Prediction of Covid-19 disease using machine-learning-based models

According to Chinese health officials, almost 250 million people in China may have caught Covid-19 in the first 20 days of December. Due to the Covid-19 pandemic and its global spread, there is a significant impact on our health system and economy, causing many deaths and slowing down worldwide economic progress. The recent pandemic continues to challenge the health systems worldwide, including a life that realizes a massive increase in various medical resource demands and leads to a critical shortage of medical equipment. Therefore, physical and virtual analysis of day-to-day death, recovery cases, and new cases by accurately providing the training data are needed to predict threats before they are outspread. Machine learning algorithms in a real-life situation help the existing cases and predict the future instances of Covid-19. Providing accurate training data to the learning algorithm and mapping between the input and output class labels minimizes the prediction error. Polynomials are usually used in statistical analysis. Furthermore, using this statistical information, the prediction of upcoming cases is more straightforward using those same algorithms. These prediction models combine many features to predict the risk of infection being developed. With the help of prediction models, many areas can be strengthened beforehand to cut down risks and maintain the health of the citizens. Many predictions before the second wave of Covid-19 were realized to be accurate, and if we had worked on it, we would have decreased the fatality rate in India. In particular, nine standard forecasting models, such as linear regression (LR), polynomial regression (PR), support vector machine (SVM), Holt's linear, Holt-Winters, autoregressive (AR), moving average (MA), seasonal autoregressive integrated moving average (SARIMA), and autoregressive combined moving average (ARIMA), are used to forecast the alarming factors of Covid-19. The models make three predictions: the number of new cases, deaths, and recoveries over the next 10 days. To identify the principal features of the dataset, we first grouped different types of cases as per the date and plotted the distribution of active and closed cases. We calculated various valuable stats like mortality and recovery rates, growth factor, and doubling rate. Our results show that the ARIMA model gives the best possible outcomes on the dataset we used with the most minor root mean squared error of 23.24, followed by the SARIMA model, which offers somewhat close results to the AR model. It provides a root mean square error (RMSE) of 25.37. Holt's linear model does not have any considerable difference with a root mean square error of 27.36. Holt's linear model has a value very close to the moving average (MA) model, which results in the root mean square of 27.43. This research, like others, is also not free from any shortcomings. We used the 2019 datasets, which missed some features due to which models like Facebook Prophet did not predict results up to the mark;so we excluded those results in our outcomes. Also, the python package for the Prophet is a little non-functional to work on massive Covid-19 datasets appropriately. The period is better, where there is a need for more robust features in the datasets to support our framework. © 2023 River Publishers.

Impact of Covid-19 Lockdown on the Glycemic Control of Type 2 Diabetes Patients

Background: The Covid-19 lockdown imposed all across the nation substantially disturbed the lifestyle and dietary habits among Indians, and this can be particular concern among individuals with diabetes. Objective(s): To understand the impact of lockdown on glycemic control in patients with type 2 diabetes, and to evaluate the healthcare practitioner (HCP) treatment preferences. Method(s): This systematic survey was done among 126 HCPs in whom a structured objective questionnaire was administered. The survey collected data related to the proportion of patients with poor glycemic control, its causes, and treatment preferences. Result(s): For the pre lockdown scenario, 37% and 48% of HCPs respectively, opined that 10-20% and 20-40% of their patients had HbA1c >8.5%. Only 10.3% HCPs reported 40-60% patients presented with high HbA1c respectively. However, for the post lockdown scenario, 8.7% and 42% of HCPs respectively, reported that 10-20% and 20-40% of their patients had HbA1c >8.5%. A notable 42% of HCPs admitted that after the lockdown 40-60% of their patients presented with HbA1c >8.5%. While 4% of HCPs reported uncontrolled glycemia in >60% of their patients before lockdown this proportion considerably increased to 7% for post lockdown scenario. HCPs perceived excess carbohydrate consumption and the lack of physical activity as the main causes of uncontrolled glycemia followed by poor medication adherence and stress. Of all the respondents, 53% agreed that they will prefer triple-drug therapy in more than 30% of their patients with HbA1c values above 8.5%. More than half of the HCPs mentioned that they would choose triple-drug therapy (Glimepiride+ metformin+ voglibose fixeddose combination) over other antidiabetics to manage the uncontrolled glycemia in their patients. Conclusion(s): The survey findings indicated an increase in the proportion of patients with HbA1c >8.5% after the lockdown as compared to the pre-lockdown phase. The altered nutritional behavior and reduced physical activity during lockdown are believed to be the major contributors to such an alarming rise in the proportion of patients with uncontrolled diabetes. Clinically, the triple-drug FDC (Glimepiride+ metformin+ voglibose) is perceived as the choice of therapy to achieve optimal glycemic control by a majority of HCPs.

