Post Pandemic Cyber Attacks Impacts and Countermeasures: A Systematic Review

COVID-19 is one of the deadliest pandemics of this century's that affected the whole world. As the COVID-19 spread the government had to impose lockdown that pushed the people to follow some new lifestyle like social distancing, work from home, hand washing, and the country have to shut down industries, businesses and public transport. At the same time, doctors were occupied in saving life's and on other side cyber criminals were busy taking this situation as advantage, which creates an another silent pandemic i.e. cyber-security pandemic. During this pandemic with overloaded ICT infrastructure, cyber space was gaining attention of more cyber attacker and number of attacks/threats increased exponentially. This is one of the rapidly growing global challenges for industry as well as for human life. In this paper a systematic surveys and review is done on recent trends of cyber security attacks during and post COVID-19 pandemic and their countermeasures. The relevant information has been collected from different trusted sources and impact landscape discussed with importance of cyber security education and future research challenges highlights. © 2023 IEEE.

Domestic Violence- A Crying Call for Change

Domestic violence or intimate partner violence, can be defined as a pattern of behaviors which could be physical, sexual, emotional, economic or psychological actions or threats of actions in any relationship that is used to gain or maintain power and control over an intimate weaker partner that influence another person. Domestic abuse can happen to anyone regardless of age, race, gender, sexual orientation, religion or socioeconomic background and education levels. It can occur within a range of relationships and not just those who live with us in our homes. These incidents are seldom isolated and escalate in frequency and severity if not opposed initial stage itself and may harm physically as well as emotionally and at times even endangers lives. Statistics for this is grave be it our country be it abroad and it became more grim covid times when staying home was not always safe. There are various factors involved from genesis to the maintainence of this menace medicolegal aspects and many more. Multidisciplinary approach for awareness that one needs to seek help, that one is not alone and various measures for curbing this grave issue is required at all levels. Domestic violence is the outcome of cumulative irresponsible behaviour which a section of society demonstrates. It is also important to note that solely the abuser is not just responsible but also those who allow this to happen and act as mere mute spectators. In this era of rights-based mental health services, such 'hidden shades' of mental wellbeing form potent challenges, which face unique conditions of demographics, prevalence of mental disorders and awareness related to this grave issue of domestic violence. Human rights are universal and are vital for promoting mental health and dignity. With this premise, this symposium intends to unfold the various factors involved and highlight the intersections of Domestic volence and approaches required to bring about and discuss strategies to curb it in light of the lessons learnt from experiences across the globe.

"Who can help me?": Knowledge Infused Matching of Support Seekers and Support Providers during COVID-19 on Reddit

During the ongoing COVID-19 crisis, subreddits on Reddit, such as r/Coronavirus saw a rapid growth in user's requests for help (support seekers - SSs) including individuals with varying professions and experiences with diverse perspectives on care (support providers - SPs). Currently, knowledgeable human moderators match an SS with a user with relevant experience, i.e, an SP on these subreddits. This unscalable process defers timely care. We present a medical knowledge-infused approach to efficient matching of SS and SPs validated by experts for the users affected by anxiety and depression, in the context of with COVID-19. After matching, each SP to an SS labeled as either supportive, informative, or similar (sharing experiences) using the principles of natural language inference. Evaluation by 21 domain experts indicates the efficacy of incorporated knowledge and shows the efficacy the matching system.

Driving Impact in Claims Denial Management Using Artificial Intelligence

A healthcare provider’s ability to quickly and efficiently process claims and quantify denial rates is critical to ensure smooth revenue cycle management and medical reimbursement. But the hospitals and medical practitioners are receiving more claim denials from payers, with the average rate of denial steadily increasing year over year. The recent COVID-19 pandemic has further accelerated the denial rate. An accurate denial detection algorithm can help to reduce the burden on healthcare providers. In this study, we propose a boosting-based machine learning framework to predict the likelihood of claims being denied along with the reason code at a line level. Prediction at a line level provides a finer-grained explanation to the administrative staff by pointing out the specific line for corrections. The list of important features provides an interpretable solution to the healthcare providers which enables them to create the right edits and correct the claim before going out to the payer. This in turn helps the healthcare provider dramatically improve both net patient revenue and cash flow. They can also put a check on their costs, as fewer denials mean less rework, resources, and time devoted to appealing and recovering denied claims. The denial model showed good performance with Area Under the Curve (AUC) of 0.80 and 0.82 for professional and institutional claims respectively. According to our estimates, the model has the potential to save 15%–50% of the denial cost for a healthcare provider. This in turn would have a tremendous impact on the healthcare costs as well as help make the healthcare process smoother. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

To Assess the Role of to Cilizumab with or without Remdesivir in Covid-19 Diabetic Patients

