Human civilization is experiencing a critical situation that presents itself for a new coronavirus disease 2019 (COVID-19). This virus emerged in late December 2019 in Wuhan city, Hubei, China. The grim fact of COVID-19 is, it is highly contagious in nature, therefore, spreads rapidly all over the world and causes severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Responding to the severity of COVID-19 research community directs the attention to the analysis of COVID-19, to diminish its antagonistic impact towards society. Numerous studies claim that the subcontinent, i.e., Bangladesh, India, and Pakistan, could remain in the worst affected region by the COVID-19. In order to prevent the spread of COVID-19, it is important to predict the trend of COVID-19 beforehand the planning of effective control strategies. Fundamentally, the idea is to dependably estimate the reproduction number to judge the spread rate of COVID-19 in a particular region. Consequently, this paper uses publicly available epidemiological data of Bangladesh, India, and Pakistan to estimate the reproduction numbers. More specifically, we use various models (for example, susceptible infection recovery (SIR), exponential growth (EG), sequential Bayesian (SB), maximum likelihood (ML) and time dependent (TD)) to estimate the reproduction numbers and observe the model fitness in the corresponding data set. Experimental results show that the reproduction numbers produced by these models are greater than 1.2 (approximately) indicates that COVID-19 is gradually spreading in the subcontinent.
The time when everyone is struggling in the cruel hands of COVID19, we present the holistic view on the effects of this pandemic in certain aspects of life. A lot of literature exists in COVID-19, but most of them talk about the social and psychological side of the COVID problems. COVID-19 has affected our day-to-day life and its effects are extensive. Most of the literature presents the adverse effect of the pandemic, but there are very few state-of-the-art approaches that discuss its beneficial effects. We see the multiple faces of the pandemic in this paper. To the best of our knowledge, th…
Pandemic is an outbreak that happens over a large geographic area affecting a greater portion of the population as new pathogens appear for which people have less immune and no vaccines are available. It can spread from person to person in a very short time, and in fact, the health workers are at greater risk of infection because of the patients who carry the disease. In the 21st century, where everyone is connected through digital technologies, Information and Communication Technology (ICT) plays a critical role in improving health care for individuals and larger communities. ICT has currentl…
During a disease outbreak, timely non-medical interventions are critical in preventing the disease from growing into an epidemic and ultimately a pandemic. However, taking quick measures requires the capability to detect the early warning signs of the outbreak. This work collects Twitter posts surrounding the 2020 COVID-19 pandemic expressing the most common symptoms of COVID-19 including cough and fever, geolocated to the United States. Through examining the variation in Twitter activities at the state level, we observed a temporal lag between the rises in the number of symptom reporting twee…
Due to the COVID-19 as a pandemic, the government has forced the nationwide shutdown of several activities, including educational activities. It has resulted in gigantic migration of universities with education over the internet serving as the educational platform. Hand-on-based learning becomes a new challenge. This paper aims to investigate the effect of computer simulation-based learning on student self-efficacy in an electric circuit analysis course. For the 17 participants included in this study, the students have overcome their existing achievements indicated by a long-term average score…
The very first infected novel coronavirus case (COVID-19) was found in Hubei, China in Dec. 2019. The COVID-19 pandemic has spread over 214 countries and areas in the world, and has significantly affected every aspect of our daily lives. At the time of writing this article, the numbers of infected cases and deaths still increase significantly and have no sign of a well-controlled situation, e.g., as of 13 July 2020, from a total number of around 13.1 million positive cases, 571, 527 deaths were reported in the world. Motivated by recent advances and applications of artificial intelligence (AI)…
As the novel coronavirus spreads across the world, concerns regarding the spreading of misinformation about it are also growing. Here we estimate the prevalence of links to low-credibility information on Twitter during the outbreak, and the role of bots in spreading these links. We find that the combined volume of tweets linking to low-credibility information is comparable to the volume of New York Times articles and CDC links. Content analysis reveals a politicization of the pandemic. The majority of this content spreads via retweets. Social bots are involved in both posting and amplifying lo…
Assessment of risks of pandemics to communities and workplaces requires an intelligent decision support system (DSS). The core of such DSS must be based on machine reasoning techniques such as inference and shall be capable of estimating risks and biases in decision making. In this paper, we use a causal network to make Bayesian inference on COVID-19 data, in particular, assess risks such as infection rate and other precaution indicators. Unlike other statistical models, a Bayesian causal network combines various sources of data through joint distribution, and better reflects the uncertainty o…