Real-Time Health Monitoring System Using Predictive Analytics

Medical specialists are primarily interested in researching health care as a potential replacement for conventional healthcare methods nowadays. COVID-19 creates chaos in society regardless of the modern technological evaluation involved in this sector. Due to inadequate medical care and timely, accurate prognoses, many unexpected fatalities occur. As medical applications have expanded in their reaches along with their technical revolution, therefore patient monitoring systems are getting more popular among the medical actors. The Internet of Things (IoT) has met the requirements for the solution to deliver such a vast service globally at any time and in any location. The suggested model shows a wearable sensor node that the patients will wear. Monitoring client metrics like blood pressure, heart rate, temperature, etc., is the responsibility of the sensor nodes, which send the data to the cloud via an intermediary node. The sensor-acquired data are stored in the cloud storage for detailed analysis. Further, the stored data will be normalized and processed across various predictive models. Among the different cloud-based predictive models now being used, the model having the highest accuracy will be treated as the resultant model. This resultant model will be further used for the data dissemination mechanism by which the concerned medical actors will be provided an alert message for a proper medication in a desirable manner. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Real-Time Health Monitoring System Using Predictive Analytics

Medical specialists are primarily interested in researching health care as a potential replacement for conventional healthcare methods nowadays. COVID-19 creates chaos in society regardless of the modern technological evaluation involved in this sector. Due to inadequate medical care and timely, accurate prognoses, many unexpected fatalities occur. As medical applications have expanded in their reaches along with their technical revolution, therefore patient monitoring systems are getting more popular among the medical actors. The Internet of Things (IoT) has met the requirements for the solution to deliver such a vast service globally at any time and in any location. The suggested model shows a wearable sensor node that the patients will wear. Monitoring client metrics like blood pressure, heart rate, temperature, etc., is the responsibility of the sensor nodes, which send the data to the cloud via an intermediary node. The sensor-acquired data are stored in the cloud storage for detailed analysis. Further, the stored data will be normalized and processed across various predictive models. Among the different cloud-based predictive models now being used, the model having the highest accuracy will be treated as the resultant model. This resultant model will be further used for the data dissemination mechanism by which the concerned medical actors will be provided an alert message for a proper medication in a desirable manner. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Olfactory and Taste Dysfunctions and Their Implications in Cases of Severe Acute Respiratory Syndrome Coronavirus 2: A Single-institute Study of 58 Cases

Importance: There is a need to identify the implications of the loss of smell and taste in cases of coronavirus disease-2019 (COVID-19). Objective: To determine whether severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is causing isolated anosmia in adult population and whether there is a role of intranasal corticosteroids (INCs) in cases of olfactory dysfunctions (ODs). Design: This was a prospective, cross-sectional, questionnaire-based study of 416 patients diagnosed with COVID-19 in a single institute. Setting: Dedicated COVID-19 facility. Participants: All patients had been tested for COVID-19 using a reverse transcription–polymerase chain reaction (RT-PCR)-based test. Patients who were hospitalized were approached in person. All patients who were discharged were then contacted by telephone up to two times to complete the study. Patients who were not reachable with two telephone calls were excluded. Demographic characteristics of the participants—age, sex, and smoking history—were collected. A standardized questionnaire was given to participants. Result: Olfactory dysfunctions (ODs) was observed in 58 patients and isolated anosmia in 3.6% of patients;82.7% showed complete recovery of smell, and 6.9% had partial recovery following INCs. Conclusion: Patients reporting recent onset of anosmia should be considered positive for SARS-CoV-2 infection until proven otherwise by a screening polymerase chain reaction test. Also, ear, nose, and throat (ENT) surgeons in particular who see patients with new-onset anosmia during the COVID-19 pandemic must take safety measures to reduce the risk of exposure and infection of healthcare workers and recommend such patients for RT-PCR test. Females and young adults are more prone to SARS-CoV-2 infection. Early intervention by INCs could be beneficial in improving olfactory and taste dysfunctions (OTDs) and other post-viral neurological manifestations. It could be beneficial in improving the quality of life of elderly patients who are at a higher risk of permanent OTDs. Smokers are at a higher risk of OTDs, but this could be reversible after smoking cessation. There is a need to put SARS-CoV-2 as a differential diagnosis in cases of sudden isolated OTDs.

