Change in Normal Health Condition Due to COVID-19 Infection: Analysis by ANFIS Technique.

The COVID-19 pandemic has crippled the world population. Our present work aims to formulate a model to analyze the change in normal health conditions due to COVID-19 infection. For this purpose, we have collected data of seven parameters, namely, age, systolic pressure (SP), diastolic paper (DP), respiratory distress (RD), fasting blood sugar (FBS), cholesterol (CHL), and insomnia (INS) of 156 persons of Birnagar municipality, Nadia, India; before and after COVID-19 infection. Ultimately, using an adaptive neuro-fuzzy inference system (ANFIS), we have formulated our desired model, a Takagi-Sugeno fuzzy inference system. Further, with the help of this model, we have established one's change in health condition with age due to COVID-19 infection. Finally, we have derived that older people are more affected by COVID-19 infection than younger people.

An ANFIS model-based approach to investigate the effect of lockdown due to COVID-19 on public health.

During the first and second quarters of the year 2020, most of the countries had implemented complete or partial lockdown policies to slow down the transmission of the COVID-19. To cultivate the effect of lockdown due to COVID-19 on public health, we have collected the data of six primary parameters, namely systolic blood pressure, diastolic blood pressure, fasting blood sugar, insomnia, cholesterol, and respiratory distress of 200 randomly chosen people from a municipality region of West Bengal, India before and after lockdown. With the help of these data and Adaptive Neuro-Fuzzy Inference System (ANFIS), we have formulated a model that has established that lockdown due to COVID-19 has negligible impacts on the individuals with better health condition but has significant effects on the health conditions to those populations who have poor health.

Forecasting the Pandemic COVID-19 in India: A Mathematical Approach

Due to the unavailability of proper antiviral therapies and high disease transmission rates, the pandemic COVID-19 is still increasing at a high rate in many countries. In each of the three countries, the USA, Brazil, and India, the COVID-19 positive cases have been crossed one million. With high population density and higher percentages of migrating workers has enabled India to be vulnerable to the disease quite more than other affected countries. In this paper, we have proposed a mathematical model with the help of a system of first-order ordinary differential equations and analyzed the model in the context of the COVID-19 pandemic. We have determined the expression of the basic reproduction number and relates it to establishing the disease-free equilibrium point’s asymptotic stability and endemic equilibrium point. As it has been observed that only ten states and union territories are carrying more than 70% infection in India, we have predicted long-term scenarios of the COVID-19 positive cases on those 10 states and India until the end of the year 2020 © 2022 L&H Scientific Publishing, LLC. All rights reserved

Modelling the risk of COVID-19 based on major clinical factors: A fuzzy rule approach

In this article, a Mamdani type fuzzy inference system is formulated in order to identify possible COVID-19 infected individuals based on three major clinical factors namely body-temperature, body-immunity level and vaccination efficacy. Measurements of the system's input and output parameters are considered as linguistic variable and assumed to follow trapezoidal type membership functions. The system based on total 27 fuzzy If-Then rules and called as Fuzzy Inference System (FIS) of Mamdani type. The system is analyzed using the Fuzzy Logic Toolbox of MATLAB. It has been found that with highly efficient vaccine a person with low body-immunity can escape the disease. On contrary, high body-temperature with high body-immunity power is not sufficient to exclude a person from the risk of having COVID-19.

Transfer learning based COVID-19 detection Using Radiological Images

Artificial Intelligence is a process that enables machines to imitate human behaviour. Both Machine Learning and Deep Learning are subsets of AI. The basic difference between ML(Machine Learning) and DL(Deep Learning) is that in Machine Learning manually defining of features is done to get the desired outcome whereas in deep learning the neural network learns of its own and publishes the result. In the present crisis due to COVID-19 pandemic the contagious power of virus has led to huge encounter of cases on daily basis. This stimulates the need for specialised and accurate methods to detect COVID-19 cases. The contribution of deep learning to this problem has been significant. The application of deep learning concepts has shown its emence importance and utility in medical domain for detection of COVID-19 cases using CT scan and X-Ray images of lungs. Our proposed method compares the accuracy of multiple pretrained models in predicting COVID-19 infected cases for a specific dataset of radiological images using three distinct optimizers for each model. This research aims to determine which model, together with its associated optimizer, is most suitable for identifying COVID-19 infected cases from radiological lungs images. © 2021 IEEE.

