Symptomatic Classes Research Articles

Monitoring Eastern White Pine Health by Using Field-Measured Foliar Traits and Hyperspectral Data.

Canopy foliar traits serve as crucial indicators of plant health and productivity, forming a vital link between plant conditions and ecosystem dynamics. In this study, the use of hyperspectral data and foliar traits for white pine needle damage (WPND) detection was investigated for the first time. Eastern White Pine (Pinus strobus L., EWP), a species of ecological and economic significance in the Northeastern USA, faces a growing threat from WPND. We used field-measured leaf traits and hyperspectral remote sensing data using parametric and non-parametric methods for WPND detection in the green stage. Results indicated that the random forest (RF) model based solely on remotely sensed spectral vegetation indices (SVIs) demonstrated the highest accuracy of nearly 87% and Kappa coefficient (K) of 0.68 for disease classification into asymptomatic and symptomatic classes. The combination of field-measured traits and remote sensing data indicated an overall accuracy of 77% with a Kappa coefficient (K) of 0.46. These findings contribute valuable insights and highlight the potential of both field-derived foliar and remote sensing data for WPND detection in EWP. With an exponential rise in forest pests and pathogens in recent years, remote sensing techniques can prove beneficial for the timely and accurate detection of disease and improved forest management practices.

A rigorous theoretical and numerical analysis of a nonlinear reaction-diffusion epidemic model pertaining dynamics of COVID-19

The spatial movement of the human population from one region to another and the existence of super-spreaders are the main factors that enhanced the disease incidence. Super-spreaders refer to the individuals having transmitting ability to multiple pathogens. In this article, an epidemic model with spatial and temporal effects is formulated to analyze the impact of some preventing measures of COVID-19. The model is developed using six nonlinear partial differential equations. The infectious individuals are sub-divided into symptomatic, asymptomatic and super-spreader classes. In this study, we focused on the rigorous qualitative analysis of the reaction-diffusion model. The fundamental mathematical properties of the proposed COVID-19 epidemic model such as boundedness, positivity, and invariant region of the problem solution are derived, which ensure the validity of the proposed model. The model equilibria and its stability analysis for both local and global cases have been presented. The normalized sensitivity analysis of the model is carried out in order to observe the crucial factors in the transmission of infection. Furthermore, an efficient numerical scheme is applied to solve the proposed model and detailed simulation are performed. Based on the graphical observation, diffusion in the context of confined public gatherings is observed to significantly inhibit the spread of infection when compared to the absence of diffusion. This is especially important in scenarios where super-spreaders may play a major role in transmission. The impact of some non-pharmaceutical interventions are illustrated graphically with and without diffusion. We believe that the present investigation will be beneficial in understanding the complex dynamics and control of COVID-19 under various non-pharmaceutical interventions.

A deterministic model of COVID-19 with differential infectivity and vaccination booster

Vaccine boosters have been recommended to mitigate the spread of the coronavirus disease 2019 (COVID-19) pandemic. A mathematical model with three vaccine doses and susceptibility is formulated. The model is calibrated using the cumulative number of hospitalized cases from Alberta, Canada. Estimated values from the fitting are used to explore the potential impact of the booster doses to mitigate the spread of COVID-19. Sensitivity analysis on initial disease transmission shows that the most sensitive parameters are the contact rate, the vaccine efficacy, the proportion of exposed individuals moving into the symptomatic and asymptomatic classes, and the recovery rate from asymptomatic infection. Simulation results support the positive population-level impact of the second and third COVID-19 vaccine boosters to reduce the number of infections and hospitalizations. Public health policy and decision-makers should continue advocating and encouraging people to get booster doses. As the end of the pandemic is in sight, there should be no complacency before it resolves.

A data-driven semi-parametric model of SARS-CoV-2 transmission in the United States.