Image Encryption Using RSA Algorithm

In the current covid pandemic situation, secure online transmission of data has the highest precedence over other activities. For providing computational hardness that is for making tough to break the key for finding the unique message, there are various algorithms are present. For secure data transmission, many researchers have applied different cryptography algorithms and in order to improve the level of information security, different hybrid cryptography algorithms have been proposed. In cryptography algorithm implementation, key management plays a major role. For this reason, we have applied an image encryption technique in which a random image is considered as the key. Using the random image as a key, we have encrypted another image as information using the RSA algorithm. The comparison of the proposed method is done with the traditional approach and concluded that the cryptography algorithm implemented using an image as key provides more security in terms of encryption and decryption time. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Review on Automated Detection of COVID-19 from X-Ray Images Using Machine Learning

COVID-19 virus has been a worldwide pandemic since its outbreak from December 2019. While coronavirus has a low fatality rate, it is extremely infectious and escalates quickly;therefore, early detection is very important for preventing its outbreak. The procedures currently used by medical personals for detection is RT-PCR test. However, it includes false negative reports and also is a time taking process;thus an alternate solution is required. Any diagnostic system that can detect COVID-19 infection can be very helpful to medical personals. The features found in COVID-19 images by X-rays are very similar to other lung diseases, which makes it very difficult to differentiate. This review includes the contribution of image processing and machine learning to make swift and precise diagnostic system from lung X-ray images. Such a system can be used by radiologists for making decisions and can be very helpful in prior detection of the virus. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Machine intelligence in dynamical systems: \A state-of-art review

This article is dedicated to study the impact of machine intelligence (MI) methods viz. various types of Neural models for investigating dynamical systems arising in interdisciplinary areas. Different types of artificial neural network (ANN) methods, viz., recurrent neural network, functional-link neural network, convolutional neural network, symplectic artificial neural network, genetic algorithm neural network, and so on, are addressed by different researchers to investigate these problems. Although various traditional methods have been developed by researchers to solve these dynamical problems but the existing traditional methods may sometimes be problem dependent, require repetitions of the simulations, and fail to solve nonlinearity behavior. In this regard, neural network model based methods are more general and solutions are continuous over the given domain of integration, self-adaptive and can be used as a black box. As such, in this article, we have reviewed and analyzed different MI methods, which are applied to investigate these problems. This article is categorized under: Technologies > Computational Intelligence Technologies > Machine Learning Application Areas > Science and Technology

Vaccine effectiveness to protect against moderate or severe disease in COVID cases: A prospective cohort study.

Background: This study was carried out to evaluate the effectiveness of partial and full vaccination with ChAdOx1 nCoV-19 (COVISHIELD) to prevent the development of moderate or severe illness among COVID-positive cases. Methods: This prospective cohort study was conducted among Armed Forces personnel deployed in Northern India who were found COVID positive during the study period between January and June 2021. Information about the vaccination status, age and comorbidities was collected at the time of diagnosis. Classification of COVID cases as moderate or severe was performed as per criteria given by the Government of India. Individuals were considered partially vaccinated three weeks after one dose and fully vaccinated two weeks after the second dose. Risk ratio and vaccine effectiveness (VE) to prevent moderate or severe disease among COVID cases were calculated. Results: A total of 2005 COVID-19 patients were included in our study. Partial vaccination and full vaccination with ChAdOx1 nCoV-19 offered 13% (95% credible interval (CI): -56.8%, 52.8%) and 66.6% (95% CI: 34.9%, 84.6%) protection against progression to moderate/severe illness among COVID-positive individuals. The risk of moderate-severe disease among COVID-positive cases occurring 4-11 weeks after the first dose was also lesser among those who had taken the second dose of vaccine than individuals who have been vaccinated with only one dose. Conclusion: Interval between the first and second doses of ChAdOx1 nCoV-19 vaccine should be reduced to 4-6 weeks, as partial vaccination offers lower protection against the development of moderate-severe illness after COVID infection.