Complex dynamics of a fractional-order SIR system in the context of COVID-19

This paper proposes and analyses a new fractional-order SIR type epidemic model with a saturated treatment function. The detailed dynamics of the corresponding system, including the equilibrium points and their existence and uniqueness, uniform-boundedness, and stability of the solutions are studied. The threshold parameter, basic reproduction number of the system which determines the disease dynamics is derived, and the condition of occurrence of backward bifurcation is also determined. Some numerical works are conducted to validate our analytical results for the commensurate fractional-order system. Hopf bifurcations for the fractional-order system are studied by taking the order of the fractional differential as a bifurcation parameter.

Complex dynamics of a fractional-order SIR system in the context of COVID-19.

This paper proposes and analyses a new fractional-order SIR type epidemic model with a saturated treatment function. The detailed dynamics of the corresponding system, including the equilibrium points and their existence and uniqueness, uniform-boundedness, and stability of the solutions are studied. The threshold parameter, basic reproduction number of the system which determines the disease dynamics is derived, and the condition of occurrence of backward bifurcation is also determined. Some numerical works are conducted to validate our analytical results for the commensurate fractional-order system. Hopf bifurcations for the fractional-order system are studied by taking the order of the fractional differential as a bifurcation parameter.

Investigation of COVID-19 Using an Artificial Intelligence Based Approach

The infectious disease COVID-19 is a disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) which has become a pandemic. RT-PCR test is implemented in most of the countries to diagnose the disease. Prioritizing individuals for RT-PCR test is necessary in this pandemic situation. In this scenario, we have attempted to propose and analyze two models on the vulnerability of COVID-19 for a person using fuzzy inference system (FIS) based on whether or not the person has a travelling history. To formulate these two models we have used Mamdani type Fuzzy Inference System. In these two models, ‘Hygiene’, ‘Immunity’, ‘Quarantine’ and ‘Home-isolation’ are taken as the inputs and ‘Vulnerability to COVID-19’ is taken as the output variable. A thorough study of these two models using FIS is done which suggests in what percentage a person is vulnerable to COVID-19. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Model-Based Analysis to Predict and Control the Dynamics of COVID-19

COVID-19 has become one of the most severe health issues of the twenty-first century. More than 30 million people have been affected by this deadly disease and more than nine lakh people have been died due to this disease worldwide. Government authorities are forced to impose lockdown in their respective countries. In a word, COVID-19 has stopped the natural flow of life in worldwide. In this situation, scientists and researchers of different fields are trying to find out appropriate mitigating measures for COVID-19. However, there is no approved pharmaceutical measures like treatment or vaccination available to the health workers. So, the governments are strengthening on the non-pharmaceutical measures like maintaining social distance, use of face mask and sanitizer and on lockdown policy. In this scenario, determining the dynamics of the disease can help governments, health workers, scientists to decide future action to be taken in order to tackle the disease. In this chapter, we have proposed and analyzed an SEAIR type fractional order epidemic model in order to investigate the transmission mechanisms of the COVID-19. We have studied the non-negativity and boundedness of the solutions, and the existence and uniqueness of the solutions of the system. The explicit expression of the most important parameter, namely the basic reproduction number is derived and the further investigations of the system are performed in terms of the basic reproduction number. Apart from studying the local dynamics of the fractional order system, we have considered the environmental fluctuations in the deterministic system. We have studied the stochastic stability of the infected equilibrium points. Finally, important results are illustrated through numerical simulation works. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Population dispersal and optimal control of an SEIR epidemic model

This paper formulates and analyses a susceptible-exposed-infected-recovered (SEIR) type epidemic model with the effect of transport-related infection between two cities in the presence of treatment control. The dispersal of populations from one city to another city has an important impact on the dynamics of disease evolution. The basic reproduction number is calculated for all the different cases of the proposed model system. It is found out that the disease-free equilibrium is disease-free if the basic reproduction is less than unity, otherwise the disease may remain in the system. In addition, the optimal control problem is constructed and solved analytically and numerically by considering the treatment control as a control variable. Further, we present a numerical simulation to confirm the analytical results. Finally, we show a comparison of the result of our predicted model with the real data of severe acute respiratory syndrome (SARS) outbreak in 2003 in Hong Kong. © 2020 Inderscience Enterprises Ltd.. All rights reserved.

COVID-Transformer: Interpretable COVID-19 Detection Using Vision Transformer for Healthcare.

