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Article ; Online: Bayesian inference for dynamical systems.

Roda, Weston C

2020 Volume 5, Page(s) 221–232

Abstract: Bayesian inference is a common method for conducting parameter estimation for dynamical systems. Despite the prevalent use of Bayesian inference for performing parameter estimation for dynamical systems, there is a need for a formalized and detailed ... ...

Abstract	Bayesian inference is a common method for conducting parameter estimation for dynamical systems. Despite the prevalent use of Bayesian inference for performing parameter estimation for dynamical systems, there is a need for a formalized and detailed methodology. This paper presents a comprehensive methodology for dynamical system parameter estimation using Bayesian inference and it covers utilizing different distributions, Markov Chain Monte Carlo (MCMC) sampling, obtaining credible intervals for parameters, and prediction intervals for solutions. A logistic growth example is given to illustrate the methodology.
Language	English
Publishing date	2020-01-10
Publishing country	China
Document type	Journal Article
ZDB-ID	3015225-2
ISSN	2468-0427 ; 2468-2152
ISSN (online)	2468-0427
ISSN	2468-2152
DOI	10.1016/j.idm.2019.12.007
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Modeling the Effects of Latency Reversing Drugs During HIV-1 and SIV Brain Infection with Implications for the "Shock and Kill" Strategy.

Roda, Weston C / Liu, Suli / Power, Christopher / Li, Michael Y

Bulletin of mathematical biology

2021 Volume 83, Issue 4, Page(s) 39

Abstract: Combination antiretroviral therapy (cART) has greatly increased life expectancy for human immunodeficiency virus-1 (HIV-1)-infected patients. Even given the remarkable success of cART, the virus persists in many different cells and tissues. The presence ... ...

Abstract	Combination antiretroviral therapy (cART) has greatly increased life expectancy for human immunodeficiency virus-1 (HIV-1)-infected patients. Even given the remarkable success of cART, the virus persists in many different cells and tissues. The presence of viral reservoirs represents a major obstacle to HIV-1 eradication. These viral reservoirs contain latently infected long-lived cells. The "Shock and Kill" therapeutic strategy aims to reactivate latently infected cells by latency reversing agents (LRAs) and kill these reactivated cells by strategies involving the host immune system. The brain is a natural anatomical reservoir for HIV-1 infection. Brain macrophages, including microglia and perivascular macrophages, display productive HIV-1 infection. A mathematical model was used to analyze the dynamics of latently and productively infected brain macrophages during viral infection and this mathematical model enabled prediction of the effects of LRAs applied to the "Shock and Kill" strategy in the brain. The model was calibrated using reported data from simian immunodeficiency virus (SIV) studies. Our model produces the overarching observation that effective cART can suppress productively infected brain macrophages but leaves a residual latent viral reservoir in brain macrophages. In addition, our model demonstrates that there exists a parameter regime wherein the "Shock and Kill" strategy can be safe and effective for SIV infection in the brain. The results indicate that the "Shock and Kill" strategy can restrict brain viral RNA burden associated with severe neuroinflammation and can lead to the eradication of the latent reservoir of brain macrophages.
MeSH term(s)	Animals ; Antiviral Agents/therapeutic use ; Brain/virology ; HIV Infections/drug therapy ; HIV Infections/prevention & control ; HIV-1 ; Humans ; Models, Biological ; Simian Acquired Immunodeficiency Syndrome/drug therapy ; Simian Acquired Immunodeficiency Syndrome/prevention & control ; Simian Immunodeficiency Virus
Chemical Substances	Antiviral Agents
Language	English
Publishing date	2021-03-12
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	184905-0
ISSN	1522-9602 ; 0007-4985 ; 0092-8240
ISSN (online)	1522-9602
ISSN	0007-4985 ; 0092-8240
DOI	10.1007/s11538-021-00875-7
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Article ; Online: Mathematical modeling of the dynamics of COVID-19 variants of concern: Asymptotic and finite-time perspectives.

Ciupeanu, Adriana-Stefania / Varughese, Marie / Roda, Weston C / Han, Donglin / Cheng, Qun / Li, Michael Y

Infectious Disease Modelling

2022 Volume 7, Issue 4, Page(s) 581–596

Abstract: The COVID-19 pandemic has seen multiple waves, in part due to the implementation and relaxation of social distancing measures by the public health authorities around the world, and also caused by the emergence of new variants of concern (VOCs) of the ... ...

