preseason vs. postseason 0.11, 95% CI 0.10-0.14). Among those wearing N95 respirators, the proportion fell from 87.9% to 53.6% (OR 0.24, 95% CI 0.21-0.28). Participants correctly identified all components of the infection control bundle for 4.9% to 38.5% of scenarios. CONCLUSIONS: Attitudes toward medical masks and N95 respirators improved significantly between the beginning and end of each season. The proportion of HCP who correctly identified the infection control precautions needed for clinical scenarios was low, but it improved over successive years of participation in the study. |
Modeling of Future COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Rates and Nonpharmaceutical Intervention Scenarios - United States, April-September 2021.
Borchering RK , Viboud C , Howerton E , Smith CP , Truelove S , Runge MC , Reich NG , Contamin L , Levander J , Salerno J , van Panhuis W , Kinsey M , Tallaksen K , Obrecht RF , Asher L , Costello C , Kelbaugh M , Wilson S , Shin L , Gallagher ME , Mullany LC , Rainwater-Lovett K , Lemaitre JC , Dent J , Grantz KH , Kaminsky J , Lauer SA , Lee EC , Meredith HR , Perez-Saez J , Keegan LT , Karlen D , Chinazzi M , Davis JT , Mu K , Xiong X , Pastore YPiontti A , Vespignani A , Srivastava A , Porebski P , Venkatramanan S , Adiga A , Lewis B , Klahn B , Outten J , Schlitt J , Corbett P , Telionis PA , Wang L , Peddireddy AS , Hurt B , Chen J , Vullikanti A , Marathe M , Healy JM , Slayton RB , Biggerstaff M , Johansson MA , Shea K , Lessler J . MMWR Morb Mortal Wkly Rep 2021 70 (19) 719-724 After a period of rapidly declining U.S. COVID-19 incidence during January-March 2021, increases occurred in several jurisdictions (1,2) despite the rapid rollout of a large-scale vaccination program. This increase coincided with the spread of more transmissible variants of SARS-CoV-2, the virus that causes COVID-19, including B.1.1.7 (1,3) and relaxation of COVID-19 prevention strategies such as those for businesses, large-scale gatherings, and educational activities. To provide long-term projections of potential trends in COVID-19 cases, hospitalizations, and deaths, COVID-19 Scenario Modeling Hub teams used a multiple-model approach comprising six models to assess the potential course of COVID-19 in the United States across four scenarios with different vaccination coverage rates and effectiveness estimates and strength and implementation of nonpharmaceutical interventions (NPIs) (public health policies, such as physical distancing and masking) over a 6-month period (April-September 2021) using data available through March 27, 2021 (4). Among the four scenarios, an accelerated decline in NPI adherence (which encapsulates NPI mandates and population behavior) was shown to undermine vaccination-related gains over the subsequent 2-3 months and, in combination with increased transmissibility of new variants, could lead to surges in cases, hospitalizations, and deaths. A sharp decline in cases was projected by July 2021, with a faster decline in the high-vaccination scenarios. High vaccination rates and compliance with public health prevention measures are essential to control the COVID-19 pandemic and to prevent surges in hospitalizations and deaths in the coming months. |
Take-home kits to detect respiratory viruses among healthcare personnel: Lessons learned from a cluster randomized clinical trial
Los J , Gaydos CA , Gibert CL , Gorse GJ , Lykken J , Nyquist AC , Price CS , Radonovich LJ Jr , Rattigan S , Reich N , Rodriguez-Barradas M , Simberkoff M , Bessesen M , Brown A , Cummings DAT , Perl TM . Am J Infect Control 2021 49 (7) 893-899 BACKGROUND: Healthcare personnel (HCP) working in outpatient settings routinely interact with patients with acute respiratory illnesses. Absenteeism following symptom development and lack of staff trained to obtain samples limit efforts to identify pathogens among infected HCP. METHODS: The Respiratory Protection Effectiveness Clinical Trial assessed respiratory infection incidence among HCP between 2011 and 2015. Research assistants (RAs) obtained anterior nasal and oropharyngeal swabs from HCP in the workplace following development of respiratory illness symptoms and randomly while asymptomatic. Participants received take-home kits to self-collect swabs when absent from work. Samples mailed to a central laboratory were tested for respiratory viruses by reverse transcription polymerase chain reaction. RESULTS: Among 2,862 participants, 3,467 swabs were obtained from symptomatic participants. Among symptomatic HCP, respiratory virus was detected in 904 of 3,467 (26.1%) samples. Self-collected samples by symptomatic HCP at home had higher rates of viral detection (40.3%) compared to 24% obtained by trained RAs in the workplace (P < 0.001). CONCLUSIONS: In this randomized clinical trial, take-home kits were an easily implemented, effective method to self-collect samples by HCP. Other studies have previously shown relative equivalence of self-collected samples to those obtained by trained healthcare workers. Take-home kit self-collection could diminish workforce exposures and decrease the demand for personnel protective equipment worn to protect workers who collect respiratory samples. |
