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1.
EBioMedicine ; 77: 103878, 2022 Mar.
Article in English | MEDLINE | ID: covidwho-1693688

ABSTRACT

BACKGROUND: Prominent early features of COVID-19 include severe, often clinically silent, hypoxia and a pronounced reduction in B cells, the latter important in defence against SARS-CoV-2. This presentation resembles the phenotype of mice with VHL-deficient B cells, in which Hypoxia-Inducible Factors are constitutively active, suggesting hypoxia might drive B cell abnormalities in COVID-19. METHODS: Detailed B cell phenotyping was undertaken by flow-cytometry on longitudinal samples from patients with COVID-19 across a range of severities (NIHR Cambridge BioResource). The impact of hypoxia on the transcriptome was assessed by single-cell and whole blood RNA sequencing analysis. The direct effect of hypoxia on B cells was determined through immunisation studies in genetically modified and hypoxia-exposed mice. FINDINGS: We demonstrate the breadth of early and persistent defects in B cell subsets in moderate/severe COVID-19, including reduced marginal zone-like, memory and transitional B cells, changes also observed in B cell VHL-deficient mice. These findings were associated with hypoxia-related transcriptional changes in COVID-19 patient B cells, and similar B cell abnormalities were seen in mice kept in hypoxic conditions. INTERPRETATION: Hypoxia may contribute to the pronounced and persistent B cell pathology observed in acute COVID-19 pneumonia. Assessment of the impact of early oxygen therapy on these immune defects should be considered, as their correction could contribute to improved outcomes. FUNDING: Evelyn Trust, Addenbrooke's Charitable Trust, UKRI/NIHR, Wellcome Trust.


Subject(s)
COVID-19 , Pneumonia , Animals , Humans , Hypoxia , Mice , Oxygen , SARS-CoV-2
2.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-317349

ABSTRACT

The outbreak of COVID-19 caused by SARS-CoV-2 in Wuhan and other cities in China in 2019 has become a global pandemic as declared by World Health Organization (WHO) in the first quarter of 2020 . The delay in diagnosis, limited hospital resources and other treatment resources leads to rapid spread of COVID-19. In this article, we consider an optimal control COVID-19 transmission model and assess the impact of some control measures that can lead to the reduction of exposed and infectious individuals in the population. We investigate three control strategies for this deadly infectious disease using personal protection, treatment with early diagnosis, treatment with delay diagnosis and spraying of virus in the environment as time-dependent control functions in our dynamical model to curb the disease spread.

3.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-317346

ABSTRACT

In this paper, we present the dynamical effects of timely and delayed diagnosis on the spread of COVID-19 in Ghana, using reported data from March 12 to June 19, 2020. The estimated basic reproduction number, R_0, for the proposed model is 1.04. One of the main focus of this study is stability results and senesitity assessment of the parameters. We show both theoretically and numerically that, the disease can be eliminated when the basic reproduction number is less or equal to a unity. Furthermore, we show that the disease persist whenever R_0>1 or whenever there is a delay in the diagnoses of infected individuals in the community. To assess the most influential parameters in the basic reproduction number, we carried out global sensitivity analysis. The scatter plots and the partial rank correlation coefficient reveal that, the most positive sensitive parameter is the recruitment rate, followed by the relative transmissibility of exposed individuals;and that the most negative sensitive parameters are the proportion of the infectious with timely diagnosis, and the transition rate of self-quarantined individuals to the susceptible population. For public health benefit, our analysis suggests that, a reduction in the inflow of new individuals into the country or a reduction in the inter community inflow of individuals will reduce the basic reproduction number and thereby reduce the number of secondary infections (multiple peaks of the infection).

4.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-305841

ABSTRACT

Cost-effectiveness analysis is a mode of determining both the cost and economic health outcomes of one or more control interventions. In this work, we have formulated a non-autonomous nonlinear deterministic model to study the control of COVID-19 to unravel the cost and economic health outcomes for the autonomous nonlinear model proposed for the Kingdom of Saudi Arabia. The optimal control model captures four time-dependent control functions, thus, $u_1$-practising physical or social distancing protocols;$u_2$-practising personal hygiene by cleaning contaminated surfaces with alcohol-based detergents;$u_3$-practising proper and safety measures by exposed, asymptomatic and symptomatic infected individuals;$u_4$-fumigating schools in all levels of education, sports facilities, commercial areas and religious worship centres. We proved the existence of the proposed optimal control model. The optimality system associated with the non-autonomous epidemic model is derived using Pontryagin's maximum principle. We have performed numerical simulations to investigate extensive cost-effectiveness analysis for fourteen optimal control strategies. Comparing the control strategies, we noticed that;Strategy 1 (practising physical or social distancing protocols) is the most cost-saving and most effective control intervention in Saudi Arabia in the absence of vaccination. But, in terms of the infection averted, we saw that strategy 6, strategy 11, strategy 12, and strategy 14 are just as good in controlling COVID-19.

