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Article: Quantification of early nonpharmaceutical interventions aimed at slowing transmission of Coronavirus Disease 2019 in the Navajo Nation and surrounding states (Arizona, Colorado, New Mexico, and Utah).

Miller, Ely F / Neumann, Jacob / Chen, Ye / Mallela, Abhishek / Lin, Yen Ting / Hlavacek, William S / Posner, Richard G

medRxiv : the preprint server for health sciences

2023

Abstract: During an early period of the Coronavirus Disease 2019 (COVID-19) pandemic, the Navajo Nation, much like New York City, experienced a relatively high rate of disease transmission. Yet, between January and October 2020, it experienced only a single period ...

Abstract	During an early period of the Coronavirus Disease 2019 (COVID-19) pandemic, the Navajo Nation, much like New York City, experienced a relatively high rate of disease transmission. Yet, between January and October 2020, it experienced only a single period of growth in new COVID-19 cases, which ended when cases peaked in May 2020. The daily number of new cases slowly decayed in the summer of 2020 until late September 2020. In contrast, the surrounding states of Arizona, Colorado, New Mexico, and Utah all experienced at least two periods of growth in the same time frame, with second surges beginning in late May to early June. To investigate the causes of this difference, we used a compartmental model accounting for distinct periods of non-pharmaceutical interventions (NPIs
Language	English
Publishing date	2023-02-16
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.02.15.23285971
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Quantification of early nonpharmaceutical interventions aimed at slowing transmission of Coronavirus Disease 2019 in the Navajo Nation and surrounding states (Arizona, Colorado, New Mexico, and Utah).

Miller, Ely F / Neumann, Jacob / Chen, Ye / Mallela, Abhishek / Lin, Yen Ting / Hlavacek, William S / Posner, Richard G

PLOS global public health

2023 Volume 3, Issue 6, Page(s) e0001490

Abstract	During an early period of the Coronavirus Disease 2019 (COVID-19) pandemic, the Navajo Nation, much like New York City, experienced a relatively high rate of disease transmission. Yet, between January and October 2020, it experienced only a single period of growth in new COVID-19 cases, which ended when cases peaked in May 2020. The daily number of new cases slowly decayed in the summer of 2020 until late September 2020. In contrast, the surrounding states of Arizona, Colorado, New Mexico, and Utah all experienced at least two periods of growth in the same time frame, with second surges beginning in late May to early June. Here, we investigated these differences in disease transmission dynamics with the objective of quantifying the contributions of non-pharmaceutical interventions (NPIs) (e.g., behaviors that limit disease transmission). We considered a compartmental model accounting for distinct periods of NPIs to analyze the epidemic in each of the five regions. We used Bayesian inference to estimate region-specific model parameters from regional surveillance data (daily reports of new COVID-19 cases) and to quantify uncertainty in parameter estimates and model predictions. Our results suggest that NPIs in the Navajo Nation were sustained over the period of interest, whereas in the surrounding states, NPIs were relaxed, which allowed for subsequent surges in cases. Our region-specific model parameterizations allow us to quantify the impacts of NPIs on disease incidence in the regions of interest.
Language	English
Publishing date	2023-06-21
Publishing country	United States
Document type	Journal Article
ISSN	2767-3375
ISSN (online)	2767-3375
DOI	10.1371/journal.pgph.0001490
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Impacts of Vaccination and Severe Acute Respiratory Syndrome Coronavirus 2 Variants Alpha and Delta on Coronavirus Disease 2019 Transmission Dynamics in Four Metropolitan Areas of the United States.

Mallela, Abhishek / Chen, Ye / Lin, Yen Ting / Miller, Ely F / Neumann, Jacob / He, Zhili / Nelson, Kathryn E / Posner, Richard G / Hlavacek, William S

Bulletin of mathematical biology

2024 Volume 86, Issue 3, Page(s) 31

Abstract: To characterize Coronavirus Disease 2019 (COVID-19) transmission dynamics in each of the metropolitan statistical areas (MSAs) surrounding Dallas, Houston, New York City, and Phoenix in 2020 and 2021, we extended a previously reported compartmental model ...

