Tomato Flu/Fever-An analysis of the Hand Foot and Mouth Disease outbreak reported in India

The term "Tomato Flu” or "Tomato Fever” is the colloquial term in India used to describe multiple diseases that present with a fever and rash, with characteristic red, "tomato” shaped blister that appears on different parts of the body, which begin small and increase in size as disease progresses. Some controversy exists on this ‘new viral "flu” that emerged in May 2022 over a period of 2 weeks in areas in the south of India. Currently, local healthcare workers have been encouraged to address the disease as a variant of Hand Foot and Mouth Disease to avoid unnecessary panic on the emergence of a "new outbreak”. With the circulation of other viruses, inadequate testing and poor-quality surveillance in a low resource setting, where healthcare systems are already burdened with ongoing monkeypox outbreak and COVID-19 pandemic, the use of colloquial terms may cause unnecessary panic in the current hypervigilant climate. Confirmation from Government is required to confirm whether this outbreak is due to a mixed infection or a variant of the highly infectious Hand Foot and Mouth Disease virus. © 2022, The authors.

Tailored intelligence to detect unusual epidemic activity following the explosion at Vector, Russia

We used open source data from the EpiWATCH observatory to monitor for early disease signals in Russia and surrounding countries following an explosion at a BSL 4 laboratory, Vector, in Siberia in September 2019. Upon news of the explosion at Vector on September 16th 2019, the EpiWATCH team added the Russian language and key words Russia, Siberia, Novosibirsk, and Koltsovo to the Standard Operating Procedures, in addition to the usual epidemic-specific keywords used in EpiWATCH. We also searched for outbreak reports in countries bordering Siberia, including Mongolia, Kazakhstan and China. Given local spread of an epidemic could manifest in these countries, we included searching in Chinese, Mongolian and Kazakh. We added "Ukraine” as a key word, given current conflict between Russia and Ukraine. Data collection began in September 2019, one week after the explosion, with this considered the baseline. We demonstrate a method for rapid epidemic intelligence following an incident of concern, the explosion at Vector. There were some unexplained outbreaks in Russia in the three months following the explosion. No unexplained outbreaks were detected in countries bordering Russia, nor in Ukraine in the three months following the explosion. We detected an accidental release of brucella from a laboratory in China in early December 2019 and two reports of severe pneumonia prior to official reports, which could have been early COVID-19 cases. Best practice in preparedness should include surveillance for disease events in the months following an event of concern at local, national and global levels. In the absence of official surveillance data, open source intelligence may be the only available means of detecting outbreaks and enabling early response and mitigation for the rest of the world. EpiWATCH was able to identify reports of Russian outbreaks in the weeks and months following the Vector explosion, which allowed monitoring of outbreaks of concern without a known cause. © 2020 The Author(s).

The Unprecedented Role of 3D Printing Technology in Fighting the COVID-19 Pandemic: A Comprehensive Review.

The coronavirus disease 2019 (COVID-19) rapidly spread to over 180 countries and abruptly disrupted production rates and supply chains worldwide. Since then, 3D printing, also recognized as additive manufacturing (AM) and known to be a novel technique that uses layer-by-layer deposition of material to produce intricate 3D geometry, has been engaged in reducing the distress caused by the outbreak. During the early stages of this pandemic, shortages of personal protective equipment (PPE), including facemasks, shields, respirators, and other medical gear, were significantly answered by remotely 3D printing them. Amidst the growing testing requirements, 3D printing emerged as a potential and fast solution as a manufacturing process to meet production needs due to its flexibility, reliability, and rapid response capabilities. In the recent past, some other medical applications that have gained prominence in the scientific community include 3D-printed ventilator splitters, device components, and patient-specific products. Regarding non-medical applications, researchers have successfully developed contact-free devices to address the sanitary crisis in public places. This work aims to systematically review the applications of 3D printing or AM techniques that have been involved in producing various critical products essential to limit this deadly pandemic's progression.

