Last data update: Dec 09, 2024. (Total: 48320 publications since 2009)
Records 1-30 (of 212 Records) |
Query Trace: Biggerstaff B[original query] |
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Development of a diagnostic IgM antibody capture ELISA for detection of anti-Cache Valley Virus human IgM
Goodman C , Powers JA , Mikula SR , Hughes HR , Biggerstaff BJ , Fitzpatrick K , Panella AJ , Machain-Williams C , Lee S , Calvert AE . Am J Trop Med Hyg 2024 Cache Valley virus (CVV), a mosquito-borne orthobunyavirus, causes epizootics in ruminants characterized by congenital malformations and fetal death in North America. Only seven human infections have been identified; limited information exists on its potential as a human teratogen. Diagnosis of CVV infections relies on the plaque reduction neutralization test (PRNT), which requires live virus, is time-consuming, and cannot differentiate between recent and past infections. To improve diagnostics for CVV, we developed an IgM antibody capture ELISA (MAC-ELISA) for detection of anti-CVV human IgM in diagnostic specimens that can be performed faster than PRNT and is specific to IgM, which is essential to determine the timing of infection. Conjointly, a cell line constitutively expressing human-murine chimeric antibody with the variable regions of monoclonal antibody CVV-17 and constant regions of human IgM was developed to provide positive control material. The new cell line produced antibody with reactivity in the assay equivalent to that of a human serum sample positive for anti-CVV IgM. Five of seven archived human specimens diagnostically confirmed as CVV positive tested positive in the MAC-ELISA, whereas 44 specimens confirmed positive for another arboviral infection tested negative, showing good initial correlation of the CVV MAC-ELISA. Two of 27 previously collected serum samples from febrile patients in Yucatán, Mexico, who tested negative for a recent flaviviral or alphaviral infection were positive in both the MAC-ELISA and PRNT, indicating a possible recent infection with CVV or related orthobunyavirus. The MAC-ELISA described here will aid in making diagnostics more widely available for CVV in public health laboratories. |
Spike and nucleocapsid antibody dynamics following SARS-CoV-2 infection and vaccination: Implications for sourcing COVID-19 convalescent plasma from routinely collected blood donations
Di Germanio C , Deng X , Balasko BG , Simmons G , Martinelli R , Grebe E , Stone M , Spencer BR , Saa P , Yu EA , Lanteri MC , Green V , Wright D , Lartey I , Kleinman S , Jones J , Biggerstaff BJ , Contestable P , Busch MP . Transfusion 2024 BACKGROUND: COVID-19 convalescent plasma (CCP) remains a treatment option for immunocompromised patients; however, the current FDA qualification threshold of ≥200 BAU/mL of spike antibody appears to be relatively low. We evaluated the levels of binding (bAb) and neutralizing antibodies (nAb) on serial samples from repeat blood donors who were vaccinated and/or infected to inform criteria for qualifying CCP from routinely collected plasma components. METHODS: Donors were categorized into four groups: (1) infected, then vaccinated, (2) vaccinated then infected during the delta, or (3) omicron waves, (4) vaccinated without infection. IgG Spike and total Nuclecapsid bAb were measured, along with S variants and nAb titers using reporter viral particle neutralization. RESULTS: Mean S IgG bAb peaks after infection alone were lower than after primary and booster vaccinations, and higher after delta and omicron infection in previously vaccinated donors. Half-lives for S IgG ranged from 34 to 66 days after first infection/vaccination events and up to 108 days after second events. The levels of S IgG bAb and nAb were similar across different variants, except for omicron, which were lower. Better correlations of nAb with bAb were observed at higher levels (hybrid immunity) than at the current FDA CCP qualifying threshold. DISCUSSION: Routine plasma donations from donors with hybrid immunity had high S bAb and potent neutralizing activity for 3-6 months after infection. In donations with high (>4000 BAU/mL) S IgG, >95% had high nAb titers (>500) against ancestral and variant S, regardless of COVID-19 symptoms. These findings provide the basis for test-based criteria for qualifying CCP from routine blood donations. |
Wastewater surveillance for influenza A virus and H5 subtype concurrent with the highly pathogenic avian influenza A(H5N1) virus outbreak in cattle and poultry and associated human cases - United States, May 12-July 13, 2024