To support decision-making and policy for managing epidemics of emerging pathogens, we present a model for inference and scenario analysis of SARS-CoV-2 transmission in the USA. The stochastic SEIR-type model includes compartments for latent, asymptomatic, detected and undetected symptomatic individuals, and hospitalized cases, and features realistic interval distributions for presymptomatic and symptomatic periods, time varying rates of case detection, diagnosis, and mortality. The model accounts for the effects on transmission of human mobility using anonymized mobility data collected from cellular devices, and of difficult to quantify environmental and behavioral factors using a latent process. The baseline transmission rate is the product of a human mobility metric obtained from data and this fitted latent process. We fit the model to incident case and death reports for each state in the USA and Washington D.C., using likelihood Maximization by Iterated particle Filtering (MIF). Observations (daily case and death reports) are modeled as arising from a negative binomial reporting process. We estimate time-varying transmission rate, parameters of a sigmoidal time-varying fraction of hospitalized cases that result in death, extra-demographic process noise, two dispersion parameters of the observation process, and the initial sizes of the latent, asymptomatic, and symptomatic classes. In a retrospective analysis covering March-December 2020, we show how mobility and transmission strength became decoupled across two distinct phases of the pandemic. The decoupling demonstrates the need for flexible, semi-parametric approaches for modeling infectious disease dynamics in real-time.

A Transmission Dynamics Model of COVID-19 With Consideration of the Vulnerability of a Population

A well documented characteristic of COVID-19 is that whereas certain infected individuals recover without ever showing symptoms, others regarded as vulnerable, usually age with comorbidities tend to succumb to more or less severe symptoms. To address pertinent issues, we formulate an $SEI_{A}I_{S}RS$ Transmission Dynamics model of COVID-19 where $I_{A}$ and $I_{S}$ respectively represent asymptomatic and symptomatic classes thus allowing the inclusion of parameters which are vulnerability sensitive. We define a vulnerability factor, $\phi$ and show that the model is globally asymptotically stable at the disease-free equilibrium when $\mathcal{R}_{0}<1$ and $\phi$ is appropriately bounded above. We also show that the model is globally asymptotically stable at the endemic equilibrium when $\mathcal{R}_{0}>1$ and $\phi$ is appropriately bounded below. Finally, we employ numerical analysis using Ghana data, to further illustrate the effect of vulnerability related parameter  values on the trajectories of key variables of the model. We thereby demonstrated that  if a dominantly young population is of sufficiently low vulnerability then $\mathcal{R}_{0}<1$, and the Transmission Dynamics exhibits global asymptotic stability at the disease-free equilibrium.

Mavacamten: a first-in-class myosin inhibitor for obstructive hypertrophic cardiomyopathy.

Mavacamten is a first-in-class, targeted, cardiac-specific myosin inhibitor approved by the US Food and Drug Administration for the treatment of adults with symptomatic New York Heart Association Classes II and III obstructive hypertrophic cardiomyopathy (oHCM). Mavacamten was developed to target the hyper-contractile phenotype, which plays a critical role in the pathophysiology of the disease. In Phase 2 and 3 clinical trials, mavacamten was well tolerated, reduced left ventricular outflow tract gradients, improved exercise capacity and symptoms, and was associated with improvements in other clinically relevant parameters, such as patient-reported outcomes and circulating biomarkers. In addition, treatment with mavacamten was associated with evidence of favourable cardiac remodelling in multi-modality imaging studies. Mavacamten substantially reduced guideline eligibility for septal reduction therapy candidates with oHCM and drug-refractory symptoms. In this article, the available efficacy and safety data from completed and ongoing clinical studies of mavacamten in patients with symptomatic oHCM are reviewed. Longer term extension studies may help address questions related to the positioning of mavacamten in current oHCM management algorithms, interactions with background therapy, as well as the potential for disease modification beyond symptomatic relief of left ventricular outflow tract obstruction.

Early predictors of chronic posttraumatic stress disorder symptom trajectories in U.S. Army soldiers deployed to the Iraq war zone.