Abstract	The COVID-19 pandemic has seen multiple waves, in part due to the implementation and relaxation of social distancing measures by the public health authorities around the world, and also caused by the emergence of new variants of concern (VOCs) of the SARS-Cov-2 virus. As the COVID-19 pandemic is expected to transition into an endemic state, how to manage outbreaks caused by newly emerging VOCs has become one of the primary public health issues. Using mathematical modeling tools, we investigated the dynamics of VOCs, both in a general theoretical framework and based on observations from public health data of past COVID-19 waves, with the objective of understanding key factors that determine the dominance and coexistence of VOCs. Our results show that the transmissibility advantage of a new VOC is a main factor for it to become dominant. Additionally, our modeling study indicates that the initial number of people infected with the new VOC plays an important role in determining the size of the epidemic. Our results also support the evidence that public health measures targeting the newly emerging VOC taken in the early phase of its spread can limit the size of the epidemic caused by the new VOC (Wu et al., 2139Wu, Scarabel, Majeed, Bragazzi, & Orbinski, Wu et al., 2021).
Language	English
Publishing date	2022-09-08
Publishing country	China
Document type	Journal Article
ZDB-ID	3015225-2
ISSN	2468-0427 ; 2468-2152
ISSN (online)	2468-0427
ISSN	2468-2152
DOI	10.1016/j.idm.2022.08.004
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Article ; Online: Why is it difficult to accurately predict the COVID-19 epidemic?

Roda, Weston C / Varughese, Marie B / Han, Donglin / Li, Michael Y

Infectious Disease Modelling

2020 Volume 5, Page(s) 271–281

Abstract: Since the COVID-19 outbreak in Wuhan City in December of 2019, numerous model predictions on the COVID-19 epidemics in Wuhan and other parts of China have been reported. These model predictions have shown a wide range of variations. In our study, we ... ...

Abstract	Since the COVID-19 outbreak in Wuhan City in December of 2019, numerous model predictions on the COVID-19 epidemics in Wuhan and other parts of China have been reported. These model predictions have shown a wide range of variations. In our study, we demonstrate that nonidentifiability in model calibrations using the confirmed-case data is the main reason for such wide variations. Using the Akaike Information Criterion (AIC) for model selection, we show that an SIR model performs much better than an SEIR model in representing the information contained in the confirmed-case data. This indicates that predictions using more complex models may not be more reliable compared to using a simpler model. We present our model predictions for the COVID-19 epidemic in Wuhan after the lockdown and quarantine of the city on January 23, 2020. We also report our results of modeling the impacts of the strict quarantine measures undertaken in the city after February 7 on the time course of the epidemic, and modeling the potential of a second outbreak after the return-to-work in the city.
Keywords	covid19
Language	English
Publishing date	2020-03-25
Publishing country	China
Document type	Journal Article
ZDB-ID	3015225-2
ISSN	2468-0427 ; 2468-2152
ISSN (online)	2468-0427
ISSN	2468-2152
DOI	10.1016/j.idm.2020.03.001
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Article ; Online: Why is it difficult to accurately predict the COVID-19 epidemic?

Roda, Weston C. / Varughese, Marie B. / Han, Donglin / Li, Michael Y.

Infectious Disease Modelling

2020 Volume 5, Page(s) 271–281

Keywords	covid19
Language	English
Publisher	Elsevier BV
Publishing country	us
Document type	Article ; Online
ZDB-ID	3015225-2
ISSN	2468-0427 ; 2468-2152
ISSN (online)	2468-0427
ISSN	2468-2152
DOI	10.1016/j.idm.2020.03.001
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Article ; Online: Why is it difficult to accurately predict the COVID-19 epidemic?

