Last data update: May 06, 2024. (Total: 46732 publications since 2009)
Records 1-30 (of 200 Records) |
Query Trace: Biggerstaff B[original query] |
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Responding to the return of influenza in the United States by applying Centers for Disease Control and Prevention surveillance, analysis, and modeling to inform understanding of seasonal influenza
Borchering RK , Biggerstaff M , Brammer L , Budd A , Garg S , Fry AM , Iuliano AD , Reed C . JMIR Public Health Surveill 2024 10 e54340 We reviewed the tools that have been developed to characterize and communicate seasonal influenza activity in the United States. Here we focus on systematic surveillance and applied analytics, including seasonal burden and disease severity estimation, short-term forecasting, and longer-term modeling efforts. For each set of activities, we describe the challenges and opportunities that have arisen because of the COVID-19 pandemic. In conclusion, we highlight how collaboration and communication have been and will continue to be key components of reliable and actionable influenza monitoring, forecasting, and modeling activities. |
Redefining pandemic preparedness: Multidisciplinary insights from the CERP modelling workshop in infectious diseases, workshop report
Nunes MC , Thommes E , Fröhlich H , Flahault A , Arino J , Baguelin M , Biggerstaff M , Bizel-Bizellot G , Borchering R , Cacciapaglia G , Cauchemez S , Barbier-Chebbah A , Claussen C , Choirat C , Cojocaru M , Commaille-Chapus C , Hon C , Kong J , Lambert N , Lauer KB , Lehr T , Mahe C , Marechal V , Mebarki A , Moghadas S , Niehus R , Opatowski L , Parino F , Pruvost G , Schuppert A , Thiébaut R , Thomas-Bachli A , Viboud C , Wu J , Crépey P , Coudeville L . Infect Dis Model 2024 9 (2) 501-518 In July 2023, the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 pandemic. This report summarizes the rich discussions that occurred during the workshop. The workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data, social media, and wastewater monitoring. Significant advancements were noted in the development of predictive models, with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease trends. The role of open collaboration between various stakeholders in modelling was stressed, advocating for the continuation of such partnerships beyond the pandemic. A major gap identified was the absence of a common international framework for data sharing, which is crucial for global pandemic preparedness. Overall, the workshop underscored the need for robust, adaptable modelling frameworks and the integration of different data sources and collaboration across sectors, as key elements in enhancing future pandemic response and preparedness. |
Intrinsic risk factors for alpha-gal syndrome in a case-control study, 2019-2020
Taylor ML , Kersh GJ , Salzer JS , Jones ES , Binder AM , Armstrong PA , Choudhary SK , Commins GK , Amelio CL , Biggerstaff BJ , Beard CB , Petersen LR , Commins SP . Ann Allergy Asthma Immunol 2024 BACKGROUND: Alpha-gal syndrome (AGS) is an allergy to galactose-α-1,3-galactose (alpha-gal), a carbohydrate found in most mammals. Evidence indicates that AGS develops following a tick bite, and in the United States, AGS is most associated with bites from Amblyomma americanum (lone star tick); however, not all persons bitten by ticks develop clinical AGS. OBJECTIVE: This study investigated intrinsic risk factors associated with the development of AGS. METHODS: We performed a case-control study among adults presenting for diagnosis or management of AGS at an allergy clinic in North Carolina during 2019-2020 and compared them to controls enrolled from two nearby internal medicine clinics. A questionnaire gathered epidemiologic and tick exposure data and blood was obtained for alpha-gal specific IgE (sIgE) and other testing. RESULTS: The 82 enrolled case patients and 191 controls did not differ significantly by age or sex. Case patients were more likely than controls to have A or O blood types (non-B-antigen), have experienced childhood allergies, and have a family history of AGS and other food allergies. Case patients were also more likely to report experiencing long healing times for insect bites or stings and a family history of allergy to stinging or biting insects. CONCLUSION: This study suggests that intrinsic factors contribute to risk of developing AGS. Some traits are genetic, but common behaviors among households and family units likely also contribute. Identification of these risk factors can inform personal risk, aid healthcare providers in understanding susceptible populations, and contribute to ongoing understanding of AGS epidemiology. |
The role of asymptomatic infections in influenza transmission: what do we really know
