ABSTRACT
A novel coronavirus causing the severe and fatal respiratory syndrome was identified in China, is now producing outbreaks in more than two hundred countries around the world, and became pandemic by the time. In this article, a modified version of the well-known mathematical epidemic model Susceptible (S)- Infected (I)- Recovered (R) is used to analyze the epidemic’s course of COVID-19 in eight different countries of the South Asian Association for Regional Cooperation (SAARC). To achieve this goal, the parameters of the SIR model are identified by using publicly available data for the corresponding countries: Afghanistan, Bangladesh, Bhutan, India, the Maldives, Nepal, Pakistan, and Sri Lanka. Based on the prediction model we estimated the epidemic trend of COVID-19 outbreak in SAARC countries for 20 days, 90 days, and 180 days respectively. An SML (short-mid-long) term prediction model has been designed to understand the early dynamics of the COVID-19 Epidemic in the southeast Asian region. The maximum and minimum basic reproduction numbers (R0 = 1.33 and 1.07) for SAARC countries are predicted to be in Pakistan and Bhutan. We equate simulation results with real data in the SAARC countries on the COVID-19 outbreak, and model potential countermeasure implementation scenarios. Our results should provide policymakers with a method for evaluating the impacts of possible interventions, including lockdown and social distancing, as well as testing and contact tracking.
Subject(s)
COVID-19 , Encephalitis, Arbovirus , Respiratory Tract InfectionsABSTRACT
This paper aims to study the fight against COVID-19 in Bangladesh and digital intervention initiatives. To achieve the purpose of our research, we conducted a methodical review of online content. We have reviewed the first digital intervention that COVID-19 has been used to fight against worldwide. Then we reviewed the initiatives that have been taken in Bangladesh. Our paper has shown that while Bangladesh can take advantage of the digital intervention approach, it will require rigorous collaboration between government organizations and universities to get the most out of it. Public health can become increasingly digital in the future, and we are reviewing international alignment requirements. This exploration also focused on the strategies for controlling, evaluating, and using digital technology to strengthen epidemic management and future preparations for COVID-19.
Subject(s)
COVID-19ABSTRACT
The next big step in combating the coronavirus disease 2019 (COVID-19) pandemic will be gaining widespread acceptance of a vaccination campaign for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but achieving high uptake need proper understandings. Many health professionals, researchers, statisticians, and programmers to track the viruses spread in different parts of the world have used various methods. However, the proliferation of vaccines produced by talented scientists around the world has sparked a strong desire to extract meaningful insights from available data. Until now, several vaccines against coronavirus disease (COVID-19) have been approved and are being distributed worldwide in various regions. This study aims to report the detailed data analysis and result-oriented storytelling of the COVID-19 vaccination program of different countries across the globe. To analyze the vaccination trend globally this research utilized two different open datasets provided by ourworldindata.org and worldometers.info. An exploratory data analysis (EDA) with interactive data visualization using various python libraries was conducted, and the results are presented in this article to better understand the impact of ongoing vaccination programs around the world. Apart from the valuable insights gained from the data of various countries, this investigation also included a comparison of the number of confirmed and death cases before and after vaccination to determine the efficacy of each vaccine in each country. The results show that a large number of people are still undecided about whether or not to get a COVID-19 vaccine, despite the viruss continued devastating effects on communities. Overall, our findings contribute to ongoing research aimed at informing policy on how to persuade the unvaccinated to be vaccinated.
Subject(s)
COVID-19ABSTRACT
We are living in the era of the fourth industrial revolution, which also treated as 4IR or Industry 4.0. Generally, 4IR considered as the mixture of robotics, artificial intelligence (AI), quantum computing, the Internet of Things (IoT) and other frontier technologies. It is obvious that nowadays a plethora of smart devices is providing services to make the daily life of human easier. However, in the morning most people around the globe use a traditional mirror while preparing themselves for daily task. The aim is to build a low-cost intelligent mirror system that can display a variety of details based on user recommendations. Therefore, in this article, Internet of Things (IoT) and AI-based smart mirror is introduced that will support the users to receive the necessary daily update of weather information, date, time, calendar, to-do list, updated news headlines, traffic updates, COVID-19 cases status and so on. Moreover, a face detection method also implemented with the smart mirror to construct the architecture more secure. Our proposed MirrorME application provides a success rate of nearly 87% in interacting with the features of face recognition and voice input. The mirror is capable of delivering multimedia facilities while maintaining high levels of security within the device.
