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Article ; Online: Assessing vaccine durability in randomized trials following placebo crossover.

Fintzi, Jonathan / Follmann, Dean

2021 Volume 40, Issue 27, Page(s) 5983–6007

Abstract: Randomized vaccine trials are used to assess vaccine efficacy (VE) and to characterize the durability of vaccine-induced protection. If efficacy is demonstrated, the treatment of placebo volunteers becomes an issue. For COVID-19 vaccine trials, there is ... ...

Abstract	Randomized vaccine trials are used to assess vaccine efficacy (VE) and to characterize the durability of vaccine-induced protection. If efficacy is demonstrated, the treatment of placebo volunteers becomes an issue. For COVID-19 vaccine trials, there is broad consensus that placebo volunteers should be offered a vaccine once efficacy has been established. This will likely lead to most placebo volunteers crossing over to the vaccine arm, thus complicating the assessment of long term durability. We show how to analyze durability following placebo crossover and demonstrate that the VE profile that would be observed in a placebo controlled trial is recoverable in a trial with placebo crossover. This result holds no matter when the crossover occurs and with no assumptions about the form of the efficacy profile. We only require that the VE profile applies to the newly vaccinated irrespective of the timing of vaccination. We develop different methods to estimate efficacy within the context of a proportional hazards regression model and explore via simulation the implications of placebo crossover for estimation of VE under different efficacy dynamics and study designs. We apply our methods to simulated COVID-19 vaccine trials with durable and waning VE and a total follow-up of 2 years.
MeSH term(s)	COVID-19 ; COVID-19 Vaccines ; Humans ; Randomized Controlled Trials as Topic ; SARS-CoV-2 ; Vaccines
Chemical Substances	COVID-19 Vaccines ; Vaccines
Language	English
Publishing date	2021-04-29
Publishing country	England
Document type	Journal Article
ZDB-ID	843037-8
ISSN	1097-0258 ; 0277-6715
ISSN (online)	1097-0258
ISSN	0277-6715
DOI	10.1002/sim.9001
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Article ; Online: A linear noise approximation for stochastic epidemic models fit to partially observed incidence counts

Fintzi, Jonathan / Wakefield, Jon / Minin, Vladimir N.

Biometrics. 2022 Dec., v. 78, no. 4 p.1530-1541

2022

Abstract: Stochastic epidemic models (SEMs) fit to incidence data are critical to elucidating outbreak dynamics, shaping response strategies, and preparing for future epidemics. SEMs typically represent counts of individuals in discrete infection states using ... ...

Abstract	Stochastic epidemic models (SEMs) fit to incidence data are critical to elucidating outbreak dynamics, shaping response strategies, and preparing for future epidemics. SEMs typically represent counts of individuals in discrete infection states using Markov jump processes (MJPs), but are computationally challenging as imperfect surveillance, lack of subject‐level information, and temporal coarseness of the data obscure the true epidemic. Analytic integration over the latent epidemic process is impossible, and integration via Markov chain Monte Carlo (MCMC) is cumbersome due to the dimensionality and discreteness of the latent state space. Simulation‐based computational approaches can address the intractability of the MJP likelihood, but are numerically fragile and prohibitively expensive for complex models. A linear noise approximation (LNA) that approximates the MJP transition density with a Gaussian density has been explored for analyzing prevalence data in large‐population settings, but requires modification for analyzing incidence counts without assuming that the data are normally distributed. We demonstrate how to reparameterize SEMs to appropriately analyze incidence data, and fold the LNA into a data augmentation MCMC framework that outperforms deterministic methods, statistically, and simulation‐based methods, computationally. Our framework is computationally robust when the model dynamics are complex and applies to a broad class of SEMs. We evaluate our method in simulations that reflect Ebola, influenza, and SARS‐CoV‐2 dynamics, and apply our method to national surveillance counts from the 2013–2015 West Africa Ebola outbreak.
Keywords	Markov chain ; Severe acute respiratory syndrome coronavirus 2 ; influenza ; monitoring ; Western Africa
Language	English
Dates of publication	2022-12
Size	p. 1530-1541.
Publishing place	John Wiley & Sons, Ltd
Document type	Article ; Online
Note	JOURNAL ARTICLE
ZDB-ID	213543-7
ISSN	0099-4987 ; 0006-341X
ISSN	0099-4987 ; 0006-341X
DOI	10.1111/biom.13538
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Web-based survey investigating cardiovascular complications in hypermobile Ehlers-Danlos syndrome after COVID-19 infection and vaccination.