The course of posttraumatic stress disorder (PTSD) symptoms varies among veterans of war zones, but sources of variation in long-term symptom course remain poorly understood. Modeling of symptom growth trajectories facilitates the understanding of predictors of individual outcomes over time. Although growth mixture modeling (GMM) has been applied to military populations, few studies have incorporated both predeployment and follow-up measurements over an extended time. In this prospective study, 1,087 U.S. Army soldiers with varying military occupational specialties and geographic locations were assessed before and after deployment to the Iraq war zone, with long-term follow-up assessment occurring at least 5 years after return from deployment. The primary outcome variable was the PTSD Checklist-Civilian Version summary score. GMM yielded four latent profiles, characterized as primarily asymptomatic (n = 194, 17.8%); postdeployment worsening symptoms (n = 84, 7.7%); mild symptoms (n = 320, 29.4%); and preexisting, with a chronic postdeployment elevation of symptoms (n = 489, 45.0%). Regression models comparing the primarily asymptomatic class to the symptomatic classes revealed that chronic symptom classes were associated with higher degrees of stress exposure, less predeployment social support, military reservist or veteran status at the most recent assessment, and poorer predeployment visual memory, ORs = 0.98-2.90. PTSD symptom course varies considerably over time after military deployment and is associated with potentially modifiable biopsychosocial factors that occur early in its course in addition to exposures and military status.

Modeling the Transmission Dynamics of Coronavirus Using Nonstandard Finite Difference Scheme

A nonlinear mathematical model of COVID-19 containing asymptomatic as well as symptomatic classes of infected individuals is considered and examined in the current paper. The largest eigenvalue of the next-generation matrix known as the reproductive number is obtained for the model, and serves as an epidemic indicator. To better understand the dynamic behavior of the continuous model, the unconditionally stable nonstandard finite difference (NSFD) scheme is constructed. The aim of developing the NSFD scheme for differential equations is its dynamic reliability, which means discretizing the continuous model that retains important dynamic properties such as positivity of solutions and its convergence to equilibria of the continuous model for all finite step sizes. The Schur–Cohn criterion is used to address the local stability of disease-free and endemic equilibria for the NSFD scheme; however, global stability is determined by using Lyapunov function theory. We perform numerical simulations using various values of some key parameters to see more characteristics of the state variables and to support our theoretical findings. The numerical simulations confirm that the discrete NSFD scheme maintains all the dynamic features of the continuous model.

Detection of soil-borne wheat mosaic virus using hyperspectral imaging: from lab to field scans and from hyperspectral to multispectral data

Hyperspectral imaging allows for rapid, non-destructive and objective assessments of crop health. Narrowband-hyperspectral data was used to select wavelength regions that can be exploited to identify wheat infected with soil-borne mosaic virus. First, leaf samples were scanned in the lab to investigate spectral differences between healthy and diseased leaves, including non-symptomatic and symptomatic areas within a diseased leaf. The potential of 84 commonly used vegetation indices to find infection was explored. A machine-learning approach was used to create a classification model to automatically separate pixels into symptomatic, non-symptomatic and healthy classes. The success rate of the model was 69.7% using the full spectrum. It was very encouraging that by using a subset of only four broad bands, sampled to simulate a data set from a much simpler and less costly multispectral camera, accuracy increased to 71.3%. Next, the classification models were validated on field data. Infection in the field was successfully identified using classifiers trained on the entire spectrum of the hyperspectral data acquired in a lab setting, with the best accuracy being 64.9%. Using a subset of wavelengths, simulating multispectral data, the accuracy dropped by only 3 percentage points to 61.9%. This research shows the potential of using lab scans to train classifiers to be successfully applied in the field, even when simultaneously reducing the hyperspectral data to multispectral data.