Variability in published rates of influenza-associated hospitalizations: A systematic review, 2007-2018
Roguski KM , Rolfes MA , Reich JS , Owens Z , Patel N , Fitzner J , Cozza V , Lafond KE , Azziz-Baumgartner E , Iuliano AD . J Glob Health 2020 10 (2) 020430 BACKGROUND: Influenza burden estimates help provide evidence to support influenza prevention and control programs at local and international levels. METHODS: Through a systematic review, we aimed to identify all published articles estimating rates of influenza-associated hospitalizations, describe methods and data sources used, and identify regions of the world where estimates are still lacking. We evaluated study heterogeneity to determine if we could pool published rates to generate global estimates of influenza-associated hospitalization. RESULTS: We identified 98 published articles estimating influenza-associated hospitalization rates from 2007-2018. Most articles (65%) identified were from high-income countries, with 34 of those (53%) presenting estimates from the United States. While we identified fewer publications (18%) from low- and lower-middle-income countries, 50% of those were published from 2015-2018, suggesting an increase in publications from lower-income countries in recent years. Eighty percent (n = 78) used a multiplier approach. Regression modelling techniques were only used with data from upper-middle or high-income countries where hospital administrative data was available. We identified variability in the methods, case definitions, and data sources used, including 91 different age groups and 11 different categories of case definitions. Due to the high observed heterogeneity across articles (I(2) >99%), we were unable to pool published estimates. CONCLUSIONS: The variety of methods, data sources, and case definitions adapted locally suggests that the current literature cannot be synthesized to generate global estimates of influenza-associated hospitalization burden. |
Identification and evaluation of epidemic prediction and forecasting reporting guidelines: A systematic review and a call for action
Pollett S , Johansson M , Biggerstaff M , Morton LC , Bazaco SL , Brett Major DM , Stewart-Ibarra AM , Pavlin JA , Mate S , Sippy R , Hartman LJ , Reich NG , Maljkovic Berry I , Chretien JP , Althouse BM , Myer D , Viboud C , Rivers C . Epidemics 2020 33 100400 INTRODUCTION: High quality epidemic forecasting and prediction are critical to support response to local, regional and global infectious disease threats. Other fields of biomedical research use consensus reporting guidelines to ensure standardization and quality of research practice among researchers, and to provide a framework for end-users to interpret the validity of study results. The purpose of this study was to determine whether guidelines exist specifically for epidemic forecast and prediction publications. METHODS: We undertook a formal systematic review to identify and evaluate any published infectious disease epidemic forecasting and prediction reporting guidelines. This review leveraged a team of 18 investigators from US Government and academic sectors. RESULTS: A literature database search through May 26, 2019, identified 1467 publications (MEDLINE n = 584, EMBASE n = 883), and a grey-literature review identified a further 407 publications, yielding a total 1777 unique publications. A paired-reviewer system screened in 25 potentially eligible publications, of which two were ultimately deemed eligible. A qualitative review of these two published reporting guidelines indicated that neither were specific for epidemic forecasting and prediction, although they described reporting items which may be relevant to epidemic forecasting and prediction studies. CONCLUSIONS: This systematic review confirms that no specific guidelines have been published to standardize the reporting of epidemic forecasting and prediction studies. These findings underscore the need to develop such reporting guidelines in order to improve the transparency, quality and implementation of epidemic forecasting and prediction research in operational public health. |