Montgomery MP , Morris SE , Rolfes MA , Kittikraisak W , Samuels AM , Biggerstaff M , Davis WW , Reed C , Olsen SJ . Lancet Infect Dis 2023 Before the COVID-19 pandemic, the role of asymptomatic influenza virus infections in influenza transmission was uncertain. However, the importance of asymptomatic infection with SARS-CoV-2 for onward transmission of COVID-19 has led experts to question whether the role of asymptomatic influenza virus infections in transmission had been underappreciated. We discuss the existing evidence on the frequency of asymptomatic influenza virus infections, the extent to which they contribute to infection transmission, and remaining knowledge gaps. We propose priority areas for further evaluation, study designs, and case definitions to address existing knowledge gaps. |
Modeling the impacts of antiviral prophylaxis strategies in mitigating seasonal influenza outbreaks in nursing homes
Morris SE , Zipfel CM , Peer K , Madewell ZJ , Brenner S , Garg S , Paul P , Slayton RB , Biggerstaff M . Clin Infect Dis 2023 BACKGROUND: Antiviral chemoprophylaxis is recommended for use during influenza outbreaks in nursing homes to prevent transmission and severe disease among non-ill residents. Centers for Disease Control and Prevention (CDC) guidance recommends prophylaxis be initiated for all non-ill residents once an influenza outbreak is detected and be continued for at least 14 days and until seven days after the last laboratory-confirmed influenza case is identified. However, not all facilities strictly adhere to this guidance and the impact of such partial adherence is not fully understood. METHODS: We developed a stochastic compartmental framework to model influenza transmission within an average-sized U.S. nursing home. We compared the number of symptomatic illnesses and hospitalizations under varying prophylaxis implementation strategies, in addition to different levels of prophylaxis uptake and adherence by residents and healthcare personnel (HCP). RESULTS: Prophylaxis implemented according to current guidance reduced total symptomatic illnesses and hospitalizations among residents by an average of 12% and 36%, respectively, compared with no prophylaxis. We did not find evidence that alternative implementations of prophylaxis were more effective: compared to full adoption of current guidance, partial adoption resulted in increased symptomatic illnesses and/or hospitalizations, and longer or earlier adoption offered no additional improvements. In addition, increasing uptake and adherence among nursing home residents was effective in reducing resident illnesses and hospitalizations, but increasing HCP uptake had minimal indirect impacts for residents. CONCLUSIONS: The greatest benefits of influenza prophylaxis during nursing home outbreaks will likely be achieved through increasing uptake and adherence among residents and following current CDC guidance. |
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. |
Effectiveness of self-collected, ambient temperature-preserved nasal swabs compared to samples collected by trained staff for genotyping of respiratory viruses by shotgun RNA sequencing: Comparative study
Soto R , Paul L , Porucznik CA , Xie H , Stinnett RC , Briggs B , Biggerstaff M , Stanford J , Schlaberg R . JMIR Form Res 2023 7 e32848 BACKGROUND: The SARS-CoV-2 pandemic has underscored the need for field specimen collection and transport to diagnostic and public health laboratories. Self-collected nasal swabs transported without dependency on a cold chain have the potential to remove critical barriers to testing, expand testing capacity, and reduce opportunities for exposure of health professionals in the context of a pandemic. OBJECTIVE: We compared nasal swab collection by study participants from themselves and their children at home to collection by trained research staff. METHODS: Each adult participant collected 1 nasal swab, sampling both nares with the single swab, after which they collected 1 nasal swab from 1 child. After all the participant samples were collected for the household, the research staff member collected a separate single duplicate sample from each individual. Immediately after the sample collection, the adult participants completed a questionnaire about the acceptability of the sampling procedures. Swabs were placed in temperature-stable preservative and respiratory viruses were detected by shotgun RNA sequencing, enabling viral genome analysis. RESULTS: In total, 21 households participated in the study, each with 1 adult and 1 child, yielding 42 individuals with paired samples. Study participants reported that self-collection was acceptable. Agreement between identified respiratory viruses in both swabs by RNA sequencing demonstrated that adequate collection technique was achieved by brief instructions. CONCLUSIONS: Our results support the feasibility of a scalable and convenient means for the identification of respiratory viruses and implementation in pandemic preparedness for novel respiratory pathogens. |
OA4-AM23-ST-23 | performance of antibody boosting thresholds for serological detection of SARS-CoV-2 reinfections
Grebe E , Stone M , Wright D , Spencer B , Akinseye A , Bruhn R , Biggerstaff B , Custer B , Jones J , Busch M . Transfusion 2023 63 34A-35A |
High influenza incidence and disease severity among children and adolescents aged <18 years - United States, 2022-23 season
