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A time-delayed model for the spread of COVID-19 with vaccination.

ABSTRACT: A mathematical model is presented in this paper to investigate the effects of time delay in vaccine production on COVID-19 spread. The model is analyzed qualitatively and numerically. The qualitative analysis indicates that the system variables are non-negative, bounded, and biologically meaningful. Moreover, the model has produced two equilibrium points: the free equilibrium point, which can exist without conditions, and the endemic equilibrium point, which can exist if the control reproduction number, [Formula: see text], is not less than one. In addition, the local stability of the equilibrium points is investigated and agrees with the numerical analysis results. Finally, a sensitivity analysis is conducted for [Formula: see text]. In particular, we examine the effect of the vaccine's time delay, vaccine rate, and vaccine efficiency on the model dynamics.

SUBMITTER: Al-Tuwairqi SM

PROVIDER: S-EPMC9659561 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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A time-delayed model for the spread of COVID-19 with vaccination.

Al-Tuwairqi Salma M SM Al-Harbi Sara K SK

Scientific reports 20221113 1

A mathematical model is presented in this paper to investigate the effects of time delay in vaccine production on COVID-19 spread. The model is analyzed qualitatively and numerically. The qualitative analysis indicates that the system variables are non-negative, bounded, and biologically meaningful. Moreover, the model has produced two equilibrium points: the free equilibrium point, which can exist without conditions, and the endemic equilibrium point, which can exist if the control reproduction ...[more]

PMID: 36372827

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Project description:BackgroundMathematical and statistical models are used to predict trends in epidemic spread and determine the effectiveness of control measures. Automatic regressive integrated moving average (ARIMA) models are used for time-series forecasting, but only few models of the 2019 coronavirus disease (COVID-19) pandemic have incorporated protective behaviors or vaccination, known to be effective for pandemic control.MethodsTo improve the accuracy of prediction, we applied newly developed ARIMA models with predictors (mask wearing, avoiding going out, and vaccination) to forecast weekly COVID-19 case growth rates in Canada, France, Italy, and Israel between January 2021 and March 2022. The open-source data was sourced from the YouGov survey and Our World in Data. Prediction performance was evaluated using the root mean square error (RMSE) and the corrected Akaike information criterion (AICc).ResultsA model with mask wearing and vaccination variables performed best for the pandemic period in which the Alpha and Delta viral variants were predominant (before November 2021). A model using only past case growth rates as autoregressive predictors performed best for the Omicron period (after December 2021). The models suggested that protective behaviors and vaccination are associated with the reduction of COVID-19 case growth rates, with booster vaccine coverage playing a particularly vital role during the Omicron period. For example, each unit increase in mask wearing and avoiding going out significantly reduced the case growth rate during the Alpha/Delta period in Canada (-0.81 and -0.54, respectively; both p < 0.05). In the Omicron period, each unit increase in the number of booster doses resulted in a significant reduction of the case growth rate in Canada (-0.03), Israel (-0.12), Italy (-0.02), and France (-0.03); all p < 0.05.ConclusionsThe key findings of this study are incorporating behavior and vaccination as predictors led to accurate predictions and highlighted their significant role in controlling the pandemic. These models are easily interpretable and can be embedded in a "real-time" schedule with weekly data updates. They can support timely decision making about policies to control dynamically changing epidemics.

Project description:BackgroundMitigation behaviors reduce the incidence of COVID-19 infection. Determining characteristics of groups defined by mitigation behaviors compliance may be useful to inform targeted public health policies and interventions. This study aimed to identify groups of individuals according to self-reported compliance with COVID-19 mitigation behaviors, define compliance class characteristics, and explore associations between compliance classes and important study and public health outcomes.Methods and findingsWe studied 1,410 participants in the Cabarrus County COVID-19 Prevalence and Immunity longitudinal cohort study (June 2020 to December 2021) who were asked 10 questions regarding compliance with recommended COVID-19 mitigation behaviors. By Latent Class Analysis, 1,381 participants were categorized into 3 classes (most [49.4%], moderately [45.0%], and least [5.6%] compliant). Compared with the most compliant class, the least and moderately compliant classes were younger (mean = 61.9 v. 59.0 v. 53.8 years), had fewer medical conditions per individual (1.37 v. 1.08 v. 0.77), and differed in Hispanic ethnicity (6.2% v. 2.8% v. 9.1%) and COVID-19 vaccine intention (65.8% v. 59.8% v. 35.1%). Compared to the most compliant class, the least compliant class had fewer women (54.6% v. 76.3%), fewer insured individuals (92.2% v. 97.4%), and more withdrew from study participation early (28.6% v. 16.0%). Relative to the most compliant class, the least compliant class had a higher likelihood of COVID-19 infection (OR = 2.08 [95% CI 1.13, 3.85]), lower rate of COVID-19 vaccination (72.6% v. 95.1%), and longer time to 50% COVID-19 vaccination following eligibility (8-9 vs 16 days).ConclusionsClasses defined by mitigation behaviors compliance had distinct characteristics, including age, sex, medical history, and ethnicity, and were associated with important study and public health outcomes. Targeted public health policies and interventions according to the compliance group characteristics may be of value in current and future pandemic responses to increase compliance.

Project description:BackgroundThe safety profile of COVID-19 vaccination is well documented, but hesitancy among people with immune-mediated inflammatory diseases, often immunocompromised, remains high, partially due to a scarcity of data on safety over a longer term. We herein aimed to assess delayed adverse events (DAEs) occurring >7 days after COVID-19 vaccination in systemic lupus erythematosus (SLE) versus other rheumatic autoimmune diseases (rAIDs), non-rheumatic AIDs (nrAIDs), and healthy controls (HCs).MethodsSelf-reported data were captured within the COVID-19 Vaccination in Autoimmune Diseases (COVAD)-2 online survey, which comprised >150 centres and responses from 106 countries, between February and June 2022. Logistic regression analysis adjusting for important confounders (age, sex, ethnicity) was used to compare groups.ResultsOf 7203 eligible individuals, 882 (12.2%) patients had SLE, 3161 (43.9%) patients had rAIDs, 426 (5.9%) patients had nrAIDs, and 2734 (38.0%) were HCs. SLE patients had a median age of 39 years (IQR: 31-50); 93.7% were women. SLE patients reported, more frequently, major DAEs (OR: 1.6; 95% CI: 1.2-2.0; p = 0.001) and hospitalisation (OR: 2.2; 95% CI: 1.4-3.4; p < 0.001) compared to HCs, severe rashes (OR: 2.4; 95% CI: 1.3-4.2; p = 0.004) compared to people with rAIDS, and hospitalisation (OR: 2.3; 95% CI: 1.1-4.9; p = 0.029) as well as several minor DAEs compared to people with nrAIDs. Differences were observed between vaccines in terms of frequency of major DAEs and hospitalisations, with the latter seen more frequently in patients receiving the Moderna vaccine. People with SLE with no autoimmune multimorbidity less frequently reported overall minor DAEs compared to SLE patients with comorbid nrAIDs (OR: 0.5; 95% CI: 0.3-1.0; p = 0.036).ConclusionHospitalisations post-vaccination were more frequent in SLE patients than in HCs. Monitoring of SLE patients following COVID-19 vaccination can help in identifying DAEs early, informing patients about expected DAEs, and supporting patients, especially those with autoimmune multimorbidity.

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A time-delayed model for the spread of COVID-19 with vaccination.

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A time-delayed model for the spread of COVID-19 with vaccination.

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