Subject(s)
COVID-19ABSTRACT
Purpose: Globally, there is an obvious concern about the fact that the evolving 2019-nCoV coronavirus is a worldwide public health threat. The appearance in China at the end of 2019 of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; previously provisionally labeled as 2019 novel coronavirus or 2019-nCoV) disease (COVID-19) caused a major global outbreak and right now is a major community health issue. As of 8 March 2020, World Health Organization (WHO) data showed that more than 105 500 confirmed cases were reported in over 100 countries/regions, with > 75% of cases being detected in China and >24% of cases detected globally. COVID-19 outbreak is evolving so rapidly; therefore, the available epidemiological data are essential to direct strategies for situational awareness and intervention. Methods: This article will present a visual exploratory data analysis (V-EDA) approach to collect and analyze COVID-19 data on epidemiological outbreaks. Various open data sources on the outbreak of COVID-19 provided by the World Health Organization (WHO), the Chinese Center for Disease Control and Prevention (CDC), the National Health Commission (NHC), Johns Hopkins University Interactive Dashboard and DXY.cn have been used in this research.Results: Therefore, an Exploratory Data Analysis (EDA) with visualizations has been designed and developed in order to understand the number of different cases reported (confirmed, death, and recovered) in different provinces of China and outside of China between 22 January 2020 to 4 March 2020. Various open data sources on the outbreak of COVID-19 provided by the World Health Organization (WHO), the Chinese Center for Disease Control and Prevention (CDC), the National Health Commission (NHC), Johns Hopkins University Interactive Dashboard and DXY.cn have been used in this research. Conclusion: In all, this is extremely important to promptly spread information to understand the risks of this pandemic and begin containment activities.
Subject(s)
COVID-19 , Coronavirus InfectionsABSTRACT
Globally, there is an obvious concern about the fact that the evolving 2019-nCoV coronavirus is a worldwide public health threat. The appearance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in China at the end of 2019 triggered a major global epidemic, which is now a major community health issue. As of April 17, 2020, according to Institute of Epidemiology, Disease Control and Research (IEDCR) Bangladesh has reported 1838 confirmed cases in between 8 March to 17 April 2020, with > 4.08% of mortality rate and >3.15% of recovery rate. COVID-19 outbreak is evolving so rapidly in Bangladesh; therefore, the availability of epidemiological data and its sensible analysis are essential to direct strategies for situational awareness and intervention. This article presents an exploratory data analysis approach to collect and analyze COVID-19 data on epidemiological outbreaks based on first publicly available COVID-19 Daily Dataset of Bangladesh. Various publicly open data sources on the outbreak of COVID-19 provided by the IEDCR, World Health Organization (WHO), Directorate General of Health Services (DGHS), and Ministry of Health and Family Welfare (MHFW) of Bangladesh have been used in this research. A Visual Exploratory Data Analysis (V-EDA) techniques have been followed in this research to understand the epidemiological characteristics of COVID-19 outbreak in different districts of Bangladesh in between 8 March 2020 to 12 April 2020 and these findings were compared with those of other countries. In all, this is extremely important to promptly spread information to understand the risks of this pandemic and begin containment activities in the country.
Subject(s)
COVID-19ABSTRACT
A novel coronavirus causing the severe and fatal respiratory syndrome was identified in China, is now producing outbreaks in more than two hundred countries around the world, and became pandemic by the time. In this article, a modified version of the well known mathematical epidemic model Susceptible (S)- Infected (I)- Recovered (R) is used to analyze the epidemic's course of COVID-19 in eight different countries of the South Asian Association for Regional Cooperation (SAARC). To achieve this goal, the parameters of the SIR model are identified by using publicly available data for the corresponding countries: Afghanistan, Bangladesh, Bhutan, India, the Maldives, Nepal, Pakistan and Sri Lanka. Based on the prediction model we estimated the epidemic trend of COVID-19 outbreak in SAARC countries for 20 days, 90 days, and 180 days respectively. An SML (short-mid-long) term prediction model has been designed to understand the early dynamics of COVID-19 Epidemic in the south-east Asian region. The maximum and minimum basic reproduction number (R0 = 1.33 and 1.07) for SAARC countries are predicted to be in Pakistan and Bhutan. We equate simulation results with real data in the SAARC countries on the COVID-19 outbreak, and model potential countermeasure implementation scenarios. Our results should provide policymakers with a method for evaluating the impacts of possible interventions, including lockdown and social distancing, as well as testing and contact tracking.
Subject(s)
COVID-19 , Respiratory InsufficiencyABSTRACT
COVID-19 or novel coronavirus disease, which has already been declared as a worldwide pandemic, at first had an outbreak in a small town of China, named Wuhan. More than two hundred countries around the world have already been affected by this severe virus as it spreads by human interaction. Moreover, the symptoms of novel coronavirus are quite similar to the general flu. Screening of infected patients is considered as a critical step in the fight against COVID-19. Therefore, it is highly relevant to recognize positive cases as early as possible to avoid further spreading of this epidemic. However, there are several methods to detect COVID-19 positive patients, which are typically performed based on respiratory samples and among them one of the critical approach which is treated as radiology imaging or X-Ray imaging. Recent findings from X-Ray imaging techniques suggest that such images contain relevant information about the SARS-CoV-2 virus. In this article, we have introduced a Deep Neural Network (DNN) based Faster Regions with Convolutional Neural Networks (Faster R-CNN) framework to detect COVID-19 patients from chest X-Ray images using available open-source dataset. Our proposed approach provides a classification accuracy of 97.36%, 97.65% of sensitivity, and a precision of 99.28%. Therefore, we believe this proposed method might be of assistance for health professionals to validate their initial assessment towards COVID-19 patients.