6.
Results Phys ; 33: 105177, 2022 Feb.
Article in English | MEDLINE | ID: covidwho-1621004

ABSTRACT

Cost-effectiveness analysis is a mode of determining both the cost and economic health outcomes of one or more control interventions. In this work, we have formulated a non-autonomous nonlinear deterministic model to study the control of COVID-19 to unravel the cost and economic health outcomes for the autonomous nonlinear model proposed for the Kingdom of Saudi Arabia. We calculated the strength number and noticed the strength number is less than zero, meaning the proposed model does not capture multiple waves, hence to capture multiple wave new compartmental model may require for the Kingdom of Saudi Arabia. We proposed an optimal control problem based on a previously studied model and proved the existence of the proposed optimal control model. The optimality system associated with the non-autonomous epidemic model is derived using Pontryagin's maximum principle. The optimal control model captures four time-dependent control functions, thus, u 1 -practising physical or social distancing protocols; u 2 -practising personal hygiene by cleaning contaminated surfaces with alcohol-based detergents; u 3 -practising proper and safety measures by exposed, asymptomatic and symptomatic infected individuals; u 4 -fumigating schools in all levels of education, sports facilities, commercial areas and religious worship centres. We have performed numerical simulations to investigate extensive cost-effectiveness analysis for fourteen optimal control strategies. Comparing the control strategies, we noticed that; Strategy 1 (practising physical or social distancing protocols) is the most cost-saving and most effective control intervention in Saudi Arabia in the absence of vaccination. But, in terms of the infection averted, we saw that strategy 6, strategy 11, strategy 12, and strategy 14 are just as good in controlling COVID-19.

7.
PLoS One ; 16(3): e0248438, 2021.
Article in English | MEDLINE | ID: covidwho-1574763

ABSTRACT

OBJECTIVES: Accurate and reliable criteria to rapidly estimate the probability of infection with the novel coronavirus-2 that causes the severe acute respiratory syndrome (SARS-CoV-2) and associated disease (COVID-19) remain an urgent unmet need, especially in emergency care. The objective was to derive and validate a clinical prediction score for SARS-CoV-2 infection that uses simple criteria widely available at the point of care. METHODS: Data came from the registry data from the national REgistry of suspected COVID-19 in EmeRgency care (RECOVER network) comprising 116 hospitals from 25 states in the US. Clinical variables and 30-day outcomes were abstracted from medical records of 19,850 emergency department (ED) patients tested for SARS-CoV-2. The criterion standard for diagnosis of SARS-CoV-2 required a positive molecular test from a swabbed sample or positive antibody testing within 30 days. The prediction score was derived from a 50% random sample (n = 9,925) using unadjusted analysis of 107 candidate variables as a screening step, followed by stepwise forward logistic regression on 72 variables. RESULTS: Multivariable regression yielded a 13-variable score, which was simplified to a 13-point score: +1 point each for age>50 years, measured temperature>37.5°C, oxygen saturation<95%, Black race, Hispanic or Latino ethnicity, household contact with known or suspected COVID-19, patient reported history of dry cough, anosmia/dysgeusia, myalgias or fever; and -1 point each for White race, no direct contact with infected person, or smoking. In the validation sample (n = 9,975), the probability from logistic regression score produced an area under the receiver operating characteristic curve of 0.80 (95% CI: 0.79-0.81), and this level of accuracy was retained across patients enrolled from the early spring to summer of 2020. In the simplified score, a score of zero produced a sensitivity of 95.6% (94.8-96.3%), specificity of 20.0% (19.0-21.0%), negative likelihood ratio of 0.22 (0.19-0.26). Increasing points on the simplified score predicted higher probability of infection (e.g., >75% probability with +5 or more points). CONCLUSION: Criteria that are available at the point of care can accurately predict the probability of SARS-CoV-2 infection. These criteria could assist with decisions about isolation and testing at high throughput checkpoints.