Paediatric Post Covid-19 Condition: Experience of a Northern Paediatric Post Covid-19 Assessment Centre

Aims Post COVID-19 condition is defined by the WHO as a 'condition (which) occurs in individuals with a history of probable or confirmed SARS-CoV-2 infection, usually 3 months from the onset of COVID-19 with symptoms that last for at least 2 months and cannot be explained by an alternative diagnosis and generally have an impact on everyday functioning [1].' South Tees' Paediatric Post COVID-19 Assessment Clinic is one of fifteen tertiary paediatric clinics commissioned in England for the multidisciplinary assessment of children and young people (CYP) with suspected post COVID-19 condition. To assess patient data from clinic to identify any patterns of susceptibility and contextualise data in terms of the national picture. Methods Data was obtained from the referral form, clinic notes and service evaluation tool. Results In the period from July 2021 to February 2022, twelve CYP completed assessment. (See figure 1 for referral and assessment pathway). Of the assessed patients, eleven were of white and one of mixed ethnicity, seven were female and five male, three were between 6-11 years, six between 12-15 years and three between 16-18 years. Four were classified as being from a deprived location defined by living in a postcode that was classed as quintile 1 or 2 in the indices of multiple deprivation. Chronic fatigue and 'brain fog' were the two most common symptoms. However, symptoms like tinnitus (one child) and chronic chesty cough with wheeze (one child) were also noted. Four patients have still not managed full-time return to school. Two of these have attendance below 25%. Conclusion Though our experience was in line with national figures in terms of symptom profile, increased prevalence in females and teenagers [2], total number of referrals for assessment is significantly lower than expected. From the local area CYP population estimates [3] and most recent Office for National Statistics 'self-reported long COVID survey' results [2], we would have expected to see significantly higher numbers of patients with post COVID-19 condition (~150-250 patients under 16 years of age at the lowest estimate) but this is not the case. It is possible that symptoms reported in the survey are not severe enough to have significant impact on daily living warranting referral. The reduced referral numbers could also be due to reduced awareness (clinicians and community) or symptoms being attributed to other causes resulting in non-referral to appropriate services. Despite small numbers, one third of CYP seen in clinic continue to have multiple symptoms and have not been able to return to full-time education. This would have significant impact on long-term health and wellbeing of these CYP. There is an urgent need for research to find rehabilitation and therapeutic strategies for these CYP. 1. 'A clinical case definition of post COVID-19 condition by a Delphi consensus'. (2021) World Health Organization. 2. 'Prevalence of ongoing symptoms following coronavirus (COVID-19) infection in the UK'. (2022) Office for National Statistics. 3. 'Local population diversity'. (2018) Middlesbrough Council.

Covid-19 Forecasting with Deep Learning-based Half-binomial Distribution Cat Swarm Optimization

About 170 nations have been affected by the COvid VIrus Disease-19 (COVID-19) epidemic. On governing bodies across the globe, a lot of stress is created by COVID-19 as there is a continuous rise in patient count testing positive, and they feel challenging to tackle this situation. Most researchers concentrate on COVID-19 data analysis using the machine learning paradigm in these situations. In the previous works, Long Short-Term Memory (LSTM) was used to predict future COVID-19 cases. According to LSTM network data, the outbreak is expected to finish by June 2020. However, there is a chance of an over-fitting problem in LSTM and true positive;it may not produce the required results. The COVID-19 dataset has lower accuracy and a higher error rate in the existing system. The proposed method has been introduced to overcome the above-mentioned issues. For COVID-19 prediction, a Linear Decreasing Inertia Weight-based Cat Swarm Optimization with Half Binomial Distribution based Convolutional Neural Network (LDIWCSO-HBDCNN) approach is presented. In this suggested research study, the COVID-19 predicting dataset is employed as an input, and the min-max normalization approach is employed to normalize it. Optimum features are selected using Linear Decreasing Inertia Weight-based Cat Swarm Optimization (LDIWCSO) algorithm, enhancing the accuracy of classification. The Cat Swarm Optimization (CSO) algorithm???s convergence is enhanced using inertia weight in the LDIWCSO algorithm. It is used to select the essential features using the best fitness function values. For a specified time across India, death and confirmed cases are predicted using the Half Binomial Distribution based Convolutional Neural Network (HBDCNN) technique based on selected features. As demonstrated by empirical observations, the proposed system produces significant performance in terms of f-measure, recall, precision, and accuracy.

Fungal Nasal Polyposis during COVID-19 Pandemic - A Review of 4 Cases

Nasal polyps are usually associated with inflammation, allergy or mucoviscidosis. The global burden of patients with COVID-19 infection, including that in the Indian sub-continent,is still very high and they are at an increased risk of developing invasive fungal infections probably due to their immunocompromised state. Here, we review 4 cases of fungal nasal polyposis in patients with an ongoing or past history of COVID-19 infection with or without associated co-morbidities. Copyright (c) 2022: Author(s).