Louis S , Mark-Carew M , Biggerstaff M , Yoder J , Boehm AB , Wolfe MK , Flood M , Peters S , Stobierski MG , Coyle J , Leslie MT , Sinner M , Nims D , Salinas V , Lustri L , Bojes H , Shetty V , Burnor E , Rabe A , Ellison-Giles G , Yu AT , Bell A , Meyer S , Lynfield R , Sutton M , Scholz R , Falender R , Matzinger S , Wheeler A , Ahmed FS , Anderson J , Harris K , Walkins A , Bohra S , O'Dell V , Guidry VT , Christensen A , Moore Z , Wilson E , Clayton JL , Parsons H , Kniss K , Budd A , Mercante JW , Reese HE , Welton M , Bias M , Webb J , Cornforth D , Santibañez S , Soelaeman RH , Kaur M , Kirby AE , Barnes JR , Fehrenbach N , Olsen SJ , Honein MA . MMWR Morb Mortal Wkly Rep 2024 73 (37) 804-809 As part of the response to the highly pathogenic avian influenza A(H5N1) virus outbreak in U.S. cattle and poultry and the associated human cases, CDC and partners are monitoring influenza A virus levels and detection of the H5 subtype in wastewater. Among 48 states and the District of Columbia that performed influenza A testing of wastewater during May 12-July 13, 2024, a weekly average of 309 sites in 38 states had sufficient data for analysis, and 11 sites in four states reported high levels of influenza A virus. H5 subtype testing was conducted at 203 sites in 41 states, with H5 detections at 24 sites in nine states. For each detection or high level, CDC and state and local health departments evaluated data from other influenza surveillance systems and partnered with wastewater utilities and agriculture departments to investigate potential sources. Among the four states with high influenza A virus levels detected in wastewater, three states had corresponding evidence of human influenza activity from other influenza surveillance systems. Among the 24 sites with H5 detections, 15 identified animal sources within the sewershed or adjacent county, including eight milk-processing inputs. Data from these early investigations can help health officials optimize the use of wastewater surveillance during the upcoming respiratory illness season. |
Estimating community-wide indirect effects of influenza vaccination: triangulation using mathematical models and bias analysis
Arinaminpathy N , Reed C , Biggerstaff M , Nguyen AT , Athni TS , Arnold BF , Hubbard A , Reingold A , Benjamin-Chung J . Am J Epidemiol 2024 Understanding whether influenza vaccine promotion strategies produce community-wide indirect effects is important for establishing vaccine coverage targets and optimizing vaccine delivery. Empirical epidemiologic studies and mathematical models have been used to estimate indirect effects of vaccines but rarely for the same estimand in the same dataset. Using these approaches together could be a powerful tool for triangulation in infectious disease epidemiology because each approach is subject to distinct sources of bias. We triangulated evidence about indirect effects from a school-located influenza vaccination program using two approaches: a difference-in-difference (DID) analysis, and an age-structured, deterministic, compartmental model. The estimated indirect effect was substantially lower in the mathematical model than in the DID analysis (2.1% (95% Bayesian credible intervals 0.4 - 4.4%) vs. 22.3% (95% CI 7.6% - 37.1%)). To explore reasons for differing estimates, we used sensitivity analyses and probabilistic bias analyses. When we constrained model parameters such that projections matched the DID analysis, results only aligned with the DID analysis with substantially lower pre-existing immunity among school-age children and older adults. Conversely, DID estimates corrected for potential bias only aligned with mathematical model estimates under differential outcome misclassification. We discuss how triangulation using empirical and mathematical modelling approaches could strengthen future studies. |
Influenza virus shedding and symptoms: Dynamics and implications from a multiseason household transmission study
Morris SE , Nguyen HQ , Grijalva CG , Hanson KE , Zhu Y , Biddle JE , Meece JK , Halasa NB , Chappell JD , Mellis AM , Reed C , Biggerstaff M , Belongia EA , Talbot HK , Rolfes MA . PNAS Nexus 2024 3 (9) pgae338 Isolation of symptomatic infectious persons can reduce influenza transmission. However, virus shedding that occurs without symptoms will be unaffected by such measures. Identifying effective isolation strategies for influenza requires understanding the interplay between individual virus shedding and symptom presentation. From 2017 to 2020, we conducted a case-ascertained household transmission study using influenza real-time RT-qPCR testing of nasal swabs and daily symptom diary reporting for up to 7 days after enrolment (≤14 days after index onset). We assumed real-time RT-qPCR cycle threshold (Ct) values were indicators of quantitative virus shedding and used symptom diaries to create a score that tracked influenza-like illness (ILI) symptoms (fever, cough, or sore throat). We fit phenomenological nonlinear mixed-effects models