Explainable COVID-19 detection using fractal dimension and vision transformer with Grad-CAM on cough sounds

The polymerase chain reaction (PCR) test is not only time-intensive but also a contact method that puts healthcare personnel at risk. Thus, contactless and fast detection tests are more valuable. Cough sound is an important indicator of COVID-19, and in this paper, a novel explainable scheme is developed for cough sound-based COVID-19 detection. In the presented work, the cough sound is initially segmented into overlapping parts, and each segment is labeled as the input audio, which may contain other sounds. The deep Yet Another Mobile Network (YAMNet) model is considered in this work. After labeling, the segments labeled as cough are cropped and concatenated to reconstruct the pure cough sounds. Then, four fractal dimensions (FD) calculation methods are employed to acquire the FD coefficients on the cough sound with an overlapped sliding window that forms a matrix. The constructed matrixes are then used to form the fractal dimension images. Finally, a pretrained vision transformer (ViT) model is used to classify the constructed images into COVID-19, healthy and symptomatic classes. In this work, we demonstrate the performance of the ViT on cough sound-based COVID-19, and a visual explainability of the inner workings of the ViT model is shown. Three publically available cough sound datasets, namely COUGHVID, VIRUFY, and COSWARA, are used in this study. We have obtained 98.45%, 98.15%, and 97.59% accuracy for COUGHVID, VIRUFY, and COSWARA datasets, respectively. Our developed model obtained the highest performance compared to the state-of-the-art methods and is ready to be tested in real-world applications.

Exploring complex-PTSD comorbidity in trauma-exposed Northern Ireland veterans

ABSTRACT Background Complex posttraumatic stress disorder (CPTSD) describes the results of complex, prolonged, and/or inescapable trauma, and is typified by avoidance, re-experiencing, sense of threat, affect dysregulation, negative self-concept, and interpersonal disturbances. Additionally, CPTSD is highly comorbid with other common psychopathologies. Objectives A study was conducted in a trauma-exposed UK Armed Forces Veteran population resident in Northern Ireland (N = 638, NI) to determine the prevalence of CPTSD and comorbid associations. Methods Data from the Northern Ireland Veterans Health and Wellbeing Study (NIVHWS), including self-report data describing traumatic stress, depression, anxiety, and suicidality, were used in a latent class analysis to identify distinct profiles of symptomology in the sample, and in a multinomial logistic regression to identify comorbidities associated with class membership. Results Three distinct classes emerged: a low endorsement ‘baseline’ class (36%), a ‘Moderate Symptomatic’ class (27%), and a high endorsement ‘Probable CPTSD’ class (37%). Both the Moderate Symptomatic and CPTSD classes were predicted by cumulative trauma exposure. Depression was highly comorbid (OR = 23.06 in CPTSD), as was anxiety (OR = 22.05 in CPTSD) and suicidal ideation (OR = 4.32 in CPTSD), with suicidal attempt associated with the CPTSD class (OR = 2.51). Conclusions Cases of probable CPTSD were more prevalent than cases of probable posttraumatic stress disorder (PTSD) without Difficulties in Self-Organisation (DSO) symptoms in a UK Armed Forces veteran sample, were associated with repeated/cumulative trauma, and were highly comorbid across a range of psychopathologies. Findings validate previous literature on CPTSD and indicate considerable distress and thus need for support in UK Armed Forces veterans resident in NI. HIGHLIGHTS CPTSD was more prevalent than PTSD in a UK Armed Forces veteran sample. CPTSD was associated with repeated/cumulative trauma, and was highly comorbid across a range of psychopathologies. These findings validate the previous literature on CPTSD