White EB , O'Halloran A , Sundaresan D , Gilmer M , Threlkel R , Colón A , Tastad K , Chai SJ , Alden NB , Yousey-Hindes K , Openo KP , Ryan PA , Kim S , Lynfield R , Spina N , Tesini BL , Martinez M , Schmidt Z , Sutton M , Talbot HK , Hill M , Biggerstaff M , Budd A , Garg S , Reed C , Iuliano AD , Bozio CH . MMWR Morb Mortal Wkly Rep 2023 72 (41) 1108-1114 During the 2022-23 influenza season, early increases in influenza activity, co-circulation of influenza with other respiratory viruses, and high influenza-associated hospitalization rates, particularly among children and adolescents, were observed. This report describes the 2022-23 influenza season among children and adolescents aged <18 years, including the seasonal severity assessment; estimates of U.S. influenza-associated medical visits, hospitalizations, and deaths; and characteristics of influenza-associated hospitalizations. The 2022-23 influenza season had high severity among children and adolescents compared with thresholds based on previous seasons' influenza-associated outpatient visits, hospitalization rates, and deaths. Nationally, the incidences of influenza-associated outpatient visits and hospitalization for the 2022-23 season were similar for children aged <5 years and higher for children and adolescents aged 5-17 years compared with previous seasons. Peak influenza-associated outpatient and hospitalization activity occurred in late November and early December. Among children and adolescents hospitalized with influenza during the 2022-23 season in hospitals participating in the Influenza Hospitalization Surveillance Network, a lower proportion were vaccinated (18.3%) compared with previous seasons (35.8%-41.8%). Early influenza circulation, before many children and adolescents had been vaccinated, might have contributed to the high hospitalization rates during the 2022-23 season. Among symptomatic hospitalized patients, receipt of influenza antiviral treatment (64.9%) was lower than during pre-COVID-19 pandemic seasons (80.8%-87.1%). CDC recommends that all persons aged ≥6 months without contraindications should receive the annual influenza vaccine, ideally by the end of October. |
Characterization of a monoclonal antibody specific to California serogroup orthobunyaviruses and development as a chimeric immunoglobulin M-positive control in human diagnostics
Powers JA , Boroughs KL , Mikula S , Goodman CH , Davis EH , Thrasher EM , Hughes HR , Biggerstaff BJ , Calvert AE . Microbiol Spectr 2023 11 (5) e0196623 California serogroup viruses (CSGVs) of medical importance in the United States include La Crosse virus, Jamestown Canyon virus (JCV), California encephalitis virus, and snowshoe hare virus. Current diagnosis of CSGVs relies heavily on serologic techniques for detecting immunoglobulin M (IgM), an indication of a recent CSGV infection. However, human-positive control sera reactive to viruses in the serogroup are scarce because detection of recent infections is rare. Here, we describe the development of new murine monoclonal antibodies (MAbs) reactive to CSGVs and the engineering of a human-murine chimeric antibody by combining the variable regions of the broadly CSGV cross-reactive murine MAb, 3-3B6/2-3B2 and the constant region of the human IgM. MAb 3-3B6/2-3B2 recognizes a tertiary epitope on the Gn/Gc heterodimer, and epitopes important in JCV neutralization were mapped to the Gc glycoprotein. This engineered human IgM constitutively expressed in a HEK-293 stable cell line can replace human-positive control sera in diagnostic serological techniques such as IgM antibody capture enzyme-linked immunosorbent assay (MAC-ELISA). Compared to the parent murine MAbs, the human-chimeric IgM antibody had identical serological activity to CSGVs in ELISA and demonstrated equivalent reactivity compared to human immune sera in the MAC-ELISA.IMPORTANCEOrthobunyaviruses in the California serogroup cause severe neurological disease in children and adults. While these viruses are known to circulate widely in North America, their occurrence is rare. Serological testing for CSGVs is hindered by the limited availability and volumes of human-positive specimens needed as controls in serologic assays. Here, we described the development of a murine monoclonal antibody cross-reactive to CSGVs engineered to contain the variable regions of the murine antibody on the backbone of human IgM. The chimeric IgM produced from the stably expressing HEK293 cell line was evaluated for use as a surrogate human-positive control in a serologic diagnostic test. |
Public health impact of the U.S. Scenario Modeling Hub