Subject(s)
COVID-19/diagnosis , COVID-19/epidemiology , Emergency Service, Hospital/trends , Adult , Aged , Clinical Decision Rules , Coronavirus Infections/diagnosis , Cough , Databases, Factual , Decision Trees , Emergency Service, Hospital/statistics & numerical data , Female , Fever , Humans , Male , Mass Screening , Middle Aged , Registries , SARS-CoV-2/pathogenicity , United States/epidemiology
8.
Alexandria Engineering Journal ; 2021.
Article in English | ScienceDirect | ID: covidwho-1212988

ABSTRACT

We propose a Caputo-based fractional compartmental model for the dynamics of the novel COVID-19 transmission dynamics. The newly proposed nonlinear fractional order differential equation epidemic model is an extension a recently formulated integer-order COVID-19 mathematical model. Using basic concepts such as continuity and Banach fixed-point theorem, the existence and uniqueness of the solution to the proposed model were shown. Furthermore, we analyze the stability of the model in the context of Ulam-Hyers and generalized Ulam-Hyers stability criteria. The concept of next-generation matrices was used to compute the basic reproduction number R0, a number that determines the spread or otherwise of the disease into the general population. We also investigated the local asymptotic stability for the derived disease-free equilibrium point. Numerical simulation of the constructed epidemic model was carried out using the fractional Adam-Bashforth-Moulton method to validate the obtained theoretical results.

9.
Chaos Solitons Fractals ; 146: 110885, 2021 May.
Article in English | MEDLINE | ID: covidwho-1141665

ABSTRACT

Optimal economic evaluation is pivotal in prioritising the implementation of non-pharmaceutical and pharmaceutical interventions in the control of diseases. Governments, decision-makers and policy-makers broadly need information about the effectiveness of a control intervention concerning its cost-benefit to evaluate whether a control intervention offers the best value for money. The outbreak of COVID-19 in December 2019, and the eventual spread to other parts of the world, have pushed governments and health authorities to take drastic socioeconomic, sociocultural and sociopolitical measures to curb the spread of the virus, SARS-CoV-2. To help policy-makers, health authorities and governments, we propose a Susceptible, Exposed, Asymptomatic, Quarantined asymptomatic, Severely infected, Hospitalized, Recovered, Recovered asymptomatic, Deceased, and Protective susceptible (individuals who observe health protocols) compartmental structure to describe the dynamics of COVID-19. We fit the model to real data from Ghana and Egypt to estimate model parameters using standard incidence rate. Projections for disease control and sensitivity analysis are presented using MATLAB. We noticed that multiple peaks (waves) of COVID-19 for Ghana and Egypt can be prevented if stringent health protocols are implemented for a long time and/or the reluctant behaviour on the use of protective equipment by individuals are minimized. The sensitivity analysis suggests that: the rate of diagnoses and testing, the rate of quarantine through doubling enhanced contact tracing, adhering to physical distancing, adhering to wearing of nose masks, sanitizing-washing hands, media education remains the most effective measures in reducing the control reproduction number R c , to less than unity in the absence of vaccines and therapeutic drugs in Ghana and Egypt. Optimal control and cost-effectiveness analysis are rigorously studied. The main finding is that having two controls (transmission reduction and case isolation) is better than having one control, but is economically expensive. In case only one control is affordable, then transmission reduction is better than case isolation. Hopefully, the results of this research should help policy-makers when dealing with multiple waves of COVID-19.

10.
Religions ; 11(7):346, 2020.
Article in English | ProQuest Central | ID: covidwho-963862

ABSTRACT

Waxing “biblical,” Donald Trump has described the COVID-19 pandemic as a “plague.” In a different but related register, millions of Christians worldwide have interpreted the pandemic as one of the eschatological plagues prophesied in the Book of Revelation. This article appropriates the reading tactics of Gilles Deleuze and Félix Guattari, together with the resources of affect theory, to connect the Book of Revelation with both the Trump phenomenon and the COVID-19 pandemic. Specifically, the article attempts to relate Revelation’s Beast to Trump (to unleash the Beast against Trump) non-eschatologically, in a non-representationalist reading strategy, and to analyze how Trump has manipulated the pandemic for his post-ideological ends.

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