There is an obvious concern globally regarding the fact about the emerging coronavirus 2019 novel coronavirus (2019-nCoV) as a worldwide public health threat. As the outbreak of COVID-19 causes by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) progresses within China and beyond, rapidly available epidemiological data are needed to guide strategies for situational awareness and intervention. The recent outbreak of pneumonia in Wuhan, China, caused by the SARS-CoV-2 emphasizes the importance of analyzing the epidemiological data of this novel virus and predicting their risks of infecting people all around the globe. In this study, we present an effort to compile and analyze epidemiological outbreak information on COVID-19 based on the several open datasets on 2019-nCoV provided by the Johns Hopkins University, World Health Organization, Chinese Center for Disease Control and Prevention, National Health Commission, and DXY. An exploratory data analysis with visualizations has been made to understand the number of different cases reported (confirmed, death, and recovered) in different provinces of China and outside of China. Overall, at the outset of an outbreak like this, it is highly important to readily provide information to begin the evaluation necessary to understand the risks and begin containment activities.

Medical researchers around the globe provide evidence that COVID-19 pandemic diseases transmitted through droplets and respirators of respiratory aerosols and wearing a face mask is an efficient infection control recommendation process. In addition, many public and private service providers demand that consumers use the service only if they wear masks properly. However, a few research studies have been found on face mask detection based on the technology of Artificial Intelligence (AI) and Image Processing. In this article, we propose, MobileNet Mask, which is a deep learning-based multi-phase face mask detection model for preventing human transmission of SARS-CoV-2. Two different face mask datasets along with more than 5,200 images have been utilized to train and test the model for detecting with and without a face mask from the images and video stream. Experiment results show that with 770 validation samples MobileNet Mask achieves an accuracy of9 3% whereas with 276 validation samples it attains an accuracy of nearly~100%. Lastly, we also discuss the possibility of implementing our proposed MobileNet Mask model on light-weighted computing devices such as mobile or embedded devices. Besides, this proposed model also introduces frontier technologies to support the efforts of government and public health guidelines with anticipation of implementing mandatory face mask regulations all over the world.

No abstract

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 August 13, 2020, according to the Institute of Epidemiology, Disease Control and Research (IEDCR), Bangladesh has reported 269,095 confirmed cases between 8 March and 13 August 2020, with > 1.30% of mortality rate and > 57% 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 the 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. 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 between 8 March 2020 and 13 August 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.

Dengue fever is a severe disease spread by Aedes mosquito-borne dengue viruses (DENVs) in tropical areas such as Bangladesh. Since its breakout in the 1960s, dengue fever has been endemic in Bangladesh, with the highest concentration of infections in the capital, Dhaka. This study aims to develop a machine learning model that can use relevant information about the factors that cause Dengue outbreaks within a geographic region. To predict dengue cases in 11 different districts of Bangladesh, we created a DengueBD dataset and employed two machine learning algorithms, Multiple Linear Regression (MLR) and Support Vector Regression (SVR). This research also explores the correlation among environmental factors like temperature, rainfall, and humidity with the rise and decline trend of Dengue cases in different cities of Bangladesh. The entire dataset was divided into an 80:20 ratio, with 80 percent used for training and 20% used for testing. The research findings imply that, for both the MLR with 67% accuracy along with Mean Absolute Error (MAE) of 4.57 and SVR models with 75% accuracy along with Mean Absolute Error (MAE) of 4.95, the number of dengue cases reduces throughout the winter season in the country and increases mainly during the rainy season in the next ten months, from August 2021 to May 2022. Importantly, Dhaka, Bangladesh’s capital, will see the maximum number of dengue patients during this period. Overall, the results of this data-driven analysis show that machine learning algorithms have enormous potential for predicting dengue epidemics.