Guerrerio, Anthony L / Mateja, Allyson / MacCarrick, Gretchen / Fintzi, Jonathan / Brittain, Erica / Frischmeyer-Guerrerio, Pamela A / Dietz, Harry C

PloS one

2024 Volume 19, Issue 3, Page(s) e0298272

Abstract: Background: Hypermobile Ehlers-Danlos syndrome is a heritable connective tissue disorder associated with generalized joint hypermobility but also other multisystem comorbidities, many of which may be exacerbated during a viral illness or after a ... ...

Abstract	Background: Hypermobile Ehlers-Danlos syndrome is a heritable connective tissue disorder associated with generalized joint hypermobility but also other multisystem comorbidities, many of which may be exacerbated during a viral illness or after a vaccination. We sought to determine whether individuals with hypermobile Ehlers Danlos syndrome report an increase in adverse events, including cardiovascular events, after COVID-19 illness or vaccination. Methods: A cross-sectional web-based survey was made available from November 22, 2021, through March 15, 2022. 368 respondents primarily from the United States self-reported data including diagnosis. We used a Cox proportional hazards model with time varying indicators for COVID-19 illness or vaccination in the previous 30 days. Results: We found a significantly increased rate of new abnormal heart rhythms reported in the 30 days following COVID-19 illness. No additional cardiovascular events were reported after COVID-19 illness. 2.5% of respondents with COVID-19 illness were hospitalized. We did not find a statistically significant increased rate of cardiovascular events in the 30 days following any COVID-19 vaccination dose. Post COVID-19 vaccination, 87.2% of hypermobile Ehlers-Danlos syndrome respondents endorsed an expected adverse event (EAE), and 3.1% reported an emergency department visit/hospitalization, of those who received at least one vaccine dose. Events possibly reflecting exacerbation of orthostasis/dysautonomia were common. Conclusion: Respondents did not report an increased rate of any cardiovascular events in the 30 days following COVID-19 vaccination; however, those with hypermobile Ehlers-Danlos syndrome experienced a high rate of expected adverse events after vaccination consistent with a high baseline prevalence of similar symptoms. No cardiovascular events other than new abnormal heart rhythms were reported at any point after a COVID-19 illness.
MeSH term(s)	Humans ; COVID-19/complications ; COVID-19/epidemiology ; COVID-19 Vaccines/adverse effects ; Cross-Sectional Studies ; Ehlers-Danlos Syndrome/chemically induced ; Ehlers-Danlos Syndrome/complications ; Heart Diseases/complications ; Internet ; Joint Instability/chemically induced ; Joint Instability/complications ; Surveys and Questionnaires ; Vaccination/adverse effects
Chemical Substances	COVID-19 Vaccines
Language	English
Publishing date	2024-03-21
Publishing country	United States
Document type	Journal Article
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0298272
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A linear noise approximation for stochastic epidemic models fit to partially observed incidence counts.