In the recent pandemic, accurate and rapid testing of patients remained a critical task in the diagnosis and control of COVID-19 disease spread in the healthcare industry. Because of the sudden increase in cases, most countries have faced scarcity and a low rate of testing. Chest X-rays have been shown in the literature to be a potential source of testing for COVID-19 patients, but manually checking X-ray reports is time-consuming and error-prone. Considering these limitations and the advancements in data science, we proposed a Vision Transformer-based deep learning pipeline for COVID-19 detection from chest X-ray-based imaging. Due to the lack of large data sets, we collected data from three open-source data sets of chest X-ray images and aggregated them to form a 30 K image data set, which is the largest publicly available collection of chest X-ray images in this domain to our knowledge. Our proposed transformer model effectively differentiates COVID-19 from normal chest X-rays with an accuracy of 98% along with an AUC score of 99% in the binary classification task. It distinguishes COVID-19, normal, and pneumonia patient's X-rays with an accuracy of 92% and AUC score of 98% in the Multi-class classification task. For evaluation on our data set, we fine-tuned some of the widely used models in literature, namely, EfficientNetB0, InceptionV3, Resnet50, MobileNetV3, Xception, and DenseNet-121, as baselines. Our proposed transformer model outperformed them in terms of all metrics. In addition, a Grad-CAM based visualization is created which makes our approach interpretable by radiologists and can be used to monitor the progression of the disease in the affected lungs, assisting healthcare.

The effectiveness of contact tracing in mitigating COVID-19 outbreak: A model-based analysis in the context of India.

The ongoing pandemic situation due to COVID-19 originated from the Wuhan city, China affects the world in an unprecedented scale. Unavailability of totally effective vaccination and proper treatment regimen forces to employ a non-pharmaceutical way of disease mitigation. The world is in desperate demand of useful control intervention to combat the deadly virus. This manuscript introduces a new mathematical model that addresses two different diagnosis efforts and isolation of confirmed cases. The basic reproductive number, R 0 , is inspected, and the model's dynamical characteristics are also studied. We found that with the condition R 0 < 1 , the disease can be eliminated from the system. Further, we fit our proposed model system with cumulative confirmed cases of six Indian states, namely, Maharashtra, Tamil Nadu, Andhra Pradesh, Karnataka, Delhi and West Bengal. Sensitivity analysis carried out to scale the impact of different parameters in determining the size of the epidemic threshold of R 0 . It reveals that unidentified symptomatic cases result in an underestimation of R 0 whereas, diagnosis based on new contact made by confirmed cases can gradually reduce the size of R 0 and hence helps to mitigate the ongoing disease. An optimal control problem is framed using a control variable u ( t ) , projecting the effectiveness of diagnosis based on traced contacts made by a confirmed COVID patient. It is noticed that optimal contact tracing effort reduces R 0 effectively over time.

Modeling and control of COVID-19: A short-term forecasting in the context of India.

The coronavirus disease 2019 (COVID-19) outbreak, due to SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), originated in Wuhan, China and is now a global pandemic. The unavailability of vaccines, delays in diagnosis of the disease, and lack of proper treatment resources are the leading causes of the rapid spread of COVID-19. The world is now facing a rapid loss of human lives and socioeconomic status. As a mathematical model can provide some real pictures of the disease spread, enabling better prevention measures. In this study, we propose and analyze a mathematical model to describe the COVID-19 pandemic. We have derived the threshold parameter basic reproduction number, and a detailed sensitivity analysis of this most crucial threshold parameter has been performed to determine the most sensitive indices. Finally, the model is applied to describe COVID-19 scenarios in India, the second-largest populated country in the world, and some of its vulnerable states. We also have short-term forecasting of COVID-19, and we have observed that controlling only one model parameter can significantly reduce the disease's vulnerability.

A model based study on the dynamics of COVID-19: Prediction and control.

As there is no vaccination and proper medicine for treatment, the recent pandemic caused by COVID-19 has drawn attention to the strategies of quarantine and other governmental measures, like lockdown, media coverage on social isolation, and improvement of public hygiene, etc to control the disease. The mathematical model can help when these intervention measures are the best strategies for disease control as well as how they might affect the disease dynamics. Motivated by this, in this article, we have formulated a mathematical model introducing a quarantine class and governmental intervention measures to mitigate disease transmission. We study a thorough dynamical behavior of the model in terms of the basic reproduction number. Further, we perform the sensitivity analysis of the essential reproduction number and found that reducing the contact of exposed and susceptible humans is the most critical factor in achieving disease control. To lessen the infected individuals as well as to minimize the cost of implementing government control measures, we formulate an optimal control problem, and optimal control is determined. Finally, we forecast a short-term trend of COVID-19 for the three highly affected states, Maharashtra, Delhi, and Tamil Nadu, in India, and it suggests that the first two states need further monitoring of control measures to reduce the contact of exposed and susceptible humans.