A Novel Framework to Forecast COVID-19 Incidence Based on Google Trends Search Data

The global outbreak of coronavirus disease 2019 (COVID-19) has spread to more than 200 countries worldwide, leading to severe health and socioeconomic consequences. As such, the topic of monitoring and predicting epidemics has been attracting a lot of interest. Previous work reported search volumes from Google Trends are beneficial in decoding influenza dynamics, implying its potential for COVID-19 prediction. Therefore, a predictive model using the Wiener methods was built based on epidemic-related search queries from Google Trends, along with climate variables, aiming to forecast the dynamics of the weekly COVID-19 incidence in Washington, DC, USA. The Wiener model, which shares the merits of interpretability, low computation costs, and adaptation to nonlinear fluctuations, was used in this study. Models with multiple sets of features were constructed and further optimized by the highest weight selecting strategy. Furthermore, comparisons to the other two commonly used prediction models based on the autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) were also performed. Our results showed the predicted COVID-19 trends significantly correlated with the actual (rho <inline-formula> <tex-math notation="LaTeX">$=$</tex-math> </inline-formula> 0.88, <inline-formula> <tex-math notation="LaTeX">$p $</tex-math> </inline-formula> <inline-formula> <tex-math notation="LaTeX">$<$</tex-math> </inline-formula> 0.0001), outperforming those with ARIMA and LSTM approaches, indicating Google Trends data as a useful tool in terms of COVID-19 prediction. Also, the model using 20 search queries with the highest weighting outperformed all other models, supporting the highest weight feature selection as a feasible criterion. Google Trends search query data can be used to forecast the outbreak of COVID-19, which might assist health policymakers to allocate health care resources and taking preventive strategies. IEEE

Chemoinformatics and bioinformatics by discrete mathematics and numbers: an adventure from small data to the realm of emerging big data

Currently, we are witnessing the emergence of big data in various fields including the biomedical and natural sciences. The size of chemoinformatics and bioinformatics databases is increasing every day. This gives us both challenges and opportunities. This chapter discusses the mathematical methods used in these fields both for the generation and analysis of such data. It is emphasized that proper use of robust statistical and machine learning methods in the analysis of the available big data may facilitate both hypothesis-driven and discovery-oriented research. © 2023 Elsevier Inc. All rights reserved.

Efficient, accurate and fast pupil segmentation for pupillary boundary in iris recognition

Iris recognition is a robust biometric system-user-friendly, accurate, fast, and reliable. This biometric system captures information in a contactless manner, making it suitable for use during the COVID-19 pandemic. Despite its advantages such as high security and high accuracy, iris recognition still suffers from pupil deformation, motion blur, eyelids blocking, reflection occlusion and eyelashes obscure. If the pupillary boundary is not accurately segmented, iris recognition may suffer tremendously. Moreover, reflections in iris image may lead to an incorrect pupillary boundary segmentation. The segmentation accuracy can also be affected and reduced because of the presence of an unwanted noise created by the motion blur effect in iris image. Additionally, the pupillary boundary might change from circular shape to uneven or irregular shape because of the interference and obstruction in pupil region. Therefore, this work is carried out to determine an accurate, efficient and fast algorithm for the segmentation of pupillary boundary. First, the iris image is pre-processed with Wiener filter. Next, the respective iris image is assigned with a specific threshold. After that, the pixel property in iris image is computed to determine the pupillary boundary coordinates which are acquired from the measured pixel list and area in iris image. Finally, morphological closing is used to remove reflections in the inner region of pupil boundary. All experiments are implemented with CASIA v4 database and Matlab R2020a.

