Unraveling the dynamics of the Omicron and Delta variants of the 2019 coronavirus in the presence of vaccination, mask usage, and antiviral treatment

Ngonghala, Calistus N.; Taboe, Hémaho B.; Safdar, Salman; Gumel, Abba B.

doi:10.1016/j.apm.2022.09.017

Cited by 35 publications

(20 citation statements)

References 89 publications

(149 reference statements)

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“…We restrict our analysis (a) to the period from June 2020 to July 2021 to avoid non-stationary effects in the evolution of the pandemic due to the appearance of new virus strains, and (b) to the 25 densest counties in California where linear models are typically a better fit. As in Ngonghala et al (2022), we use cross-validation to evaluate the accuracy of our model; 6/7 of our data is used for fitting and the remaining data is for performance evaluation (i.e. 2 months of data).…”

Section: Methodsmentioning

confidence: 99%

The Generalized Elastic Net for least squares regression with network-aligned signal and correlated design

Tran¹,

Wei²,

Donnat³

2022

Preprint

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We propose a novel 1 + 2 -penalty, which we refer to as the Generalized Elastic Net, for regression problems where the feature vectors are indexed by vertices of a given graph and the true signal is believed to be smooth or piecewise constant with respect to this graph. Under the assumption of correlated Gaussian design, we derive upper bounds for the prediction and estimation errors, which are graph-dependent and consist of a parametric rate for the unpenalized portion of the regression vector and another term that depends on our network alignment assumption. We also provide a coordinate descent procedure based on the Lagrange dual objective to compute this estimator for large-scale problems. Finally, we compare our proposed estimator to existing regularized estimators on a number of real and synthetic datasets and discuss its potential limitations.

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Section: Methodsmentioning

confidence: 99%

The Generalized Elastic Net for least squares regression with network-aligned signal and correlated design

Tran¹,

Wei²,

Donnat³

2022

Preprint

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“…In the current post-pandemic period, however, top-down emergency measures have been discontinued, and the responsibility for adopting protective measures is left to individuals. Possible protective measures encompass both aspects of each individual’s social life, such as reducing social gatherings, and hygiene- or health-related practices, including mask-wearing and vaccination decisions [19]. Disease perception, i.e., the way individuals perceive how the disease might impact them, plays an important role in the adoption of such protective behaviors as evidenced by numerous studies and taken into account in psychological models (e.g.…”

Section: Introductionmentioning

confidence: 99%

Modeling the interplay between disease spread, behaviors, and disease perception with a data-driven approach

De Gaetano,

Barrat,

Paolotti

2024

Preprint

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Individuals' perceptions of disease influence their adherence to preventive measures, shaping the dynamics of disease spread. Despite extensive research on the interaction between disease spread, human behaviors, and interventions, few models have incorporated real-world behavioral data on disease perception, limiting their applicability. This study novelly integrates disease perception, represented by perceived severity, as a critical determinant of behavioral change into a data-driven compartmental model to assess its impact on disease spread. Using survey data, we explore scenarios involving a competition between a COVID-19 wave and a vaccination campaign, where individuals' behaviors vary based on their perceived severity of the disease. Results demonstrate that behavioral heterogeneities influenced by perceived severity affect epidemic dynamics, with high heterogeneity yielding contrasting effects. Longer adherence to protective measures by groups with high perceived severity provides greater protection to vulnerable individuals, while premature relaxation of behaviors by low perceived severity groups facilitates virus spread. Epidemiological curves reveal that differences in behavior among groups can eliminate a second infection peak, resulting in a higher first peak and overall more severe outcomes. The specific modeling approach for how perceived severity modulates behavior parameters does not strongly impact the model's outcomes. Sensitivity analyses confirm the robustness of our findings, emphasizing the consistent impact of behavioral heterogeneities across various scenarios. Our study underscores the importance of integrating risk perception into infectious disease transmission models and highlights the necessity of extensive data collection to enhance model accuracy and relevance.