Borchering RK , Healy JM , Cadwell BL , Johansson MA , Slayton RB , Wallace M , Biggerstaff M . Epidemics 2023 44 100705 Beginning in December 2020, the COVID-19 Scenario Modeling Hub has provided quantitative scenario-based projections for cases, hospitalizations, and deaths, aggregated across up to nine modeling groups. Projections spanned multiple months into the future and provided timely information on potential impacts of epidemiological uncertainties and interventions. Projections results were shared with the public, public health partners, and the Centers for Disease Control COVID-19 Response Team. The projections provided insights on situational awareness and informed decision-making to mitigate COVID-19 disease burden (e.g., vaccination strategies). By aggregating projections from multiple modeling teams, the Scenario Modeling Hub provided rapidly synthesized information in times of great uncertainty and conveyed possible trajectories in the presence of emerging variants. Here we detail several use cases of these projections in public health practice and communication, including assessments of whether modeling results directly or indirectly informed public health communication or guidance. These include multiple examples where comparisons of projected COVID-19 disease outcomes under different vaccination scenarios were used to inform Advisory Committee for Immunization Practices recommendations. We also describe challenges and lessons learned during this highly beneficial collaboration. |
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 |
Using real-time data to guide decision-making during an influenza pandemic: a modelling analysis (preprint)
Haw DJ , Biggerstaff M , Prasad P , Walker J , Grenfell B , Arinaminpathy N . medRxiv 2021 2021.06.09.21258618 Influenza pandemics typically occur in multiple waves of infection, often associated with initial emergence of a novel virus, followed (in temperate regions) by a later resurgence accompanying the onset of the annual influenza season. Here, we examined whether data collected from an initial pandemic wave could be informative, for the need to implement non-pharmaceutical measures in any resurgent wave. Drawing from the 2009 H1N1 pandemic in 10 states in the USA, we calibrated simple mathematical models of influenza transmission dynamics to data for virologically confirmed hospitalisations during the initial ‘spring’ wave. We then projected pandemic outcomes (cumulative hospitalisations) during the fall wave, and compared these projections with data. Model results show reasonable agreement for all states that reported a substantial number of cases in the spring wave. Using this model we propose a probabilistic decision framework that can be used to determine the need for pre-emptive measures such as postponing school openings, in advance of a fall wave. This work illustrates how model-based evidence synthesis, in real-time during an early pandemic wave, could be used to inform timely decisions for pandemic response.Competing Interest StatementThe authors have declared no competing interest.Funding StatementAll work funded by the USA Centre for Disease Control and Prevention.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:NAAll 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 (FluSurNet) is freely available online. https://www.cdc.gov/flu/weekly/influenza-hospitalization-surveillance.htm |
Evaluation of commercially available high-throughput SARS-CoV-2 serological assays for serosurveillance and related applications (preprint)
Stone M , Grebe E , Sulaeman H , Di Germanio C , Dave H , Kelly K , Biggerstaff BJ , Crews BO , Tran N , Jerome KR , Denny TN , Hogema B , Destree M , Jones JM , Thornburg N , Simmons G , Krajden M , Kleinman S , Dumont LJ , Busch MP . medRxiv 2021 2021.09.04.21262414 SARS-CoV-2 serosurveys can estimate cumulative incidence for monitoring epidemics but require characterization of employed serological assays performance to inform testing algorithm development and interpretation of results. We conducted a multi-laboratory evaluation of 21 commercial high-throughput SARS-CoV-2 serological assays using blinded panels of 1,000 highly-characterized blood-donor specimens. Assays demonstrated a range of sensitivities (96%-63%), specificities (99%-96%) and precision (IIC 0.55-0.99). Durability of antibody detection in longitudinal samples was dependent on assay format and immunoglobulin target, with anti-spike, direct, or total Ig assays demonstrating more stable, or increasing reactivity over time than anti-nucleocapsid, indirect, or IgG assays. Assays with high sensitivity, specificity and durable antibody detection are ideal for serosurveillance. Less sensitive assays demonstrating waning reactivity are appropriate for other applications, including characterizing antibody responses after infection and vaccination, and detection of anamnestic boosting by reinfections and vaccine breakthrough infections. Assay performance must be evaluated in the context of the intended use.Competing Interest StatementThe authors have declared no competing interest.Funding StatementThis work was supported by research contracts from the Centers for Disease Control and Prevention (CDC Contract 75D30120C08170).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:All blood donors consented to use of de-identified, residual specimens for further research purposes. UCSF IRB provided explicit approval for VRI self-certification that use of the de-identified CCP donations in this study does not meet the criteria for human subjects research. CDC investigators reviewed and relied on this determination as consistent with applicable federal law and CDC policy (45 C.F.R. part 46, 21 C.F.R. part 56; 42 U.S.C. Sect. 241(d); 5 U.S.C. Sect. 552a; 44 U.S.C. Sect. 3501).All 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.YesThe analytic data set is available upon request. |