Comparison of alternative full and brief versions of functional status scales among older adults in China
Reich J , Thompson MG , Cowling BJ , Iuliano AD , Greene C , Chen Y , Phadnis R , Leung NHL , Song Y , Fang VJ , Xu C , Dai Q , Zhang J , Zhang H , Havers F . PLoS One 2020 15 (8) e0234698 BACKGROUND: Brief assessments of functional status for community-dwelling older adults are needed given expanded interest in the measurement of functional decline. METHODS: As part of a 2015 prospective cohort study of older adults aged 60-89 years in Jiangsu Province, China, 1506 participants were randomly assigned to two groups; each group was administered one of two alternative 20-item versions of a scale to assess activities of daily living (ADL) and instrumental activities of daily living (IADL) drawn from multiple commonly-used scales. One version asked if they required help to perform activities (ADL-IADL-HELP-20), while the other version provided additional response options if activities could be done alone but with difficulty (ADL-IADL-DIFFICULTY-20). Item responses to both versions were compared using the binomial test for differences in proportion (with Wald 95% confidence interval [CI]). A brief 9-item scale (ADL-IADL-DIFFICULTY-9) was developed favoring items identified as difficult or requiring help by ≥4%, with low redundancy and/or residual correlations, and with significant correlations with age and other health indicators. We repeated assessment of the measurement properties of the brief scale in two subsequent samples of older adults in Hong Kong in 2016 (aged 70-79 years; n = 404) and 2017 (aged 65-82 years; n = 1854). RESULTS: Asking if an activity can be done alone but with difficulty increased the proportion of participants reporting restriction on 9 of 20 items, for which 95% CI for difference scores did not overlap with zero; the proportion with at least one limitation increased from 28.6% to 34.2% or an absolute increase of 5.6% (95% CI = 0.9-10.3%), which was a relative increase of 19.6%. The brief ADL-IADL-DIFFICULTY-9 maintained excellent internal consistency (α = 0.93) and had similar ceiling effect (68.1%), invariant item ordering (H trans = .41; medium), and correlations with age and other health measures compared with the 20-item version. The brief scale performed similarly when subsequently administered to older adults in Hong Kong. CONCLUSIONS: Asking if tasks can be done alone but with difficulty can modestly reduce ceiling effects. It's possible that the length of commonly-used scales can be reduced by over half if researchers are primarily interested in a summed indicator rather than an inventory of specific types of deficits. |
Risk Factors for Healthcare Personnel Infection with Endemic Coronaviruses (HKU1, OC43, NL63, 229E): Results from the Respiratory Protection Effectiveness Clinical Trial (ResPECT).
Cummings DAT , Radonovich LJ , Gorse GJ , Gaydos CA , Bessesen MT , Brown AC , Gibert CL , Hitchings MDT , Lessler J , Nyquist AC , Rattigan SM , Rodriguez-Barradas MC , Price CS , Reich NG , Simberkoff MS , Perl TM . Clin Infect Dis 2020 73 (11) e4428-e4432 BACKGROUND: SARS-CoV-2 presents a large risk to healthcare personnel. Quantifying the risk of coronavirus infection associated with workplace activities is an urgent need. METHODS: We assessed the association of worker characteristics, occupational roles and behaviors, and participation in procedures with the risk of endemic coronavirus infection among healthcare personnel who participated in the Respiratory Protection Effectiveness Trial (ResPECT), a cluster randomized trial to assess personal protective equipment to prevent respiratory infections and illness conducted from 2011 to 2016. RESULTS: Among 4,689 HCP-seasons, we detected coronavirus infection in 387 (8%). HCP who participated in an aerosol generation procedure (AGP) at least once during the viral respiratory season were 105% (95% CI 21%, 240%) more likely to be diagnosed with a laboratory-confirmed coronavirus infection. Younger individuals, those who saw pediatric patients and those with household members under the age of five were at increased risk of coronavirus infection. CONCLUSIONS: Our analysis suggests the risk of HCP becoming infected with an endemic coronavirus increases approximately two-fold with exposures to AGP. Our findings may be relevant to the Coronavirus Disease 2019 (COVID-19) pandemic; however, SARS-COV-2, the virus that causes COVID-19, may differ from endemic coronaviruses in important ways. |
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