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“…Furthermore, numerous studies have shown that math-ematical models for disease transmission that did not explicitly incorporate human behavior and heterogeneities failed to accurately capture the correct trajectory and burden of the pandemic [22, 33, 34]. For example, although some mathematical models have correctly captured the trajectory and burden of the ongoing COVID-19 pandemic [31, 35–37], numerous others that did not explicitly account for social and human behavior aspects have failed to correctly capture current and/or future course/trajectory of the pandemic (the agents-based model developed by the United Kingdom’s Scientific Advisory Group on Emergencies overestimated the burden of the SARS-CoV-2 Omicron at its peak by a factor of 20; and this discrepancy is attributed, in part, by the lack of explicit incorporation of human behavior elements into the model [38]).…”

Section: Introductionmentioning

confidence: 99%

Mathematical assessment of the role of human behavior changes on SARS-CoV-2 transmission dynamics

Pant,

Safdar,

Santillana

et al. 2024

Preprint

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The COVID-19 pandemic has not only presented a major global public health and socio-economic crisis, but has also significantly impacted human behavior towards adherence (or lack thereof) to public health intervention and mitigation measures implemented in communities worldwide. The dynamic nature of the pandemic has prompted extensive changes in individual and collective behaviors towards the pandemic. This study is based on the use of mathematical modeling approaches to assess the extent to which SARS-CoV-2 transmission dynamics is impacted by population-level changes of human behavior due to factors such as (a) the severity of transmission (such as disease-induced mortality and level of symptomatic transmission), (b) fatigue due to the implementation of mitigation interventions measures (e.g., lockdowns) over a long (extended) period of time, (c) social peer-pressure, among others. A novel behavior-epidemiology model, which takes the form of a deterministic system of nonlinear differential equations, is developed and fitted using observed cumulative SARS-CoV-2 mortality data during the first wave in the United States. Rigorous analysis of the model shows that its disease-free equilibrium is locally-asymptotically stable whenever a certain epidemiological threshold, known as the control reproduction number (denoted by R_C) is less than one, and the disease persists (i.e., causes significant outbreak or outbreaks) if the threshold exceeds one. The model fits the observed data, as well as makes a more accurate prediction of the observed daily SARS-CoV-2 mortality during the first wave (March 2020 -June 2020), in comparison to the equivalent model which does not explicitly account for changes in human behavior. Of the various metrics for human behavior changes during the pandemic considered in this study, it is shown that behavior changes due to the level of SARS-CoV-2 mortality and symptomatic transmission were more influential (while behavioral changes due to the level of fatigue to interventions in the community was of marginal impact). It is shown that an increase in the proportion of exposed individuals who become asymptomatically-infectious at the end of the exposed period (represented by a parameter r) can lead to an increase (decrease) in the control reproduction number (R_C) if the effective contact rate of asymptomatic individuals is higher (lower) than that of symptomatic individuals. The study identifies two threshold values of the parameter r that maximize the cumulative and daily SARS-CoV-2 mortality, respectively, during the first wave. Furthermore, it is shown that, as the value of the proportion r increases from 0 to 1, the rate at which susceptible non-adherent individuals change their behavior to strictly adhere to public health interventions decreases. Hence, this study suggests that, as more newly-infected individuals become asymptomatically-infectious, the level of positive behavior change, as well as disease severity, hospitalizations and disease-induced mortality in the community can be expected to significantly decrease (while new cases may rise, particularly if asymptomatic individuals have higher contact rate, in comparison to symptomatic individuals).

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Unraveling the dynamics of the Omicron and Delta variants of the 2019 coronavirus in the presence of vaccination, mask usage, and antiviral treatment

Cited by 35 publications

References 89 publications

The Generalized Elastic Net for least squares regression with network-aligned signal and correlated design

The Generalized Elastic Net for least squares regression with network-aligned signal and correlated design

Modeling the interplay between disease spread, behaviors, and disease perception with a data-driven approach

Mathematical assessment of the role of human behavior changes on SARS-CoV-2 transmission dynamics

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