Fintzi, Jonathan / Wakefield, Jon / Minin, Vladimir N

ArXiv

2021

Abstract	Stochastic epidemic models (SEMs) fit to incidence data are critical to elucidating outbreak dynamics, shaping response strategies, and preparing for future epidemics. SEMs typically represent counts of individuals in discrete infection states using Markov jump processes (MJPs), but are computationally challenging as imperfect surveillance, lack of subject-level information, and temporal coarseness of the data obscure the true epidemic. Analytic integration over the latent epidemic process is impossible, and integration via Markov chain Monte Carlo (MCMC) is cumbersome due to the dimensionality and discreteness of the latent state space. Simulation-based computational approaches can address the intractability of the MJP likelihood, but are numerically fragile and prohibitively expensive for complex models. A linear noise approximation (LNA) that approximates the MJP transition density with a Gaussian density has been explored for analyzing prevalence data in large-population settings, but requires modification for analyzing incidence counts without assuming that the data are normally distributed. We demonstrate how to reparameterize SEMs to appropriately analyze incidence data, and fold the LNA into a data augmentation MCMC framework that outperforms deterministic methods, statistically, and simulation-based methods, computationally. Our framework is computationally robust when the model dynamics are complex and applies to a broad class of SEMs. We evaluate our method in simulations that reflect Ebola, influenza, and SARS-CoV-2 dynamics, and apply our method to national surveillance counts from the 2013--2015 West Africa Ebola outbreak.
Language	English
Publishing date	2021-04-27
Publishing country	United States
Document type	Preprint
ISSN	2331-8422
ISSN (online)	2331-8422
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A linear noise approximation for stochastic epidemic models fit to partially observed incidence counts.

Fintzi, Jonathan / Wakefield, Jon / Minin, Vladimir N

Biometrics

2021 Volume 78, Issue 4, Page(s) 1530–1541

Abstract	Stochastic epidemic models (SEMs) fit to incidence data are critical to elucidating outbreak dynamics, shaping response strategies, and preparing for future epidemics. SEMs typically represent counts of individuals in discrete infection states using Markov jump processes (MJPs), but are computationally challenging as imperfect surveillance, lack of subject-level information, and temporal coarseness of the data obscure the true epidemic. Analytic integration over the latent epidemic process is impossible, and integration via Markov chain Monte Carlo (MCMC) is cumbersome due to the dimensionality and discreteness of the latent state space. Simulation-based computational approaches can address the intractability of the MJP likelihood, but are numerically fragile and prohibitively expensive for complex models. A linear noise approximation (LNA) that approximates the MJP transition density with a Gaussian density has been explored for analyzing prevalence data in large-population settings, but requires modification for analyzing incidence counts without assuming that the data are normally distributed. We demonstrate how to reparameterize SEMs to appropriately analyze incidence data, and fold the LNA into a data augmentation MCMC framework that outperforms deterministic methods, statistically, and simulation-based methods, computationally. Our framework is computationally robust when the model dynamics are complex and applies to a broad class of SEMs. We evaluate our method in simulations that reflect Ebola, influenza, and SARS-CoV-2 dynamics, and apply our method to national surveillance counts from the 2013-2015 West Africa Ebola outbreak.
MeSH term(s)	Humans ; Hemorrhagic Fever, Ebola/epidemiology ; Incidence ; COVID-19/epidemiology ; SARS-CoV-2 ; Markov Chains ; Epidemics ; Monte Carlo Method ; Stochastic Processes ; Bayes Theorem
Language	English
Publishing date	2021-09-07
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	213543-7
ISSN	1541-0420 ; 0099-4987 ; 0006-341X
ISSN (online)	1541-0420
ISSN	0099-4987 ; 0006-341X
DOI	10.1111/biom.13538
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Assessing Vaccine Durability in Randomized Trials Following Placebo Crossover

Fintzi, Jonathan / Follmann, Dean

2021

Abstract: Randomized vaccine trials are used to assess vaccine efficacy and to characterize the durability of vaccine induced protection. If efficacy is demonstrated, the treatment of placebo volunteers becomes an issue. For COVID-19 vaccine trials, there is broad ...

Abstract	Randomized vaccine trials are used to assess vaccine efficacy and to characterize the durability of vaccine induced protection. If efficacy is demonstrated, the treatment of placebo volunteers becomes an issue. For COVID-19 vaccine trials, there is broad consensus that placebo volunteers should be offered a vaccine once efficacy has been established. This will likely lead to most placebo volunteers crossing over to the vaccine arm, thus complicating the assessment of long term durability. We show how to analyze durability following placebo crossover and demonstrate that the vaccine efficacy profile that would be observed in a placebo controlled trial is recoverable in a trial with placebo crossover. This result holds no matter when the crossover occurs and with no assumptions about the form of the efficacy profile. We only require that the vaccine efficacy profile applies to the newly vaccinated irrespective of the timing of vaccination. We develop different methods to estimate efficacy within the context of a proportional hazards regression model and explore via simulation the implications of placebo crossover for estimation of vaccine efficacy under different efficacy dynamics and study designs. We apply our methods to simulated COVID-19 vaccine trials with durable and waning vaccine efficacy and a total follow-up of two years.
Keywords	Statistics - Applications
Subject code	610 ; 310
Publishing date	2021-01-04
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Impact of Surge Strain and Pandemic Progression on Prognostication by an Established COVID-19-Specific Severity Score.