Improved Linear Factor based Grasshopper Optimization Algorithm with Ensemble Learning for Covid-19 Forecasting

Recently the COVID’19 is extensively increasing around the world with many challenges for researchers. Rigorous respiratory disease corona virus 2 show aggression to many parts of COVID’19 affected patients, together with brain and lungs. The changeableness of Corona virus with likely to infect Central Nervous System emphasize the necessity for technological development to identify, handle, and take care of brain damages in COVID’19 patients. An exact short-term predicting the quantity of newly infected and cured cases is vital for resource optimization to stop or reduce the growth of infection. The previous system designed a Linear Decreasing Inertia Weight based Cat Swarm Optimization with Half Binomial Distribution based Convolutional Neural Network (LDIWCSO-HBDCNN) approach for COVID-19 forecasting. However, the ensemble learning is required to improve the prediction outcome via integrating many approaches. This approach allows the production of better predictive performance compared to a single model. For solving this problem, the proposed system designed an Improved Linear Factor based Grasshopper Optimization Algorithm with Ensemble Learning (ILFGOA with EL) for covid-19 forecasting. Initially, the COVID-19 forecasting dataset is taken as an input. With the help of min-max approach, data normalization is done. Then the optimal features are selected by using Improved Linear Factor based Grasshopper Optimization Algorithm (ILFGOA) algorithm to improve the prediction accuracy. Based on the selected features, Ensemble Learning (EL) which includes Hyperparameter based Convolutional Neural Network (HCNN) is utilized to identify infected and demise cases across india for a period of time. The outcome of analysis shows that the introduced method attains better execution against previous system with regard to error rate, accuracy, precision, recall and f-measure.

LSTM-Based Prediction of COVID-19 Vaccination Drive in India

The vaccination drive for the much dangerous and contagious Coronavirus (COVID-19) has started successfully in India. This paper proposes to predict the vaccination drive of COVID-19 using the time series data for India. The proposed model was used for predicting the number of people to be vaccinated once per day in the country. The proposed model was compared with the direct input-based Long Short Term Memory (LSTM) cell model using various performance parameters and the proposed model was found to perform better. The actual closeness of the model's prediction from the actual data was depicted through line graphs. The proposed model was further used to predict the short-term and long-term future values. Herd immunity is another key ongoing research area when it comes to COVID-19. The Herd Immunity Threshold (HIT) of COVID-19 has not been found yet. However, this paper has proposed the expected number of days for different population thresholds. The proposed model predicts 174 days for obtaining a population threshold of 50% and 319 days for obtaining a population threshold of 90%. © 2021 IEEE.

Secured college bus management system using IoT for Covid-19 pandemic situation

College bus transportation system plays a vital role in students' life. They provide safe and secured journey compared to other mode of transportation. The current COVID19 pandemic situation has driven the State Governments and all the college authorities around India to enforce limitations on transportation and movement of students in order to prevent the spread of disease. But taking the students education into considerations, many state governments have advised to open the colleges with proper precautionary measures. However, student health has to be given more importance as they are the backbone of the country. So, a Secured College Bus Management System (SCBMS) has been introduced which monitors the health of the student and the safety measures followed by the student before entering into the bus. Alert information will be passed to the parent and the college management if a person fails to cross these two stages of screening process. Vehicle location information is passed to the parents periodically in order to prepare their child for the school on scheduled time before the bus reaches their boarding point. College bus attendance is monitored efficiently and updated to college authorities for further actions. © 2021 IEEE.

Assessment of anxiety level and practice modifications among dentist during novel corona outbreak (Covid-19) in Tamilnadu

Background: Pandemic and panic are two sides of the same coin. The sudden outbreak of coronavirus COVID-19 has influ-enced the routine practices of the dentist. Dentists were categorized as a high-risk profession during this pandemic, which cre-ated anxiety and stress regarding their personal and financial issues. Such a survey would help us to understand the impact of psychological burden among the dentist during the pandemic. Objective: The study aimed to assess the anxiety level among the dentist and also the practice modifications in their dental clinics during a novel COVID-19 outbreak in Tamil Nadu. Methods: The cross-sectional questionnaire-based survey was conducted amongst 250 dental professionals with various years of clinical experiences residing in Tamil Nadu. The questionnaire consisted of two sections;it addressed the level of anxiety and practice modification adopted in dental clinics during this pandemic. Results: There were significant differences in anxiety scores and practice management questionnaire scores between clinicians with varying years of clinical experience (P=0.0001). Anxiety level was found to be higher in clinicians with less than 5 years of experience. Dentists with clinical experience of more than 10 years were found to have better practice management. Conclusion: Anxiety and stress have been the main psychological burden among dentist during this covid-19 outbreak. Ad-ditional practice modifications during this pandemic have a severe impact on their financial investment in the clinic. © IJCRR.