Background: The clinical presentation of Covid-19 positive person can range from asymptomatic to severe pneumonia with acute respiratory diseases. The major impact of Covid-19 is identified on respiratory system of the human and leading to death. There are different types of treatment options available for managing the health of the people but first was remdesivir that approved by the FDA. The dexamethasone has been associated with decreased mortality in recovery of the medicine trail. Apart from this, the major benefits of interleukin 6 antagonists are still subject of debate as tocilizumab FDA approved the interleukin 6 for treatment considering the side effects too. Aim: The study aims to assess the role of tocilizumab with or without remdesivir in COVID-19 diabetic patients Method: The current study is retrospective, single centered, observational cohort and based on the patients who were diagnosed with Covid-19 considering the PCR test and hospitalized at ESI Chitrakoot Nagar, Udaipur under RNT Medical College, Udaipur from October-20 to December-21. The study has involved the patients who were 18 years and older and had the clinical association with diabetes mellitus. Moreover, the respiratory findings also defined as infiltrates, SPO2 < 93% on room air and requirements of respiratory assistance. For the current study, the data was collected related to demographics, co-morbidities, symptom, oxygen support category, laboratory values and outcome of the therapies. The level of oxygen support was analyzed considering the ACTT-1. Results: There were total of 127 patients considered for analyzing the role of tocilizumab with or without remdesivir in COVID-19 diabetic patients. The group 1 is involving the 54 patients and group 2 has 73 covid-19 patients. According to the outcome of the analysis, the mean age of both groups were 62 and 64 years for group 1 and 2. There was significant difference identified for respiratory support received by the patients and obesity, COPD and CVD. However, there was no significant difference found for diabetes patients as the P value was more than 0.05. As per the outcome of the study focusing on the Chi Square, most of the variables have shown significant difference but Remdesivir, and low vitamin D levels have shown the no significant difference Conclusion: From the analysis, it has been concluded that the combinations of tocilizumab and remdesivir did not have any significant difference in mortality but the patients who recovered from the covid-19 has influenced with diabetic issues. The improvement in practice and advancement in laboratory trail has helped to improve the effectiveness of these treatment options.

Persuasive Factors and Weakness for Security Vulnerabilities in BIG IOT Data in Healthcare Solution

In the present scenario, public health is a global challenge. In view of COVID-19 pandemic,interventions of emerging technologies hasbeen highly increased and postpandemica big technological shift is expected for providing information and communication Technology-enabled solutions to healthcare as well a meeting other social challenges. Internet of Things or IoT and Big data are the technologies prominently being used in healthcare applications. In smart city visualization to provide ubiquitous computing environment, urge of smart, small but powerful sensor devices or IoT technology-enabled healthcare solutions deployments done over open networked infrastructure and underlying architecture. Such highly dynamic and heterogeneous environment with rapid digital transformation enforcing trusted security resource-restrictions and performance implication. In this paper, firstly, we explore the existing security, privacy and authentication weakness in reference to IoT or IoMT and big data enabled healthcare applications. Secondly, scaling the low to high security risks done based on the major weaknesses. In this work primarily we focus on most challenging attacks like Denial of Services (DoS), Man in the middle and dynamic intrusions. In winding-up machine learning based intelligent adaptive approach proposed for underlying deficiencies and insufficiencies in IoT enabled Healthcare application security. The key driving forces for the imprecision of trust and security with emerging Big IoT also presented as future scope. © 2021 Institute of Physics Publishing. All rights reserved.

COVID-19 in Spain and India: Comparing policy implications by analyzing epidemiological and social media data

The COVID-19 pandemic has forced public health experts to develop contingent policies to stem the spread of infection, including measures such as partial/complete lockdowns. The effectiveness of these policies has varied with geography, population distribution, and effectiveness in implementation. Consequently, some nations (e.g., Taiwan, Haiti) have been more successful than others (e.g., United States) in curbing the outbreak. A data-driven investigation into effective public health policies of a country would allow public health experts in other nations to decide future courses of action to control the outbreaks of disease and epidemics. We chose Spain and India to present our analysis on regions that were similar in terms of certain factors: (1) population density, (2) unemployment rate, (3) tourism, and (4) quality of living. We posit that citizen ideology obtainable from twitter conversations can provide insights into conformity to policy and suitably reflect on future case predictions. A milestone when the curves show the number of new cases diverging from each other is used to define a time period to extract policy-related tweets while the concepts from a causality network of policy-dependent sub-events are used to generate concept clouds. The number of new cases is predicted using sentiment scores in a regression model. We see that the new case predictions reflects twitter sentiment, meaningfully tied to a trigger sub-event that enables policy-related findings for Spain and India to be effectively compared. Copyright © 2020 for this paper by its authors.