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 |
Estimating incidence of infection from diverse data sources: Zika virus in Puerto Rico, 2016 (preprint)
Quandelacy TM , Healy JM , Greening B , Rodriguez DM , Chung KW , Kuehnert MJ , Biggerstaff BJ , Dirlikov E , Mier YTeran-Romero L , Sharp TM , Waterman S , Johansson MA . medRxiv 2020 2020.10.14.20212134 Emerging epidemics are challenging to track. Only a subset of cases is recognized and reported, as seen with the Zika virus (ZIKV) epidemic where large proportions of infection were asymptomatic. However, multiple imperfect indicators of infection provide an opportunity to estimate the underlying incidence of infection. We developed a modeling approach that integrates a generic Time-series Susceptible-Infected-Recovered epidemic model with assumptions about reporting biases in a Bayesian framework and applied it to the 2016 Zika epidemic in Puerto Rico using three indicators: suspected arboviral cases, suspected Zika-associated Guillain-Barré Syndrome cases, and blood bank data. Using this combination of surveillance data, we estimated the peak of the epidemic occurred during the week of August 15, 2016 (the 33rd week of year), and 120 to 140 (50% credible interval [CrI], 95% CrI: 97 to 170) weekly infections per 10,000 population occurred at the peak. By the end of 2016, we estimated that approximately 890,000 (95% CrI: 660,000 to 1,100,000) individuals were infected in 2016 (26%, 95% CrI: 19% to 33%, of the population infected). Utilizing multiple indicators offers the opportunity for real-time and retrospective situational awareness to support epidemic preparedness and response.Competing Interest StatementThe authors have declared no competing interest.Funding StatementThe author(s) received no specific funding for this work.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:Exemption was obtained from the CDC Human Subjects Research Office as the data were collected as part of regular surveillance activities.All 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 relevant data are within the manuscript and its Supporting Information files. |
A systematic review and evaluation of Zika virus forecasting and prediction research during a public health emergency of international concern (preprint)
Kobres PY , Chretien JP , Johansson MA , Morgan JJ , Whung PY , Mukundan H , Del Valle SY , Forshey BM , Quandelacy TM , Biggerstaff M , Viboud C , Pollett S . bioRxiv 2019 634832 INTRODUCTION Epidemic forecasting and prediction tools have the potential to provide actionable information in the midst of emerging epidemics. While numerous predictive studies were published during the 2016-2017 Zika Virus (ZIKV) pandemic, it remains unknown how timely, reproducible and actionable the information produced by these studies was.METHODS To improve the functional use of mathematical modeling in support of future infectious disease outbreaks, we conducted a systematic review of all ZIKV prediction studies published during the recent ZIKV pandemic using the PRISMA guidelines. Using MEDLINE, EMBASE and grey literature review, we identified studies that forecasted, predicted or simulated ecological or epidemiological phenomenon related to the Zika pandemic that were published as of March 01, 2017. Eligible studies underwent evaluation of objectives, data sources, methods, timeliness, reproducibility, accessibility and clarity by independent reviewers.RESULTS 2034 studies were identified, of which n = 73 met eligibility criteria. Spatial spread, R0 (basic reproductive number) and epidemic dynamics were most commonly predicted, with few studies predicting Guillain-Barré Syndrome burden (4%), sexual transmission risk (4%) and intervention impact (4%). Most studies specifically examined populations in the Americas (52%), with few African-specific studies (4%). Case count (67%), vector (41%) and demographic data (37%) were the most common data sources. Real-time internet data and pathogen genomic information were used in 7% and 0% of studies, respectively, and social science and behavioral data were typically absent in modeling efforts. Deterministic models were favored over stochastic approaches. Forty percent of studies made model data entirely available, 29% provided all relevant model code, 43% presented uncertainty in all predictions and 54% provided sufficient methodological detail allowing complete reproducibility. Fifty-one percent of predictions were published after the epidemic peak in the Americas. While the use of preprints improved the accessibility of ZIKV predictions by a median 119 days sooner than journal publication dates, they were used in only 30% of studies.CONCLUSIONS Many ZIKV predictions were published during the 2016-2017 pandemic. The accessibility, reproducibility, timeliness, and incorporation of uncertainty in these published predictions varied and indicates that there is substantial room for improvement. To enhance the utility of analytical