Forecasting COVID-19 Confirmed Cases in China Using an Optimization Method

COVID-19, a brand-new coronavirus, was found in Wuhan, China, in December 2019 and has since spread to 24 additional nations as well as numerous locations in China. The number of confirmed cases continues to rise every day, reaching 34,598 on February 8, 2021. We present our findings a new method was used in this investigation, predictive framework, for such number of reported COVID-19 cases in the China. During the next 10 days, predicated on recently known cases in China. The suggested upgraded adaptable neuro-fuzzy powerful instrument (ANFIS) with an updated floral modeling is used in this model. The salp swarm algorithm (SSA) was used to implement the pollination algorithm (FPA). Generally, SSA is used to enhance FPA in order to minimize its shortcomings. The fundamental theme of the essay FPASSA-ANFIS seems to be a proposed paradigm of improving ANFIS effectiveness through determining FPASSA which was used to determine the ANFIS specifications. The world is also used to analyze the FPASSA-ANFIS model. Statistical figures from the World Health Organization (WHO) on the COVID-19 pandemic for forecast the cases reported these following are indeed the cases for the next 10 days. Most specifically, the FPASSA-ANFIS model in comparison to such a number of other models outperformed them in terms of computing time, root mean squared error (RMSE), and mean absolute percentage (MAP). Researchers also put the suggested model to the tests utilizing two distinct datasets of week pandemic confirmed cases from two or more countries: the USA and China. These results also indicated incredible performance. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

COVID-19 Disease Classification Model Using Deep Dense Convolutional Neural Networks

Preventing the transmission of COVID-19 necessitates diagnosis and identification. Researchers have developed algorithms to detect the presence of COVID-19 in X-ray and CT scans and images. These methodologies produce skewed data and incorrect disease detection. So, in the case of COVID-19 forecasting utilizing CT scans in an IoT setting, the current study paper established an oppositional-based deep dense convolutional neural network (DDCNN) and chimp optimization algorithm. The framework proposed is divided into two stages: preprocessing and estimation. Previously, a CT scan pictures generated from anticipated COVID-19 are acquired utilizing IoT devices from an open-source system. After that, the photos are preprocessed with a Gaussian function. A Gaussian filter can be used to remove undesirable noise from CT scan pictures that have been obtained. The preprocessed photos are then transmitted to the prediction process. DDCNN is applied to the images preprocessed in this step. The recommended classifier is designed to be as efficient as possible using the oppositional-based chimp optimization algorithm (OCOA). This approach is used to choose the best classifier parameters under consideration. Furthermore, the suggested method is applied to forecast COVID-19 and categorizes the findings as COVID-19 or non-COVID-19. The proposed technique was used in Python, and results were assessed using statistical analysis. CNN-EPO and CNN-FA were compared to the new method. The results proved that the proposed model was optimal. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Smart Quarantine Environment Privacy through IoT Gadgets Using Blockchain

The coronavirus, formerly known as COVID-19, has caused massive global disasters. As a precaution, most governments imposed quarantine periods ranging from months to years and postponed significant financial obligations. Furthermore, governments around the world have used cutting-edge technologies to track citizens’ activity. Thousands of sensors were connected to IoT (Internet of Things) devices to monitor the catastrophic eruption with billions of connected devices that use these novel tools and apps, privacy and security issues regarding data transmission and memory space abound. In this study, we suggest a block-chain-based methodology for safeguarding data in the billions of devices and sensors connected over the internet. Various trial secrecy and safety qualities are based on cutting-edge cryptography. To evaluate the proposed model, we recom-mend using an application of the system, a Raspberry Pi single-board computer in an IoT system, a laptop, a computer, cell phones and the Ethereum smart contract platform. The models ability to ensure safety, effectiveness and a suitable budget is proved by the Gowalla dataset results. © 2023, Tech Science Press. All rights reserved.

An Experimental Approach to Diagnose Covid-19 Using Optimized CNN

The outburst of novel corona viruses aggregated worldwide and has undergone severe trials to manage medical sector all over the world. A radiologist uses x-rays and Computed Tomography (CT) scans to analyze images through which the existence of corona virus is found. Therefore, imaging and visualization systems contribute a dominant part in diagnosing process and thereby assist the medical experts to take necessary precautions and to overcome these rigorous conditions. In this research, a Multi-Objective Black Widow Optimization based Convolutional Neural Network (MBWO-CNN) method is proposed to diagnose and classify covid-19 data. The proposed method comprises of four stages, preprocess the covid-19 data, attribute selection, tune parameters, and classify cov-id-19 data. Initially, images are fed to preprocess and features are selected using Convolutional Neural Network (CNN). Next, Multi-objective Black Widow Optimization (MBWO) method is imparted to finely tune the hyper parameters of CNN. Lastly, Extreme Learning Machine Auto Encoder (ELM-AE) is used to check the existence of corona virus and further classification is done to classify the covid-19 data into respective classes. The suggested MBWO-CNN model was evaluated for effectiveness by undergoing experiments and the outcomes attained were matched with the outcome stationed by prevailing methods. The outcomes confirmed the astonishing results of the ELM-AE model to classify cov-id-19 data by achieving maximum accuracy of 97.53%. The efficacy of the proposed method is validated and observed that it has yielded outstanding outcomes and is best suitable to diagnose and classify covid-19 data. © 2022, Tech Science Press. All rights reserved.