Knowledge, awareness and outlook on the COVID-19 disease among the dental professionals in Tamil Nadu

Aim: The aim of this study was to analyze the knowledge, level of awareness, and the attitude of the dental professionals towards the pandemic disease (COVID-19). Materials and Methods: This questionnaire-based survey was conducted among 295 dental professionals residing in Tamil Nadu through an online portal. The questionnaire consisted of four sections that addressed the demographic data, knowledge, awareness, and outlook of the COVID-19 disease by dental professionals. All the received responses were tabulated and the results were represented graphically. Result: The result of the study showed significant awareness among dental professionals towards COVID-19. The dental professionals needed more attention towards the precautions to be followed during this pandemic as personal protection and a safe environment are essential for a secure practice. Conclusion: This study emphasizes the role of oral health professionals in the prevention of the transmission of coronavirus among the public along with the management of dental emergencies with appropriate personal protective measures. This study also enforced the need to enrich the knowledge about infection, transmission, prevention, and control towards COVID-19. This may act as a source of information for the future pandemic crisis. © 2021 Wolters Kluwer Medknow Publications. All rights reserved.

Economic declination due to lockdown

To analyze and create awareness about the economic declination of a country due to lockdown. Due to sudden outbreaks of pandemic diseases a country might face economic declination. With fresh coronavirus cases on the rise in India the nation witnesses a second round effect of the virus spread and a complete halt to economic activity. Hence it is important to have an economically literate population to understand and evaluate the socio-economic crisis.Widespread lockdowns and social distancing in economies affected by the coronavirus outbreak are set to cause a massive negative short-term impact on consumer spending and GDP. To minimise the effect in the economy caused by the COVID -19 outbreak, the Union Finance & Corporate Affairs Minister, on 24.03.2020, announced several important relief measures taken by the Government of India, especially on statutory and regulatory compliance matters related to several sectors. A well structured questionnaire containing socio-demographic information,knowledge,attitude,and perception was framed, and circulated through an online survey link. This was conducted through an online survey link, the results were analyzed using statistical analysis. The results were collected and represented in pie charts. From this survey , we can conclude that it is the responsibility of every citizen in a country to be literate about the economy, to evaluate the socio-economic crisis.

Effective Transfer Learning for Identifying Similar Questions: Matching User Questions to COVID-19 FAQs

People increasingly search online for answers to their medical questions but the rate at which medical questions are asked online significantly exceeds the capacity of qualified people to answer them. This leaves many questions unanswered or inadequately answered. Many of these questions are not unique, and reliable identification of similar questions would enable more efficient and effective question answering schema. COVID-19 has only exacerbated this problem. Almost every government agency and healthcare organization has tried to meet the informational need of users by building online FAQs, but there is no way for people to ask their question and know if it is answered on one of these pages. While many research efforts have focused on the problem of general question similarity, these approaches do not generalize well to domains that require expert knowledge to determine semantic similarity, such as the medical domain. In this paper, we show how a double fine-tuning approach of pretraining a neural network on medical question-answer pairs followed by fine-tuning on medical question-question pairs is a particularly useful intermediate task for the ultimate goal of determining medical question similarity. While other pretraining tasks yield an accuracy below 78.7% on this task, our model achieves an accuracy of 82.6% with the same number of training examples, an accuracy of 80.0% with a much smaller training set, and an accuracy of 84.5% when the full corpus of medical question-answer data is used. We also describe a currently live system that uses the trained model to match user questions to COVID-related FAQs. © 2020 ACM.

Effects of quarantine lockdown on physical healtha survey

Physical health is a condition of the body. Coronavirus outbreak leads to the need for self isolation. Quarantine is a way of separating and restricting people exposed to the disease from other healthy individuals. Keeping up physical health-improves the way of life and keeps us physically active. Proper diet, eliminating unhealthy food, maintaining social distancing and proper hygiene improves immunity and reduces the spread of viruses. A well-structured questionnaire containing socio-demographic information, knowledge, attitude and perception was framed and circulated through an online survey link. In this prospective study, the advantages are economical, easy to create, gathers large data, wide reach, heterogeneous population and disadvantages maybe response bias and survey fatigue. This survey was approved by the Scientific Review Board Saveetha Dental College, Chennai. The sample size was 100 volunteers. The results were collected and represented in pie charts. In the present survey, we found that the majority of participants took good care of their physical health, and were aware about the importance of physical health in reducing the risk of virus. Hence, it is important to improve and keep up good physical health during this lockdown in order to not fear the COVID19 occurrence and reduce the risk of virus.