tools for outbreak response, it is essential to improve the sharing of model data, code, and preprints for future outbreaks, epidemics and pandemics.Author summary Researchers published many studies which sought to predict and forecast important features of Zika virus (ZIKV) infections and their spread during the 2016-2017 ZIKV pandemic. We conducted a comprehensive review of such ZIKV prediction studies and evaluated their aims, the data sources they used, which methods were used, how timely they were published, and whether they provided sufficient information to be used or reproduced by others. Of the 73 studies evaluated, we found that the accessibility, reproducibility, timeliness, and incorporation of uncertainty in these published predictions varied and indicates that there is substantial room for improvement. We identified that the release of study findings before formal journal publication (‘pre-prints’) increased the timeliness of Zika prediction studies, but note they were infrequently used during this public health emergency. Addressing these areas can improve our understanding of Zika and other outbreaks and ensure that forecasts can inform preparedness and response to future outbreaks, epidemics and pandemics. |
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. |
COVID-19 reopening strategies at the county level in the face of uncertainty: Multiple Models for Outbreak Decision Support (preprint)
Shea K , Borchering RK , Probert WJM , Howerton E , Bogich TL , Li S , van Panhuis WG , Viboud C , Aguás R , Belov A , Bhargava SH , Cavany S , Chang JC , Chen C , Chen J , Chen S , Chen Y , Childs LM , Chow CC , Crooker I , Valle SYD , España G , Fairchild G , Gerkin RC , Germann TC , Gu Q , Guan X , Guo L , Hart GR , Hladish TJ , Hupert N , Janies D , Kerr CC , Klein DJ , Klein E , Lin G , Manore C , Meyers LA , Mittler J , Mu K , Núñez RC , Oidtman R , Pasco R , Piontti APY , Paul R , Pearson CAB , Perdomo DR , Perkins TA , Pierce K , Pillai AN , Rael RC , Rosenfeld K , Ross CW , Spencer JA , Stoltzfus AB , Toh KB , Vattikuti S , Vespignani A , Wang L , White L , Xu P , Yang Y , Yogurtcu ON , Zhang W , Zhao Y , Zou D , Ferrari M , Pannell D , Tildesley M , Seifarth J , Johnson E , Biggerstaff M , Johansson M , Slayton RB , Levander J , Stazer J , Salerno J , Runge MC . medRxiv 2020 Policymakers make decisions about COVID-19 management in the face of considerable uncertainty. We convened multiple modeling teams to evaluate reopening strategies for a mid-sized county in the United States, in a novel process designed to fully express scientific uncertainty while reducing linguistic uncertainty and cognitive biases. For the scenarios considered, the consensus from 17 distinct models was that a second outbreak will occur within 6 months of reopening, unless schools and non-essential workplaces remain closed. Up to half the population could be infected with full workplace reopening; non-essential business closures reduced median cumulative infections by 82%. Intermediate reopening interventions identified no win-win situations; there was a trade-off between public health outcomes and duration of workplace closures. Aggregate results captured twice the uncertainty of individual models, providing a more complete expression of risk for decision-making purposes. |
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. |
School-located influenza vaccination and community-wide indirect effects: Reconciling mathematical models to epidemiologic models (preprint)
Arinaminpathy N , Reed C , Biggerstaff M , Nguyen A , Athni TS , Arnold BF , Hubbard A , Colford JM , Reingold A , Benjamin-Chung J . medRxiv 2022 13 Background: Mathematical models and empirical epidemiologic studies (e.g., randomized and observational studies) are complementary tools but may produce conflicting results for a given research question. We used sensitivity analyses and bias analyses to explore such discrepancies in a study of the indirect effects of influenza vaccination. Method(s): We fit an age-structured, deterministic, compartmental model to estimate indirect effects of a school-based influenza vaccination program in California that was evaluated in a previous matched cohort study. To understand discrepancies in their results, we used 1) a model with constrained parameters such that projections matched the cohort study; and 2) probabilistic bias analyses to identify potential biases (e.g., outcome misclassification due to incomplete influenza testing) that, if corrected, would align the empirical results with the mathematical model. Result(s): The indirect effect estimate (% reduction in influenza hospitalization among older adults in intervention vs. control) was 22.3% (95% CI 7.6% - 37.1%) in the cohort study but only 1.6% (95% Bayesian credible intervals 0.4 - 4.4%) in the mathematical model. When constrained, mathematical models aligned with the cohort study when there was substantially lower pre-existing immunity among school-age children and older adults. Conversely, empirical estimates corrected for potential bias aligned with mathematical model estimates only if influenza testing rates were 15-23% lower in the intervention vs. comparison site. Conclusion(s): Sensitivity and bias analysis can shed light on why results of mathematical models and empirical epidemiologic studies differ for the same research question, and in turn, can improve study and model design. 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. |