Research on Prediction Model of COVID-19 Based on Deep Learning

The accurate prediction of COVID-19 is of great significance for the prevention and control of the epidemic. Based on the current situation and demand of COVID-19 prediction research, this paper mainly analyzes the convolutional neural network (CNN) model by using the deep learning algorithm, It uses 1dcnn model and Gabor filter to build the G-ldcnn model, and introduces back propagation to update The model has the high-efficiency learning ability of CNN model and the feature extraction ability of Gabor filter at the model. The model has the high-efficiency learning ability of CNN model and the feature extraction ability of Gabor filter at the same time, improves the prediction efficiency of the model while ensuring the accuracy, and can better adapt to By comparing the prediction model proposed in this paper with the current By comparing the prediction model proposed in this paper with the current mature model, it shows that the improved and optimized model has a high accuracy. © 2022 IEEE.

Efficient, accurate and fast pupil segmentation for pupillary boundary in iris recognition

Iris recognition is a robust biometric system—user-friendly, accurate, fast, and reliable. This biometric system captures information in a contactless manner, making it suitable for use during the COVID-19 pandemic. Despite its advantages such as high security and high accuracy, iris recognition still suffers from pupil deformation, motion blur, eyelids blocking, reflection occlusion and eyelashes obscure. If the pupillary boundary is not accurately segmented, iris recognition may suffer tremendously. Moreover, reflections in iris image may lead to an incorrect pupillary boundary segmentation. The segmentation accuracy can also be affected and reduced because of the presence of an unwanted noise created by the motion blur effect in iris image. Additionally, the pupillary boundary might change from circular shape to uneven or irregular shape because of the interference and obstruction in pupil region. Therefore, this work is carried out to determine an accurate, efficient and fast algorithm for the segmentation of pupillary boundary. First, the iris image is pre-processed with Wiener filter. Next, the respective iris image is assigned with a specific threshold. After that, the pixel property in iris image is computed to determine the pupillary boundary coordinates which are acquired from the measured pixel list and area in iris image. Finally, morphological closing is used to remove reflections in the inner region of pupil boundary. All experiments are implemented with CASIA v4 database and Matlab R2020a.

Performance Analysis of Wiener filter in Restoration of Covid-19 Chest X-Ray Images, Ultrasound Images and Mammograms

Medical images such as X-Ray images, Mammograms and Ultrasound images are very useful diagnostic techniques used for understanding the functions of different internal organs, bones, tissues, etc. Most of the times these medical images are degraded by some noises and different kinds of blur. Image blurring and degradation leads to loss of quality of images which in hand causes difficulty in proper diagnosis. This paper emphases on the efficacy of Wiener filter in image de blurring and denoising Chest X-Ray of Covid-19 patients, ultrasound images of fetal abdominal cyst, umbilical cord cyst and Common Carotid Artery, Mammogram of both pathological and non-pathological breasts. Performance of Wiener filter is analyzed using image restoration parameters like Structural Similarity (SSIM), Histogram, Peak Signal to Noise Ratio and Mean Square Error. © 2022 IEEE.

Human visualization system based intensive contrast improvement of the collected COVID-19 images

Enhancement and color correction of images play an important role and can be considered as one of the fundamental and basic operations in image analysis for the purpose of speeding up the diagnosis of the medical images. Improving the quality and contrast of the medical image is the basic requirement for clinicians for obtaining an accurate and accurate medical diagnosis. Thus, getting a clear X-ray image reduces the effort and time-wasting. In this study a new idea will be applied for improving image contrast of the collected COVID-19 X-ray images, this idea is based on using Wiener filter, multilevel of histogram equalization (HE) technique with OpenCV library and then using contrast limited adaptive histogram equalization (CLAHE) techniques with OpenCV library. The proposed methodology programmed in MATLAB software and then implemented using Rasperry Pi 3 model B. The size and resolution of images are different as inputted images and this difference succeeded in proving the strength of the proposed idea. The collected X-ray images have undergone experiential evaluations which clearly showed the effective performance of the proposed methodology. © 2022 Institute of Advanced Engineering and Science. All rights reserved.

On Some Stochastic Epidemic Models

In this article, we present some stochastic non-linear epidemic models related to Covid-19. It is very hard to get exact solutions for the non-linear such models, but we have successfully obtained exact solutions of some suitable non-linear stochastic cases. The general stochastic logistic model is solved;and some properties related to functions of Wiener process are studied.