Abstract	To characterize Coronavirus Disease 2019 (COVID-19) transmission dynamics in each of the metropolitan statistical areas (MSAs) surrounding Dallas, Houston, New York City, and Phoenix in 2020 and 2021, we extended a previously reported compartmental model accounting for effects of multiple distinct periods of non-pharmaceutical interventions by adding consideration of vaccination and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants Alpha (lineage B.1.1.7) and Delta (lineage B.1.617.2). For each MSA, we found region-specific parameterizations of the model using daily reports of new COVID-19 cases available from January 21, 2020 to October 31, 2021. In the process, we obtained estimates of the relative infectiousness of Alpha and Delta as well as their takeoff times in each MSA (the times at which sustained transmission began). The estimated infectiousness of Alpha ranged from 1.1x to 1.4x that of viral strains circulating in 2020 and early 2021. The estimated relative infectiousness of Delta was higher in all cases, ranging from 1.6x to 2.1x. The estimated Alpha takeoff times ranged from February 1 to February 28, 2021. The estimated Delta takeoff times ranged from June 2 to June 26, 2021. Estimated takeoff times are consistent with genomic surveillance data.
MeSH term(s)	United States/epidemiology ; Humans ; SARS-CoV-2/genetics ; COVID-19/epidemiology ; COVID-19/prevention & control ; Mathematical Concepts ; Models, Biological ; Vaccination
Language	English
Publishing date	2024-02-14
Publishing country	United States
Document type	Journal Article
ZDB-ID	184905-0
ISSN	1522-9602 ; 0007-4985 ; 0092-8240
ISSN (online)	1522-9602
ISSN	0007-4985 ; 0092-8240
DOI	10.1007/s11538-024-01258-4
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Article: Impacts of vaccination and Severe Acute Respiratory Syndrome Coronavirus 2 variants Alpha and Delta on Coronavirus Disease 2019 transmission dynamics in four metropolitan areas of the United States.

Mallela, Abhishek / Chen, Ye / Lin, Yen Ting / Miller, Ely F / Neumann, Jacob / He, Zhili / Nelson, Kathryn E / Posner, Richard G / Hlavacek, William S

medRxiv : the preprint server for health sciences

2024

Abstract	To characterize Coronavirus Disease 2019 (COVID-19) transmission dynamics in each of the metropolitan statistical areas (MSAs) surrounding Dallas, Houston, New York City, and Phoenix in 2020 and 2021, we extended a previously reported compartmental model accounting for effects of multiple distinct periods of non-pharmaceutical interventions by adding consideration of vaccination and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants Alpha (lineage B.1.1.7) and Delta (lineage B.1.617.2). For each MSA, we found region-specific parameterizations of the model using daily reports of new COVID-19 cases available from January 21, 2020 to October 31, 2021. In the process, we obtained estimates of the relative infectiousness of Alpha and Delta as well as their takeoff times in each MSA (the times at which sustained transmission began). The estimated infectiousness of Alpha ranged from 1.1x to 1.4x that of viral strains circulating in 2020 and early 2021. The estimated relative infectiousness of Delta was higher in all cases, ranging from 1.6x to 2.1x. The estimated Alpha takeoff times ranged from February 1 to February 28, 2021. The estimated Delta takeoff times ranged from June 2 to June 26, 2021. Estimated takeoff times are consistent with genomic surveillance data. One-sentence summary: Using a compartmental model parameterized to reproduce available reports of new Coronavirus Disease 2019 (COVID-19) cases, we quantified the impacts of vaccination and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants Alpha (lineage B.1.1.7) and Delta (lineage B.1.617.2) on regional epidemics in the metropolitan statistical areas (MSAs) surrounding Dallas, Houston, New York City, and Phoenix.
Language	English
Publishing date	2024-01-08
Publishing country	United States
Document type	Preprint
DOI	10.1101/2021.10.19.21265223
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Bayesian Inference of State-Level COVID-19 Basic Reproduction Numbers across the United States

Mallela, Abhishek / Neumann, Jacob / Miller, Ely F. / Chen, Ye / Posner, Richard G. / Lin, Yen Ting / Hlavacek, William S.