COVID-19 Pandemic and Post-pandemic: Impact and Technical Threats in India

World Health Organization declared Coronavirus as a pandemic. More than 6 million confirmed cases of COVID-19 have been found leading to more than 367166 deaths till May 31, 2020. With every passing day, the number of cases and deaths is expanding. The widespread of this epidemic has not only threatened human health but also production, economy, social functioning, education, etc. In this critical pandemic situation, a large number of the population are fighting for their lives and economic challenges for survival. Although digital health would not be the main contributor in combating COVID-19, it could play a very important supporting role in control and prevention work. During this isolation period, various digital applications are needed to ensure a normal life for most of the people. Artificial intelligence, machine learning, data analytics, big data, cloud computing, Internet of things (IoT) and other digital technologies are playing a vital role in managing routine activities through work from home, online education, remote patient treatment, citizen protection, risk communication, and medical supplies. On the downside, various technical threats like online fraud and cyber-attacks are rising and increasing challenges in the COVID-19 pandemic. The objective of this paper is to explore the available COVID-19 statistics and understand the impacts with technical threats to relief measures in India caused in the current pandemic. To realize social responsibility and comprehend response capacity in foreseeing COVID-19 extortions and foster the community awareness toward population and public health allocation where upholding local health with technical risk prevention is alarming. In the winding up, post-pandemic open challenges are also discussed. © 2021, Springer Nature Singapore Pte Ltd.

Knowledge infused policy gradients for adaptive pandemic control

COVID-19 has impacted nations differently based on their policy implementations. The effective policy requires taking into account public information and adaptability to new knowledge. Epidemiological models built to understand COVID-19 seldom provide the policymaker with the capability for adaptive pandemic control (APC). Among the core challenges to be overcome include (a) inability to handle a high degree of non-homogeneity in different contributing features across the pandemic timeline, (b) lack of an approach that enables adaptive incorporation of public health expert knowledge, and (c) transparent models that enable understanding of the decision-making process in suggesting policy. In this work, we take the early steps to address these challenges using Knowledge Infused Policy Gradient (KIPG) methods. Prior work on knowledge infusion does not handle soft and hard imposition of varying forms of knowledge in disease information and guidelines to necessarily comply with. Furthermore, the models do not attend to non-homogeneity in feature counts, manifesting as partial observability in informing the policy. Additionally, interpretable structures are extracted post-learning instead of learning an interpretable model required for APC. To this end, we introduce a mathematical framework for KIPG methods that can (a) induce relevant feature counts over multi-relational features of the world, (b) handle latent non-homogeneous counts as hidden variables that are linear combinations of kernelized aggregates over the features, and (b) infuse knowledge as functional constraints in a principled manner. The study establishes a theory for imposing hard and soft constraints and simulates it through experiments. In comparison with knowledge-intensive baselines, we show quick sample efficient adaptation to new knowledge and interpretability in the learned policy, especially in a pandemic context. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org)

Depressive, drug abusive, or informative: Knowledge-aware study of news exposure during COVID-19 outbreak

The COVID-19 pandemic is having a serious adverse impact on the lives of people across the world. COVID-19 has exacerbated community-wide depression, and has led to increased drug abuse brought about by isolation of individuals as a result of lockdown. Further, apart from providing informative content to the public, the incessant media coverage of COVID-19 crisis in terms of news broadcasts, published articles and sharing of information on social media have had the undesired snowballing effect on stress levels (further elevating depression and drug use) due to uncertain future. In this position paper, we propose a novel framework for assessing the spatio-temporal-thematic progression of depression, drug abuse, and informativeness of the underlying news content across the different states in the United States. Our framework employs an attention-based transfer learning technique to apply knowledge learned on a social media domain to a target domain of media exposure. To extract news articles that are related to COVID-19 communications from the streaming news content on the web, we use neural semantic parsing, and background knowledge bases in a sequence of steps called semantic filtering. We achieve promising preliminary results on three variations of Bidirectional Encoder Representations from Transformers (BERT) model. We compare our findings against a report from Mental Health America and the results show that our fine-tuned BERT models perform better than vanilla BERT. Our study can benefit epidemiologists by offering actionable insights on COVID-19 and its regional impact. Further, our solution can be integrated into end-user applications to tailor news for users based on their emotional tone measured on the scale of depressiveness, drug abusiveness, and informativeness. © 2020 Copyright held by the author(s).

A new delay differential equation model for COVID-19

In this work we give a delay differential equation, the retarded logistic equation, as a mathematical model for the global transmission of COVID-19. This model accounts for asymptomatic carriers, pre-symptomatic or latent transmission as well as contact tracing and quarantine of suspected cases. We find that the equation admits varied classes of solutions including self-burnout, progression to herd immunity and multiple states in between. We use the term “partial herd immunity” to refer to these states, where the disease ends at an infection fraction which is not negligible but is significantly lower than the conventional herd immunity threshold. We believe that the spread of COVID-19 in every localized area can be explained by one of our solution classes. © 2020, Copyright held by the author(s).