Yek, Christina / Wang, Jing / Fintzi, Jonathan / Mancera, Alex G / Keller, Michael B / Warner, Sarah / Kadri, Sameer S

Critical care explorations

2023 Volume 5, Issue 12, Page(s) e1021

Abstract: Importance: Many U.S. State crisis standards of care (CSC) guidelines incorporated Sequential Organ Failure Assessment (SOFA), a sepsis-related severity score, in pandemic triage algorithms. However, SOFA performed poorly in COVID-19. Although disease- ... ...

Abstract	Importance: Many U.S. State crisis standards of care (CSC) guidelines incorporated Sequential Organ Failure Assessment (SOFA), a sepsis-related severity score, in pandemic triage algorithms. However, SOFA performed poorly in COVID-19. Although disease-specific scores may perform better, their prognostic utility over time and in overcrowded care settings remains unclear. Objectives: We evaluated prognostication by the modified 4C (m4C) score, a COVID-19-specific prognosticator that demonstrated good predictive capacity early in the pandemic, as a potential tool to standardize triage across time and hospital-surge environments. Design: Retrospective observational cohort study. Setting: Two hundred eighty-one U.S. hospitals in an administrative healthcare dataset. Participants: A total of 298,379 hospitalized adults with COVID-19 were identified from March 1, 2020, to January 31, 2022. m4C scores were calculated from admission diagnosis codes, vital signs, and laboratory values. Main outcomes and measures: Hospital-surge index, a severity-weighted measure of COVID-19 caseload, was calculated for each hospital-month. Discrimination of in-hospital mortality by m4C and surge index-adjusted models was measured by area under the receiver operating characteristic curves (AUC). Calibration was assessed by training models on early pandemic waves and measuring fit (deviation from bisector) in subsequent waves. Results: From March 2020 to January 2022, 298,379 adults with COVID-19 were admitted across 281 U.S. hospitals. m4C adequately discriminated mortality in wave 1 (AUC 0.779 [95% CI, 0.769-0.789]); discrimination was lower in subsequent waves (wave 2: 0.772 [95% CI, 0.765-0.779]; wave 3: 0.746 [95% CI, 0.743-0.750]; delta: 0.707 [95% CI, 0.702-0.712]; omicron: 0.729 [95% CI, 0.721-0.738]). m4C demonstrated reduced calibration in contemporaneous waves that persisted despite periodic recalibration. Performance characteristics were similar with and without adjustment for surge. Conclusions and relevance: Mortality prediction by the m4C score remained robust to surge strain, making it attractive for when triage is most needed. However, score performance has deteriorated in recent waves. CSC guidelines relying on defined prognosticators, especially for dynamic disease processes like COVID-19, warrant frequent reappraisal to ensure appropriate resource allocation.
Language	English
Publishing date	2023-12-12
Publishing country	United States
Document type	Journal Article
ISSN	2639-8028
ISSN (online)	2639-8028
DOI	10.1097/CCE.0000000000001021
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: A linear noise approximation for stochastic epidemic models fit to partially observed incidence counts

Fintzi, Jonathan / Wakefield, Jon / Minin, Vladimir N.