Viruses. 2022 Jan. 15, v. 14, no. 1

2022

Abstract: Although many persons in the United States have acquired immunity to COVID-19, either through vaccination or infection with SARS-CoV-2, COVID-19 will pose an ongoing threat to non-immune persons so long as disease transmission continues. We can estimate ... ...

Abstract	Although many persons in the United States have acquired immunity to COVID-19, either through vaccination or infection with SARS-CoV-2, COVID-19 will pose an ongoing threat to non-immune persons so long as disease transmission continues. We can estimate when sustained disease transmission will end in a population by calculating the population-specific basic reproduction number ℛ0, the expected number of secondary cases generated by an infected person in the absence of any interventions. The value of ℛ0 relates to a herd immunity threshold (HIT), which is given by 1−1/ℛ0. When the immune fraction of a population exceeds this threshold, sustained disease transmission becomes exponentially unlikely (barring mutations allowing SARS-CoV-2 to escape immunity). Here, we report state-level ℛ0 estimates obtained using Bayesian inference. Maximum a posteriori estimates range from 7.1 for New Jersey to 2.3 for Wyoming, indicating that disease transmission varies considerably across states and that reaching herd immunity will be more difficult in some states than others. ℛ0 estimates were obtained from compartmental models via the next-generation matrix approach after each model was parameterized using regional daily confirmed case reports of COVID-19 from 21 January 2020 to 21 June 2020. Our ℛ0 estimates characterize the infectiousness of ancestral strains, but they can be used to determine HITs for a distinct, currently dominant circulating strain, such as SARS-CoV-2 variant Delta (lineage B.1.617.2), if the relative infectiousness of the strain can be ascertained. On the basis of Delta-adjusted HITs, vaccination data, and seroprevalence survey data, we found that no state had achieved herd immunity as of 20 September 2021.
Keywords	Bayesian theory ; COVID-19 infection ; Severe acute respiratory syndrome coronavirus 2 ; basic reproduction number ; herd immunity ; models ; reproduction ; seroprevalence ; surveys ; vaccination ; New Jersey ; Wyoming
Language	English
Dates of publication	2022-0115
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2516098-9
ISSN	1999-4915
ISSN	1999-4915
DOI	10.3390/v14010157
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Bayesian Inference of State-Level COVID-19 Basic Reproduction Numbers across the United States.

Mallela, Abhishek / Neumann, Jacob / Miller, Ely F / Chen, Ye / Posner, Richard G / Lin, Yen Ting / Hlavacek, William S

