Daily Forecasting of Regional Epidemics of Coronavirus Disease with Bayesian Uncertainty Quantification, United States

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

doi:10.3201/eid2703.203364

Cited by 16 publications

(126 citation statements)

References 15 publications

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“…Model To obtain regional ℛ ! and HIT estimates, we used a compartmental model developed previously (22). We found region-specific parameterizations that allow the model to reproduce surveillance data (daily reports of new confirmed COVID-19 cases) available for each region of interest over a defined period (e.g., January 21 to June 21, 2020).…”

Section: Methodsmentioning

confidence: 99%

“…The number of social-distancing periods deemed best (i.e., to provide the most parsimonious explanation of the data) for a given time period was determined using the model selection procedure described by Lin et al (22). As in the study of Lin et al (22), the model has 14 parameters with universal fixed values (applicable to all regions). The model also has 3( + 1) + 3 parameters with region-specific adjustable values determined through Bayesian inference, where + 1 denotes the number of social-distancing periods.…”

Section: Methodsmentioning

confidence: 99%

“…We also specified the onset time of the first social-distancing period as the earliest day on which the cumulative reported case count was 200 cases or more. A full description of model parameters is given in Lin et al (22).…”

Section: Methodsmentioning

confidence: 99%

“…To infer region-specific values of adjustable model parameters (and ℛ ! and HIT estimates), we followed the Bayesian inference approach of Lin et al (22). In inferences, we used all region-relevant confirmed COVID-19 case-count data available in the GitHub repository maintained by The New York The maximum a posteriori (MAP) estimate of a parameter is the value of the parameter corresponding to the peak of its marginal posterior distribution, where probability density is highest.…”

Section: Bayesian Inferencementioning

confidence: 99%

“…Following the Bayesian inference approach of Lin et al (22), we quantified uncertainty in predicted trajectories of confirmed case counts for all 50 states, using data from January 21 to June 21, S2. These tables also provide 95% credible intervals.…”

Section: Bayesian Uncertainty Quantificationmentioning

confidence: 99%

See 4 more Smart Citations

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

Mallela

Neumann

Miller

et al. 2021

Preprint

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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_0, the expected number of secondary cases generated by an infected person in the absence of any interventions. The value of R_0 relates to a herd immunity threshold (HIT), which is given by 1-1/R_0. 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 R_0 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. R_0 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 R_0 estimates characterize 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 find that no state has achieved herd immunity as of 20-September-2021.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Bayesian Inferencementioning

confidence: 99%

Section: Bayesian Uncertainty Quantificationmentioning

confidence: 99%

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

Mallela

Neumann

Miller

et al. 2021

Preprint

Self Cite

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A Multiscale Model of COVID-19 Dynamics

Wang

Wang²,

Wang

et al. 2022

Bull Math Biol

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COVID-19, caused by the infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been a global pandemic and created unprecedented public health challenges throughout the world. Despite significant progresses in understanding the disease pathogenesis and progression, the epidemiological triad of pathogen, host, and environment remains unclear. In this paper, we develop a multiscale model to study the coupled within-host and between-host dynamics of COVID-19. The model includes multiple transmission routes (both human-to-human and environment-to-human) and connects multiple scales (both the population and individual levels). A detailed analysis on the local and global dynamics of the fast system, slow system and full system shows that rich dynamics, including both forward and backward bifurcations, emerge with the coupling of viral infection and epidemiological models. Model fitting to both virological and epidemiological data facilitates the evaluation of the influence of a few infection characteristics and antiviral treatment on the spread of the disease. Our work underlines the potential role that the environment can play in the transmission of COVID-19. Antiviral treatment of infected individuals can delay but cannot prevent the emergence of disease outbreaks. These results highlight the implementation of comprehensive intervention measures such as social distancing and wearing masks that aim to stop airborne transmission, combined with surface disinfection and hand hygiene that can prevent environmental transmission. The model also provides a multiscale modeling framework to study other infectious diseases when the environment can serve as a reservoir of pathogens.

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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,

Chen,

Lin

et al. 2024

Bull Math Biol

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Daily Forecasting of Regional Epidemics of Coronavirus Disease with Bayesian Uncertainty Quantification, United States

Cited by 16 publications

References 15 publications

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

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

A Multiscale Model of COVID-19 Dynamics

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

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