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

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

doi:10.1101/2021.10.19.21265223

Cited by 4 publications

(2 citation statements)

References 57 publications

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“…Thirdly, we enhanced only one forecasting model, the β-AR model, to assess the usefulness of incorporating Ct values. There are other publicly available forecasting models employing a compartmental model methodology 21,23,24,33-35 36 37,38 , machine learning techniques 39 30,40 , deep learning approaches 41-43, 44, 44,45 , ensemble methods 9,46,47 , statistical methods 48-51 , hybrid approaches 52,53 54 55-57 that could be enhanced to validate our approach.…”

Section: Discussionmentioning

confidence: 99%

Cross-sectional Ct distributions from qPCR tests can provide an early warning signal for the spread of COVID-19 in communities

Sharmin

Manivannan

Woo

et al. 2023

Preprint

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Background: SARS-CoV-2 PCR testing data has been widely used for COVID-19 surveillance. Existing COVID-19 forecasting models mainly rely on case counts, even though the binary PCR results provide a limited picture of the pandemic trajectory. Most forecasting models have failed to accurately predict the COVID-19 waves before they occur. Recently a model utilizing cross-sectional population cycle threshold (Ct) values obtained from PCR tests (Ct-based model) was developed to overcome the limitations of using only binary PCR results. In this study, we aimed to improve on COVID-19 forecasting models using features derived from the Ct-based model, to detect epidemic waves earlier than case-based trajectories. Methods: PCR data was collected weekly at Northeastern University (NU) between August 2020 and January 2022. The NU campus epidemic trajectories were generated from the campus incidence rates. In addition, epidemic trajectories were generated for Suffolk County, where NU is located, based on publicly available case-counts. A novel forecasting approach was developed by enhancing a recent deep learning model with Ct-based features, along with the default features of the model. For this, cross-sectional Ct values from PCR data were used to generate Ct-based epidemic trajectories, including effective reproductive rate (Rt) and incidence. The improvement in forecasting performance was compared using absolute errors and residual squared errors with respect to actual observed cases at the 7-day and 14-day forecasting horizons. The model was also tested prospectively over the period January 2022 to April 2022. Results: Rt estimates from the Ct-based model preceded NU campus and Suffolk County cases by 12 and 14 days respectively, with a three-way synched Spearman correlation of 0.57. Enhancing the forecasting models with Ct-based information significantly decreased absolute error and residual squared error compared to the original model without Ct features (p-value <0.001 for both 7 and 14-days forecasting horizons). Conclusion: Ct-based epidemic trajectories can herald an earlier signal for impending epidemic waves in the community and forecast transmission peaks. Moreover, COVID-19 forecasting models can be enhanced using these Ct features to improve their forecasting accuracy. Policy implications: We make the case that public health agencies should publish Ct values along with the binary positive/negative PCR results. Early and accurate forecasting

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Section: Discussionmentioning

confidence: 99%

Cross-sectional Ct distributions from qPCR tests can provide an early warning signal for the spread of COVID-19 in communities

Sharmin

Manivannan

Woo

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…in flexible SEIR models which can characterize arbitrary waiting-time distributions for the exposed and infected compartments. As a result, we do not need to use multiple compartments for modeling non-Markovian transitions between the E/I/R compartments as most models [2,26,27] using ordinary differential equations do.…”

Section: Epidemic Model Specificationmentioning

confidence: 99%

Nonparametric inference for the reproductive rate in generalized compartmental models

Trejo

Lin

Patrick

et al. 2022

Preprint

Self Cite

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We develop a tractable non-parametric model for the time-varying reproductive rate of infectious diseases that combines the structure of a deterministic compartmental model and a stochastic model for incidence data. We use Bayesian inference to estimate, with uncertainty, the reproductive rate of the Coronavirus 2019 outbreak in the U.S. states of California, Florida, Michigan, New Mexico, New York, and Texas from January 2020 to March 2022. We use the inferred reproductive rates to estimate the posterior distribution of the time-varying reproduction numbers for each state. Compering the time-varying reproduction numbers across the states, we identify some epidemic waves, potentially driven from changes in human behavior and virus mutations.

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Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review

Espinosa,

Mora,

Sanabria

et al. 2024

Syst Rev

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Background The interaction between modelers and policymakers is becoming more common due to the increase in computing speed seen in recent decades. The recent pandemic caused by the SARS-CoV-2 virus was no exception. Thus, this study aims to identify and assess epidemiological mathematical models of SARS-CoV-2 applied to real-world data, including immunization for coronavirus 2019 (COVID-19). Methodology PubMed, JSTOR, medRxiv, LILACS, EconLit, and other databases were searched for studies employing epidemiological mathematical models of SARS-CoV-2 applied to real-world data. We summarized the information qualitatively, and each article included was assessed for bias risk using the Joanna Briggs Institute (JBI) and PROBAST checklist tool. The PROSPERO registration number is CRD42022344542. Findings In total, 5646 articles were retrieved, of which 411 were included. Most of the information was published in 2021. The countries with the highest number of studies were the United States, Canada, China, and the United Kingdom; no studies were found in low-income countries. The SEIR model (susceptible, exposed, infectious, and recovered) was the most frequently used approach, followed by agent-based modeling. Moreover, the most commonly used software were R, Matlab, and Python, with the most recurring health outcomes being death and recovery. According to the JBI assessment, 61.4% of articles were considered to have a low risk of bias. Interpretation The utilization of mathematical models increased following the onset of the SARS-CoV-2 pandemic. Stakeholders have begun to incorporate these analytical tools more extensively into public policy, enabling the construction of various scenarios for public health. This contribution adds value to informed decision-making. Therefore, understanding their advancements, strengths, and limitations is essential.

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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

Cited by 4 publications

References 57 publications

Cross-sectional Ct distributions from qPCR tests can provide an early warning signal for the spread of COVID-19 in communities

Cross-sectional Ct distributions from qPCR tests can provide an early warning signal for the spread of COVID-19 in communities

Nonparametric inference for the reproductive rate in generalized compartmental models

Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review

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