COVID-19 pandemic prediction and forecasting using machine learning classifiers

COVID-19 is a novel virus that spreads in multiple chains from one person to the next. When a person is infected with this virus, they experience respiratory problems as well as rise in body temperature. Heavy breathlessness is the most severe sign of this COVID-19, which can lead to serious illness in some people. However, not everyone who has been infected with this virus will experience the same symptoms. Some people develop cold and cough, while others suffer from severe headaches and fatigue. This virus freezes the entire world as each country is fighting against COVID-19 and endures vaccination doses. Worldwide epidemic has been caused by this unusual virus. Several researchers use a variety of statistical methodologies to create models that examine the present stage of the pandemic and the losses incurred, as well as considered other factors that vary by location. The obtained statistical models depend on diverse aspects, and the studies are purely based on possible preferences, the pattern in which the virus spreads and infects people. Machine Learning classifiers such as Linear regression, Multi-Layer Perception and Vector Auto Regression are applied in this study to predict the various COVID-19 blowouts. The data comes from the COVID-19 data repository at Johns Hopkins University, and it focuses on the dissemination of different effect patterns of Covid-19 cases throughout Asian countries. © 2022, Tech Science Press. All rights reserved.

Prevalence of covid-19 infection and identification of risk factors among asymptomatic healthcare workers: A serosurvey involving multiple hospitals in west bengal

Background: The declining trend of COVID-19 infection in India has made healthcare personnel (HCP) and general public lenient about personal-protective-measures. Serosurveys to estimate the prevalence of SARS-CoV2 IgG antibodies, particularly in high-risk-zones like hospitals can give the real scenario and risk-factors can help prioritise the target population for urgent, effective vacccination. Methods: 1470 consecutive HCP from 4 tertiary-care-hospitals in Kolkata filled a questionnaire and were tested for serum SARS-CoV2-IgG by Enzyme-linked Immunosorbent Assay (ELISA). The prevalence of SARS-CoV2-IgG among asymptomatic HCPs was studied and the work environment, clinical comorbidities, personal habits and protective measures and pharmacologic prophylaxes were compared between those with and without SARS-CoV2-IgG. Parameters of asymptomatic seroconverters were also compared to those with personal history of COVID-19-Infection. Logistic regression was done to identify independent risk-factors. Results: Prevalence of asymptomatic seroconversion was 15.8%. Asymptomatic seroconverters (n=208) were mostly working in mixed hospitals (having both COVID-19 and non-COVID-19 wards, 57.7%), were non-doctors by profession (nurses-25.1%, others–51.4%). Among asymptomatic HCP, indepedendent positive risk factors for SARS-CoV2 IgG-positivity were Diabetes Mellitus (DM) and multiple comorbidities (pboth <0.001) and prophylactic use of Hydroxychloroquine and Famotidine (pboth < 0.03). However, for symptomatic COVID-19 infection, working in COVID-19 dedicated hospitals, and personal h/o COPD were positive risk-factors and Ivermectin prophylaxis a negative risk-factor (pall < 0.03). Conclusion:In our study conducted i n the i mmedi ate pre-i mmuni sati on peri od, rate of asymptomati c seroconversion among HCPs is too low to presume herd immunity. Those working in mixed hospitals and DM, multiple comorbidities are at particularly high risk.

A retrospective analysis of influence of environmental/ air temperature and relative humidity on sARs-CoV-2 outbreak

Coronaviruses are a family of viruses causing mild to severe upper respiratory tract syndrome. Recent pandemic threat caused by SARS-CoV-2 first appeared in Wuhan, China in December 2019. Whether the COVID-19 might be affected by warming global temperatures like some of previous pandemic flues. Therefore, the current study aims to analyze the effect of temperature and relative humidity (RH) on the spreading of the SARS-CoV-2 infection. The confirmed cases of COVID-19 in 31 different provinces in China and 274 provinces and/or countries were obtained from an online database. The real time temperature and humidity of the respective regions were taken from another online weather reporting data source. Spearman [R(s)] rank correlation was performed to identify the relationship between the variables (e.g., temperature, number of confirmed cases etc.). The overall spreading of SARS-CoV-2 in relations to temperature was inversely correlated. Among 29 of 31 provinces of China the overall correlation coeffient of the relationship between temperature and viral spread was negative [-R(s)] where in 15 provinces the correlation was at significant level (p<0.05). Furthermore, there was a significant negative relationship observed between the SARS-CoV-2 spreading and air temperature throughout the 274 provinces and/or countries of the world. However, there was no significant co-relationship between humidity and COVID-19 spreading either in China or among countries and/ or various regions of the world. The SARS-CoV-2 infection seems to be spread in a wide range of temperature throughout the world. Thus, several factors including temperature, may influnce the SARS-CoV-2 spreading. As a results, relatively elevated air temperature could not completely prevent viral spread but it might be one of the important detrimental factors for SARS-CoV-2 rapid spread.