Physiological quality of peanut seed affected by tomato spotted wilt disease

AbstractTomato spotted wilt disease (TSW) caused by tomato spotted wilt virus (TSWV) (genus Orthotospovirus) is a relevant disease in the peanut (Arachis hypogaea L.) crop. The effects of TSW in the plant are well documented. However, its effect on the seed physiology is unknown. The objective of this study was to evaluate the vigor and germination of peanut seeds from different genotypes with TSW symptoms. One cultivar and six germplasm lines related to A. ipaënsis Krapov. & W.C. Gregory and A. duranensis Krapov. & W.C. Gregory in addition to two U.S. cultivars (resistant and susceptible checks) were evaluated. Seeds were separated into symptomatic and asymptomatic classes. The mass of 100 seeds varied from 31 to 72 g among the genotypes. Germination and vigor were assessed by using a thermal gradient table (12–32 °C). Seeds that did not germinate during the test on the table were transferred to an incubator at 25 °C for three additional days to quantify total germination. Seed vigor was high across all genotypes and symptomology classes. Total germination was reduced for seeds kept at low temperature (12–16 °C) in the thermal table, regardless of genotype or symptomology. Overall, TSW symptoms did not affect germination, vigor, and seed size negatively.

How optimal allocation of limited testing capacity changes epidemic dynamics

Insufficient testing capacity has been a critical bottleneck in the worldwide fight against COVID-19. Optimizing the deployment of limited testing resources has therefore emerged as a keystone problem in pandemic response planning. Here, we use a modified SEIR model to optimize testing strategies under a constraint of limited testing capacity. We define pre-symptomatic, asymptomatic, and symptomatic infected classes, and assume that positively tested individuals are immediately moved into quarantine. We further define two types of testing. Clinical testing focuses only on the symptomatic class. Non-clinical testing detects pre- and asymptomatic individuals from the general population, and a concentration parameter governs the degree to which such testing can be focused on high infection risk individuals. We then solve for the optimal mix of clinical and non-clinical testing as a function of both testing capacity and the concentration parameter. We find that purely clinical testing is optimal at very low testing capacities, supporting early guidance to ration tests for the sickest patients. Additionally, we find that a mix of clinical and non-clinical testing becomes optimal as testing capacity increases. At high but empirically observed testing capacities, a mix of clinical testing and non-clinical testing, even if extremely unfocused, becomes optimal. We further highlight the advantages of early implementation of testing programs, and of combining optimized testing with contact reduction interventions such as lockdowns, social distancing, and masking.

Predicting transitions between longitudinal classes of post-traumatic stress disorder, adjustment disorder and well-being during the COVID-19 pandemic: protocol of a latent transition model in a general Dutch sample

BackgroundA growing body of literature shows profound effects of the COVID-19 pandemic on mental health, among which increased rates of post-traumatic stress disorder (PTSD) and adjustment disorder (AD). However, current...

Stratification of carotid atheromatous plaque using interpretable deep learning methods on B-mode ultrasound images.

Carotid atherosclerosis is the major cause of ischemic stroke resulting in significant rates of mortality and disability annually. Early diagnosis of such cases is of great importance, since it enables clinicians to apply a more effective treatment strategy. This paper introduces an interpretable classification approach of carotid ultrasound images for the risk assessment and stratification of patients with carotid atheromatous plaque. To address the highly imbalanced distribution of patients between the symptomatic and asymptomatic classes (16 vs 58, respectively), an ensemble learning scheme based on a sub-sampling approach was applied along with a two-phase, cost-sensitive strategy of learning, that uses the original and a resampled data set. Convolutional Neural Networks (CNNs) were utilized for building the primary models of the ensemble. A six-layer deep CNN was used to automatically extract features from the images, followed by a classification stage of two fully connected layers. The obtained results (Area Under the ROC Curve (AUC): 73%, sensitivity: 75%, specificity: 70%) indicate that the proposed approach achieved acceptable discrimination performance. Finally, interpretability methods were applied on the model's predictions in order to reveal insights on the model's decision process as well as to enable the identification of novel image biomarkers for the stratification of patients with carotid atheromatous plaque.Clinical Relevance-The integration of interpretability methods with deep learning strategies can facilitate the identification of novel ultrasound image biomarkers for the stratification of patients with carotid atheromatous plaque.