Viruses

2022 Volume 14, Issue 1

Abstract	Although many persons in the United States have acquired immunity to COVID-19, either through vaccination or infection with SARS-CoV-2, COVID-19 will pose an ongoing threat to non-immune persons so long as disease transmission continues. We can estimate when sustained disease transmission will end in a population by calculating the population-specific basic reproduction number ℛ0, the expected number of secondary cases generated by an infected person in the absence of any interventions. The value of ℛ0 relates to a herd immunity threshold (HIT), which is given by 1-1/ℛ0. When the immune fraction of a population exceeds this threshold, sustained disease transmission becomes exponentially unlikely (barring mutations allowing SARS-CoV-2 to escape immunity). Here, we report state-level ℛ0 estimates obtained using Bayesian inference. Maximum a posteriori estimates range from 7.1 for New Jersey to 2.3 for Wyoming, indicating that disease transmission varies considerably across states and that reaching herd immunity will be more difficult in some states than others. ℛ0 estimates were obtained from compartmental models via the next-generation matrix approach after each model was parameterized using regional daily confirmed case reports of COVID-19 from 21 January 2020 to 21 June 2020. Our ℛ0 estimates characterize the infectiousness of ancestral strains, but they can be used to determine HITs for a distinct, currently dominant circulating strain, such as SARS-CoV-2 variant Delta (lineage B.1.617.2), if the relative infectiousness of the strain can be ascertained. On the basis of Delta-adjusted HITs, vaccination data, and seroprevalence survey data, we found that no state had achieved herd immunity as of 20 September 2021.
MeSH term(s)	Basic Reproduction Number ; Bayes Theorem ; COVID-19/epidemiology ; COVID-19/immunology ; COVID-19/transmission ; Epidemics ; Epidemiological Models ; Humans ; Immunity, Herd ; SARS-CoV-2 ; Uncertainty ; United States/epidemiology
Language	English
Publishing date	2022-01-15
Publishing country	Switzerland
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2516098-9
ISSN	1999-4915 ; 1999-4915
ISSN (online)	1999-4915
ISSN	1999-4915
DOI	10.3390/v14010157
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Quantification of early nonpharmaceutical interventions 1 aimed at slowing transmission of Coronavirus Disease 2019 in the Navajo Nation and surrounding states (Arizona, Colorado, New Mexico, and Utah)

Miller, Ely F / Neumann, Jacob / Chen, Ye / Mallela, Abhishek / Lin, Yen Ting / Hlavacek, William s / Posner, richard G

medRxiv

Abstract	During an early period of the Coronavirus Disease 2019 (COVID-19) pandemic, the Navajo Nation, much like New York City, experienced a relatively high rate of disease transmission. Yet, between January and October 2020, it experienced only a single period of growth in new COVID-19 cases, which ended when cases peaked in May 2020. The daily number of new cases slowly decayed in the summer of 2020 until late September 2020. In contrast, the surrounding states of Arizona, Colorado, New Mexico, and Utah all experienced at least two periods of growth in the same time frame, with second surges beginning in late May to early June. To investigate the causes of this difference, we used a compartmental model accounting for distinct periods of non-pharmaceutical interventions (NPIs) (e.g., behaviors that limit disease transmission) to analyze the epidemic in each of the five regions. We used Bayesian inference to estimate region-specific model parameters from regional surveillance data (daily reports of new COVID-19 cases) and to quantify uncertainty in parameter estimates and model predictions. Our results suggest that NPIs in the Navajo Nation were sustained over the period of interest, whereas in the surrounding states, NPIs were relaxed, which allowed for subsequent surges in cases. Our region-specific model parameterizations allow us to quantify the impacts of NPIs on disease incidence in the regions of interest.
Keywords	covid19
Language	English
Publishing date	2023-02-16
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2023.02.15.23285971
Database	COVID19

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Article: Bayesian Inference of State-Level COVID-19 Basic Reproduction Numbers across the United States.

Mallela, Abhishek / Neumann, Jacob / Miller, Ely F / Chen, Ye / Posner, Richard G / Lin, Yen Ting / Hlavacek, William S

medRxiv : the preprint server for health sciences

2021

Abstract	Although many persons in the United States have acquired immunity to COVID-19, either through vaccination or infection with SARS-CoV-2, COVID-19 will pose an ongoing threat to non-immune persons so long as disease transmission continues. We can estimate when sustained disease transmission will end in a population by calculating the population-specific basic reproduction number ℛ Significance statement: COVID-19 will continue to threaten non-immune persons in the presence of ongoing disease transmission. We can estimate when sustained disease transmission will end by calculating the population-specific basic reproduction number ℛ
Language	English
Publishing date	2021-09-28
Publishing country	United States
Document type	Preprint
DOI	10.1101/2021.09.27.21264188
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Implementation of a practical Markov chain Monte Carlo sampling algorithm in PyBioNetFit.