Weston C. Roda / Marie B. Varughese / Donglin Han / Michael Y. Li

Infectious Disease Modelling, Vol 5, Iss , Pp 271-

2020 Volume 281

Abstract	Since the COVID-19 outbreak in Wuhan City in December of 2019, numerous model predictions on the COVID-19 epidemics in Wuhan and other parts of China have been reported. These model predictions have shown a wide range of variations. In our study, we demonstrate that nonidentifiability in model calibrations using the confirmed-case data is the main reason for such wide variations. Using the Akaike Information Criterion (AIC) for model selection, we show that an SIR model performs much better than an SEIR model in representing the information contained in the confirmed-case data. This indicates that predictions using more complex models may not be more reliable compared to using a simpler model. We present our model predictions for the COVID-19 epidemic in Wuhan after the lockdown and quarantine of the city on January 23, 2020. We also report our results of modeling the impacts of the strict quarantine measures undertaken in the city after February 7 on the time course of the epidemic, and modeling the potential of a second outbreak after the return-to-work in the city. Keywords: COVID-19 epidemic in Wuhan, SIR and SEIR models, Bayesian inference, Model selection, Nonidentifiability, Quarantine, Peak time of epidemic
Keywords	Infectious and parasitic diseases ; RC109-216 ; covid19
Subject code	612
Language	English
Publishing date	2020-01-01T00:00:00Z
Publisher	KeAi
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Why is it difficult to accurately predict the COVID-19 epidemic?

Roda, Weston C. / Varughese, Marie B. / Han, Donglin / Li, Michael Y.

Infect. Dis. Modelling

Abstract	Since the COVID-19 outbreak in Wuhan City in December of 2019, numerous model predictions on the COVID-19 epidemics in Wuhan and other parts of China have been reported. These model predictions have shown a wide range of variations. In our study, we demonstrate that nonidentifiability in model calibrations using the confirmed-case data is the main reason for such wide variations. Using the Akaike Information Criterion (AIC) for model selection, we show that an SIR model performs much better than an SEIR model in representing the information contained in the confirmed-case data. This indicates that predictions using more complex models may not be more reliable compared to using a simpler model. We present our model predictions for the COVID-19 epidemic in Wuhan after the lockdown and quarantine of the city on January 23, 2020. We also report our results of modeling the impacts of the strict quarantine measures undertaken in the city after February 7 on the time course of the epidemic, and modeling the potential of a second outbreak after the return-to-work in the city.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #15074
Database	COVID19

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Article ; Online: Why is it difficult to accurately predict the COVID-19 epidemic?

Roda, Weston C. / Varughese, Marie B. / Han, Donglin / Li, Michael

Infectious disease modelling, 5:271-281

2020

Abstract	Since the COVID-19 outbreak in Wuhan City in December of 2019, numerous model predictions on the COVID-19 epidemics in Wuhan and other parts of China have been reported. These model predictions have shown a wide range of variations. In our study, we demonstrate that nonidentifiability in model calibrations using the confirmed-case data is the main reason for such wide variations. Using the Akaike Information Criterion (AIC) for model selection, we show that an SIR model performs much better than an SEIR model in representing the information contained in the confirmed-case data. This indicates that predictions using more complex models may not be more reliable compared to using a simpler model. We present our model predictions for the COVID-19 epidemic in Wuhan after the lockdown and quarantine of the city on January 23, 2020. We also report our results of modeling the impacts of the strict quarantine measures undertaken in the city after February 7 on the time course of the epidemic, and modeling the potential of a second outbreak after the return-to-work in the city.
Keywords	Quarantine ; COVID-19 ; Wuhan ; Model selection ; Nonidentifiability ; SIR and SEIR models ; Bayesian inference ; Peak time of epidemic ; epidemic ; covid19
Language	English
Publishing country	de
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Effectiveness of maternal influenza vaccination during pregnancy against laboratory-confirmed seasonal influenza among infants under 6 months of age in Ontario, Canada.

Fell, Deshayne B / Russell, Margaret / Fung, Stephen G / Swayze, Sarah / Chung, Hannah / Buchan, Sarah A / Roda, Weston / Smolarchuk, Christa / Wilson, Kumanan / Crowcroft, Natasha / Schwartz, Kevin L / Gubbay, Jonathan B / McGeer, Allison / Smieja, Marek / Richardson, David C / Katz, Kevin / Zahariadis, George / Campigotto, Aaron / Mubareka, Samira /

McNally, Dayre / Karnauchow, Timothy / Zelyas, Nathan / Svenson, Lawrence W / Kwong, Jeffrey C

The Journal of infectious diseases

2023

Abstract: Background: Randomized trials conducted in low- and middle-income settings demonstrated efficacy of influenza vaccination during pregnancy against influenza infection among infants <6 months of age. However, vaccine effectiveness (VE) estimates from ... ...