Estimation of epidemiological parameters for COVID-19 cases using a stochastic SEIRS epidemic model with vital dynamics

We estimate and analyze the time-dependent parameters: transmission rate, symptomatic recovery rate, immunity rate, infection noise intensities, and the effective reproduction number for the United States COVID-19 cases for the period 01/22/2020-02/25/2021 using an innovative generalized method of moments estimation scheme. We assume the disease-dynamic is described by a stochastic susceptible–exposed–infected–recovered–susceptible (SEIRS) epidemic model, where the infected class is divided into the asymptomatic infected, and symptomatic infectious classes. Stochasticity appears in the model due to fluctuations in the disease’s transmission and recovery rates. The disease eradication threshold is derived from the reproduction number. The estimated parameters are used to model the disease outbreak’s possible trajectories. Our analysis reveals that current interventions are having positive effects on the transmission and recovery rates. The analysis is demonstrated using the daily United States COVID-19 infection and recovered cases for the period: 01/22/2020-02/25/2021.

A mathematical COVID-19 model considering asymptomatic and symptomatic classes with waning immunity

The spread of COVID-19 to more than 200 countries has shocked the public. Therefore, understanding the dynamics of transmission is very important. In this paper, the COVID-19 mathematical model has been formulated, analyzed, and validated using incident data from West Java Province, Indonesia. The model made considers the asymptomatic and symptomatic compartments and decreased immunity. The model is formulated in the form of a system of differential equations, where the population is divided into seven compartments, namely Susceptible Population (S0), Exposed Population (E), Asymptomatic Infection Population (IA), Symptomatic Infection Population (YS), Recovered Population (Z), Susceptible Populations previously infected (Z0), and Quarantine population (Q). The results show that there has been an outbreak of COVID-19 in West Java Province, Indonesia. This can be seen from the basic reproduction number of this model, which is 3.180126127 (R0>1). Also, the numerical simulation results show that waning immunity can increase the occurrence of outbreaks; and a period of isolation can slow down the process of spreading COVID-19. So if a strict social distancing policy is enforced like a quarantine, the outbreak will lessen.

Burnout Phenotypes Among U.S. General Surgery Residents

Although well-established metrics exist to measure workplace burnout, researchers disagree about how to categorize individuals based on assessed symptoms. Using a person-centered approach, this study identifies classes of burnout symptomatology in a large sample of general surgery residents in the United States. A survey was administered following the 2018 American Board of Surgery In-Training Examination (ABSITE) to study wellness among U.S. general surgery residents. Latent class models identified distinct classes of residents based on their responses to the emotional exhaustion and depersonalization questions of the modified abbreviated Maslach Burnout Inventory (aMBI). Classes were assigned representative names, and the characteristics of their members and residency programs were compared. The survey was completed by 7415 surgery residents from 263 residency programs nationwide (99.3% response rate). Five burnout classes were found: Burned Out (unfavorable score on all six items, 9.8% of total), Fully Engaged (favorable score on all six items, 23.1%), Fatigued (favorable on all items except frequent fatigue, 32.2%), Overextended (frequent fatigue and burnout from work, 16.7%), and Disengaged (weekly symptoms of fatigue and callousness, 18.1%). Within the more symptomatic classes (Burned Out, Overextended, and Disengaged), men manifested more depersonalization symptoms, whereas women reported more emotional exhaustion symptoms. Burned Out residents were characterized by reports of mistreatment (abuse, sexual harassment, and gender-, racial-, or pregnancy and/or childcare-based discrimination), duty hour violations, dissatisfaction with duty hour regulations or time for rest, and low ABSITE scores. Burnout is multifaceted, with complex and variable presentations. Latent class modeling categorizes general surgery residents based on their burnout symptomatology. Organizations should tailor their efforts to address the unique manifestations of each class as well as shared drivers.