Neumann, Jacob / Lin, Yen Ting / Mallela, Abhishek / Miller, Ely F / Colvin, Joshua / Duprat, Abell T / Chen, Ye / Hlavacek, William S / Posner, Richard G

Bioinformatics (Oxford, England)

2022 Volume 38, Issue 6, Page(s) 1770–1772

Abstract: Summary: Bayesian inference in biological modeling commonly relies on Markov chain Monte Carlo (MCMC) sampling of a multidimensional and non-Gaussian posterior distribution that is not analytically tractable. Here, we present the implementation of a ... ...

Abstract	Summary: Bayesian inference in biological modeling commonly relies on Markov chain Monte Carlo (MCMC) sampling of a multidimensional and non-Gaussian posterior distribution that is not analytically tractable. Here, we present the implementation of a practical MCMC method in the open-source software package PyBioNetFit (PyBNF), which is designed to support parameterization of mathematical models for biological systems. The new MCMC method, am, incorporates an adaptive move proposal distribution. For warm starts, sampling can be initiated at a specified location in parameter space and with a multivariate Gaussian proposal distribution defined initially by a specified covariance matrix. Multiple chains can be generated in parallel using a computer cluster. We demonstrate that am can be used to successfully solve real-world Bayesian inference problems, including forecasting of new Coronavirus Disease 2019 case detection with Bayesian quantification of forecast uncertainty. Availability and implementation: PyBNF version 1.1.9, the first stable release with am, is available at PyPI and can be installed using the pip package-management system on platforms that have a working installation of Python 3. PyBNF relies on libRoadRunner and BioNetGen for simulations (e.g. numerical integration of ordinary differential equations defined in SBML or BNGL files) and Dask.Distributed for task scheduling on Linux computer clusters. The Python source code can be freely downloaded/cloned from GitHub and used and modified under terms of the BSD-3 license (https://github.com/lanl/pybnf). Online documentation covering installation/usage is available (https://pybnf.readthedocs.io/en/latest/). A tutorial video is available on YouTube (https://www.youtube.com/watch?v=2aRqpqFOiS4&t=63s). Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Humans ; Markov Chains ; Bayes Theorem ; COVID-19 ; Algorithms ; Software ; Monte Carlo Method
Language	English
Publishing date	2022-01-06
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btac004
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Article: Daily Forecasting of New Cases for Regional Epidemics of Coronavirus Disease 2019 with Bayesian Uncertainty Quantification.

Lin, Yen Ting / Neumann, Jacob / Miller, Ely F / Posner, Richard G / Mallela, Abhishek / Safta, Cosmin / Ray, Jaideep / Thakur, Gautam / Chinthavali, Supriya / Hlavacek, William S

medRxiv : the preprint server for health sciences

2021

Abstract: To increase situational awareness and support evidence-based policy-making, we formulated a mathematical model for COVID-19 transmission within a regional population. This compartmental model accounts for quarantine, self-isolation, social distancing, a ... ...

Abstract	To increase situational awareness and support evidence-based policy-making, we formulated a mathematical model for COVID-19 transmission within a regional population. This compartmental model accounts for quarantine, self-isolation, social distancing, a non-exponentially distributed incubation period, asymptomatic individuals, and mild and severe forms of symptomatic disease. Using Bayesian inference, we have been calibrating region-specific models daily for consistency with new reports of confirmed cases from the 15 most populous metropolitan statistical areas in the United States and quantifying uncertainty in parameter estimates and predictions of future case reports. This online learning approach allows for early identification of new trends despite considerable variability in case reporting. Article summary line: We report models for regional COVID-19 epidemics and use of Bayesian inference to quantify uncertainty in daily predictions of expected reporting of new cases, enabling identification of new trends in surveillance data.
Keywords	covid19
Language	English
Publishing date	2021-01-19
Publishing country	United States
Document type	Preprint
DOI	10.1101/2020.07.20.20151506
Database	MEDical Literature Analysis and Retrieval System OnLINE

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