Performance of Chip Based Real Time RT-PCR (Truenat) and Conventional Real Time RT-PCR for Detection of SARS-CoV-2

Introduction: Coronavirus Disease-2019 (COVID-19), caused by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is ravaging the globe due to its rapid spread. Since providing fast results is of critical importance in a time of shortage of medical personnel and beds in isolation wards and to ensure timely treatment for patients, developing high quality rapid Point Of Care (POC) diagnostics is essential. Aim: To compare the diagnostic performance of chip based real time Reverse Trancriptase Polymerase Chain Reaction (RTPCR) (Truenat) which has a shorter turnaround time compared to conventional real time RT-PCR in samples of suspected COVID-19 patients. Materials and Methods: The present cross-sectional observational study was carried out in a tertiary care hospital in New Delhi, India. Five hundred randomly selected Oropharyngeal (OP) swabs samples received from May-July 2020, were included in the study to compare the diagnostic performance of chip based real time RT-PCR (Truenat) with conventional real time RT-PCR for diagnosis of SARS-CoV-2 infection. All statistical analysis was performed using STATA version 16.1 software (College station, Texas, USA). Results: The sensitivity of Truenat test was 100% while the specificity was found to be 99.12% at 95% confidence intervals. The positive predictive value was 91.84% and the negative predictive value was 100%. Conclusion: The short turnaround time, good sensitivity and specificity makes Truenat a reliable and affordable option to provide rapid results in cases requiring urgent interventions and to augment SARS-CoV-2 testing capacity at peripheral settings where sample load is less.

Multiple etiological factors led septicemic disease with mass mortality of fishes in freshwater reservoir of Odisha, India

A detailed investigation on mass mortality of fishes was conducted in a small tropical reservoir- Derjang (20o50'32.0"N, 85o01'14.8″E), Odisha, India. Mortality mostly occurred in Systomus sarana followed by Labeo rohita , Cirrhinus mrigala , Labeo catla , Ompok bimaculatus , Labeo calbasu and Mastacembelus armatus. During 20 days of disease occurrence in May–June 2019, a total of about 3000 kg of fish died. The clinical signs in Cyprinid group were haemorrhagic spots, ulcerative lesions, rotten and pale patches in gills due to septicemic disease whereas haemorrhagic spots were the only prominent symptoms observed in Silurid group. Bacteriological isolation and identification through conventional and molecular techniques revealed that Klebsiella pneumoniae was the most common pathogen recovered from S. sarana , C. mrigala and O. bimaculatus. Further Aeromonas hydrophila , Acinetobacter baumannii were isolated from L. rohita and L. catla respectively. The role of these pathogens for this disease outbreak in multiple fish species is discussed in perspective of environmental factors. Sudden environmental alternation by the supercyclone Fani (3rd May 2019) on the coastal part of Odisha might have played a key role to translate the aquatic bacteria into the virulent infective pathogens. In the experimental challenge study, isolated bacteria showed pathogenicity in respective hosts as that in the reservoir. Thus this further revealed both bacteria and fish specific virulency with a variation in LD 50 values. All the gram negative bacterial isolates were found to resist ampicillin and amoxicillin-clavulanic acid and most of them were TEM gene positive. However, the bacteria were found to be susceptible to the rest of the nineteen antibiotics. These findings suggested that the sudden cyclone is an enormous threat to reservoir aquaculture, and should be taken into consideration before breeding, stocking and harvesting of fishes. • Disease outbreak in reservoir during Summer, 2019 caused mass mortality of many freshwater fishes with septicemic symptoms • Klebsiella pnumoniae was the most dominating pathogen recovered, besides Aeromonas hydrophila and Acinetobacter baumannii • Sudden environmental alternation by the cyclone Fani might have played a key role to flare up the virulent pathogens. • Specificity and virulence of bacteria were found host-dependent with variable LD 50 as revealed by challenge experiments [ FROM AUTHOR] Copyright of Aquaculture is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

A black intruder into the Pandemic: Rhino-Orbital Mucormycosis Complicating COVID-19

There is an increased incidence of rhino orbital mucormycosis during the Coronavirus Disease-2019 (COVID-19) pandemic attributed to Diabetes Mellitus, corticosteroid usage and immunocompromise caused by COVID-19. In this series, seven biopsy-proven cases of mucormycosis (six male patients and 1 female patients) are presented from a tertiary center in eastern India from (May 2021 to June 2021). Empirical systemic liposomal amphotericin B, radical sinus surgery with orbital decompression and irrigation of sinus and orbit with amphotericin B was performed. The mean age of the patients was 42.71 +/- 7.34 years with a male preponderance (85.7%). Five patients had orbital involvement (71.42%), and two had cerebral involvement (28.6%). All of them had elevated blood glucose levels, though only three (42.85%) were known cases of type 2 diabetes. The most common manifestations were sinus tenderness (100%), paresthesia (100%), facial swelling (71.42%) and nasal discharge (28.57%). Follow-up at two months showed zero mortality. Timely diagnosis, appropriate management with intravenous amphotericin B and endoscopic radical sinus surgery, debridement of the necrotic tissue proved to be necessary for a good outcome in rhino-orbito-cerebral mucormycosis.