2020

Abstract	Stochastic epidemic models (SEMs) fit to incidence data are critical to elucidating outbreak dynamics, shaping response strategies, and preparing for future epidemics. SEMs typically represent counts of individuals in discrete infection states using Markov jump processes (MJPs), but are computationally challenging as imperfect surveillance, lack of subject-level information, and temporal coarseness of the data obscure the true epidemic. Analytic integration over the latent epidemic process is impossible, and integration via Markov chain Monte Carlo (MCMC) is cumbersome due to the dimensionality and discreteness of the latent state space. Simulation-based computational approaches can address the intractability of the MJP likelihood, but are numerically fragile and prohibitively expensive for complex models. A linear noise approximation (LNA) that approximates the MJP transition density with a Gaussian density has been explored for analyzing prevalence data in large-population settings, but requires modification for analyzing incidence counts without assuming that the data are normally distributed. We demonstrate how to reparameterize SEMs to appropriately analyze incidence data, and fold the LNA into a data augmentation MCMC framework that outperforms deterministic methods, statistically, and simulation-based methods, computationally. Our framework is computationally robust when the model dynamics are complex and applies to a broad class of SEMs. We evaluate our method in simulations that reflect Ebola, influenza, and SARS-CoV-2 dynamics, and apply our method to national surveillance counts from the 2013--2015 West Africa Ebola outbreak. Comment: 67 pages, 22 pages of main text, the rest are appendices
Keywords	Statistics - Methodology ; Quantitative Biology - Populations and Evolution
Subject code	612
Publishing date	2020-01-14
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Asthma is associated with increased risk of intubation but not hospitalization or death in coronavirus disease 2019.

Rosenthal, Jamie A / Awan, Seemal F / Fintzi, Jonathan / Keswani, Anjeni / Ein, Daniel

Annals of allergy, asthma & immunology : official publication of the American College of Allergy, Asthma, & Immunology

2020 Volume 126, Issue 1, Page(s) 93–95

MeSH term(s)	Adult ; Aged ; Asthma/epidemiology ; Asthma/mortality ; Asthma/pathology ; Asthma/virology ; COVID-19/epidemiology ; COVID-19/mortality ; COVID-19/pathology ; COVID-19/virology ; Comorbidity ; Coronary Artery Disease/epidemiology ; Coronary Artery Disease/mortality ; Coronary Artery Disease/pathology ; Coronary Artery Disease/virology ; Diabetes Mellitus/epidemiology ; Diabetes Mellitus/mortality ; Diabetes Mellitus/pathology ; Diabetes Mellitus/virology ; Electronic Health Records ; Heart Failure/epidemiology ; Heart Failure/mortality ; Heart Failure/pathology ; Heart Failure/virology ; Humans ; Hypertension/epidemiology ; Hypertension/mortality ; Hypertension/pathology ; Hypertension/virology ; Intensive Care Units ; Intubation, Intratracheal/statistics & numerical data ; Length of Stay/statistics & numerical data ; Male ; Middle Aged ; Pandemics ; Prevalence ; Renal Insufficiency, Chronic/epidemiology ; Renal Insufficiency, Chronic/mortality ; Renal Insufficiency, Chronic/pathology ; Renal Insufficiency, Chronic/virology ; Retrospective Studies ; SARS-CoV-2/pathogenicity ; Spain/epidemiology ; Survival Analysis
Keywords	covid19
Language	English
Publishing date	2020-10-12
Publishing country	United States
Document type	Journal Article
ZDB-ID	1228189-x
ISSN	1534-4436 ; 0003-4738 ; 1081-1206
ISSN (online)	1534-4436
ISSN	0003-4738 ; 1081-1206
DOI	10.1016/j.anai.2020.10.002
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Article ; Online: Contributions of lipopolysaccharide and the type IVB secretion system to Coxiella burnetii vaccine efficacy and reactogenicity.

Long, Carrie M / Beare, Paul A / Cockrell, Diane C / Fintzi, Jonathan / Tesfamariam, Mahelat / Shaia, Carl I / Heinzen, Robert A

NPJ vaccines

2021 Volume 6, Issue 1, Page(s) 38

Abstract: Coxiella burnetii is the bacterial causative agent of the zoonosis Q fever. The current human Q fever vaccine, Q- ... ...

Abstract	Coxiella burnetii is the bacterial causative agent of the zoonosis Q fever. The current human Q fever vaccine, Q-VAX
Language	English
Publishing date	2021-03-19
Publishing country	England
Document type	Journal Article
ISSN	2059-0105
ISSN (online)	2059-0105
DOI	10.1038/s41541-021-00296-6
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