Geo-Spatial Disease Clustering for Public Health Decision Making

An explosion of interest has been observed in disease mapping with the developments in advanced spatial statistics, data visualization and geographic information system (GIS) technologies. This technique is known as "Geo-Spatial Disease Clustering," mainly used for visualization and future disease expansion prediction. Its importance has been overwhelmingly observed since the COVID-19 pandemic outbreak. Government, Medical Institutes, and other medical practices gather large amounts of data from surveys and other sources. This data is in the form of notes, databases, spread sheets and text data files. Mostly this information is in the form of feedback from different groups like age group, gender, provider (doctors), region, etc. Incorporating such heterogeneous nature of data is quite challenging task. In this regard, variety of techniques and algorithms have been proposed in the literature, but their effectiveness varies due to data types, volume, format and structure of data and disease of interest. Mostly, the techniques are confined to a specific data type. To overcome this issue, in this research, a data visualization technique combined with data warehousing and GIS for disease mapping is proposed. This includes data cleansing, data fusion, data dimensioning, analysis, visualization, and prediction. Motivation behind this research is to create awareness about the disease for the guidance of patients, healthcare providers and government bodies. By this, we can extract information that describes the association of disease with respect to age, gender, and location. Moreover, the temporal analysis helps earlier prediction and identification of disease, to be care of and necessary avoiding arrangements can be taken.

Classifying COVID-19 Disinformation on Twitter using a Convolutional Neural Network

Disinformation regarding COVID-19 is spreading rapidly on social media platforms and can cause undesirable consequences for people who rely on such content. To combat disinformation, several platform providers have implemented intelligent systems to detect disinformation and provide measurements that apprise users of the quality of information being disseminated on social media platforms. For this purpose, intelligent systems employing deep learning approaches are often applied, hence, their effectivity requires closer analysis. The study begins with a thorough literature review regarding the concept of disinformation and its classification. This paper models and evaluates a disinformation detector that uses a convolutional neural network to classify samples of social media content. The evaluation of the proposed deep learning model showed that it performed well overall in discriminating the fake-labelled tweets from the real-labelled tweets;the model yielded an accuracy score of 97.2%, a precision score of 95.7% and a recall score of 99.8%. Consequently, the paper contributes an effective disinformation detector, which can be used as a tool to combat the substantial volume of disinformation scattered throughout social media platforms. A more standardised feature extraction for disinformation cases should be the subject of subsequent research.

A Covid 19: QR Code based Health Monitoring System

The epic Covid 2019 is likewise known as Coronavirus is a continuous infection that was spread rapidly around the world. It's the outbreak affects hundreds of thousands of people and it has a growing impact on daily lives. It affected the global economy and environment too. The main problem is that in the densely populated country it's very difficult to detect the positive cases earlier as much as possible and to prevent the rapid spread of this and to treat the patients quickly. This project is aimed at supplying healthcare providers with ways to use the 'Health Monitoring System' to reduce such viral flues. This project is mainly focused on android application development. It is mainly directed to the people in quarantine. Their information will be collected from these people themselves. By this application collecting the details of people in a questionnaire mode by using a QR code who has gone for the test of covid'19. This application provides physically contactless and wireless technology to help prevent the further spreading of this disease at the time of collecting the details of the people. Here the details and results of people are transferred to the applications are done by scanning the QR codes. The main advantages of this project are that there is no need of enquiring about health for health workers by contacting the patient's details directly. The significance and purpose of this project are to providing user-friendly support to reduce the number of infected people and helping to maintain social distance and also reduce physical contact and find the exact location of the patient. © 2021 IEEE.

Identifying and Characterizing COVID-19 Themed Malicious Domain Campaigns

Ever since the beginning of the outbreak of the COVID-19 pandemic, attackers acted quickly to exploit the confusion, uncertainty and anxiety caused by the pandemic and launched various attacks through COVID-19 themed malicious domains. Malicious domains are rarely deployed independently, but rather almost always belong to much bigger and coordinated attack campaigns. Thus, analyzing COVID-themed malicious domains from the angle of attack campaigns would help us gain a deeper understanding of the scale, scope and sophistication of the threats imposed by such malicious domains. In this paper, we collect data from multiple sources, and identify and characterize COVID-themed malicious domain campaigns, including the evolution of such campaigns, their underlying infrastructures and the different strategies taken by attackers behind these campaigns. Our exploration suggests that some malicious domains have strong correlations, which can guide us to identify new malicious domains and raise alarms at the early stage of their deployment. The results shed light on the emergency for detecting and mitigating public event related cyber attacks. © 2021 ACM.

Racial Inequity in COVID-19 ECMO Outcomes

Racial health inequities have been widely recognized in many healthcare arenas in the United States. The ongoing COVID-19 pandemic is no exception, with many reports of minority populations being disproportionately affected by the disease. Our experience at a major urban medical center in Washington DC mirrors these reports, with further burden of disease on Latino individuals, specifically. Despite making up less than 10% of the general population of the DC Metro Area, 26% of patients admitted to our facility identified as Latino. Further, 17% of deaths were people who self-identified as Latino. Additionally, 63% of our COVID-19 patients supported with ECMO were Latino, and 64% of our COVID-19 ECMO deaths were also Latino. This suggests that not only is the incidence of disease disproportionately higher in Latino populations, but the severity as well. There was no significant difference in comorbidities between Latino and non-Latino populations in regard to age, pre-existing conditions, duration of illness and duration of ECMO run. These observations demand more investigation to the etiology of these striking disparities. Additionally, it should prompt study into other factors such as socioeconomic status contributing to living conditions, and improving effective public health messaging and ensuring its accessibility to all populations.