Research and Development of a COVID-19 Tracking System in Order to Implement Analytical Tools to Reduce the Infection Risk

The whole world is currently focused on COVID-19, which causes considerable economic and social damage. The disease is spreading rapidly through the population, and the effort to stop the spread is entirely still failing. In our article, we want to contribute to the improvement of the situation. We propose a tracking system that would identify affected people with greater accuracy than medical staff can. The main goal was to design hardware and construct a device that would track anonymous risky contacts in areas with a highly concentrated population, such as schools, hospitals, large social events, and companies. We have chosen a 2.4 GHz proprietary protocol for contact monitoring and mutual communication of individual devices. The 2.4 GHz proprietary protocol has many advantages such as a low price and higher resistance to interference and thus offers benefits. We conducted a pilot experiment to catch bugs in the system. The device is in the form of a bracelet and captures signals from other bracelets worn at a particular location. In case of contact with an infected person, the alarm is activated. This article describes the concept of the tracking system, the design of the devices, initial tests, and plans for future use.

Covid-19 in India-Emergence, Implications and Possible Precautionary Measure for Disease Transmission in Indian Healthcare Workers

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a potentially fatal disease that prompted health disasters worldwide. The virus was reported first from Wuhan, China, in December 2019. SARS-CoV-2 majorly transmits through direct contactwith an infected person or inhalation.The spread rate of SARS-CoV-2 is much higher than the other virus of family. The virus is very harmful to the children, people with old age, low immunity, or suffering from other critical diseases. A total of 29.6 million infected cases and approximately 936000 death were reported worldwide. Whereas in India reached 5.02 Million cases are reported with 82000 deaths. In this paper, the authors had study the Origin of viruses, Symptoms, actions taken by the Indian government, and precautions suggested to healthcare workers. The biometric system's adverse effects in hospitals are highlighted, and authors emphasize IoT-based smart door-lock that works without direct contact. The proposed system helps in reducing contamination at healthcare centers.

Covid-19 in India-Emergence, Implications and Possible Precautionary Measure for Disease Transmission in Indian Healthcare Workers

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a potentially fatal disease that prompted health disasters worldwide. The virus was reported first from Wuhan, China, in December 2019. SARS-CoV-2 majorly transmits through direct contactwith an infected person or inhalation.The spread rate of SARS-CoV-2 is much higher than the other virus of family. The virus is very harmful to the children, people with old age, low immunity, or suffering from other critical diseases. A total of 29.6 million infected cases and approximately 936000 death were reported worldwide. Whereas in India reached 5.02 Million cases are reported with 82000 deaths. In this paper, the authors had study the Origin of viruses, Symptoms, actions taken by the Indian government, and precautions suggested to healthcare workers. The biometric system's adverse effects in hospitals are highlighted, and authors emphasize IoT-based smart door-lock that works without direct contact. The proposed system helps in reducing contamination at healthcare centers.

Multi-Objective Big Data View Materialization using MOGA

The COVID 19 Pandemic, has resulted in large scale of generation of Big data. This Big data is heterogeneous and includes the data of people infected with corona virus, the people who were in contact of infected person, demographics of infected person, data on corona testing, huge amount of GPS data of people location, and large number of unstructured data about prevention and treatment of COVID 19. Thus, the pandemic has resulted in producing several Zeta bytes of structured, semi-structured and unstructured data. The challenge is to process this Big data, which has the characteristics of very large volume, brisk rate of generation and modification and large data redundancy, in a time bound manner to take timely predictions and decisions. Materialization of views for Big data is one of the ways to enhance the efficiency of processing of the data. In this paper, Big data view selection problem is addressed, as a bi-objective optimization problem, using Multi-objective genetic algorithm.

Get vaccinated when it is your turn and follow the local guidelines

The nurse has an important role caring for the patient infected with the coronavirus disease (COVID- 19). It is an infectious disease caused by the SARS-CoV-2 virus. The virus can spread from the mouth or nose of an infected person in small liquid particles when they cough, sneeze, speak, sing, or breathe. These particles range from larger respiratory droplets to smaller aerosols. It is important to practice respiratory etiquette, for example coughing into a flexed elbow, and to stay home and self-isolate until you recover if you feel unwell. Most people infected with the virus will experience mild to moderate respiratory illness and will recover without requiring special treatment. Older people and people with underlying medical conditions such as cardiovascular disease, diabetes, chronic respiratory disease, or cancer are more likely to develop serious illnesses. The goal of the research is to propose the best way to prevent and slow down transmission is to be well informed about the disease and how the virus spreads. Protect yourself and others from infection by staying at least 1 meter away from others, wearing a well-fitting mask, and washing your hands or using an alcohol-based rub frequently.