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. |
Multimodeling approach to evaluating the efficacy of layering pharmaceutical and nonpharmaceutical interventions for influenza pandemics
Prasad PV , Steele MK , Reed C , Meyers LA , Du Z , Pasco R , Alfaro-Murillo JA , Lewis B , Venkatramanan S , Schlitt J , Chen J , Orr M , Wilson ML , Eubank S , Wang L , Chinazzi M , Pastore YPiontti A , Davis JT , Halloran ME , Longini I , Vespignani A , Pei S , Galanti M , Kandula S , Shaman J , Haw DJ , Arinaminpathy N , Biggerstaff M . Proc Natl Acad Sci U S A 2023 120 (28) e2300590120 When an influenza pandemic emerges, temporary school closures and antiviral treatment may slow virus spread, reduce the overall disease burden, and provide time for vaccine development, distribution, and administration while keeping a larger portion of the general population infection free. The impact of such measures will depend on the transmissibility and severity of the virus and the timing and extent of their implementation. To provide robust assessments of layered pandemic intervention strategies, the Centers for Disease Control and Prevention (CDC) funded a network of academic groups to build a framework for the development and comparison of multiple pandemic influenza models. Research teams from Columbia University, Imperial College London/Princeton University, Northeastern University, the University of Texas at Austin/Yale University, and the University of Virginia independently modeled three prescribed sets of pandemic influenza scenarios developed collaboratively by the CDC and network members. Results provided by the groups were aggregated into a mean-based ensemble. The ensemble and most component models agreed on the ranking of the most and least effective intervention strategies by impact but not on the magnitude of those impacts. In the scenarios evaluated, vaccination alone, due to the time needed for development, approval, and deployment, would not be expected to substantially reduce the numbers of illnesses, hospitalizations, and deaths that would occur. Only strategies that included early implementation of school closure were found to substantially mitigate early spread and allow time for vaccines to be developed and administered, especially under a highly transmissible pandemic scenario. |
Evaluation of Ortho VITROS and Roche Elecsys S and NC immunoassays for SARS-CoV-2 serosurveillance applications
Sulaeman H , Grebe E , Dave H , McCann L , Di Germanio C , Sanghavi A , Sclar V , Bougie DW , Chatelain G , Biggerstaff BJ , Jones JM , Thornburg NJ , Kleinman S , Stone M , Busch MP . Microbiol Spectr 2023 11 (4) e0323422 SARS-CoV-2 seroprevalence studies are instrumental in monitoring epidemic activity and require well-characterized, high-throughput assays, and appropriate testing algorithms. The U.S. Nationwide Blood Donor Seroprevalence Study performed monthly cross-sectional serological testing from July 2020 to December 2021, implementing evolving testing algorithms in response to changes in pandemic activity. With high vaccine uptake, anti-Spike (S) reactivity rates reached >80% by May 2021, and the study pivoted from reflex Roche anti-nucleocapsid (NC) testing of Ortho S-reactive specimens to parallel Ortho S/NC testing. We evaluated the performance of the Ortho NC assay as a replacement for the Roche NC assay and compared performance of parallel S/NC testing on both platforms. Qualitative and quantitative agreement of Ortho NC with Roche NC assays was evaluated on preselected S/NC concordant and discordant specimens. All 190 Ortho S+/Roche NC+ specimens were reactive on the Ortho NC assay; 34% of 367 Ortho S+/Roche NC- specimens collected prior to vaccine availability and 43% of 37 Ortho S-/Roche NC+ specimens were reactive on the Ortho NC assay. Performance of parallel S/NC testing using Ortho and Roche platforms was evaluated on 200 specimens collected in 2019 and 3,903 study specimens collected in 2021. All 200 pre-COVID-19 specimens tested negative on the four assays. Cross-platform agreement between Roche and Ortho platforms was 96.4% (3,769/3,903); most discordant results had reactivity close to the cutoffs on the alternate assays. These findings, and higher efficiency and throughput, support the use of parallel S/NC testing on either Roche or Ortho platforms for large serosurveillance studies. IMPORTANCE Seroprevalence studies like the U.S. Nationwide Blood Donor Seroprevalence Study (NBDS) have been critical in monitoring SARS-CoV-2 epidemic activity. These studies rely on serological assays to detect antibodies indicating prior infection. It is critical that the assays and testing algorithms used in seroprevalence studies have adequate performance (high sensitivity, high specificity, ability to discriminate vaccine-induced and infection-induced antibodies, etc.), as well as appropriate characteristics to support large-scale studies, such as high throughput and low cost. In this study we evaluated the performance of Ortho's anti-nucleocapsid assay as a replacement for the Roche anti-nucleocapsid assay and compared performance of parallel anti-spike and anti-nucleocapsid testing on both platforms. These data demonstrate similar performance of the Ortho and Roche anti-nucleocapsid assays and that parallel anti-spike and anti-nucleocapsid testing on either platform could be used for serosurveillance applications. |