As of July 31, 2020, the COVID-19 pandemic has over 17 million reported cases, causing more than 667,000 deaths. Countries irrespective of economic status have succumbed to this pandemic. Many aspects of the lives, including health, economy, freedom of movement have been negatively affected by the coronavirus outbreak. Numerous strategies have been taken in order to prevent the outbreak. Some countries took severe resections in the form of full-scale lockdown, while others took a moderate approach of dealing with the pandemics, for example, mass testing, prohibiting large-scale public gatherin…
Internet of Things (IoT) has grown rapidly in the last decade and continue to develop in terms of dimension and complexity offering wide range of devices to support diverse set of applications. With ubiquitous Internet, connected sensors and actuators, networking and communication technology, and artificial intelligence (AI), smart cyber-physical systems (CPS) provide services rendering assistance to humans in their daily lives. However, the recent outbreak of COVID-19 (also known as coronavirus) pandemic has exposed and highlighted the limitations of current technological deployments to curta…
The rapid spread of the novel corona virus, SARS-CoV-2, has prompted an unprecedented response from governments across the world. A third of the world population have been placed in varying degrees of lockdown, and the Internet has become the primary medium for conducting most businesses and schooling activities. This paper aims to provide a multi-prospective account of Internet performance during the first wave of the pandemic. We investigate the performance of the Internet control plane and data plane from a number of globally spread vantage points. We also look closer at two case studies. F…
The rapid spread of COVID-19 has affected thousands of people from different socio-demographic groups all over the country. A decisive step in preventing or slowing the outbreak is the use of mobility interventions, such as government stay-at-home orders. However, different socio-demographic groups might have different responses to these orders and regulations. In this paper, we attempt to fill the current gap in the literature by examining how different communities with different age groups performed social distancing by following orders such as the national emergency declaration on March 13,…
University of Toronto（多伦多大学）；University of Pittsburgh（匹兹堡大学）；Chinese Academy of Sciences（中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）
The study aims to understand Twitter users' discourse and psychological reactions to COVID-19. We use machine learning techniques to analyze about 1.9 million Tweets (written in English) related to coronavirus collected from January 23 to March 7, 2020. A total of salient 11 topics are identified and then categorized into ten themes, including "updates about confirmed cases," "COVID-19 related death," "cases outside China (worldwide)," "COVID-19 outbreak in South Korea," "early signs of the outbreak in New York," "Diamond Princess cruise," "economic impact," "Preventive measures," "authorities…
Pandemic influenza has great potential to cause large and rapid increases in deaths and serious illness. The objective of this paper is to develop an agent-based model to simulate the spread of pandemic influenza (novel H1N1) in Egypt. The proposed multi-agent model is based on the modeling of individuals' interactions in a space time context. The proposed model involves different types of parameters such as: social agent attributes, distribution of Egypt population, and patterns of agents' interactions. Analysis of modeling results leads to understanding the characteristics of the modeled pan…
The COVID-19 pandemic has profoundly impacted the world, having taken the lives of over 6 million individuals. Accordingly, this pandemic has caused a shift in conversations surrounding the burden of diseases worldwide, welcoming insights from multidisciplinary fields including digital health and artificial intelligence. Africa faces a heavy disease burden that exacerbates the current COVID-19 pandemic and limits the scope of public health preparedness, response, containment, and case management. Herein, we examined the potential impact of transformative digital health technologies in mitigati…
The COVID-19 pandemic has resulted in significant social and economic impacts throughout the world. In addition to the health consequences, the impacts on traffic behaviors have also been sudden and dramatic. We have analyzed how the road traffic safety of New York City, Los Angeles, and Boston in the U.S. have been impacted by the pandemic and corresponding local government orders and restrictions. To be specific, we have studied the accident hotspots' distributions before and after the outbreak of the pandemic and found that traffic accidents have shifted in both location and time compared t…
The COVID-19 pandemic has shed light on how the spread of infectious diseases worldwide are importantly shaped by both human mobility networks and socio-economic factors. Few studies, however, have examined the interaction of mobility networks with socio-spatial inequalities to understand the spread of infection. We introduce a novel methodology, called the Infection Delay Model, to calculate how the arrival time of an infection varies geographically, considering both effective distance-based metrics and differences in regions' capacity to isolate -- a feature associated with socioeconomic ine…
University of Virginia（弗吉尼亚大学）；Global Infectious Disease Institute, University of Virginia（弗吉尼亚大学全球传染病研究所）
To mitigate the spread of COVID-19 pandemic, decision-makers and public authorities have announced various non-pharmaceutical policies. Analyzing the causal impact of these policies in reducing the spread of COVID-19 is important for future policy-making. The main challenge here is the existence of unobserved confounders (e.g., vigilance of residents). Besides, as the confounders may be time-varying during COVID-19 (e.g., vigilance of residents changes in the course of the pandemic), it is even more difficult to capture them. In this paper, we study the problem of assessing the causal effects …