Spreading of Corona Virus in Central Europe: The Cases of Italy and Spain and the Very Beginning of Pandemic

In this paper a mathematical model that focuses at the very beginning of pandemic at Europe is presented. In essence it is assumed that once the virus arrived to Italy then the geographical propagation was done through probabilistic rules among then to Spain. Because of this the model of propagation of Feynman in conjunction to Wiener schemes have been used to model the displacement of virus from Wuhan to Milan as well from Milan to Spain, as seen at the end of 2019 triggering the beginning of European pandemic at January of 2020. As seen at official data Italy and Spain have presented same statistics at the first months of local pandemic. From the usage of the proposed formalism, it is found that the country data are following Gaussian-like distributions due to the space-time propagation of virus. © 2021 IEEE.

Optimizing Hammerstein-Wiener Model for Forecasting Confirmed Cases of Covid-19

Noise poses challenge to nonlinear Hammerstein-Wiener (HW) subsystem model application, because HW subsystem need large number of parameter interactions. However, flexibility, soft computing, and automatic adjustment to dynamic observation for best model fitting make it potential for forecasting nonlinear data. In this article, we adopted improved HW inference from Levenberg-Marquardt optimization algorithm to optimize HW subsystem and to select best model parameters. Therefore, the adopted model is tested on COVID-19 confirmed reported cases, to estimate transmission rate of COVID-19 virus for period from 15th March 2020 to 29th April 2020. Model validation is carried out on small dataset, which outperforms some existing models. The adopted model is further evaluated using statistical metrics and reported best accuracy of 0.127 and 0.998 for Mean Absolute percentage error (MAPE) and coefficient of determination (R2) respectively, with best model complexity of 1.86. The obtained results are promising enough in predicting spread of COVID-19 virus and may inspire as guidance to relax lockdown restriction policies. © 2022, IAENG International Journal of Applied Mathematics. All Rights Reserved.

Pre-study for facilitating the discovery of microfluidic properties in blood vessels using retinal fundus images

COVID-19 disease may cause alterations of microfluidic properties of blood circulation in the retinal tissue. For the pre-study of microfluidic blood physiology and transport, retinal fundus images are applied for clinical screening of abnormal vessels forms. However, fundus images captured by operators with various levels of experience have diversity in quality. Low-quality fundus images increase uncertainty in clinical observation and lead to the risk of misdiagnosis. Due to the optical beam of fundus image acquisition and vessel structure of the retina, natural image restoration methods cannot be applied straight to address this problem. The semi-automatic blind deblurring is a useful technique to restore the underlying sharp image given some assumed or known information about the cause of degradation. In this work, we propose a new hybrid algorithm for image restoration that does not require a priori knowledge of the noise distribution. The degraded image is first de-convoluted in Fourier space by parametric Wiener filtering;then, it is smoothed by using the anisotropic diffusion. The filtering model was tested on 177 fundus images. Experiment filtering results show the efficiency of our algorithm with a superlative performance (p-value < 0.05) when compared with state of the art methods.

Distance based and bond additive topological indices of certain repurposed antiviral drug compounds tested for treating COVID-19.

The entire world is struggling to control the spread of coronavirus (COVID-19) as there are no proper drugs for treating the disease. Under clinical trials, some of the repurposed antiviral drugs have been applied to COVID-19 patients and reported the efficacy of the drugs with the diverse inferences. Molecular topology has been developed in recent years as an influential approach for drug design and discovery in which molecules that are structurally related show similar pharmacological properties. It permits a purely mathematical description of the molecular structure so that in the development of identification of new drugs can be found through adequate topological indices. In this paper, we study the structural properties of the several antiviral drugs such as chloroquine, hydroxychloroquine, lopinavir, ritonavir, remdesivir, theaflavin, nafamostat, camostat, umifenovir and bevacizumab by considering the distance and bond measures of chemical compounds. Our quantitative values of the topological indices are extremely useful in the recent development of designing new drugs for COVID-19.