Cost-effectiveness of routine annual influenza vaccination by age and risk status
Kim DeLuca E , Gebremariam A , Rose A , Biggerstaff M , Meltzer MI , Prosser LA . Vaccine 2023 41 (29) 4239-4248 BACKGROUND: The epidemiology of circulating seasonal influenza strains changed following the 2009 pandemic influenza A(H1N1). A universal influenza vaccination recommendation has been implemented and new vaccine types have become available post-2009. The objective of this study was to evaluate the cost-effectiveness of routine annual influenza vaccination in the context of this new evidence. METHODS: A state transition simulation model was constructed to estimate the health and economic outcomes of influenza vaccination compared to no vaccination for hypothetical US cohorts stratified by age and risk status. Model input parameters were derived from multiple sources, including post-2009 vaccine effectiveness data from the US Flu Vaccine Effectiveness Network. The analysis used societal and healthcare sector perspectives and a one-year time horizon, except permanent outcomes were also included. The primary outcome was the incremental cost-effectiveness ratio (ICER) in dollars per quality-adjusted life years (QALYs) gained. RESULTS: Compared to no vaccination, vaccination yielded ICERs lower than $95,000/QALY for all age and risk groups, except for non-high-risk adults 18-49 years ($194,000/QALY). Vaccination was cost-saving for adults ≥50 years at higher risk for influenza-related complications. Results were most sensitive to changes in the probability of influenza illness. Performing the analysis from the healthcare sector perspective, excluding vaccination time costs, delivering vaccinations in lower-cost settings, and including productivity losses improved the cost-effectiveness of vaccination. Sensitivity analysis revealed that vaccination remains below $100,000/QALY for older persons ≥65 years at vaccine effectiveness estimates as low as 4 %. CONCLUSIONS: Cost-effectiveness of influenza vaccination varied by age and risk status and was less than $95,000/QALY for all subgroups, except for non-high-risk working-age adults. Results were sensitive to the probability of influenza illness and vaccination was more favorable under certain scenarios. Vaccination for higher risk subgroups resulted in ICERs below $100,000/QALY even at low levels of vaccine effectiveness or circulating virus. |
Multiple models for outbreak decision support in the face of uncertainty
Shea K , Borchering RK , Probert WJM , Howerton E , Bogich TL , Li SL , van Panhuis WG , Viboud C , Aguás R , Belov AA , Bhargava SH , Cavany SM , Chang JC , Chen C , Chen J , Chen S , Chen Y , Childs LM , Chow CC , Crooker I , Del Valle SY , España G , Fairchild G , Gerkin RC , Germann TC , Gu Q , Guan X , Guo L , Hart GR , Hladish TJ , Hupert N , Janies D , Kerr CC , Klein DJ , Klein EY , Lin G , Manore C , Meyers LA , Mittler JE , Mu K , Núñez RC , Oidtman RJ , Pasco R , Pastore YPiontti A , Paul R , Pearson CAB , Perdomo DR , Perkins TA , Pierce K , Pillai AN , Rael RC , Rosenfeld K , Ross CW , Spencer JA , Stoltzfus AB , Toh KB , Vattikuti S , Vespignani A , Wang L , White LJ , Xu P , Yang Y , Yogurtcu ON , Zhang W , Zhao Y , Zou D , Ferrari MJ , Pannell D , Tildesley MJ , Seifarth J , Johnson E , Biggerstaff M , Johansson MA , Slayton RB , Levander JD , Stazer J , Kerr J , Runge MC . Proc Natl Acad Sci U S A 2023 120 (18) e2207537120 Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020. |
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. |
Discussion of "Is group testing ready for prime-time in disease Identification?" by Haber, Malinovsky, and Albert, Statistics in Medicine, 2021
Biggerstaff BJ . Stat Med 2021 40 (17) 3887-3888 I congratulate the authors on an interesting, instructive, and timely paper. Certainly, including the testing for SARS‐CoV‐2 example in a paper on a topic—group testing—of wide interest during the current COVID‐19 pandemic emphasizes the relevance of the topic in public health and diagnostic medicine generally. But the thoughtful inclusion of the remaining examples—HIV, HPV, and cancer biomarker detection—illustrates that the issues considered are important even in less urgent times. In this note of discussion I will comment on two particular aspects in the paper that I found particularly informative, and then I will comment on two other, related areas that I believe the conclusions inform. |
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. |
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