The biotherapics Influenzinum and Oscilococcinum in the treatment of influenza virus flu

Introduction: The influenza virus flu is a widespread illness which is responsible for hundreds of thousands of deaths annually. About 20% of children and 5% of adults are infected with this virus every year. The disease is highly contagious and its transmission occurs by saliva particles of the infected person, expelled by breathing, talking and coughing [1]. Flu pandemics are generally caused by the appearance of a new subtype of the virus in humans, which occurs as a result of the existing flu in animal species transmitted to humans [2]. Despite the fact there are antiviral drugs, the virus develops mutations, creating resistance to these drugs in few days. Thus, the development of new therapies, including homeopathy, that can prevent and/or treat this disease becomes increasingly necessary. In this scenario, biotherapics appear as drugs that are made from biological products, such as secretions, tissues, organs whose compounding follows the homeopathic pharmacopeia. Objective: This study is a literature review on the treatment of flu with biotherapics used in clinical medicine, namely Influenzinum and Oscilococcinum. Method: Studies on the prescription of biotherapics for the prevention and cure of the flu as well as literature about the history and evolution of Homeopathy were reviewed in the present work. Influenzinum is a biotherapic made from the influenza vaccine from Pasteur Laboratory, while Oscillococcinum is obtained from the lysate of the liver and heart of the goose Anas barbaries. Results: Preliminary results showed that both medicines are widely used in clinical medicine. Influenzinum 9CH is prescribed for flu prevention and treatment, while Oscilococcinum is more used to reduce the severe symptoms in patients who already have the flu. Conclusion: Based on these results, it is possible to say that Influenzinum has a very important role in the prevention and cure of the influenza and Oscilococcinnum is useful in the relief of the symptoms caused by this disease

COVID -19 Pandemic: A Review Article

Corona virus infection is caused by a virus of COVID- 19 strain. this infection is spread by close contact with infected person and mainly due to droplets, this virus may remain alive on surfaces which was touched by an infected person .so to stop its spread it was necessary to reduce human contact, for that all countries around the world including India declared complete of all places where mass people gatherings were happens. WHO announced Covid- 19 infection as a global pandemic and this was a global emergency required complete lockdown. This necessary lockdown was for stoppage of infection spread but it also impacted very badly on every individual’s lifestyle. Conclusion: PHC are the easily assessable and cost friendly for rural peoples but during this pandemic situation these PHC are not prepared for facing COVID -19 pandemic. Due to less accessibility to media and less media coverage exact situation in rural areas was not stated properly also less knowledge in rural peoples about COVID -19 infection was reason of high risk of spread of infection amongst them. Children in rural areas who are economically not that much well can’t get access to internet and cannot afford the mobile or laptops for online education this made the chances of drop outs in these children. During lockdown due to pandemic all the farmers and small shop holders faced huge loss ,this was the period of great earnings from various rabi crops but due to shortage of labor and non-availability of various machineries delay harvest and this lead to huge loss for farmers.

Panic Spreading Model with Different Emotions under Emergency

Emotion plays an important role in decision making. In an emergency, panic can spread among crowds through person-to-person communications and can cause harmful effects on society. The aim of this paper is to propose a new theoretical model in the context of epidemiology to describe the spread of panic under an emergency. First, according to divisions in personality in the context of psychology, groups are divided into a level-headed group and an impatient group. Second, individuals in the two groups have unique personalities. Thus, the level-headed group only infects within the group, while the impatient group considers emotional infection within the group and cross infection between the groups. Then, a nonlinear infection rate is used to describe the probability of infection after an infected person contacts a susceptible person, which is more in line with the real situation. After that, the level-headed group–impatient group nonlinear SIRS panic spreading model is developed. Stable analysis of the model is obtained using the Lyapunov function method to study the stability of the panic-free equilibrium and panic-permanence equilibrium. Finally, simulations are carried out to dynamically describe the spread process of group emotional contagion.

Computing the daily reproduction number of COVID-19 by inverting the renewal equation using a variational technique

The COVID-19 pandemic has undergone frequent and rapid changes in its local and global infection rates, driven by governmental measures or the emergence of new viral variants. The reproduction number Rt indicates the average number of cases generated by an infected person at time t and is a key indicator of the spread of an epidemic. A timely estimation of Rt is a crucial tool to enable governmental organizations to adapt quickly to these changes and assess the consequences of their policies. The EpiEstim method is the most widely accepted method for estimating Rt. But it estimates Rt with a significant temporal delay. Here, we propose a method, EpiInvert, that shows good agreement with EpiEstim, but that provides estimates of Rt several days in advance. We show that Rt can be estimated by inverting the renewal equation linking Rt with the observed incidence curve of new cases, it. Our signal-processing approach to this problem yields both Rt and a restored it corrected for the “weekend effect” by applying a deconvolution and denoising procedure. The implementations of the EpiInvert and EpiEstim methods are fully open source and can be run in real time on every country in the world and every US state.

COVID-19: Face Mask Detector with Open CV and CNN Algorithm

Now a day, covering our faces with a mask has become a new normal habit in this pandemic, as face masks are effective in preventing the virus outbreak. Masks reduce risk from an infected person whether they have symptoms or not. In this paper, we propose a system that restrict the growth of COVID-19 by finding out peoples with mask and without mask. Where all the public places are monitored with CCTV cameras. A deep learning architecture is trained on a dataset which consists of images of people with wearing mask and without wearing is masks collected from various sources. By using image processing analysis and machine learning method we can find out face mask wearied or not. Face mask detection can be done using various methods. Mainly convolutional neural network and OpenCV method is used. The accuracy and decision making of CNN algorithm is higher than other algorithms.