stratified by age and vaccination status and estimated two quantities influencing isolation effectiveness: shedding before symptom onset and shedding that might occur once isolation ends. We considered different isolation end points (including 24 h after fever resolution or 5 days after symptom onset) and assumptions about the infectiousness of Ct shedding trajectories. Of the 116 household contacts with ≥2 positive tests for longitudinal analyses, 105 (91%) experienced ≥1 ILI symptom. On average, children <5 years experienced greater peak shedding, longer durations of shedding, and elevated ILI symptom scores compared with other age groups. Most individuals (63/105) shed <10% of their total shed virus before symptom onset, and shedding after isolation varied substantially across individuals, isolation end points, and infectiousness assumptions. Our results can inform strategies to reduce transmission from symptomatic individuals infected with influenza. |
Asymptomatic and mildly symptomatic influenza virus infections by season -- Case-ascertained household transmission studies, United States, 2017-2023
Biddle JE , Nguyen HQ , Talbot HK , Rolfes MA , Biggerstaff M , Johnson S , Reed C , Belongia EA , Grijalva CG , Mellis AM . medRxiv 2024 Asymptomatic influenza virus infection occurs but may vary by factors such as age, influenza vaccination status, or influenza season. We examined the frequency of influenza virus infection and associated symptoms using data from two case-ascertained household transmission studies (conducted from 2017-2023) with prospective, systematic collection of respiratory specimens and symptoms. From the 426 influenza virus infected household contacts that met our inclusion criteria, 8% were asymptomatic, 6% had non-respiratory symptoms, 23% had acute respiratory symptoms, and 62% had influenza-like illness symptoms. Understanding the prevalence of asymptomatic and mildly symptomatic influenza cases is important for implementing effective influenza prevention strategies and enhancing the effectiveness of symptom-based surveillance systems. |
Title evaluation of FluSight influenza forecasting in the 2021-22 and 2022-23 seasons with a new target laboratory-confirmed influenza hospitalizations
Mathis SM , Webber AE , León TM , Murray EL , Sun M , White LA , Brooks LC , Green A , Hu AJ , Rosenfeld R , Shemetov D , Tibshirani RJ , McDonald DJ , Kandula S , Pei S , Yaari R , Yamana TK , Shaman J , Agarwal P , Balusu S , Gururajan G , Kamarthi H , Prakash BA , Raman R , Zhao Z , Rodríguez A , Meiyappan A , Omar S , Baccam P , Gurung HL , Suchoski BT , Stage SA , Ajelli M , Kummer AG , Litvinova M , Ventura PC , Wadsworth S , Niemi J , Carcelen E , Hill AL , Loo SL , McKee CD , Sato K , Smith C , Truelove S , Jung SM , Lemaitre JC , Lessler J , McAndrew T , Ye W , Bosse N , Hlavacek WS , Lin YT , Mallela A , Gibson GC , Chen Y , Lamm SM , Lee J , Posner RG , Perofsky AC , Viboud C , Clemente L , Lu F , Meyer AG , Santillana M , Chinazzi M , Davis JT , Mu K , Pastore YPiontti A , Vespignani A , Xiong X , Ben-Nun M , Riley P , Turtle J , Hulme-Lowe C , Jessa S , Nagraj VP , Turner SD , Williams D , Basu A , Drake JM , Fox SJ , Suez E , Cojocaru MG , Thommes EW , Cramer EY , Gerding A , Stark A , Ray EL , Reich NG , Shandross L , Wattanachit N , Wang Y , Zorn MW , Aawar MA , Srivastava A , Meyers LA , Adiga A , Hurt B , Kaur G , Lewis BL , Marathe M , Venkatramanan S , Butler P , Farabow A , Ramakrishnan N , Muralidhar N , Reed C , Biggerstaff M , Borchering RK . Nat Commun 2024 15 (1) 6289 Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. For the 2021-22 and 2022-23 influenza seasons, 26 forecasting teams provided national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one-to-four weeks ahead. Forecast skill is evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperform the baseline model across forecast weeks and locations in 2021-22 and 12 out of 18 models in 2022-23. Averaging across all forecast targets, the FluSight ensemble is the 2(nd) most accurate model measured by WIS in 2021-22 and the 5(th) most accurate in the 2022-23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degrade over longer forecast horizons. In this work we demonstrate that while the FluSight ensemble was a robust predictor, even ensembles face challenges during periods of rapid change. |
Detection of nucleocapsid antibodies associated with primary SARS-CoV-2 infection in unvaccinated and vaccinated blood donors