We examine the implementation of social robots in real-world settings during the COVID-19 pandemic. In particular, we analyze the areas in which social robots are being adopted, the roles and tasks being fulfilled, and the robot models being implemented. For this, we traced back and analyzed 240 deployment cases with 86 different social robots worldwide that have been adopted since the coronavirus outbreak. We found that social robot adoption during this period was strongly related to the use of this technology for crisis management. The social robots' capacity to perform the roles of liaison …
Novel coronavirus-19 (2019-nCoV or COVID-19) is by far the most dangerous coronavirus ever identified for the third time in the three decades capable of infecting not only the animals but also the humans across the globe. Nearly 6000 deaths have been recorded due mainly to COVID-19 outbreak worldwide and more than 50% of these deaths appeared to have evolved from China where the virus was thought to originate. The endemicity of COVID-19 dramatically surpassed severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) that were so far di…
The outbreak of the COVID-19 pandemic triggers infodemic over online social media, which significantly impacts public health around the world, both physically and psychologically. In this paper, we study the impact of the pandemic on the mental health of influential social media users, whose sharing behaviours significantly promote the diffusion of COVID-19 related information. Specifically, we focus on subjective well-being (SWB), and analyse whether SWB changes have a relationship with their bridging performance in information diffusion, which measures the speed and wideness gain of informat…
Background: The outbreak of the new coronavirus disease (COVID-19) has affected human life to a great extent on a worldwide scale. During the coronavirus pandemic, public health professionals at the early outbreak faced an extraordinary challenge to track and quantify the spread of disease. Objective: To investigate whether a digital surveillance model using google trends (GT) is feasible to monitor the outbreak of coronavirus in the Kingdom of Saudi Arabia. Methods: We retrieve GT data using ten common COVID-19 symptoms related keywords from March 2, 2020, to October 31, 2020. Spearman correl…
The outbreak of the SARS-CoV-2 novel coronavirus (COVID-19) has been accompanied by a large amount of misleading and false information about the virus, especially on social media. During the pandemic social media gained special interest as it went on to become an important medium of communication. This made the information being relayed on these platforms especially critical. In our work, we aim to explore the percentage of fake news being spread on Twitter as well as measure the sentiment of the public at the same time. We further study how the sentiment of fear is present among the public. I…
After the pandemic of SARS-CoV-2, it has influenced the health care practices around the world. Initial investigations indicate that patients with comorbidities are more fragile to this SARS-CoV-2 infection. They suggested postponing the routine treatment of cancer patients. However, few meta-analyses suggested evidences are not sufficient to hold the claim of the frailty of cancer patients to COVID-19, and they are not in favour of shelving the scheduled procedures. There are recent studies in which medical professionals, according to their competence, are referring to change the routine prac…
COVID-19 resulted in an infodemic, which could erode public trust, impede virus containment, and outlive the pandemic itself. The evolving and fragmented media landscape is a key driver of the spread of misinformation. Using misinformation identified by the fact-checking platform by Tencent and posts on Weibo, our results showed that the evolution of misinformation follows an issue-attention cycle, pertaining to topics such as city lockdown, cures, and preventions, and school reopening. Sources of authority weigh in on these topics, but their influence is complicated by peoples' pre-existing b…
The outbreak of the COVID-19 pandemic has deeply influenced the lifestyle of the general public and the healthcare system of the society. As a promising approach to address the emerging challenges caused by the epidemic of infectious diseases like COVID-19, Internet of Medical Things (IoMT) deployed in hospitals, clinics, and healthcare centers can save the diagnosis time and improve the efficiency of medical resources though privacy and security concerns of IoMT stall the wide adoption. In order to tackle the privacy, security, and interoperability issues of IoMT, we propose a framework of bl…
Female researchers may have experienced more difficulties than their male counterparts since the COVID-19 outbreak because of gendered housework and childcare. Using Microsoft Academic Graph data from 2016 to 2020, this study examined how the proportion of female authors in academic journals on a global scale changed in 2020 (net of recent yearly trends). We observed a decrease in research productivity for female researchers in 2020, mostly as first authors, followed by last author position. Female researchers were not necessarily excluded from but were marginalised in research. We also identi…
The unprecedented behavioural responses of societies have been evidently shaping the COVID-19 pandemic, yet it is a significant challenge to accurately monitor the continuously changing social mixing patterns in real-time. Contact matrices, usually stratified by age, summarise interaction motifs efficiently, but their collection relies on conventional representative survey techniques, which are expensive and slow to obtain. Here we report a data collection effort involving over $2.3\%$ of the Hungarian population to simultaneously record contact matrices through a longitudinal online and seque…
