ABSTRACT
It is now widely known that the hazards can be natural, but most disasters are ‘human-made’. The failure to properly implement developmental policies and practices with due consideration to disaster risk management is the leading cause of turning a hazard into a disaster.25 This, in return, negatively affects sustainable development which ultimately affects the weakest and the poorest sections of society. Disaster impacts have been felt on a wide range of sectors and sections of the population. They are curbing progress made toward achieving the Sendai Framework targets, and SDGs. Climate and human-induced disaster events have exposed several underlying facets of risks' systemic and cascading nature. There is an urgent need to identify, analyse and better understand the multi-hazard, systemic and cascading nature of the disaster and climate risks, their inter-linkages, and interplay. A holistic understanding of risk is crucial for furthering the priorities of action laid under the Sendai Framework and the envisioned SDGs and ensuring a better, greener, resilient and sustainable society. We have tried to study the disaster management frameworks, plans and policies of 10 countries including India to understand the institutional mechanisms and integration of critical aspects of dual/multi disaster scenarios. When the traditional disasters hit the community following the COVID-19 pandemic, the need arises to have an integrated model that can assisting in the preparation and response to the dual situation simultaneously. Efforts are made to put the experiences into a framework for an integrated approach preparing for dual/multi-disaster scenarios. © 2022, World Research Association. All rights reserved.
ABSTRACT
The novel coronavirus and its emerging variants have continued to affect 50.4 million people worldwide, increasing the need for safe and effective vaccines. According to the World Health Organization guidelines, the efficacy of a vaccine should be at least 30% in all age groups and protect for a longer duration without any life-threatening adverse effects. At present, there are 319 vaccines in various stages of development, of which 16 are authorized for emergency use. Of these 16 vaccines, five vaccines are based on adenoviral vectors. This review is focused on understanding the safety and efficacy of the approved adenoviral vector vaccines for COVID-19, particularly highlighting the interim analysis of phase 3 clinical trials of AZD1222, Gam-Covid-Vac, Ad26.COV2.S, and AD5-nCOV vaccine. The efficacy of AZD1222, Gam-Covid-Vac, Ad26.COV2.S, and AD5-nCOV vaccine were found to be 70.4%, 95%, 66%, and 65.7%, respectively. Some serious adverse events such as deep vein thrombosis and thrombosis with thrombocytopenia syndrome were observed among AZD1222 and Ad26.COV2.S vaccinated individuals. Meanwhile, Gam-Covid-Vac and AD5-nCOV vaccines did not report any significant adverse events. In addition, we have also focused on the efficacy of these vaccines against SARS-CoV-2 variants such as B.1.1.7, B.1.351, and P.1. Although the efficacy of these approved vaccines against novel SARS-CoV-2 variants, pediatric and geriatric population and long-term efficacy remains uncertain, they are reasonably efficient in preventing mortality due to COVID-19.
ABSTRACT
Technology advancements have a rapid effect on every field of life, be it medical field or any other field. Artificial intelligence has shown the promising results in health care through its decision making by analysing the data. COVID-19 has affected more than 100 countries in a matter of no time. People all over the world are vulnerable to its consequences in future. It is imperative to develop a control system that will detect the coronavirus. One of the solutions to control the current havoc can be the diagnosis of disease with the help of various AI tools. The proposed system contains textual data analysis as well as real time physiological data analysis concept. The embedded platform reads the body temperature and heart rate of the patients. The patient is automatically induced to attend the pre-screening survey designed using the software GUI that collects most of the information on symptoms persists. A COVID-19 dataset is collected from publicly available websites. The read survey values and sensor values are pre-processed and extracted the unique features present in it. Those unique parameters are compared with the database to produce the output showing COVID positive status or Negative status and immediate medicine suggestions for them using the global collective medicine suggestions box. © 2021 Institute of Physics Publishing. All rights reserved.