Abstract	Background: Randomized trials conducted in low- and middle-income settings demonstrated efficacy of influenza vaccination during pregnancy against influenza infection among infants <6 months of age. However, vaccine effectiveness (VE) estimates from settings with different population characteristics and influenza seasonality remain limited. Methods: We conducted a test-negative study in Ontario, Canada. All influenza virus tests among infants <6 months from 2010-2019 were identified and linked with health databases to ascertain information on maternal-infant dyads. VE was estimated from the odds ratio for influenza vaccination during pregnancy among cases versus controls, computed using logistic regression with adjustment for potential confounders. Results: Among 23,806 infants tested for influenza, 1,783 (7.5%) were positive and 1,708 (7.2%) were born to mothers vaccinated against influenza during pregnancy. VE against laboratory-confirmed infant influenza infection was 64% (95% confidence interval [CI]: 50%-74%). VE was similar by trimester of vaccination (1st/2nd: 66%, 40%-80%; 3rd: 63%, 46%-74%), infant age at testing (0-<2 months: 63%, 46%-75%; 2-<6 months: 64%, 36%-79%), and gestational age at birth (≥37 weeks: 64%, 50%-75%; < 37 weeks: 61%, 4%-86%). VE against influenza hospitalization was 67% (95%CI: 50%-78%). Conclusions: Influenza vaccination during pregnancy offers effective protection to infants <6 months, for whom vaccines are not currently available.
Language	English
Publishing date	2023-11-29
Publishing country	United States
Document type	Journal Article
ZDB-ID	3019-3
ISSN	1537-6613 ; 0022-1899
ISSN (online)	1537-6613
ISSN	0022-1899
DOI	10.1093/infdis/jiad539
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Article ; Online: Modeling brain lentiviral infections during antiretroviral therapy in AIDS.

Roda, Weston C / Li, Michael Y / Akinwumi, Michael S / Asahchop, Eugene L / Gelman, Benjamin B / Witwer, Kenneth W / Power, Christopher

Journal of neurovirology

2017 Volume 23, Issue 4, Page(s) 577–586

Abstract: Understanding HIV-1 replication and latency in different reservoirs is an ongoing challenge in the care of patients with HIV/AIDS. A mathematical model was created to describe and predict the viral dynamics of HIV-1 and SIV infection within the brain ... ...

Abstract	Understanding HIV-1 replication and latency in different reservoirs is an ongoing challenge in the care of patients with HIV/AIDS. A mathematical model was created to describe and predict the viral dynamics of HIV-1 and SIV infection within the brain during effective combination antiretroviral therapy (cART). The mathematical model was formulated based on the biology of lentiviral infection of brain macrophages and used to describe the dynamics of transmission and progression of lentiviral infection in brain. Based on previous reports quantifying total viral DNA levels in brain from HIV-1 and SIV infections, estimates of integrated proviral DNA burden were made, which were used to calibrate the mathematical model predicting viral accrual in brain macrophages from primary infection. The annual rate at which susceptible brain macrophages become HIV-1 infected was estimated to be 2.90×10
MeSH term(s)	Animals ; Anti-HIV Agents/pharmacology ; Antiretroviral Therapy, Highly Active ; Brain/drug effects ; Brain/virology ; Disease Eradication/statistics & numerical data ; HIV Infections/drug therapy ; HIV Infections/virology ; HIV-1/drug effects ; HIV-1/growth & development ; Humans ; Macaca mulatta ; Models, Statistical ; Simian Acquired Immunodeficiency Syndrome/drug therapy ; Simian Acquired Immunodeficiency Syndrome/virology ; Simian Immunodeficiency Virus/drug effects ; Simian Immunodeficiency Virus/growth & development ; Viral Load/drug effects
Chemical Substances	Anti-HIV Agents
Language	English
Publishing date	2017-05-16
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	1283265-0
ISSN	1538-2443 ; 1355-0284
ISSN (online)	1538-2443
ISSN	1355-0284
DOI	10.1007/s13365-017-0530-3
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