A versatile web app for identifying the drivers of COVID-19 epidemics

BackgroundNo versatile web app exists that allows epidemiologists and managers around the world to comprehensively analyze the impacts of COVID-19 mitigation. The http://covid-webapp.numerusinc.com/ web app presented here fills this gap.MethodsOur web app uses a model that explicitly identifies susceptible, contact, latent, asymptomatic, symptomatic and recovered classes of individuals, and a parallel set of response classes, subject to lower pathogen-contact rates. The user inputs a CSV file of incidence and, if of interest, mortality rate data. A default set of parameters is available that can be overwritten through input or online entry, and a user-selected subset of these can be fitted to the model using maximum-likelihood estimation (MLE). Model fitting and forecasting intervals are specifiable and changes to parameters allow counterfactual and forecasting scenarios. Confidence or credible intervals can be generated using stochastic simulations, based on MLE values, or on an inputted CSV file containing Markov chain Monte Carlo (MCMC) estimates of one or more parameters.ResultsWe illustrate the use of our web app in extracting social distancing, social relaxation, surveillance or virulence switching functions (i.e., time varying drivers) from the incidence and mortality rates of COVID-19 epidemics in Israel, South Africa, and England. The Israeli outbreak exhibits four distinct phases: initial outbreak, social distancing, social relaxation, and a second wave mitigation phase. An MCMC projection of this latter phase suggests the Israeli epidemic will continue to produce into late November an average of around 1500 new case per day, unless the population practices social-relaxation measures at least 5-fold below the level in August, which itself is 4-fold below the level at the start of July. Our analysis of the relatively late South African outbreak that became the world’s fifth largest COVID-19 epidemic in July revealed that the decline through late July and early August was characterised by a social distancing driver operating at more than twice the per-capita applicable-disease-class (pc-adc) rate of the social relaxation driver. Our analysis of the relatively early English outbreak, identified a more than 2-fold improvement in surveillance over the course of the epidemic. It also identified a pc-adc social distancing rate in early August that, though nearly four times the pc-adc social relaxation rate, appeared to barely contain a second wave that would break out if social distancing was further relaxed.ConclusionOur web app provides policy makers and health officers who have no epidemiological modelling or computer coding expertise with an invaluable tool for assessing the impacts of different outbreak mitigation policies and measures. This includes an ability to generate an epidemic-suppression or curve-flattening index that measures the intensity with which behavioural responses suppress or flatten the epidemic curve in the region under consideration.

A mathematical model of Coronavirus Disease (COVID-19) containing asymptomatic and symptomatic classes

The research work in this paper attempts to describe the outbreak of Coronavirus Disease 2019 (COVID-19) with the help of a mathematical model using both the Ordinary Differential Equation (ODE) and Fractional Differential Equation. The spread of the disease has been on the increase across the globe for some time with no end in sight. The research used the data of COVID-19 cases in Nigeria for the numerical simulation which has been fitted to the model. We brought in the consideration of both asymptomatic and symptomatic infected individuals with the fact that an exposed individual is either sent to quarantine first or move to one of the infected classes with the possibility that susceptible individual can also move to quarantined class directly. It was found that the proposed model has two equilibrium points; the disease-free equilibrium point (DFE) and the endemic equilibrium point (E1). Stability analysis of the equilibrium points shows (E0) is locally asymptotically stable whenever the basic reproduction number, R0<1 and (E1) is globally asymptotically stable whenever R0>1. Sensitivity analysis of the parameters in the R0 was conducted and the profile of each state variable was also depicted using the fitted values of the parameters showing the spread of the disease. The most sensitive parameters in the R0 are the contact rate between susceptible individuals and the rate of transfer of individuals from exposed class to symptomatically infected class. Moreover, the basic reproduction number for the data is calculated as R0≈1.7031. Existence and uniqueness of solution established via the technique of fixed point theorem. Also, using the least square curve fitting method together with the fminsearch function in the MATLAB optimization toolbox, we obtain the best values for some of the unknown biological parameters involved in the proposed model. Furthermore, we solved the fractional model numerically using the Atangana-Toufik numerical scheme and presenting different forms of graphical results that can be useful in minimizing the infection.