Physician's perception of E-consultation adoption amid of COVID-19 pandemic

Purpose The purpose of this paper is to investigate physicians' perceptions of e-consultation adoption, which has the potential to bridge existing gaps in the current health-care system, using the unified theory of acceptance and use of technology (UTAUT2) framework. Design/methodology/approach The judgemental sampling method was embraced to collect primary data from 337 physicians from Delhi-National Capital Region who had experience with e-consultation. A number of hypotheses was developed and tested using structural equation model based on UTAUT2. Findings The study's findings revealed an affirmative and significant relation between a physician's intention to embrace e-consultation and facilitating conditions, effort expectancy, social influence and performance expectancy;however, habit and experience are not significantly linked to it. Originality/value This study will not only add to the existing body of knowledge about e-consultation adoption, but it will also assist electronic health service providers in devising strategies to encourage the usage of e-consultation services in emerging economies such as India where people are deprived of the right to access better health care due to lack of physical infrastructure.

Moderating effect of gender on adoption of digital health consultation: a patient perspective study

Purpose: The purpose of this paper is to understand the factors influencing the adoption decision of patients towards digital consultation in India with gender as a moderating variable. This study is based on the unified theory of acceptance and use of technology (UTAUT2) framework for examining the factors influencing adoption decisions for digital consultation and to what extent this leads to continuous usage intention. Design/methodology/approach: Based on the UTAUT2 framework, this study proposed a set of hypotheses that were tested using structural equation modeling. This study was based on primary data collected from 462 sample respondents using the judgemental sampling method who had experience of using digital health consultation in India. Findings: Findings of this study revealed significant and positive causation in the behavioural intention (BI) of a patient to adopt digital health consultation arising out of performance expectancy, effort expectancy, social influence, facilitating condition and price value;however, habit is insignificantly associated with the same. Furthermore, the results of this study also revealed that the BI of a patient towards digital health consultation is significantly moderated by their gender. Originality/value: This study conceptually strengthens the present body of literature on the adoption behaviour by contributing certain new dimensions in the context of digital health consultations and will also help policymakers and service providers in crafting their strategy for promoting the adoption of digital health consultation. © 2021, Emerald Publishing Limited.

The golden spice turmeric (Curcuma longa) and its feasible benefits in prospering human health - a review

From the evolution of the mankind, Turmeric has been used in conventional medication. India is in lead for producing, marketing and exporting the Turmeric and its value added products. Curcumalonga (Turmeric) is an Indian rhizomatous medicinal herb from the Zingiberaceae family that is common and widely available across the globe. The components of Turmeric are curcumin, demethoxycurcumin and bisdemethoxycurcumin and these are collectively known as curcuminoids. Curcumin, the active ingredient of Turmeric is generally investigated by the scientific community for its wide range of antioxidant activity, anti-Inflammatory properties and anti-cancer activity, anti-metabolic syndrome activities, neuroprotective activity, antimicrobial effects, anti-arthritis effects, anti-viral effects, anti-asthma and anti-diabetic effects, anti-obesity, cardio and liver toxicity protection activity, anti-depression and anxiety activities. Turmeric has been widely used as a typical household treatment for cough, sore throat, respiratory ailments and could be an effective immunity booster against SARS-CoV-2 therapy during the ongoing pandemic situation. Safety evaluation studies indicate that both turmeric and curcumin are well tolerated at a very high dose without any toxic effects. Thus, turmeric and its constituents have the potential for the development of modern medicine for the treatment of various diseases. So in this review, we describe the various metabolic roles of curcumin and activities for the benefit of human health.

In Silico Molecular Docking-Based Screening Reveals Phytomolecules Against SARS-COV-2 Main Protease

In past two decades, the globe has faced many infectious disease outbreaks. 2019 Novel Corona-virus (2019-nCoV) or the severe acute respiratory syndrome Corona-virus 2 (SARS-CoV-2) emerged as a global risk and put the entire globe into unrest. Unavailability of specific drug against the virus is more imperative. This demanding situation requires development of bio molecules for competent treatment against the SARS-CoV-2. The crystal structure of SARS-CoV-2 main protease (Mpro) may be used for fast in silico docking and novel pharmacophores can be discovered. This may result into identification of active bio-molecules largely phytochemicals. In silico Molecular Docking revealed that the phytochemical, Gallic acid effectively binds to the active pocket of the SARS-CoV-2 main protease.