Grebe E , Stone M , Spencer BR , Akinseye A , Wright D , Di Germanio C , Bruhn R , Zurita KG , Contestable P , Green V , Lanteri MC , Saa P , Biggerstaff BJ , Coughlin MM , Kleinman S , Custer B , Jones JM , Busch MP . Emerg Infect Dis 2024 30 (8) Nucleocapsid antibody assays can be used to estimate SARS-CoV-2 infection prevalence in regions implementing spike-based COVID-19 vaccines. However, poor sensitivity of nucleocapsid antibody assays in detecting infection after vaccination has been reported. We derived a lower cutoff for identifying previous infections in a large blood donor cohort (N = 142,599) by using the Ortho VITROS Anti-SARS-CoV-2 Total-N Antibody assay, improving sensitivity while maintaining specificity >98%. We validated sensitivity in samples donated after self-reported swab-confirmed infections diagnoses. Sensitivity for first infections in unvaccinated donors was 98.1% (95% CI 98.0-98.2) and for infection after vaccination was 95.6% (95% CI 95.6-95.7) based on the standard cutoff. Regression analysis showed sensitivity was reduced in the Delta compared with Omicron period, in older donors, in asymptomatic infections, <30 days after infection, and for infection after vaccination. The standard Ortho N antibody threshold demonstrated good sensitivity, which was modestly improved with the revised cutoff. |
binGroup2: Statistical tools for infection identification via group testing
Bilder CR , Hitt BD , Biggerstaff BJ , Tebbs JM , McMahan CS . R j 2023 15 (4) 21-36 Group testing is the process of testing items as an amalgamation, rather than separately, to determine the binary status for each item. Its use was especially important during the COVID-19 pandemic through testing specimens for SARS-CoV-2. The adoption of group testing for this and many other applications is because members of a negative testing group can be declared negative with potentially only one test. This subsequently leads to significant increases in laboratory testing capacity. Whenever a group testing algorithm is put into practice, it is critical for laboratories to understand the algorithm's operating characteristics, such as the expected number of tests. Our paper presents the binGroup2 package that provides the statistical tools for this purpose. This R package is the first to address the identification aspect of group testing for a wide variety of algorithms. We illustrate its use through COVID-19 and chlamydia/gonorrhea applications of group testing. |
Outbreak of highly pathogenic avian influenza A(H5N1) viruses in U.S. dairy cattle and detection of two human cases - United States, 2024
Garg S , Reed C , Davis CT , Uyeki TM , Behravesh CB , Kniss K , Budd A , Biggerstaff M , Adjemian J , Barnes JR , Kirby MK , Basler C , Szablewski CM , Richmond-Crum M , Burns E , Limbago B , Daskalakis DC , Armstrong K , Boucher D , Shimabukuro TT , Jhung MA , Olsen SJ , Dugan V . MMWR Morb Mortal Wkly Rep 2024 73 (21) 501-505 |
Detection of novel influenza viruses through community and healthcare testing: Implications for surveillance efforts in the United States
Morris SE , Gilmer M , Threlkel R , Brammer L , Budd AP , Iuliano AD , Reed C , Biggerstaff M . Influenza Other Respir Viruses 2024 18 (5) e13315 BACKGROUND: Novel influenza viruses pose a potential pandemic risk, and rapid detection of infections in humans is critical to characterizing the virus and facilitating the implementation of public health response measures. METHODS: We use a probabilistic framework to estimate the likelihood that novel influenza virus cases would be detected through testing in different community and healthcare settings (urgent care, emergency department, hospital, and intensive care unit [ICU]) while at low frequencies in the United States. Parameters were informed by data on seasonal influenza virus activity and existing testing practices. RESULTS: In a baseline scenario reflecting the presence of 100 novel virus infections with similar severity to seasonal influenza viruses, the median probability of detecting at least one infection per month was highest in urgent care settings (72%) and when community testing was conducted at random among the general population (77%). However, urgent care testing was over 15 times more efficient (estimated as the number of cases detected per 100,000 tests) due to the larger number of tests required for community testing. In scenarios that assumed increased clinical severity of novel virus infection, median detection probabilities increased across all healthcare settings, particularly in hospitals and ICUs (up to 100%) where testing also became more efficient. CONCLUSIONS: Our results suggest that novel influenza virus circulation is likely to be detected through existing healthcare surveillance, with the most efficient testing setting impacted by the disease severity profile. These analyses can help inform future testing strategies to maximize the likelihood of novel influenza detection. |
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. |
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. |
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